Nicolas

Corinne

Katell

Stéphane Ch

Stephane Boutayna

Jean-William Cl

Anne Marie

Michael

Florent, Vasek Francesca Landry

Diana Craig Patch

Jasyon Dobney

Melanie Holcomb

Maia Nuku

Manu Frederickx

Janice Mandrus

Natanya Schiff

Jennifer Schnitker

Anna Serotta

Jack Soultanian

Vasek Krupicka

Aleksandra Popowich

Quentin Scanvion

Benoit Bertrand

Valery Hedouin

Benjamin Dartigues

Nadia From Adoc

Corentin, Bastien, Medhi Marie Charlotte

Unité De Recherche

Gal Galactose

Hlnl Hydroxylysinorleucine

Teng Triboelectric

FAIMS High field asymmetric waveform ion mobility spectrometry FASP Filter aided sample preparation FDR False discovery rate

Keywords: Bottom up proteomics, high resolution mass spectrometry, collagen, ivory, archaeological bones, museum objects, trace analysis, cross-linking v Analyse protéomique (bottom up), spectrométrie de masse de haute résolution, collagène, ivoires, ossements archéologiques, objets de musées, analyse AGC Automatic gain control AGE Advanced glycation products AI Artificial intelligence

for providing samples for method

Les chapitres 1, 2 et 3 sont consacrés à des études protéomiques par spectrométrie de masse de haute résolution d'échantillons provenant de musées et de sites archéologiques. L'objectif principal des analyses est l'identification taxonomique d'échantillons d'os ou d'ivoire. Des études préliminaires d'échantillons modèles ont été nécessaires afin de construire les méthodes peu invasives ainsi que les bases de données protéomiques, permettant ainsi la reconstruction des séquences protéiques pour leur utilisation lors de l'étude des échantillons historiques. accessibles; cette étape a été réalisée à partir de spécimens provenant de l'American Museum of Natural History (c) le développement de méthodes de spectrométrie de masse permettant de distinguer la leucine de l'isoleucine et (d), l'analyse de poussières d'échantillons historiques permettant l'identification taxonomique à partir de plusieurs peptides spécifiques aux espèces, et (e) le développement de nouvelles stratégies d'analyse des données pour procéder à l'identification des espèces à partir d'échantillons fortement dégradés. Ce travail a permis l'identification de multiples espèces des familles de Cervidés, Elephantidés, Hippopotamidés, entre autres, mais aussi de l'infra-ordre des cétacés (par ex. Physeter macrocephalus). Par ailleurs, ce travail a également permis de confirmer certaines imprécisions des banques de données disponibles. L'assignation taxonomique des protéines par les peptides et contigs associe l'utilisation de différents algorithmes dont certains basés sur l'intelligence artificielle permettant ainsi des ajustements dans le code source de l'outil afin de gérer les nouvelles modifications des protéines anciennes dans son modèle d'apprentissage.

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Les chapitres 4, 5 et 6 sont consacrés à l'analyse exploratoire des réticulations des collagènes de type I dans l'os. Afin de mieux comprendre les mécanismes de dégradation ou de préservation présents dans les tissus minéralisés, il est nécessaire de comprendre comment le réseau de collagène évolue dans son environnement.

Le Chapitre 4 décrit le développement d'une nouvelle procédure de préparation des échantillons minéralisés; ici, le protocole ne vise pas à être peu invasif, mais il cible le matériel peu soluble. Cette étude propose l'utilisation de l'hydroxylamine comme étape d'extraction complémentaire. L'hydroxylamine, un réactif d'hydrolyse chimique, est proposé pour effectuer une digestion en phase solide, ce qui permet de solubiliser efficacement le matériel protéique insoluble par les protocoles protéomiques classiques, par exemple, à base de guanidine. Cette procédure de digestion chimique permet de solubiliser efficacement les matières protéiques insolubles dans les échantillons modernes et archéologiques. Elle permet donc d'analyser la fraction insoluble de la matrice extracellulaire des os et d'accéder à des informations qui sont généralement perdues dans les matériaux rejetés dans le cadre d'un protocole expérimental de protéomique classique. Ce protocole a également été développé pour l'analyse des peptides de collagène réticulés, où il solubilise efficacement la partie hautement réticulée du réseau de collagène, enrichissant l'extrait en peptides réticulés.

Le Chapitre 5 est une analyse bibliographique approfondie des méthodes d'analyses utilisées pour l'analyse des réticulations des collagènes. Les réticulations des collagènes sont liées à de nombreux mécanismes (par ex. architectures tumorales, maladies osseuses, solidité des os et des tendons, etc.). Cette revue couvre les sujets suivants : (a) les types de réticulations enzymatiques des collagènes, (b) le mécanisme de formation des réticulations des collagènes, (c) les méthodes d'analyse quantitative pour l'analyse des réticulations dans le collagène, (d) les méthodes d'analyse qualitative pour caractériser l'effet de la réticulation sur les tissus collagéniques, et (e) les techniques basées sur la spectrométrie de masse pour analyser les peptides de collagène réticulés, et les perspectives dans le domaine. Cette analyse de la littérature a été essentielle pour la conception des expériences menées au chapitre 6. Elle a xi permis d'identifier les techniques déjà employées pour la caractérisation des réticulations des collagènes, de mesurer leurs apports dans le domaine, et d'évaluer les possibilités offertes par les techniques d'analyse actuelles pour augmenter la quantité d'informations pouvant être extraites dans ce domaine d'étude. En particulier, cette revue a révélé que les études proposées à ce jour ciblent une analyse globale de la réticulation dans les tissus et étudient les effets globaux. Et en conséquence, cette étude a mis en évidence la pertinence de proposer une approche basée sur l'analyse protéomique et l'analyse structurale par spectrométrie de masse pour la localisation des réticulations sur les protéines de collagène elle-mêmes.

Enfin, le Chapitre 6 présente l'analyse exploratoire des peptides réticulés dans le collagène à l'aide de la spectrométrie de masse de haute résolution. À la suite de l'examen approfondi de la littérature au chapitre 5 et de la mise au point d'un protocole de digestion chimique ciblant le collagène au chapitre 4, des stratégies d'analyse ciblant les liaisons divalentes et trivalentes ont été mises au point. L'analyse des peptides de collagène réticulés est très complexe pour plusieurs raisons, notamment l'hétérogénéité de la protéine de collagène elle-même et des structures de réticulation. En outre, les réticulations les plus stables dans le collagène sont des peptides trivalents, et il n'existe pas de support bioinformatique pour l'identification de ces peptides. Ce chapitre détaille : (a) l'analyse ciblée des modifications post traductionnelles associées à la réticulation dans les os archéologiques, (b) une approche par spectrométrie de masse de type bottom up pour l'identification des réticulations divalentes dans les os, (c) une procédure d'enrichissement ciblée pour la purification des peptides réticulés trivalents dans les os, et (d) des approches par spectrométrie de masse de type top down pour l'analyse des peptides trivalents, intégrant une fragmentation MS3 ciblée pour faciliter l'identification des séquences.

Ce chapitre traite de l'analyse de structures très complexes et du challenge analytique provenant à la fois de l'hétérogénéité des structures réticulées et de la protéine de collagène elle-même.

Ici, le plus grand défi a été l'analyse du collagène réticulé mature, qui a nécessité des méthodes de fragmentation adaptées et le séquençage manuel de la séquence peptidique.

En conclusion, ce travail de thèse a démontré plusieurs avancées méthodologiques en analyse protéomique appliquée à l'étude objets du patrimoine culturel. Tout d'abord, la miniaturisation et l'analyse de haute sensibilité développées dans les chapitres 1 à 3 permettront d'accéder à des collections archéologiques et muséales plus précieuses. Deuxièmement, la méthode de screening d'échantillons de type high throughput permettra à la communauté d'archéologistes une montée en puissance par l'accès facilité à ce type d'analyse et par les nouvelles informations obtenues en conséquence. Enfin, les développements méthodologiques des chapitres 4-6 permettront une compréhension plus approfondie des mécanismes de dégradation associés aux collagènes des matériaux étudiés. A proposed illustration of the data using Prometa, where the number of peptides identified is plotted against the genes of different species. The top two plots correspond to model reindeer sample rang150, and the bottom two to rang1709. The colour of the bars in the histogram correspond to the species, in this case, we focused on the species in the Cervidae family that have collagen sequences available in the NCBI database. If there is more than one gene per species in each plot, this corresponds to an isoform. Assignment of the peptide sequences to a gene and species was performed using MMSeqs2. (35) The sequence coverage of collagen 1α(i) from Homo sapiens from sample LIP001, highlighted in grey, with two informative peptide sequences identified by blue rectangles. B and C show more detail regarding the identified peptide sequences with, i, the number of peptides identified for each sequence, ii, MS/MS spectra of the peptides, and iii, the species in which this peptide sequence is observed, ie; 32 species in the Primate order contain the peptide sequence B in their collagen 1α(i) sequence. *This peptide present as a polymorphism that is unique to Homo sapiens (discussed on the coming sections) .. EICs of the peptides corresponding to the polymorphism at position 1075 in collagen α1(i) that allows for sample LIP001 to be confirmed as human. A: The EIC of the peptide with T at position 1075, B: EIC of the peptide with A at position 1075. One can see that the peptide shown in B is significantly higher in abundance than A, corresponding to the calculated frequencies discussed in (13) Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 .........................

Chapter 6: Analysis of enzymatic cross-links in bone collagen

List of Tables

Chapter 1: Species identification of ivory and bone museum objects using minimally invasive proteomics Chapter 2: Proteomic analysis of elephant ivory for proteotype differentiation Figure S2-1: Sequence alignment of collagen A 1α(i), B 1α(ii), C 1α(iii), and D 2α(v), of Loxodonta africana and Elephas maximus. While some differences between the two species were identified in these proteins, they were not targeted as markers of proteotype because either (a), the portions of the sequences where the markers were identified were not detected in the microsamples (the case for B), or (b) the differences identified by the sequence alignment were in fact unknowns in the sequence from the DNA translation (A and C).

List of Supplementary Tables

Chapter 1: Species identification of ivory and bone museum objects using minimally invasive proteomics S1-10: Peptides identified using MS/MS sequencing in the three exemplar samples from Hippopotamus amphibius where the amino acid substitution from P to A at position 455 of collagen α2(i) was identified. .............. 209 Table S1-11: Peptides identified using MS/MS sequencing in the three exemplar samples from Loxodonta africana where the G was identified at residue 326 of collagen α1(i).

Chapter 6: Analysis of enzymatic cross-links in bone collagen

Introduction

1.1.Introducing definitions

Proteomics. By homology to the term "genome", the word "proteome" was proposed for the first time in 1995 to designate a protein set expressed by the genome of a cell, a tissue, or an organism at a precise moment of its development and in a precise environment. (1) The word proteomics designates the study of proteins, including their identification, quantification, and the study of their modifications.

Methods in proteomics are mainly based on the use of complementary techniques: chromatography for analyte separation or purification, mass spectrometry for peptide or protein analysis, and bioinformatics for protein identification and/or structural characterization. (2) The bottom up proteomics approach is the current mainstream technique in proteomics. (3) It consists of analysing the peptide mixture resulting from protein hydrolysis (commonly through digestion with trypsin) either by peptide mass fingerprinting (PMF) or by peptide sequencing (MS/MS). ( 4) For PMF, protein identification is performed based on the molecular weights of peptides, whereas for MS/MS, peptides are fragmented in the source or analyser of the mass spectrometer and amino acid sequences are obtained by the elucidation of fragment ions resulting from the peptide fragmentation. Top down proteomics is an alternative analytical approach, involving the accurate molecular weight measurement of an intact protein and the direct protein fragmentation without preliminary chemical or enzymatic hydrolysis.(5)

1.2.Proteomics in cultural heritage analysis

The first application of proteomic analysis to cultural heritage samples was reported in the early 2000s with first experiments performed on paintings and archaeological bones involving PMF and sequencing experiments in both applications. (6,7) From these first pioneering works, the methods were deployed to the study of other cultural heritage samples including other artworks and museum objects, archaeological potsherds, and archaeological bones and teeth. (8)(9)(10)(11)(12)(13)(14) Now however, with increasingly sensitive and high resolution instrumentation along with more sophisticated and miniaturized sample preparation methods, and more adapted bioinformatic tools, the amount of information that can be extracted from such samples has increased from the type of protein present, to the species-specific amino acid sequences of the proteins, providing significantly more contextual information to the objects that are studied. (15) Proteomics gives us the keys to reveal new historical insights or aid in cases of preservation issues. For art materials, this has implications on understanding how an artwork was created, or how it has changed over time; improving conservation and preservation strategies; and addressing questions related to authentication. (16)(17)(18) In archaeology, human habits and commercial exchanges from the past are revealed; information on extinct species and evolutionary links are drawn; and ancient pathologies and pathogens are studied. (19)(20)(21)(22) Several excellent reviews describe and summarise the current achievements in proteomics and related mass spectrometry-based methods applied to cultural heritage. (23)(24)(25)(26)(27)(28)(29) Here, this chapter considers the challenges currently facing the field of proteomic analysis applied to cultural heritage, and considers how the current developments in the proteomics field can be applied to such analytical questions.

Bottom up proteomics

Proteomics as a field is moving towards high throughput and big data for the analysis of thousands of samples. (30) With the introduction of more sensitive and faster instrumentation, the field is at a point where massive amounts of data are produced. In order for the field to progress to the stage of the more mature field of genomics, moves towards 'population-scale' proteomics are being made. (31) With this expansion in capabilities, proteomic studies can be used to answer a wider range of biological questions. As such, additional instrumentation and bioinformatic developments are required, such as the expansion of online public databases, the development of robust de novo sequencing workflows, and the miniaturisation of both sample preparation and analysis procedures for single cell analysis. While the main challenges and questions of clinical proteomics and proteomics applied to cultural heritage samples may differ, they can both take advantage of the current technological developments in order to realise their goals.

High throughput

As proteomics becomes a mature technique, the demands for analysis increase significantly, with a typical proteomic experiment moving from needing tens to hundreds of samples to extract meaningful biological information. In the case of single cell analysis, which is becoming an area of increasing interest, a typical analytical campaign can include several thousand cells. (32,33) Therefore, instrumental developments are necessary in order to increase the speed of analysis without losing information from the sample.

One such way is the use of automated technologies developed for high throughput sample preparation, which are frequently used in large proteomics facilities to increase throughput, but also reproducibility for quantitative studies. (34)(35)(36) In addition to this, the development of automation focused on robustness and reproducibility increases the quality of the data produced in high throughput experiments. (37) Furthermore, the use of data independent acquisition (DIA) methodologies significantly decreases the time spent on spectra acquisition per peptide, as multiple peptides are fragmented in a single m/z window. (38) This increases the sensitivity of the instrument, while also reducing the required analysis time. (39,40) The resulting data has a higher complexity, but recent advances in data analysis pipelines for DIA data, are continually improving the proteome coverage and reproducibility of this data acquisition methodology. (41) Of course, such a high throughput data production also requires significantly more data processing power, resulting in many software providers moving to a model where data processing is performed on remote servers, as well as models where data processing is started automatically, immediately after the data acquisition, so that the proteomics analysis pipeline is automated throughout. (42,43) Such challenges are also present in the context of cultural heritage. A particular example is the screening of morphologically unidentifiable bones in archaeological sites, in order to (a) have an overview of the species present in the site, giving information on the diet of a population and the biological sources of various tools, and (b) to identify samples of particular interest, for example human or Neanderthal bones that would prove that humans inhabited a certain area. (44)(45)(46) Such questions would benefit from the automated, high throughput screening of large numbers of samples, for which both PMF and LC-MS methods are developed. (47) Regarding LC-MS approaches, automated sample preparation is combined with short chromatography gradients and DIA-MS acquisition in order to rapidly collect data that would allow for species identification. There are several challenges that are specifically related to the application of such analysis methods to archaeological samples, namely that, in some cases, very little protein is present in highly degraded bones, so the analysis must have a sufficiently high sensitivity to detect trace levels of peptides. Additionally, while archaeological samples have a significantly lower complexity in comparison to clinical samples, the data analysis remains challenging, as the dominant protein detected is collagen, where only a few amino acids differ between different species. Therefore, it is challenging to design a data analysis strategy that is sensitive to these small changes in protein sequence which allow for species identification.

High sensitivity

As proteomics is applied to a wider range of research areas, the samples that are used consequently can become more precious, and thus less material is available for analysis. (48) Sample preparation and data acquisition methodologies must therefore be optimised in order to extract the maximum amount of information possible from, for example, single cells, needle biopsies or samples taken using laser capture microdissection. (32,49,50) Developments have been made in the development of automated micro-sample preparation pipelines, such as nanodroplet processing platforms. (51)(52)(53) Such platforms can allow for the use of ultra-high precision dispensing of small amounts of buffer, as well as reducing the impact of contamination, and the need for sample clean up. In such circumstances, it is clear that the main factors affecting the resulting data are less the instrumental parameters than the number of steps in the sample preparation procedure, which must be reduced as much as possible, including limiting the transfer of sample from one vial to another. (51) To increase the sensitivity of the data acquisition itself, post LC separation fractionation steps, such as high field asymmetric waveform ion mobility spectrometry (FAIMS) or trapped ion mobility spectrometry (TIMS) have both been developed in order to increase the sensitivity and accuracy of the MS detection, resulting in virtually noise-free spectra. (54,55) Such fractionation methods have shown to increase the depth of proteome coverage during the same length of LC gradients but without post-column fractionation, by allowing for the distinction between isobaric peptides that elute at the same time using ion mobility in acquisition with TIMS-TOF, or by using targeted compensation voltage (CV) values to filter uninformative peptides in the case of FAIMS. (56,57) Finally, the use of novel data acquisition strategies, such as wide window data dependant acquisition (DDA) workflows, and real-time searches, coupled with more powerful artificial intelligence (AI)-driven algorithms for peptide identification means that more peptide identifications from smaller and more complex samples are possible. (58)(59)(60)(61) In the analysis of cultural heritage samples, very often the amount of material that is permitted to be extracted is miniscule, with the final concentration of the extracted peptides being close to the limit of detection for many instruments. (15) Cultural heritage samples additionally often require harsher, or highly specific, extraction steps due to the high complexity of the matrices, and additional optimisation steps are always required when moving away from clinical samples. (15,(62)(63)(64) Furthermore, when sampling cultural heritage objects, often specific sampling techniques are used in order to remove as little sample as possible and to minimise any impact on the appearance of the object, such as functionalised or micro abrasive films, and the following sample preparation must account for and be compatible with such sampling techniques. (65,66) Finally, the high heterogeneity of degraded peptides can often result in the coelution or overlapping of peptide species making the identification of these peptides particularly challenging. (67,68)

Database building

Proteomics has been mainly used to study and model diseases of high clinical importance, and as such, online public databases are most complete for species including humans and animals that are frequently used as human models, such as monkeys, mice and dogs. The availability of protein sequences on public databases is strongly driven by research, therefore, a lot of the most recent additions are related to, for example, COVID-19. (69) Having said this, there is growing recognition for the utility of both genomics and proteomics in other research areas, such as ocean research and conservation, and therefore, the genomes and proteomes of species outside of traditional biochemical research fields are being annotated. (70,71) Consequently, the lack of availability of protein sequences for certain species or poorly characterised proteins can severely inhibit the progress of proteomics in novel applications. The challenge presented by the lack of complete databases is twofold; firstly, in the case of species identification, classification can only be performed based on the information that is currently available in these databases, and secondly, how to improve the coverage of these databases. The best methodologies use both genomic and proteomic approaches in order to identify the amino acid sequence. (72) However, in many cases both approaches are not available, and, for proteomics analysis, the strict analysis of known model samples with error-tolerant search algorithms can be used to identify potential amino acid substitutions between closely related species. (20) Having said this, the benefits of public data repositories means that it is always possible to re-search old data with new databases and algorithms in order to extract new information. (73) This challenge is frequently encountered when applying proteomics to cultural heritage samples, as there are rarely protein sequences available for the species that are commonly of interest in the context of museums and archaeology. When performing species identification, often the key question in the analysis of archaeological bone fragments, the result is often based on incomplete protein databases, wherein the protein sequences have been reconstructed from DNA sequences, or based on the species that are available in the databases. For example, there are 251 species in the family Cervidae (deer), but, of those 251, only 6 have available collagen sequences (as of 02/05/2023). This can therefore lead to potential misassignment of the species, and for many species of interest it can be challenging to obtain reference samples in order to construct homemade databases (see Chapter 3 for more details).

De novo sequencing

Another approach to the challenge of the lack of available databases is the use of de novo sequencing algorithms to construct the amino acid sequences of proteins from scratch. This is predominantly applied to antibody sequencing, but also has applications in, for example, archaeology, forensics and snake venom analysis. (74,75) Several de novo sequencing algorithms are available, most of which have been designed specifically for the sequencing of antibodies, such as Novor, pNovo, DeepNovo, and PointNovo, wherein samples are typically prepared with multiple different enzymes in order to have the most confident sequencing result. (76,77) Such algorithms are able to reconstruct the amino acid sequence of a protein, although confident sequence reconstruction using only proteomics still remains a challenge. One can also distinguish between leucine and isoleucine through the use of EThCD (Electron transfer dissociation with supplemental collision induced dissociation) fragmentation and then searching for characteristic w-ion losses, which can be identified in an automated fashion using PEAKS AB. (78) De novo sequencing has been used for the reconstruction of ancient proteins for evolutionary studies. (79,80) Challenges here are frequently related to the quality of the data that originates from ancient samples, where the peptides are not tryptic and often result in unusual fragmentation patterns that are less easy to predict, as well as lower intensity spectra overall.

(81) Therefore, in many cases it is important to manually control the data, especially the identified substitutions, to ensure accurate reporting of the peptide and protein sequences.

Structural proteomics

In many cases when using proteomics to analyse cultural heritage samples, the goal of the analysis is to identify the amino acid sequence of the proteins present in the sample. Through the use of standardised methodologies, often a conclusion can be made on the species of origin of an object, or the materials used to make it. (12,16,47) Therefore, it is now of increasing interest to use these emerging technologies to obtain a deeper level of information related to the degradation mechanisms and changes to the tertiary structure of the proteins themselves, in environments such as paints, parchments, glues and mineralised tissues.

Top down proteomics

Top down proteomics studies the intact, denatured protein, and can allow for the identification of different proteoforms of a protein, thus giving information regarding how posttranslational modifications (PTMs) are related to each other, as this information is lost during the digestion step in bottom up proteomics. (82) With the maturation of the bottom up proteomics field, it has been clear that the PTMs on a protein, and the combinations in which they are present, are highly indicative of the function of a protein. (83,84) A frequent example of this is the study of histones, proteins that act as structural supports for DNA in chromosomes, wherein PTMs only appear in certain combinations. (85) There are many challenges related to top down proteomics analysis, namely the complexity and dynamic range of the proteome and the complexity of the data obtained. (86,87) For many top down proteomics studies, the amount of starting material and the level of fractionation required to reduce the complexity of the sample is significantly higher than in bottom up strategies. (88,89) When applying this to cultural heritage samples, materials with a low complexity should be targeted, and larger samples must be taken. (23,29) In addition to this, proteins from cultural heritage samples are highly degraded. In an example of top down analysis of casein proteins from Gainsborough's milk dipped drawings, proteoforms with up to 20 deamidations were identified. (90) Such complex modifications can cause difficulties in the data analysis, in terms of localising the modifications as well as being able to include such a high number of modifications when searching the data. The additional information gained from top down, compared to bottom up proteomics, is that it can reveal the true extent of modifications on a protein. For example, in bottom up proteomics of archaeological samples, the bulk deamidation of a protein is often calculated to give an indication of the age and preservation quality of a sample. (91) However, with top down proteomics and intact mass measurements, it is possible, for example, to identify the ratio between proteoforms with none, one, two or three oxidation or deamidation modifications, in order to fully understand the effect of a treatment or degradation. (92)

Hydrogen/Deuterium Exchange-Mass Spectrometry

Hydrogen/Deuterium Exchange-Mass Spectrometry (HDX-MS) has emerged as a valuable tool in structural biology as it provides contrasting information about a protein's solution phase dynamics with a high complementarity to conventional tools used to acquire high resolution structural information, such as X-ray crystallography and structural nuclear magnetic resonance spectroscopy (NMR). (93) During an HDX-MS experiment, a protein is incubated in D2O-based buffers for various time intervals, during which labile hydrogens on backbone amides are exchanged with deuterium.

These incorporations into the protein backbone are detected as a mass shift of 1.006 Da using mass spectrometry, and can reveal information regarding the relative accessibility of each amide in the protein backbone. (94,95) One of the benefits of HDX-MS is its versatility, as it can be applied to systems that are either too large or flexible to be analysed using traditional structural biology tools. (96) In addition to this, very low samples amounts are required due to the inherent sensitivity of mass spectrometry. Thus, HDX-MS has been used to characterise the complex systems in egg tempera paints, wherein the H/D exchange rate in the egg protein lysozyme was compared in the presence of different historical pigments. Through comparing the H/D exchange rate of and extent of oxidation induced in lysozyme in the presence of various pigments, information on the structural changes in proteins in the presence of metal based pigments was possible to extract. (92) This analysis could easily be applied to other cultural heritage-based model systems in order to better understand the degradation mechanisms of proteins in a variety of environments.

Native protein analysis

Native protein mass spectrometry is an emerging technique wherein proteins are analysed in their native conformations, often still bound in non-covalent complexes. (97) Challenges associated with native protein analysis are related to the ionisation of such large macromolecules, and the need for high mass range instruments since native proteins are still folded in their tertiary structure, and therefore less of the protein surface is available to hold a charge. (98) Native mass spectrometry coupled to ion mobility mass spectrometry has been used to further extract information related to a protein's tertiary structure in the gas phase, by tracking the impact of different treatments on the collision cross-section of a protein. Such changes can relate to an (un)folding event in the protein chain, revealing additional biological information. (99,100) As with HDX-MS, native mass spectrometry can reveal insights into the interactions of proteins with pigments in complex paint matrices. Native mass spectrometry coupled with ion mobility has been applied in the first studies of proteins relevant to cultural heritage, by coupling to a triboelectric nanogenerator (TENG) device, which allows for a significant decrease in the amount of sample required. (101)

Cross-linking Mass Spectrometry

Finally, cross-linking mass spectrometry offers another approach to identifying inter-and intramolecular protein interactions, through the use of chemical cross-linking reagents to covalently connect amino acids residues in close proximity to each other before denaturation and digestion with trypsin. (102, 103) Cross-linked peptides can then be enriched, for example, with the use of chemical cross-linkers, or analysed as a mixture of tryptic peptides. (104) Specific bioinformatic tools have been developed to search for cross-linked peptides, the main challenge being that cross-linked peptides are typically present at a very low concentration in comparison to linear tryptic peptides. Following the identification of cross-linked peptides, they can be plotted onto known protein structures to verify the accuracy of prediction tools. (105-108) Cross-linking mass spectrometry can complement traditional structural biology tools (X-ray crystallography, NMR) as it can characterise dynamic interactions and disordered sections in the protein that are not well characterised using typical crystal structures. (109) When searching for cross-links that are not caused by chemical cross-linking, such as enzymatic cross-links, or cross-links caused by degradation in works of art, the identification becomes significantly more complex. In such cases, model systems can be used in order to identify the cross-links that can be formed. (92) Studying protein cross-linking in works of art and archaeological samples can be very informative with regards to the degradation mechanisms occurring in the objects, such as cross-links resulting from oxidation found in egg tempera paints, and through the identification of formaldehyde-based cross-linking resulting from a parchment manuscript restoration. (90) The identification of cross-links in ancient samples potentially has a huge impact on the information that can be extracted, both in terms of understanding the preservation mechanisms occurring in the samples, as well increasing the sequence coverage of the protein by identifying peptide sequences that would otherwise not be identified because they were involved in cross-links.

Proteomics in the museum

The way in which museums are viewed and present their collections is rapidly changing. Many museums suffer from perceived inclusivity issues, and struggle to have an accessible public image. (110) In addition to this, many scientists, for example, those working with large natural history collections, find effective scientific communication challenging, and are increasingly relying upon science and technology to communicate their work in the museum to the public.

(111, 112) More and more, museums are demonstrating the usually 'behind the scenes' activities relating to, for example, biodiversity research and how the museums work with and benefit the communities they work with. (113-116) In addition to this, museums now rely heavily on technology to improve the visitor experience and to more efficiently transmit information about exhibits, including additional context that is the result of scientific research. (117) What can chemical analyses such as proteomics bring to the museum? Firstly, a better understanding of the materials that make up valuable artworks helps objects conservators and curators to better understand the treatment needs of the objects analysed, as well as having a clearer understanding of the context in which the object was created. However, arguably more importantly, this information is also passed on to the visitors of the museum where the objects are housed, allowing the general public to have a clearer understanding of the historical aspects of the pieces, as well as it being an opportunity for science communication in an unusual setting.

A notable example here is the Night's Watch project at the Rijksmuseum, where the in-depth study of this painting has been incredibly successfully used as a means for scientific communication in a broader context. (118) Other research projects, such as the projects led by the Metropolitan Museum of Art to identify the use of chia oil in Hispanic artworks, and the analysis of food stuffs remaining in Edgefield pottery, have highly relevant social impacts. (119, 120) The communication of science in the context of cultural heritage analysis makes science more accessible for a very wide audience. This is an opportunity to improve the public's general understanding of research and the scientific method, and, more importantly, to attract more young people to STEM subjects. (121, 122) Therefore, having interactive, informative exhibits that include science in the story of objects, will help to keep museums relevant for future generations.

Conclusions

In the introduction of this thesis, the challenges related to and opportunities for proteomics analysis applied to cultural heritage samples have been outlined. Firstly, the pressing challenges for both clinical and cultural heritage proteomics were considered, such as the need for higher throughput and more sensitive analysis. Secondly, the key developments that have been made in the clinical proteomics field were considered, such as instrumental developments like FAIMS and TIMS-TOF, as well as bioinformatic developments like in the case of data analysis solutions for DIA data and de novo sequencing algorithms. Finally, their application to the analysis of cultural heritage samples was considered.

In general, clinical proteomics is working towards the automation and standardisation of analysis, with the aim to have data that can be compared across platforms. For this field, it is a key goal, since very often different groups are working on the same sample type. While this is somewhat applicable to cultural heritage samples, such as archaeological bone samples, normally the question originates from one single sample, and the sample preparation needs to be adapted to that specific sample. Therefore, it is necessary to consider which of the novel developments in the field of clinical proteomics can be successfully applied to samples from cultural heritage.

In this thesis, I will demonstrate how the technological advancements that are being developed in the field of clinical proteomics can be repurposed and applied to the analysis of cultural heritage objects. In particular, I will show how advances in instrumentation and data processing have allowed for more sensitive and faster analysis of cultural heritage samples, coupled with automated data processing. In addition to this, I will demonstrate novel analysis strategies for the characterisation of cross-linking in the collagen network, and how this can be applied to cultural heritage samples in the future.

Chapter 1: Species identification of ivory and bone museum objects using minimally invasive proteomics Abstract Chapter 1 describes a wide-ranging study of ivory objects at the Metropolitan Museum of Art, New York. The goal of this analysis was to identify the species from which each ivory object was sourced. Due to the many challenges associated with this analysis, including that we were targeting understudied species, very little sample was available, and that some objects were significantly degraded, several developments were made during this study, including:

• Development of a minimally invasive sampling and analysis workflow to perform proteomics analysis on minimal amounts of ivory and bone samples.

• Analysis of ivory exemplars obtained from the Mammology American Museum of Natural History collection to build home-made proteomic databases of understudied species to compare to historical samples.

• Development of mass spectrometry methods to discriminate between leucine and isoleucine (targeted and untargeted).

• Confident species identification of ivory and bone objects from the collection of The Metropolitan Museum of Art dating from as early as 4000 B.C. based on the identification of multiple species-specific peptides.

• Development of novel data analysis strategies to perform species identification on samples with poor collagen preservation in order to extract the maximum amount of information possible.

The main goal of this study was to extract the maximum amount of information possible from the minimal amounts of starting material that were available to us. All the above developments were necessary in order to increase the sensitivity of the data analysis. For example, building our own databases based on proteomics data allowed for the identification of inconsistencies in protein sequences available on public databases, which in turn resulted in a significant increase in the number of peptides identified. In addition to this, having a database based on proteomic data allowed for the comparison of the two sets of raw data, giving an additional level of confidence in the species assignment. This study allowed us to demonstrate the criteria required to perform an accurate species identification, as well as demonstrating that the species identification is not always clear cut, either due to the quality of the data, or the information available on public databases. This chapter has been written in the format of Science Advances and was submitted in May 2023.

Introduction

Ivory has been used as a carving material in many cultures in part because of its numerous aesthetic qualities; it has a very fine grain, polishes well, and has a warm texture and colour. (1,2) As such, ivory is a material commonly found in museum collections, in the form of statuettes, buttons, and chess pieces, and configured into a variety of spiritual and household objects such as inlays or individual components (furniture legs, stair newels, etc.). Strictly speaking, the term ivory refers to material deriving from tusks, that is, teeth that grow continuously throughout an animal's life, such as those of elephants. However, the term is also more widely used to describe any teeth that have been carved, such as those of hippopotamus and sperm whales. (3,4) Ivory taxonomic identification is of great interest not only for museums (for the identification of materials and movement of cultural heritage objects around the world) but also for the regulation of the trade or trafficking of materials from endangered species. Several species from which ivory can be sourced are protected under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), and thus an accurate species identification of seized objects is vital to prosecutors. CITES is also relevant when artworks are purchased or requested for loan, which involves conservators as well as curators and registrars/collections managers. When species cannot be determined by visual methods, museums often find full characterization of an object nearly impossible. (1, 5) A common question for wildlife law enforcement officers is the distinction between mammoth ivory and elephant ivory, as the trade of mammoth ivory is unregulated, whereas the trade of elephant ivory is prohibited under CITES, and is thus frequently passed as mammoth ivory. In addition to this, in 2020 there was a notable increase in hippopotamus teeth being seized at EU borders, with calls now starting for hippopotamus to be added to the list of CITES protected species. (6) Therefore, the development of both visual identification techniques, and analytical-based confirmation is vital to conservation efforts. (7)(8)(9) The World Wildlife Foundation's (WWF) 'Identification Guide for Ivory and Ivory Substitutes' cites a variety of analytical procedures for ivory species identification, including UV spectroscopy, microscopic analysis of Schreger lines, and DNA analysis, but there is an absence of data and studies to show the strength of proteomics for this application. (10) The identification of elephant ivory using DNA analysis is becoming a vital tool in prosecutions relating to hunting and poaching, where methods typically require 200 mg of material. (11,12) DNA analysis has also been used to identify the species of origin of museum objects, including an elephant ivory chess piece, and sperm whale teeth, where tens of milligrams of material were required to confidently identify the species of origin. (13,14) Other non-destructive methods such as Infra-Red and Raman spectroscopies are also used in order to classify species specific features. (15,16) As mentioned above, ivory is essentially an elongated tooth, and therefore the majority of the material is made up of enamel and dentin. Ivory is composed of a thin outer layer of cementum, an enamel derivative, and a main core of dentin. (17) Dentin has a higher organic content than enamel of around 20% by weight, of which 90% are collagenous proteins, predominantly collagen type I. (18) These collagenous proteins form mineralised collagen fibrils throughout the dentinal matrix, the structure of which is very ordered, making up of a series of tubules which align themselves into sheets of microlaminae. (17) The orientation of these microlaminae sheets relative to each other produce characteristic Schreger lines which are commonly used to identify the species from which the ivory originated. (17) Since the structure of the mineral matrix is specific to both the species and the tissue type, X-ray diffractometry (XRD) has been used to discriminate between mineralised tissues, such as bone, antler and ivory. (19) Furthermore, small and wide angle X-ray scattering, cross-polarised light microscopy and scanning electron microscopy have all been used to characterise the orientation of the microlaminae sheets present in ivory, and have been used to identify species specific characteristics. (20,21) In addition to this, synchrotron radiation micro-computed tomography (SR-µCT) has been used to characterise the 3D morphology of ivory, antler, land mammal, and whale bones at a high resolution in order to differentiate them. (22) SR-µCT has also been used to study the 3D microtubule network to explain the appearance of the Schreger pattern. (23) In addition to species identification, the structure of the inorganic matrix of ivory can be studied using particle-induced X-ray emission (micro-PIXE) analysis in order to better understand the conservation state of an object. (24,25) Micro-PIXE can also be used to study trace metal compositions, which can allow for the distinction between ivory and bone objects, as well as the identification of the geographical origin of ivory objects, and the state of ivory preservation based on the fluorine content and MgO/CaO ratios. (26)(27)(28)(29)(30)(31) Destructive analysis can yield more specific or conclusive information regarding the age and taxonomy of a particular object. Of course, in such cases, a balance must be found between sampling enough material to have a conclusive result, and protecting the integrity of the object. For example, radiocarbon dating ( 13 C, 15 N and 86 Sr) of historic samples has been used to track the ivory trade throughout Africa, although such analyses typically require up to 50 mg of sample. (32)(33)(34)(35) Another destructive technique commonly used for species identification is peptide mass fingerprinting (PMF), also referred to as zooarchaeology by mass spectrometry (ZooMS). (36) ZooMS has proven to be an excellent tool to screen large numbers of samples quickly during large scale archaeological investigations. (37) However, when a limited amount of sample is available, a clear result is not always guaranteed. Liquid chromatography coupled to mass spectrometry (LC-MS)-based proteomics has a higher sensitivity and accuracy due to the additional separation dimension of the liquid chromatography, and the additional information gained through the MS/MS sequencing of the peptides. This technique has also successfully been used in archaeological investigations for the species identification of morphologically unidentifiable bone fragments, both in the mainstream data dependent acquisition (DDA), and, more recently, in a high throughput data independent acquisition (DIA). (38,39) In this work, we present the first example of minimally invasive LC-MS/MS-based proteomics used to perform taxonomic identification of ivory museum objects. This methodology was applied to a highly diverse group of objects based on their age, preservation state, and suspected species of origin from several of The Metropolitan Museum of Art's collections: Egyptian Art, Medieval Art, Musical Instruments, and The Michael C. Rockefeller Wing, that includes sub-Saharan Africa, the Pacific Islands, and North, Central, and South America collections. In addition to the analysis of museum objects, elephant tusks and hippopotamus teeth and bones collected in the early 1900s from the Mammalogy department of the American Museum of Natural History (AMNH) were sampled. These samples were used to construct an inhouse proteomic database of underrepresented species. Novel data analysis methodologies were developed to extract information from historical objects, as well as MS3 fragmentation methods to enable the differentiation between leucine and isoleucine isomeric residues for more confident species identification between species such as elephant and hippopotamus. Here we show the successful identification of a variety of species from the protein extracted from a minimal sample amount (i.e. small amount of powdered bone) from the studied objects based on the identification of multiple unique peptides that were fully fragmented across the peptide backbone.

Results

While proteomics was originally developed for biological applications, in recent years, it has been increasingly widely used to study museum and archaeological objects. Typically, proteomic analysis is used to either perform accurate identification of proteinaceous material, taxonomy identification and phylogenetic analysis, or better understand the preservation of ancient samples. (40)(41)(42)(43) A key benefit of using proteomics in comparison to DNA sequencing for such analysis is that generally, proteins persist longer in mineralised tissues than DNA. (44)(45)(46) Collagens, in particular type I, are the most prevalent structural proteins in mineralised tissues such as bones and ivory, and therefore the taxonomic identification detailed in this study is based on peptides identified from these proteins. However, the collagen sequence is highly conserved between species, and between closely related species, very few amino acid substitutions are present. In addition to this, many of the species from which ivory originates have not had their proteome fully sequenced, and there is little information available on public proteomic/genomic databases. For these reasons, in order to increase the likelihood of identifying taxonomically informative peptides, the sample preparation and analysis must be optimised to target collagens and maximise their coverage. In this study, a total of 15 objects in the Metropolitan Museum of Art collections were analysed varying in age? from ~4000 to 800 years old, provenance, and preservation condition (Table 1-1, Table S1-1). Table 1-2 shows the sequence coverage and the number of collagen peptides detected for each sample where a taxonomic assignment was possible.

2.1.Minimally invasive analysis

The development of proteomic protocols that require minimal amounts of sample material can enable access to precious museum and archaeological collections, as the sampling has a negligible impact on the integrity of the object. In order to perform successful minimally invasive analysis, it is necessary to miniaturise and optimise both the sample preparation and data acquisition methods.

Various sampling techniques have been proposed in order to limit the potential effect of the sampling on the object, such as the use of PVC-free erasers and microgrit films. (47,48) Other sampling techniques that have been proposed include enzyme functionalised films and sampling strips designed for clinical use. (49,50) It is important to note that the type of sampling performed must be designed and optimised for the specific material that will be sampled. Furthermore, in order to minimise the sample loss during protein extraction, steps such as integrating different chemical treatments into a single or few steps, and/or using miniaturized analytical workflows can be taken, both of which have already been demonstrated to improve recovery in the most challenging ancient samples. (47,51) In this analysis, we utilised a highly effective minimally invasive sampling technique using 6 µm diamond microgrit polishing films. (47,52) During sampling, 3-4 sequential rubbings were taken from an object, with the sampling taken at the same location each time. Each of these samples were visible as a small amount of powdered material on the surface of the microgrit film, pictured in Optimisation of this sampling technique using exemplar archaeological teeth showed that performing at least one rubbing before taking the sample that would be analysed significantly increases the sequence coverage of collagen, from around 30% coverage of collagen α1(i) and α2(i) in the first samples, to 70% of coverage for both in the second sampling. The effects of this sampling technique are summarised in Table S1-2.

A key step of the protocol used in this study was the evaporation of the TFA-based demineralisation solution, as this allowed for all the material extracted from the samples to be analysed in one injection. This means that the proteins solubilised by the demineralisation step, which would have usually been analysed as a different fraction or discarded, were combined with the acid insoluble fraction to increase the final signal of the sample. In addition to this, the extraction, reduction, and alkylation steps were performed simultaneously, in order to reduce sample loss caused by additional reaction steps. The benefits of this are demonstrated by the high sequence coverage and number of peptide identifications not only in the exemplar samples, but also in the museum objects. In the following examples, we present three objects that are made from a single piece of bone or ivory that originate from species whose collagen sequences are represented in public databases. In the first case we applied the minimally invasive method described above to the samples taken from the polished white keys of the Muselar Virginal (11.176.1, Johannes Ruckers, 1622). The resulting data showed an excellent sequence coverage of over 70% for collagens α1(i) and α2(i) (Table 1-2).

All three samples taken from the different keys were found to be made from bone from the Bos genus. An example is shown in Figure 1-2 of a peptide specific to the Bos genus, that was detected across all three samples taken. Details of the number of collagen peptides detected, can be found in Table 1-2, and a full list of the peptides identified across the three samples can be found in Table S1-3. Table S1-3 includes peptides that are also specific to the level of the Bovinae subfamily as well as to the Bos genus, as there is one species outside of the Bos genus to which many of the peptides listed are also specific. This case study benefited from the high sequence coverage of collagen identified using our minimally invasive method along with available sequence data in the public databases for the Bos genus and related species. In the next cases, a 13 th century Scandinavian ivory queen chess piece (2012.346) and an 18-19 th century Hawaii bone pendant (1979.206.1587) were identified as sperm whale (Physeter macrocephalus), based on the identification of 66 and 68 species specific peptides respectively (See Table S1-4 and Table S1-5). Unique portions of the collagen sequence were detected in the form of multiple peptides with different modifications, as well as missed and unspecific cleavages, with spectra that were fully fragmented across the peptide backbone, as shown in Figure 1-3. After the initial search with an untargeted database containing all the collagen sequences in Uniprot including whale collagens, a targeted database search was performed using the Odontoceti parvorder in NCBI, which is made up of toothed whales. This was performed as Physeter microcephalus is closely related to many other species of whale, including narwhal (Monodon monoceros), another common source of ivory. It should be noted that this identification of Physeter microcephalus was made based on the information available in online databases, and that, of the 138 species part of the Odontoceti parvorder, only 33 species have collagen sequenced (importantly, Monodon monoceros is sequenced and not identified here). Therefore, while this was a confident species assignment based on collagen sequences currently available, the species identification may be refined as public databases are updated.

Multi-component Studies

Other assignments were more difficult because of the complexity of the object's construction and preservation history. For example, it was challenging to assign a taxonomy to a ceremonial medieval saddle decorated with bone plaques (40.66.a,b, ca. 1400-1420) because, unlike the other objects in this study, the saddle is an assemblage of different materials, rather than a carving of a single piece of bone or ivory. It was believed to be made from antler, bone, and bone-based glue from various animals, including deer and cows. This significantly complicated the analysis as there was a possibility that samples could contain a mixture of collagen from different species. For this object, three samples from three different locations were analysed, detailed in Table 1-1. The different samples were concluded to be from the Bos genus or Cervidae family, but it was not possible to assign a specific species to the samples, due to the high conservation of the collagen sequences between species in both the Bos genus and Cervidae family. For Cervidae, the availability of data in the current databases was also a limitation (of the 251 descendants that make up the Cervidae family, only six have collagen sequenced on the NCBI database). Following the untargeted database search, a more targeted search was performed using a database from the infraorder Pecora (an infraorder of even toed hoofed mammals, including Bovinae and Cervidae).

For the body of the saddle (S2), 85 peptides unique to the Bos genus were identified, an example is shown in Figure 1-4A, and a list of the peptides identified is shown in Table S1-6. For the samples taken from the decorative trim (S1 and S3 from object 40.66a,b), both were concluded to contain collagen from the Cervidae family. The collagen α2(i) sequence between residues 707-736, which is specific to both the Cervidae and Elephantidae families was identified in both samples (Figure 1-4B). The collagen α2(i) sequence showed differences in other regions, which allowed confident differentiation between Elephantidae and Cervidae: the collagen α2(i) sequence between residues 947-972 is unique to the Cervidae family. It was therefore concluded that sample S1 and S3 both contained collagen from Cervidae, based on the identification of 16 and 30 informative peptides respectively, as listed in Table S1-7and Table S1-8.

2.3.Taxonomic analysis of collagens from underrepresented species in databases

2.3.1. Creation of databases of underrepresented species.

As discussed above, the samples analysed in this study were very diverse, and some were particularly challenging due to the suspected species of origin of the ivory. For example, ivory originating from Egypt is typically elephant (Loxodonta africana) tusks or hippopotamus (Hippopotamus amphibius) teeth. ( 53) These two species have not been well-studied using proteomics, and there is limited information available on public proteomic and genomic databases.

The collagen protein sequences present in public databases are either fragments (Hippopotamus amphibius) or reconstructed from DNA sequences (Loxodonta africana). (54,55) Considering this lack of reference information, we built our own proteomic database for the species Loxodonta africana and Hippopotamus amphibius. Samples were taken from the collections of the Department of Mammalogy at the American Museum of Natural History (AMNH) using the same minimally invasive sampling method applied to the museum objects. The information related to the samples from which the objects were taken is available in Table 1-1.

Using these exemplars, we were not only able to validate the species identification of the museum objects, but also identify residues in the collagen sequences that did not align with those in the public databases. For example, residue #455 in collagen α2(i) of Hippopotamus amphibius is reported to be P in the public databases, but multiple instances were found where residue #455 is in fact A. Table S1-9 shows an increase in the number of peptides identified for all the exemplar samples of Hippopotamus amphibius with an additional 30 collagen α2(i) peptides detected, when incorporating this substitution during the database search. This was confirmed through the identification of the same peptide sequence with different modifications, missed, or unspecific cleavages in all three exemplar samples (Table S1-10), of which an example spectrum is shown in Figure 1-5. This is a significant identification, as this peptide sequence is unique to Hippopotamus amphibius, and, when searching the data with a classical database search with P at residue #455, this part of the sequence was not identified, while when searching with A in this position, this portion of the sequence was covered by a significant number of peptides, as demonstrated in Table S1-10. This substitution was identified in all three exemplar samples, and in all of the historical samples that were identified to originate from Hippopotamus amphibious. In addition to the exemplar samples from Hippopotamus amphibius, three samples from Loxodonta africana (Savannah elephant) ivory were analysed to identify unique peptide markers that could be used to validate the species identification of the historical samples. A detailed study of the collagen sequence of Loxodonta africana used to search the data from the historical samples was required, as the sequences available on both the Uniprot and NCBI databases are reconstructed from DNA sequences. In particular, for collagen α1(i), there are inconsistencies between the sequences on the two databases, as shown in the sequence realignment in Figure S1-1. Therefore, we were able to use the data collected from the exemplar Loxodonta africana samples to confirm the collagen α1(i) sequence and confirm it across all three exemplar samples, as detailed in Table S1-11 and Table S1-12. In particular, a G residue was identified at position 326 of collagen α1(i) sequence (Table S1-11) and residues 548-551 of collagen α1(i) were confirmed to be GPAG (Table S1 -12). With these validated sequences, very high sequence coverage and peptides identification were identified for the exemplar samples, for sample 51949, 1293 collagen α1(i) peptides were detected, and 991 for α2(i)with a coverage of 73 and 76% respectively, more details are available in Table S1-9.

MS3 method to discriminate Leucine/Isoleucine.

A final hurdle in the challenge of identifying the taxonomy of a sample using LC-MS/MS-based proteomics is the presence of amino acids that have the same mass. When confirming a speciesspecific peptide, the same peptide, but with the equal mass amino acid, must also be controlled, e.g. leucine (L)/isoleucine (I) which have the same mass but two different chemical structures.

When trying to investigate historical objects, where we expected to identify either Hippopotamus amphibius or Loxodonta africana ivory, but a challenge stemmed from the fact that the publicly available protein sequences for Hippopotamus amphibius has a sequence uncertainty at every leucine or isoleucine residue. (54) When comparing the collagen sequences of Hippopotamus amphibius and Loxodonta africana, many of the identified differences between the sequences are between I/L (illustrated in Figure S1-2). Therefore, we have developed an MS3 fragmentation method to distinguish between them in order to have a higher confidence species identification. It should be noted that all the data discussed originates from samples taken using the minimally invasive sampling procedure.

Electron transfer dissociation (ETD) fragmentation was used in MS2, and low energy collision induced dissociation (HCD) fragmentation was used in MS3 targeting the z-ions from the MS2 fragmentation. This double fragmentation causes a radical rearrangement that results in a characteristic loss of either 29 Da (isoleucine), or 43 Da (leucine). (56,57) Using this fragmentation pathway, targeted and untargeted methods were tested. Both methods were successful, but it was found that the targeted method allowed for a more conclusive assignment. Figure S1-3 shows some examples of spectra that were observed when using an untargeted method, where it can be seen that the informative peaks are at a very low intensity. In the other method, peptides that contained either I or L were targeted to univocally confirm the amino acid identification. The method developed targeted specific regions of interest in the collagen sequences of both Hippopotamus amphibius and Loxodonta africana. Through the use of a targeted loss trigger, the signal to noise ratio of the MS3 spectra was greatly improved without losing sensitivity for the MS2 fragmentation in case the expected rearrangement reaction in MS3 does not take place. The samples from objects originating from Egypt presented more challenges than the others referenced in Table 1-1 for several reasons: the level of preservation due to the age of the objects (3900 -1070 B.C.) or their environment, the species that were expected to be identified, and potential previous conservation work that may be undocumented. These objects are precious and fragile archaeological objects and may have been treated when they were first collected. For three objects, species identification was performed using MS/MS spectra, and were expected to contain underrepresented species in databases such as Loxodonta africana and Hippopotamus amphibius.

Therefore, using the proteomic data collected from the AMNH exemplar samples, as well as the new protein sequences deduced from this data, a confident species assignment was reached for several objects. Hippopotamus amphibius, as this peptide has two amino acid substitutions compared to this region of the collagen α2(i) sequence of Loxodonta africana. It should be noted that this peptide contains a deamidated asparagine (N) which is chemically equivalent to aspartic acid (D), therefore the same sequence was also searched against the NCBI database with N substituted for D, in order to validate that the peptide was unique to Hippopotamus amphibius. The MS3 method was applied to historic samples and allowed us to distinguish between leucine and isoleucine. Figure 1-7 shows the use of this method applied to sample 66.99.1, where a characteristic loss of 29 Da, indicative of isoleucine, was observed for three peptides corresponding to the same collagen sequence with differing modifications and cleavages. Such experiments can serve as an additional tool to identify markers of specific peptides that are indicative of a certain species when those peptide markers contain leucine or isoleucine residues. For a number of the objects, it was not possible to identify the species, genus or family from which the ivory originated (Table S1-1). In many cases, collagen peptides were detected, but with low signal to noise ratio for MS and/or MS/MS spectra to confidently confirm the peptide sequence.

The unknown history of several objects makes it difficult to hypothesise on the causes of the poor preservation. For example, the sample Figurine or amulet of a hippo on a sled (26.7.1299, Egypt, ca. 2960-2649 B.C.), was identified to be Hippopotamus amphibius, but had very poor collagen recovery. Because of the small size of the object (2.5 cm/1 in.) and its fragile nature, only one rubbing was possible. As we previously demonstrated, a significantly reduced number of collagen peptides are detected in the first rubbing from an object. With this low collagen recovery, only 11 peptides unique to hippopotamus were identified during the preliminary database search (See Table S1-16), with poor quality MS/MS spectra. In order to extract the maximum amount of information, we used two approaches in order to more confidently perform the species identification: (i) by summing MS/MS spectra (for MS methods without dynamic exclusion) of a targeted peptide marker to increase the signal-to-noise ratio to more confidently confirm the amino acid sequence of the peptide (see Figure 1-8A) and, (ii) with the availability of the proteomic data from exemplar samples of Hippopotamus amphibius, we used a peptide mass fingerprint approach (PMF), but with the additional dimensions of the retention time and charge state (LC-MS-PMF) in order to confirm the presence of informative peptide peaks. The peptide-based approach applied to sample 26.7.1299 is shown in Table S1-17, where the peptides unique to Hippopotamus amphibius were detected both in sample 26.7.1299 and sample 113816, one of the exemplar samples that was analysed during the same sequence. The identification of multiple peptides based on the observed m/z values, peptide charge state and retention time alone, allowed for a more confident species assignment of Hippopotamus amphibius.

Discussion

We present here a proteomics workflow adapted to the analysis of ancient proteins from an extremely small amount of material. The successful analysis was made possible by the optimisation of the whole analytical workflow from the miniaturised and simplified sample preparation procedure to the optimized data acquisition and data processing steps. This study is also the first example of the use of LC-MS/MS-based proteomics to perform identification of a variety of species of ivory objects in a museum collection. Materials originating from hippopotamus, elephant, sperm whale, deer and cow were identified using these combined techniques. We were able to attribute a taxon with a high level of confidence to a number of objects where the species of origin was previously unknown, based on the detection of multiple unique peptide sequences. To achieve these results, another challenge to take into consideration for the differentiation between species or phylogenetic analysis was the high collagen sequence homology. Depending on the species from which the material originated, the type of object, the availability of sequence information, and the preservation state of the object, we have shown that a species identification can be reached with different levels of targeted data interrogation. For some objects and species that are of particular significance to cultural heritage but had been previously understudied using proteomics techniques, a more in-depth investigation into the collagen sequence itself was required, specifically to enrich the existing databases through the study of specimens in natural history collections. Our work has highlighted several inconsistencies in the current public databases (e.g. for Hippopotamus amphibius and Loxodonta africana), providing new and more specific information for species discrimination. Furthermore, we have proposed the use of two consecutive stages of mass spectrometric fragmentation to discriminate between leucine and isoleucine, a methodology that has not previously been applied to cultural heritage. In the future, if sequence reconstruction using proteomics becomes more prevalent, such a methodology can allow for the discrimination between leucine and isoleucine residues without the need to refer to DNA data. For samples where organic material was too poorly preserved to confidently assign a species based on a classical database search, we have proposed LC-MS-PMF mapping as an alternative taxonomic identification technique. Several pieces of information originating from the LC-MS data were successfully used including the m/z, the retention time and the peptide charge state in order to assign a taxon to poorly preserved objects.

Regarding the studied objects, this minimally invasive protocol allowed the unequivocal identification of animal species for ivory objects, which has a high impact on the analysis of cultural heritage objects where sampling is not possible. Previously, analysis would not have been permitted In relation to the other objects included in this study, identifying the taxonomy of the ivory or bone provides new information regarding material sourcing for several cultures and insight into the process of creating the object. For example, the Medieval Saddle (40.66a,b, Central European, ca. 1400-1420), was constructed from an assemblage of materials: a large ornately carved bone from the Bos genus, likely a pelvic bone given its size, was used for the body of the saddle, while small pieces of bone or antler from Cervidae was used to create a decorative trim. The different materials were possibly used because of their physical properties, most notably the size of the single Bos bone. (64) The species identification of ivory objects can also provide context and insight related to the societies from which they originate, such as the Queen chess piece made of whale ivory (2021.364, 13 th century), originating from Scandinavia, a region that has a history of nautical exploration, including whale hunting. (65 based on visual assessment and yet were identified as Bos from proteomic analysis. This suggests either contamination from animal glue from a previous undocumented treatment or that these objects are made from cow bone. The latter scenario is of particular interest in that the objects do not have the expected morphological features of bone from an archaeological context. The methods developed here will become part of the Metropolitan Museum of Art's routine scientific analysis and will be extended to the international cultural heritage community, allowing better characterisation and attribution of objects when desired and deemed appropriate by the collections, providing new information on the ivory trade, distribution, and use across cultures. Based on the demonstrated capabilities of the methods, we also anticipate the use of this new workflow in the context of ivory-related prosecutions and seizures.

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Materials and Methods

4.1.Samples

The objects analysed in this study are detailed in Table 1-1 and Table S1-1. 22 samples from 16

objects from the Metropolitan Museum of Art were analysed. In addition to this, 6 samples of ivory originating from Hippopotamus amphibius and Loxodonta africana were analysed from the Department of Mammalogy at the American Museum of Natural History. The human archaeological tooth and bone used to test the minimally invasive sampling procedure and as a control for the MS analysis respectively, were obtained from PACEA, UMR CNRS 5199, University of Bordeaux, France.

4.2.Sample Preparation

Samples were taken from collections at the Metropolitan Museum of Art, and Mammalogy at the American Museum of Natural History. Sampling was performed in areas that already displayed a flaw or damage, or areas that are not directly visible when the object is on display. There was little to no visible disruption of the surface appearance. Sampling was performed by rubbing three to four different pieces of 6 µm diamond microgrit film (Precision Fiber Products) successively over the same area of the surface of the object, removing an immeasurably small amount of material.

The successive rubbings were labelled 1, 2, 3, and 4. Table 1-1 and Table S1-1 show the rubbing that was analysed for each sample. During the preparation of samples, a microgrit film that had been used to sample a human archaeological bone was used as a control standard, and a protocol blank was prepared and analysed alongside the samples.

Unless otherwise stated, all reagents were obtained from Merck (Darmstadt, Germany).

The samples were prepared for proteomic analysis using a filter aider digestion protocol, optimised for minimal sample amounts, adapted from. ( 67 Following the extraction step, the digestion was performed by transferring the extraction solution into a 10 kDa Amicron filter (Merck, Darmstadt, Germany). A buffer exchange was performed by washing each sample three times with a digestion buffer (50 mM ammonium bicarbonate, pH 8.8), after which 100 µL of digestion buffer was added to the filter. The collection tube for the filters was replaced, 10 µL of 0.01 µg/µL trypsin (Promega, Madison, USA) was then added to each filter, and parafilm was wrapped around the top of each tube to prevent evaporation during the digestion.

Digestion was performed overnight at 37°C, in the dark.

In order to recover the peptides after digestion, samples were centrifuged at 13,200 g for 10 min, 50 µL of digestion buffer was then added to the filters, and the samples were centrifuged for another 10 min (this step was performed 3 times in total). At this point, the filtrate (containing the digested peptides) was transferred to a new Eppendorf tube, acidified with 2 µL of TFA. An extraction with ethyl acetate was then performed, by adding 200 µL of ethyl acetate, aspirating several times with the pipette to mix fully. An additional 800 µL of ethyl acetate was then added to the samples, which were then vortexed, and centrifuged for another 10 min at 13,200 g. The top, organic, phase was then removed, and the extraction with 800 µL of ethyl acetate was performed two more times. After this, samples were heated at 60°C for 5 min to remove residual ethyl acetate, and then were evaporated to dryness in a vacuum centrifuge. Once dry, 100 µL of 1:1 water:methanol was added, and the samples were evaporated to dryness again, in order to remove residual volatile salts.

Samples were then reconstituted with 2 µL of 0.1% formic acid for analysis.

4.3.Mass Spectrometry Analysis

LC-MS/MS analysis.

Samples were analysed using a Dionex Ultimate 3000RSLCnano system coupled to an Orbitrap Eclipse mass spectrometer (ThermoFisher Scientific). Both settings with and without preconcentration column were evaluated as follows, but it was confirmed that the setting without precolumn allowed for a higher sensitivity. 1 µL of sample was injected onto a C18 Pepmap Neo trap column (ThermoFisher Scientific), which was then washed in backflush mode before being injected onto a PepMap RSLC C18 column (2 µm, 100 Å, 75 µm x 50 cm, ThermoFisher Scientific), with a gradient of 146 min and a flow rate of 300 nL/min. As mentioned earlier, tests were also performed without the trap column. Where buffer A was 0.1% formic acid in water, and buffer B was 20% water/80% acetonitrile with 0.1% formic acid, the following gradient was used: 0-3 min, 4% B, 3-96 min, increase to 40% B, 96-121 min, increase to 90% B, 121-125 min, hold at 90% B, 125-126 min, decrease to 4% B, 126-146 min, hold at 4% B. Peptides were detected using an Orbitrap Eclipse mass spectrometer in positive mode. Both MS1 and MS2 spectra were collected in the Orbitrap analyser, with a mass range of 375-1500 and 120k resolving power for MS1 and a mass range of 50-2000 and 30k resolving power for MS2. MS2 spectra were acquired using a data dependent analysis method, including the following MS2 filters; MIPS (Peptide), charge state (2-7), dynamic exclusion (30 s), with a 100% AGC target. MS2 fragmentation was performed with HCD at 28%, using the automated injection time mode.

During analysis, a tryptic digest of human archaeological bone was analysed at the beginning and end of the sequence in order to monitor the performance of the instrument during analysis. In addition to this, a minimum of two analytical blanks were placed between every sample in order to avoid carry over between the runs, and before samples were injected, the data from the blank runs were searched with PEAKS to confirm that there was no carry over.

MS3 method for leucine/isoleucine discrimination

In order to distinguish between leucine and isoleucine residues, both targeted and untargeted methods were tested. These tests were developed using the same Dionex Ultimate 3000RSLCnano system coupled to an Orbitrap Eclipse mass spectrometer as discussed above, with the same 146 min gradient. For the targeted tests, peptides of interest containing leucine or isoleucine residues were identified and m/z values with a charge state of more that 3+ were selected using the targeted mass trigger followed by the targeted mass inclusion filters available on Xcalibur (version 4.2, ThermoFisher Scientific, Massachusetts, USA). MS2 fragmentation was performed using ETD, with an accumulation time fixed at 300 ms, using the calibrated charge dependent ETD parameters.

z-ions corresponding to the fragment beside the leucine/isoleucine residue were then targeted using the mass trigger and inclusion filters and fragmented using low energy HCD fragmentation (12%).

A targeted loss trigger was then included so that if a loss of either 29 or 43 Da was detected, three more MS3 spectra would be collected. 100% AGC target and 200 ms injection time were used for MS3. The data were then manually analysed to identify the characteristic loss of either 43 or 29

Da, indicative of leucine or isoleucine, and spectra originating from the same z-ion fragment were averaged to reduce the signal to noise ratio.

For the untargeted tests, a method was adapted from (68). In short, for each ion that is fragmented, two MS2 spectra are recorded, one using HCD fragmentation (stepped collision energy, 25, 35, 50 %), the second using EThcD fragmentation (27% supplemental activation). For the analysis of the peptide sequence, the scans using HCD fragmentation are more informative, but to distinguish between leucine and isoleucine, the scan using EThcD fragmentation is used. In order to perform the differentiation, data were searched manually, for characteristic w-ions that have a loss of either 29 or 43 Da, which can indicate the residue at a certain position.

4.4.Data processing

Proteomic data analysis was performed with PEAKS X (Bioinformatic Solutions, Waterloo, Canada). Multiple interrogations were performed on each dataset. The first interrogation was performed using a database containing all of the collagen sequences in the Uniprot database (downloaded 16/06/2022), while the second interrogation was performed with a targeted database containing the sequences of species known to produce ivory from the NCBI database (downloaded 02/03/2022, Odontoceti database downloaded 03/04/2022, Pecora downloaded 08/03/2022). The search was performed with a parent mass error tolerance of 10.0 ppm, and a fragment ion error tolerance of 0.02 Da. The enzyme used in the search was trypsin, with a semi-specific cleavage and a maximum of three missed cleavages. Carbamidomethylation was set as a fixed modification, while deamidation (NQ), and oxidation (MP) were set as variable modifications. An FDR of 0.1% was used, with 5 variable modifications allowed. Peptides identified as species specific through the targeted database search were then manually controlled by searching the peptide sequence using the NCBI BLAST database search tool. The COBALT algorithm, also available on NCBI, was used when performing sequence alignments to identify informative regions of the sequence. If a speciesspecific peptide contained leucine or isoleucine, the same peptide sequence with the respective amino acid was searched, and only considered a species specific peptide if no other species were hits for the second search. For manual species identification, data visualisation was performed in Xcalibur version 4.2.

Introduction

The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) is an international agreement that aims to restrict the trade of materials originating from endangered species. CITES currently recognises two species of elephant, the Asian (Elephas maximus) and Therefore, the species identification of ivory is crucial in the context of prosecutions relating to elephant poaching and ivory trafficking, as well as in the context of CITES compliance. The official guidelines from CITES and the Word Wildlife Fund (WWF) cite the use of morphological characterisation, particularly the identification of species specific Schreger lines, and spectroscopic analysis, namely Fourier transform infrared spectroscopy (FT-IR) and Raman, as non-invasive analysis techniques to identify the species from which the ivory originated. (1) However, these techniques are highly dependent on the state in which the ivory was received, as, for example, when ivory is carved, the identifying morphological information is often lost. DNA analysis is considered the gold standard for species identification of ivory, despite being a destructive technique that requires larger amounts of sample. It is particularly used for differentiating between different elephant species, as their ivories are morphologically similar. (6-9) Finally, the analysis of the crystal structure of the mineral matrix itself can also be used to differentiate between species, using techniques such as X-ray diffractometry, small and wide angle X-ray scattering, and synchrotron radiation micro-computed tomography. (10)(11)(12)(13) In this study, we propose the use of mass spectrometry-based proteomics in order to classify elephant ivories. The peptide mass fingerprinting technique ZooMS (Zooarchaeology by mass spectrometry), which uses matrix-assisted laser dissociation ionisation time of flight mass spectrometry (MALDI-TOF-MS), is routinely used for species identification of archaeological materials. ( 14) Here species identification is performed through the analysis of tryptic collagen peptides, through the comparison of the peak lists from the acquired mass spectra against database values. ( 15) ZooMS has previously been used for the identification of elephant ivory, in combination with isotope analysis to have additional geographic information. ( 16) ZooMS is a particularly useful technique for the large-scale screening of a high number of samples, but lacks information related to the amino acid sequences of the peptides analysed. For this reason, we use LC-MS/MS based proteomics, in order to analyse the protein sequences in elephant ivories. As stated in the previous chapters, LC-MS/MS methods have been used for the species identification of bone samples, using DIA-based acquisition methods, (17) or DDA acquisition methods as also shown in Chapter 1 with the identification of the species of bone and ivory objects from the Metropolitan Museum of Art. Benefits of such tandem mass spectrometry methods include higher sensitivity due to the LC separation, and that the fragmentation of the peptides allows for identification of the amino acid sequence.

Here, we present the analysis of ivory samples from three different species of elephant, Elephas maximus, Loxodonta africana, and Loxodonta cyclosis. The goal of this study was to demonstrate the identification of different proteotypes of ivory, proteotype being the protein equivalent of the genotype. (18) The analysis was performed on microsamples obtained from the Department of Mammalogy at the American Museum of Natural History, New York, with the goal of identifying discriminating substitutions in the collagen proteins. This included comparison of the collagen sequences currently available on online proteomic databases to obtained proteomic data, as well as the use of error tolerant search engines to identify unreported amino acid substitutions. Finally, key peptide markers were identified to differentiate between proteotypes more quickly in future analysis.

Table 2-4, where two to three individuals from Elephas maximus (A2-3), Loxodonta africana (S1-3), and Loxodonta cyclosis (F1-3) were sampled both at the base and end of the tusk (shown by either a 1 or 2 following the sample number, i.e. A11 and A12). From each location, four samples were taken using the successive minimally invasive sampling method described in Chapter 1, wherein a microgrit film was used to remove a small amount of dust from the surface of the tusk (the successive samples labelled from A to D, i.e. sample A12D).

2.2.Sample preparation

The microgrit samples were prepared using the optimised sample preparation procedure described in Chapter 1. In short, microgrit films were places in 1.5 mL Eppendorf tubes, and 200 µL of 5%

TFA was added to each sample. Demineralisation was performed for 30 min at room temperature, at which point the microgrit was removed using tweezers, and the TFA was evaporated using a vacuum centrifuge. Following this, 200 µL of extraction buffer (6 M GuHCl, 100 mM Tris HCl, 5 mM TCEP and 15 mM CAA) was added to each sample, and extraction was performed at 60 °C for one hour, in the dark. After extraction, the entirety of the extraction buffer was transferred to a 10 kDa Amicon centrifugal filter, and centrifuged at 13,200 g for 20 min. Following this, a buffer exchange was performed by adding 200 µL digestion buffer (50 mM ammonium bicarbonate, pH 8.8) and centrifuging at 13,200 g for 20 min. This step was performed three times. Finally, the Amicon filter was transferred to a new collection tube, 100 µL of digestion buffer was added to the filter, along with 10 µL of 0.01 µg/µL trypsin. Digestion was performed at 37 °C overnight, in the dark, and digestion was stopped by centrifuging the sample at 13,200 g for 20 min. 50 µL of digestion buffer was then added to the filter, and the samples centrifuged again. This step was performed three times. The filtrate was then transferred to a new Eppendorf tube, an ethyl acetate extraction was performed, and the samples were evaporated in a vacuum centrifuge. For analysis, samples were resuspended in 3 µL of 0.1% formic acid.

2.3.LC MS/MS analysis

100 min gradient

Samples S11D, S21D and S31D were analysed in the context of Chapter 1, but the data obtained were reanalysed in this study.

Samples A21C, A31C, F12D, F22D, F32D, S12C, S11D, S12D, S22D, S31D and S32C were analysed using an Ultimate 3000 nanoLC system coupled to an Orbitrap Eclipse mass spectrometer.

1 µL of sample was injected onto a C18 Pepmap trap column (0.3 × 5 mm, C18, 5 µm, ThermoFisher Scientific), which was then washed in backflush mode before being injected onto a

PepMap RSLC C18 column (2 µm, 100 Å, 75 µm x 50 cm, ThermoFisher Scientific), with a gradient of 146 min and a flow rate of 300 nL/min. Where buffer A was 0.1% formic acid in water, and buffer B was 20% water/80% acetonitrile with 0.1% formic acid, the following gradient was used: 0-3 min, 4% B, 3-96 min, increase to 40% B, 69-121 min, increase to 90% B, 121-125 min, hold at 90% B, 125-126 min, decrease to 4% B, 126-146 min, hold at 4% B. Peptides were detected using an Orbitrap Eclipse mass spectrometer in positive mode. Both MS1 and MS2 spectra were collected in the Orbitrap analyser, with a mass range of 375-1500 and 120k resolving power for MS1 and a mass range of 50-2000 and 30k resolving power for MS2. MS2 spectra were acquired using a data dependent analysis method, including the following MS2 filters; MIPS (Peptide), charge state (2-7), dynamic exclusion (30 s), with a 100% AGC target. MS2 fragmentation was performed with HCD at 28%, using a fixed accumulation time of 100 ms.

Samples F31D, and S12D were analysed using a Vanquish Neo nanoLC system coupled to an Orbitrap Eclipse mass spectrometer. 1 µL of sample was injected using the direct injection mode onto a PepMap Neo column (75 µm × 500 mm, C18, 2 µm, 100 Å, ThermoFisher Scientific), using the fast loading injection method with CombinedControl. A gradient of 96 min was used with a flow rate of 0.3 µL, where buffer A was 0.1% formic acid, and buffer B was 20% 0.1% formic acid with 80% acetonitrile. Following the injection, the following gradient was used: 0 -93 min: linear gradient from 4 to 40% B, 93 -94 min: increase from 40 to 90% B, 94 -98 min: hold at 90% B.

Following this, 5 column volumes of 100% A were used to re-equilibrate the column using the fast equilibration method with CombinedControl. The same mass spectrometry method described above was used.

10 min gradient

A short 10 min, targeted method was also developed. Here, analysis was prformed ona Vanquish Neo coupled to an Orbitrap Eclipse mass spectrometer (ThermoFisher Scientific). 1 µL of sample was injected using the direct injection mode onto a PepMap Neo column (75 µm × 150 mm, C18, 2 µm, 100 Å, ThermoFisher Scientific), using the fast loading injection method with

CombinedControl. An active gradient of 10 min was used with a flow rate of 0.3 µL, where buffer A was 0.1% formic acid, and buffer B was 20% 0.1% formic acid with 80% acetonitrile. Following the injection, the following gradient was used: 0-1 min: hold a 4% B; 1 -8.5 min: linear gradient from 4 to 25% B, 8.5 -11 min: increase from 25 to 35% B, 11 -12 min: increase to 90% B; 12 -20 min: hold at 90% B. Following this, 5 column volumes of 100% A were used to re-equilibrate the column using the fast equilibration method with CombinedControl.

Peptide were analysed using the Orbitrap Eclipse mass spectrometer. MS1 spectra were collected in the Orbitrap analyser at a resolution of 60,000, with a mass range from 375-1500. MS2 spectra were acquired in the ion trap using a data dependent analysis method, including the following MS2 filters; MIPS (Peptide), charge state (2-7), dynamic exclusion (30 s), precursor intensity (1.0e3).

An isolation window of 1.6 m/z was used, with HCD fragmentation at 30%, and the ion trap scanning in rapid mode. The AGC target was set at 75%, with an automated injection time.

This MS method was used both with and without an inclusion list. When the inclusion list was included, the mass and charge state of the peptides identified in Table 2-2 were included as a filter before the MS2 fragmentation.

2.4.Data Analysis

Data analysis was performed using PEAKS X. Data were searched with a mass tolerance of 10 ppm in MS1 and 0.02 Da in MS2. A semi-specific tryptic digestion was specified, with an allowance of 3 missed cleavages. The following modifications were specified, variable: oxidation (MP), deamidation (NQ) and fixed: carbamidomethylation (C). Data were searched against databases for the species Elephas maximus and Loxodonta africana (downloaded from NCBI on 10/01/2023). Data were analysed using the DE NOVO, PEAKS, PEAKS PTM and SPIDER algorithms.

Results and Discussion

As demonstrated in Chapter 1, the microsamples allow for a very strong signal with high quality data, as shown in Figure 2-1. As such, it is possible to confidently identify amino acid substitutions based on the identification of multiple peptides with different modifications, with missed and unspecific cleavages, based on high quality MS2 fragmentation spectra.

3.1.Analysis of collagen sequences available on public databases

Five proteins were consistently identified, collagen 1α(i), 2α(i), 1α(ii), 1α(iii), and 2α(v), as described in Table 2-1. Therefore, these proteins were targeted when identifying residues that could be used to discriminate between elephant proteotypes.

Identification of amino acid substitutions between species

The sequences of the identified proteins of both Elephas maximus and Loxodonta africana were then aligned using the COBALT algorithm available on NCBI, in order to identify substitutions between the two species based on sequences calculated from DNA sequences, as shown in Figure 2-2 and Figure S2-1. Based in the sequence coverage identified in the microsamples, the substitutions at position 384 and 993 in collagen 2α(i) were identified as sites that could be confidently used to discriminate between the different proteotypes, as they were consistently detected in the microsamples. for the identification of two potential positions that can be used to discriminate between elephant proteotypes.

As such, the data from the three species of elephant were all searched using a FASTA database of both Elephas maximus and Loxodonta africana, this search was performed using the DE NOVO, PEAKS DB, PEAKS PTM and SPIDER algorithms available on PEAKS X. Due to the heterogeneity of the collagen protein, multiple peptides can be identified that correspond to the same portion of the peptide sequence, which can be used as additional pieces of evidence in the case of the identification of a substitution. Using the SPIDER algorithm, which searches for amino acid substitutions based on sequence homology, it was possible to identify the same substitutions identified in Figure 2-2 in the proteomic data in multiple different peptides. As there were no collagen sequences available for Loxodonta cyclosis, the data from these samples were search using both FASTA databases from Elephas maximus and Loxodonta africana, and it was found that the collagen type I sequences between Loxodonta cyclosis and Loxodonta africana were identical. This was to be somewhat expected, since collagen is a structural protein, and as such is highly conserved between species. Therefore, since the two species are very closely related, it was not necessarily expected that it would be possible to distinguish between the two species based on the sequences in collagen type I alone.

3.3.Identification of specimens

Nine individual tusks were analysed from the Department of Mammalogy at the American Museum of Natural History. Of the ivory specimens in the collection, these tusks were chosen because their species assignment was the most confident, either based on DNA analysis, or the context in which the specimen was collected. We have shown that proteomics can be used to confidently differentiate between elephant proteotypes based on the identification of high quality MS2 fragmentation spectra, and on the identification of peptide markers that can be used to differentiate based on a peptide mass fingerprinting approach (as shown in Figure 2-3, Figure 2-4 and Table 2-2). This analysis was performed through sequence homology searches with the PEAKS SPIDER algorithm, which allowed for the identification of key markers in the sequence, which can be used as markers so that future samples can be analysed more rapidly. The taxonomic identification of the samples from the AMNH collection are summarised in Collections in natural history museums are an untapped source of biological information, and represent a large resource that could be used to study biodiversity of both extinct and extant animal and plant species. However, using such collections can come with some caveats, as museums move towards digitising their collections, their vast size and age makes them vulnerable to human error from a multitude of sources. (19) As museums revisit their collections in the process of digitising and better characterising them, there are known cases where samples have been found to be misassigned. One key example is the model sample on which the species Elephas maximus was defined, held at the Swedish Museum of Natural History, which, when reanalysed with DNA sequencing, was reassigned as a specimen from the Loxodonta genus. (20) In the case of the samples analysed from AMNH, the species assignment was mostly performed based on geographical data received at the time that the piece was added to the collection. In addition to this, the collection was reorganised in 2020-2021 with a new geotagging system to better catalogue the collection. Having identified key peptide markers that can be used to differentiate between elephants species, a rapid targeted LC-MS method was developed in order to allow for a fast discrimination of the two species. Here, an active gradient of 10 min was used, made possible with the use of a 15 cm capillary column, rather than the 50 cm column that was used in the previous analyses, as shown in Figure 2-5. Here, a targeted inclusion list of the ions and their charge states detailed in Table 2-2 was included in the method to decrease the data complexity and allow for rapid data analysis. As shown in Figure 23456, where sample A3 was analysed, one can confidently identify peaks corresponding to the informative peptide sequences. Data processing of MS and MS/MS spectra was performed as previously described. The strength of this method is that confident species discrimination can be performed on the bases of a few peaks, and the EIC trace is sufficient to differentiate between the species, without the need for complex, and more expensive, proteomics methods.

A2 (n=1) N I A3 (n=2) N I S1 (n=3) S V S2 (n=2) S V S3 (n=2) S V F1 (n=1) S V F2 (n=1) S V F3 (n=2) S V

Conclusions

Here, we have shown the confident identification of peptide markers using mass spectrometry, that enables the distinction of elephant proteotypes. The identification of these highly informative amino acid substitutions was performed based on high quality MS/MS fragmentation spectra, as well as the identification of multiple peptides corresponding to the same sequence with different modifications and missed/unspecific cleavages. This allowed for the identification of informative peptide markers that were identified across multiple individuals, and in repeated samplings from the same individual. Further work on this topic should include the integration of more genomic data from elephants as and when it becomes available, to better understand the genetic variance within the Elephantidae family.

Chapter 3: High throughput analysis of bone fragments from Le Piage Abstract

Chapter 3 builds on the developments of Chapter 1 and Chapter 2. While in Chapter 1, the main goals were related to maximising the sensitivity of the proteomics analysis, here, an additional goal was to develop a faster analysis workflow. The samples used in this study originate from Le Piage, an Upper Pleistocene archaeological site in southwest France, and consist of 12 samples that were assumed to be reindeer, and one sample suspected to be human, based on previous morphological analysis. In this study, several developments were made:

• Adaptation of the minimally invasive protocol developed in Chapter 1 for the analysis of archaeological samples.

• Development of more rapid analysis methods fitted to the study of large cohorts of archaeological samples.

• Involvement in the development of a new bioinformatic tool based on combined data processing algorithms, including artificial intelligence and allowing for a semiautomated species identification of samples based on the collagen peptides detected.

This study, through the species identification of several bone fragments from Le Piage, gave more context based on proteomic evidence of the occupation of the site during the Proto-Aurignacian period. Here we show the first molecular evidence that Anatomically Modern Humans were inhabiting the site, through the identification of collagen peptides specific to

Homo sapiens. This study has laid the groundwork for future high throughput campaigns of large number of archaeological samples, both through the development of faster analysis methods, and the initial development of a data analysis tool to perform automated species identification.

Introduction

Molecular analysis techniques have quickly become a new tool to characterise objects found in archaeological sites. The use of DNA sequencing, species specific assays, and, more recently, proteomics, have provided new tools to analyse the preserved organic material in such objects.

(1, 2) All of these techniques are widely used in the field, with the chosen technique depending on the question that is being asked for the object of interest. (3)

The information that can be obtained from proteins and DNA differs in the fact that DNA can provide information relating to a sample on an individual level, whereas proteins can provide information on a species level. Therefore ancient DNA analysis can provide information relating to both evolution and migratory patterns of a population, and is a field that has exploded since the development of high throughput sequencing methods. (4, 5) However, the technique has significant disadvantages such as the sample size required for analysis, amplification bias, and the poor preservation of DNA in particular environments. (5,6) In contrast, proteins are better preserved than DNA, and significantly less sample is required to perform a successful analysis, as demonstrated in Chapters 1 and 2. (7,8) The preservation of proteins in mineralised tissues such as bones and teeth is generally due to their roles as structural proteins or their strong interaction with the mineral matrix. (9)(10)(11) In archaeological analysis, structural proteins, predominately collagens, are used for taxonomic identification, while proteins found in enamel are more phylogenetically informative. (12,13) For the analysis of large cohorts of archaeological bones and teeth for species identification, the protein collagen type I is targeted. Due to its role as a structural protein, the sequence is highly repetitive and there are very few amino acid differences between species. ZooMS (Zooarchaeology by Mass Spectrometry), is based on the targeted peptide mass fingerprinting of collagen peptides extracted from archaeological bones and teeth to perform species identification. ( 14

19)

There are, however, some disadvantages to the use of MALDI-TOF-MS in comparison to other protein analysis methods, such as LC-MS/MS-based techniques, namely the limited dynamic range and the low confidence in identification since assignment is done manually based on mass lists and not peptide sequences. LC-MS/MS-based proteomics, which has a higher sensitivity and allows for MS/MS-based peptide sequencing, tends to only be used for targeted applications, due to the cost and time associated with the analysis. (20,21) However, recent advances in proteomics have resulted in the field moving towards high throughput analysis methods targeting the analysis of single cells and population proteomics. (22,23) This is in part due to instrumentation developments that allow for shorter analysis times using higher flow rates in the chromatographic separation dimension, and significantly faster data acquisition in new generation mass spectrometers. ( 24) Such methods have previously been applied to large cohorts of archaeological samples using a DIA acquisition method. ( 25) On a limited number of species, direct DIA and library-based DIA showed respectively a better species separation or the best species identification, however, the developments showed that an extension of the protein database and the set of spectral libraries are required.

Here, a new analysis workflow for the species identification of archaeological bones based on a DDA LC-MS/MS method is presented. Shorter LC gradients with MS methods adapted for faster analysis were developed based on archaeological bone collagen extracts. In addition to this, a new semi-automated data analysis workflow is presented in order to aid in species identification, which has previously been performed manually. This workflow was applied in the high throughput analysis of a cohort of archaeological samples from Le Piage archaeological site in southwestern France, with an age of about 40,000 years.

Methods

2.1.Samples

15 bone fragments from Le Piage Upper Pleistocene site were analysed, detailed in Table S3-1. All samples were taken from the b8 lithofacies and the square I5, subsquare C (Bordes, com.

pers.). This b8 lithofacies defined by Bordes is the equivalent to the layers J and K identified by Champagne and Espitalié as Proto-Aurignacian (circa 42.8 to 40.2 Ky cal BP). (26)(27)(28) These samples were chosen as one sample (LIP001) had previously been identified as human based on morphological analysis, and so other samples excavated in the same layer were also analysed to provide context for this sample . (29) Modern reindeer samples were also analysed as references. The model reindeer samples were collected by Cédric Beauval in 1999, in northern Norway, between Varangerbotn and Neiden.

They consist of one proximal metacarpal fragment (rang 150) and a phalanx 2 (rang 1709), it is unlikely that they originate from the same individual.

2.2.Sample Preparation

The sample preparation procedure developed in Chapter 1 for the minimally invasive analysis of ivory samples was followed. In short, sampling was performed using 6µm microgrit polishing films, three samplings were performed on the same area of the object, and the third sampling was taken for proteomic sample preparation. Pictures identifying the sampling site and the visual effect of the sampling are shown in Figure 3-1.

Microgrit films were places in 1.5 mL Eppendorf tubes, and 200 µL of 5% TFA was added to each sample. Demineralisation was performed for 30 min at room temperature, at which point the microgrit was removed using tweezers, and the TFA was evaporated using a vacuum g for 20 min. 50 µL of digestion buffer was then added to the filter, and the samples centrifuged again. This step was performed three times. The filtrate was then transferred to a new Eppendorf tube, an ethyl acetate extraction was performed in order to remove contaminants, and the samples were evaporated in a vacuum centrifuge. For analysis, samples were resuspended in 3 µL of 0.1% formic acid.

All objects except for object LIP001 were sampled using the minimally invasive sampling procedure described in Chapter 1. Object LIP001 was sampled used a small hand drill, and 10 mg of bone powder were available for analysis. The same sample preparation procedure detailed above was followed, but on a different day, except that the remaining pellet after demineralisation was also prepared, and the two fractions were combined before analysis.

2.3.LC-MS/MS Analysis

Samples that were sampled using the minimally invasive protocol were analysed using a Dionex Ultimate 3000RSLCnano system coupled to an Orbitrap Eclipse mass spectrometer (ThermoFisher Scientific). 1 µL of sample was injected onto a C18 Pepmap Neo trap column (ThermoFisher Scientific), which was then washed in backflush mode before being injected onto a PepMap RSLC C18 column (2 µm, 100 Å, 75 µm x 50 cm, ThermoFisher Scientific), with a gradient of 45 min and a flow rate of 300 nL/min. Where buffer A was 0.1% formic acid in water, and buffer B was 20% water/80% acetonitrile with 0.1% formic acid, the following 45 min gradient was used: 0-3 min, 4% B, 3-20 min, increase to 40% B, 22 min, increase to 90% B, 24 min, hold at 90% B, 25 min, decrease to 4% B, 25-45 min, hold at 4% B. Peptides were detected using an Orbitrap Eclipse mass spectrometer in positive mode. Both MS1 and MS2 spectra were collected in the Orbitrap analyser, with a mass range of 375-1500 for MS1 and the 'Normal' mass mode for MS2, with a resolution of 60,000 and 30,000 in MS1 and MS2 respectively. MS2 spectra were acquired using a data dependent analysis method, including the following MS2 filters; MIPS (Peptide), intensity (4.5e5), charge state (2-7), dynamic exclusion (30 s). MS2 fragmentation was performed with HCD at 28%, using a 100 ms injection time.

During the analysis, one blank injection between each sample was used. A standard tryptic digest of collagen from a human archaeological bone was additionally used as a standard sample in order to track the performance of the instrument during the analysis, with an injection at the beginning and end of the sequence, as well as two during the sequence in between the samples from Le Piage.

For the analysis of sample LIP001, a Vanquish Neo chromatography system was used, coupled to an Orbitrap Eclipse mass spectrometer. Here, 1 µL of sample was injected directly onto a Neo PepMap column (0.3 × 5 mm, C18, 5 µm, ThermoFisher Scientific), with the following gradient 0-93 min, increase from 4 to 40% B, 94 min, increase to 90% B, 98 min, hold at 90% B. Following this gradient, a column equilibration of 5 column volumes was used, with a maximum allowed pressure of 1400 bar. Peptides were detected using an Orbitrap Eclipse mass spectrometer in positive mode. Both MS1 and MS2 spectra were collected in the Orbitrap analyser, with a mass range of 375-1500 for MS1 and the 'Normal' mass mode for MS2, with a resolution of 120,000 and 30,000 in MS1 and MS2 respectively. MS2 spectra were acquired using a data dependent analysis method, including the following MS2 filters; MIPS (Peptide), intensity (4.5e5), charge state (2-7), dynamic exclusion (30 s). MS2 fragmentation was performed with HCD at 28%, using a 100 ms injection time. After de novo sequencing, the PSMs identified by each de novo tool are mapped to each spectrum, and the scores from each of the search algorithms are converted into a score that can be compared between the four results. The de novo assignment with the best score (score < threshold) for each analysis is retained.

2.4.Data Analysis

Following the PSM consensus step, peptide assembly is performed by aligning the PSM peptide sequences and using kmer and De Bruijn graph methods to produce contigs. The first peptide sequences identified in PSMs are considered as kmers, and aligned based on their similarity with a specific overlap size. After this first alignment, the kmer sequences are condensed into contigs by removing the repeating sequences in the alignment. Following this, the contigs are then realigned in a second phase to produce the consensus contigs file. Such contigs therefore represent sections of the protein sequence that are well covered by the identified PSMs. Here, the following parameters can be specified; the size of the kmer, the permitted size of the kmer overlap, then permitted overlap size for the second alignment of the merged contigs, and then the number of gaps authorised.

Following the consensus peptide assembly, a list of peptide sequences from the de novo consensus step, and a list of contigs from the assembly step, is produced, which can then be mapped onto the protein sequence, and used for taxonomic assignment. For the taxonomic assignment, the final contig and consensus peptides list are compared against the protein sequences available on the online public databases UniProt and NCBI, using the MMseqs2 and Proteoclade tools. ProteoClade is a library tool that allows for the taxonomic annotation of peptide sequences from bottom up data, while MMseq2 allows the user to search and cluster large protein data sets according to their sequence. (35,36) For the analysis of the data generated in this project, the following parameters were used; an MS1 error of 10 ppm, MS2 error of 0. is considered to be made by Neandertals, although this has now been brought into question.

(13) Whether the lithic industries from the Proto-Aurignacian period can be attributed to Neanderthals or Anatomically Modern Humans is unknown.

In the initial excavations at Le Piage, it was noted that the two key stratigraphic layers corresponding to the early Aurignacien and Châtelperronien periods were somewhat mixed. In particular, it was noted that tools from the Aurignacien period were found in the Châtelperronien layer. (26,38,39) This complex stratigraphy of the site was at first attributed to the two populations living in the site at the same time. However, later analysis of the observed location of the Aurignacien lithics found in the Châtelperronien layer concluded that these lithics had in fact been deposited in this layer from above at a later time. (40,41) Later excavations lead by Jean-Guillaume Bordes and Foni Lebrun-Ricalens from 2000-2019 further confirmed that this mixed stratigraphy was caused by sedimentological and taphonomical processes related to natural changes to the landscape over time. (27,41) Until now, the conclusions drawn at Le Piage have been based mostly on the stratigraphic analysis of sediment and morphological analysis of lithics found at the site, as well as the discoveries at other sites in South-West Eurasia. The samples analysed in this study were found in the b8 lithofacies of the site modernly excavated, which corresponds to the Proto-Aurignacian site human occupation. Due to their exceptional preservation, minimally invasive proteomic analysis was performed in order to identify the species of the faunal remains found at the site, as well as to confirm the presence of a human fossil for which previously attempted DNA analysis was unsuccessful (Maureille, com. pers.).

3.2.Sampling

The minimally invasive sampling methodology described in Chapter 1 was applied to the samples from Le Piage. Sampling was performed three times in the same area using a 6 µm microgrit polishing film in order to remove a small amount of dust from the surface of the object. The visual effect of the sampling depended significantly on the preservation state of the object, as demonstrated in Figure 3-1Figure 3-1:Table S3-1. For example, some samples, such as object no. 1470 had an exceptionally high level of preservation, and the effect of the sampling was not so apparent and only resulted in the sample surface seeming a little more polished than the surrounding area (Figure 3-1Figure 3-1:B). However, two samples, including as object no.1471 was observed to be extremely brittle, and the discolouration caused by the sampling was visible with the naked eye (Table S3-1). The visual effect of the minimally invasive sampling is demonstrated for all samples in Table S3-1.

Object

Method Development

In the previous study in Chapter 1, the goal of the analysis was to have the highest sensitivity possible because the amount of sample available was extremely limited. Here we also successfully applied the minimally invasive analysis workflow developed in Chapter 1 to samples that were 40,000 years old. The purpose of this study was the development of a full analysis workflow to allow for species identification of archaeological samples, specifically targeting the LC-MS/MS and data analysis workflows.

Throughout the method development, a tryptic digest of a model archaeological human bone was used as a standard. Initially the time of the analysis was reduced by reducing the length of the gradient in the nanoLC separation. As shown in Figure 3-2, the length of nanoLC active gradient was reduced from 100 min, Figure 3-2 (Figure 3-2A(i)), to only 10 min (Figure 3-2A(iii)). Understandably, this reduction in analysis time has direct impact on the number of peptides identified, which decreased by about 65% going from a 100 min active gradient to a 10 min method, as demonstrated in Figure 3-2B. However, it is important to note that the coverage of collagen (for clarity, the coverage of collagen α2(i) is shown throughout) does not decrease, shown by the black lines and the secondary axis in Figure 3-2B and C. This is due to the fact that since collagen is a highly heterogeneous protein, many peptides are detected corresponding to the same portion of the protein sequence, but with different modifications and missed/unspecific cleavages. Therefore, the loss in sensitivity reduces the number of peptide identifications for the lower intensity peptides, for example, those with an unspecific cleavage, but the high intensity abundant peptides corresponding to the same sequence continue to be detected, in particular, fully tryptic peptides. Using the 10 min active gradient, the slope of the gradient was then optimised specifically for archaeological collagen peptides. Figure 3-2C demonstrates the effect of the slope of the gradient, wherein the maximum percentage of solvent B was either 30, 40 or 50%, with the maximum of 30% B resulting in the highest number of identified peptides. Following this, the parameters for the mass spectrometer were adjusted to increase the speed of the analysis and account for the reduced gradient time. Figure 3-3A demonstrates the effect of the set resolution in the MS1 acquisition, with a noticeable gain in identifications when lower resolutions are used as expected, due the decrease in accumulation time required. Figure 3-3B shows the effect of different acquisition settings in the MS2 analysis, using either a fixed injection time, the automated injection time mode, and using the ion trap in rapid speed mode.

Previous tests with the longer gradient (Figure S3-1) had previously shown that a fixed injection time resulted in increased identifications over the automated injection time mode, and this held true for the shorter gradient as well. As shown in Figure 3-3A, the automated inject time in MS2 results in more peptide identifications at lower resolutions, however, it was decided to remain with a fixed injection time, as the species identification using Prometa is based on de novo sequencing, and requires high quality spectra. Finally, one should also note that the % coverage of collagen α2(i) remains constant throughout. For the analysis of the archaeological samples from Le Piage site, a resolution of 60,000 in MS1 was used, with a fixed injection time of 100 ms.

3.4.Species Identification

Reindeer collagen sequencing

Prior to the analysis of the samples from Le Piage, many of the fragments had been visually or contextually identified as reindeer (Rangifer tarandus). Since proteomic analysis of reindeer collagens has previously been reported, and there was limited information on the collagen type I sequence available on online databases, model samples from northern Norway, excavated in 1999, were analysed in order to add to a home-made database of understudied species. Data obtained from the model reindeer samples were searched against database for Odocoileus virignianus texanus, the closest relative that had collagen sequenced in the NCBI database, and then analysed using the SPIDER algorithm from PEAKS in order to identify amino acid substitutions. Figure 3-4 shows two amino acid substitutions in collagen α2(i) that were identified in the search, namely S→T@829 and I→F@864. These substitutions were identified based on high quality fragmentation spectra, as shown in Figure 3-4A and B, with fragments across the whole peptide sequence identified. Several peptides corresponding to the same sequence were identified with different modifications and missed or unspecific cleavages, adding further evidence for the identification of these substitutions. Furthermore, when re-searching the same data with these substitutions included in the FASTA file, the number of peptide identifications for collagen α2(i) increased, as demonstrated in Table 3-1. data. Since the reconstruction of the collagen sequences from reindeer was performed before the protein sequences from Rangifer tarandus platyrhyncus were added to NCBI, it was an opportunity to control the accuracy of this sequence reconstruction. In order to evaluate the efficiency of the protein reconstruction of reindeer collagen, the identified amino acid substitutions were compared with the sequence derived from DNA data, shown in Figure 345.

Here, two of the seven amino acid differences between the two species presented in the Figure 3-4 were identified using proteomics, highlighted in blue. This means that when the data was searched again with the Rangifer tarandus platyrhyncus database derived from DNA data, a significant increase in peptide identifications was observed, demonstrated in Table 3-1. The SPIDER algorithm in PEAKS identifies amino acid substitutions based on sequence homologies, and since collagen has a very repetitive amino acid sequence, it is challenging to align the identified peptides with a high confidence during the reconstruction of reindeer collagen sequences.

Species Identification with Prometa

In this study, both PEAKS and the novel data analysis tool Prometa were used to perform the species identification of the samples from Le Piage. Prometa uses several available tools to perform de novo sequencing, peptide alignment and taxonomic assignment of data originating from archaeological samples. The full workflow is depicted in Figure 34567.

Figure 34567: Workflow of the Prometa data processing tool. In red, the key data processing steps, in blue, file files generated, and in black, the parameters that can be input for the analysis.

A targeted workflow was developed using Prometa, whereby PSMs were grouped based on their taxonomic specificity. Here, taxonomic assignment at each level from class to species was performed using the Proteoclade and MMSeq2 tool. In this way, it is possible to identify peptide sequences that are specific to a certain taxonomic rank, even if they were not specific to a single species. Presenting the data in this way is an easy to read output for species identification, the concept is visualised in Figure 345678.

Figure 3-8:

A proposed illustration of the data using Prometa, where the number of peptides identified is plotted against the genes of different species. The top two plots correspond to model reindeer sample rang150, and the bottom two to rang1709. The colour of the bars in the histogram correspond to the species, in this case, we focused on the species in the Cervidae family that have collagen sequences available in the NCBI database. If there is more than one gene per species in each plot, this corresponds to an isoform. Assignment of the peptide sequences to a gene and species was performed using MMSeqs2. (35) Figure 3-8 shows how Prometa can be used for target species identification of unknown archaeological smaples, but also that further development is necessary, for example the COL1A2 gene is not identified for Rangifer tarandus, when it should be one of the most dominant genes in the sample. Since Prometa is a tool that is still in development, one of the goals of this work was to identify points that can be improved both in the data analysis, and presentation of the results. Challenges were encountered in different steps of the analysis during this development, and steps continue to be taken to overcome these difficulties.

First, related to the sequence reconstruction. Here, the challenge stems from the repetitive sequence in collagen, meaning that larger overlaps in the contigs were necessary in order to have a meaningful sequence alignment, as an overlap of 6 amino acids in collagen won't necessarily be enough to reconstruct the correct peptide sequence. The alignment of contigs shown in Figure 3-6 corresponds to a sequence of the osteocalcin protein rather than collagen as this protein does not have a repeating sequence. In order to comprehensively understand the effects of the contig assembly parameters, we will perform a comprehensive benchmarking study in order to identify the critical parameters and the effect they can have on the sequence reconstruction.

Another, related to the assignment of peptide sequences to species, is the over filtering of assignment tools. For example, in MMseqs2, the standard number of species assignments to a peptide sequence is 10, but in the case of collagen, one peptide sequence can be found in hundreds of different species. Therefore, the challenge is to limit this filtering before meaningful information can be extracted from the data, as there is not particular way of scoring the assignment to prioritise them, but avoid slowing down the data processing by producing unnecessarily large results files. Following this, we aim to visualise the resulting data in a way that will allow for a relatively easy species identification, based on the grouping of peptide identifications based on taxonomy.

Finally, for the presentation of data, the incomplete annotation of protein sequences, in particular in NCBI, makes it challenging to show data by protein, as the name of the protein is not necessarily always the same in the database. For example, in NCBI, the collagen 1α(i)

protein can be named 'collagen alpha1(I),' or, for unannotated sequences, 'hypothetical protein' or 'unnamed protein product.' To produce Figure 3-8, the proteins were manually annotated with the genes. To address this issue, the NCBI BLASTP tool is being incorporated into the Prometa workflow in order to annotate protein sequences based on sequence homology.

Species Identification with PEAKS

Species identification was also performed using PEAKS as a benchmark with which to compare the output from Prometa. Species identification was performed based on the identification of species or genus specific peptides that were identified with high quality fragmentation spectra, as described in Chapter 1. Key informative peptides that were used to perform the species identification are shown in Figure 3-9A for Rangifer tarandus, and B for the Bos genus. A summary of the species identifications can be found in Unlike the samples discussed in Section 0, the data obtained from sample LIP001 indicated that it was very poorly preserved. Therefore, this sample was analysed using PEAKS and manually confirmed. As with the other samples analysed in this study, collagen type 1 was the predominant protein identified in this sample, with 64% coverage of collagen 1α(i) (296 peptides,), and 60% coverage of collagen 2α(i) (262 peptides). However, overall intensity of the signal from the extract was relatively low, so additional controls were used to perform the species identification. This sample was identified to be Homo sapiens, based on the identification of two peptide sequences that are almost exclusively identified in species that belong to the Primate taxonomic order, with the additional identification of one peptide sequence that is unique to the Homo sapiens species. For a more confident identification of the peptides indicating that sample LIP001 is Homo sapiens, the PMF-like analysis method developed in Chapter 1 for the species identification of poorly conserved collagen was applied to this sample, screening the combination of m/z precursor ion and retention time. A standard tryptic digest of an archaeological human bone was analysed after sample LIP001 in the same sequence, which was then used as a model sample to compare the raw data between the two samples. the PMF method was used as an additional control for the presence of these peptides, to ensure that they are not a misassignment in the bioinformatic search. Since this peptide was not identified using the PMF method in the model sample, we do not include the identification of this peptide in the evidence used to identify sample LIP001 as Homo sapiens. The EIC of the peptide with T at position 1075, B: EIC of the peptide with A at position 1075. One can see that the peptide shown in B is significantly higher in abundance than A, corresponding to the calculated frequencies discussed in (13).

While sample LIP001 has been recognised as human since the 1980s, this analysis is the first molecular evidence connecting Homo sapiens with the Le Piage site. This can therefore be used to evidence that the lithic industry found in the Proto-Aurignacian period can be attributed to activity from Anatomically Modern Humans. Due to the poor preservation of this sample in comparison to the other samples analysed from the same time period, a targeted search had to be performed to confirm the species identification. Future steps in the analysis of samples from the site at Le Piage could include the analysis of earlier identified hominin samples to compare the preservation, as well as to be able to better differentiate between Anatomically Modern Human and Neandertal collagen sequences through the construction of a home-made proteomics-based database.

Conclusions

This study demonstrates new LC-MS/MS and data analysis workflows aimed towards the rapid species identification of archaeological bones. Shorter LC gradients adapted to collagen tryptic peptides from archaeological bones were developed, along with adapted MS acquisition methods to increase the analysis speed. Following this, a novel data analysis workflow, Prometa, was developed for targeted species identification based on collagen sequences. This new workflow was successfully applied to a cohort of samples from Le Piage archaeological site to confirm the assumed species of 15 samples, including one identified as human.

Introduction

In recent years the proteomic analysis of bone has become increasingly widely studied in the context of biological, clinical, forensic and archaeological research. (1-5) Both circulating and structural proteins can be targeted for their sequences and isoforms, chemical/posttranslational modifications and differential expression or quantitative analysis. (6)(7)(8) Proteomics also offers insight regarding interactions with the surrounding biological environment, for example in biomaterial engineering. ( 9) Bone proteomics requires consistent and robust protocols to achieve maximal protein sequence recovery in order to extract meaningful biological information.

Collagen type I is the most abundant protein in bone, making up more than 90% of the organic content. It is a fibrillous protein, composed of two collagen alpha-1(I) chains and one alpha-2(I) chain, arranged in a triple helix. These fibril helices are then arranged in a staggered formation as part of a highly structured network, held in place by both inter-molecular forces and covalent cross-links . (10) While the formation of these cross-links is initiated by enzymatic activity, the di-and trivalent cross-links form through non-enzymatic condensation reactions. (11) The high degree of cross-linking in the collagen network significantly decreases the solubility of the proteinaceous content in bones, even in the presence of strong denaturing agents. ( 12) It has been shown that, of the insoluble fraction of the extracellular matrix (i.e. that which is not solubilised by detergents and denaturing agents), more than 80% is made up of collagen type I. (13) The full solubilisation of these complex, highly cross-linked proteins, will allow access to the most informative parts of the extracellular matrix with a direct impact in related research. For example considering osteogenesis imperfecta and scurvy, (14, 15) levels of stable and labile lysyl-oxidase-mediated cross-links contribute to bone strength. Another example is that the increased solubility of bone proteins can result in the improvement of sequence coverages, which has a major impact in forensic and archaeological research.

Sample preparation is a key step in proteomics protocols. Our group and others have investigated protocols with or without sample demineralization (e.g. HCl, TFA, EDTA), the use of various denaturants (e.g. urea/thiourea, guanidine, TFE) and detergents (SDS), and digestion with different enzymes (e.g. trypsin/lysC) or digestion free protocols. (3,(16)(17)(18)(19)(20)(21)(22)(23) Chemical hydrolysis with CNBr has also been used as a method for solubilising bone collagen, (24)(25)(26) and is a commonly used chemical digestion reagent targeting membrane proteins and extracellular matrix proteins to increase their solubility. (13,27) In this study, we propose the use of hydroxylamine as a chemical digestion agent, which cleaves proteins between N and G residues. As demonstrated in Figure 4

Materials and Methods

Samples

Fresh, forensic and archaeological samples of different preservation states were used in this study, information of where they were sourced and their age can be found in

Sample reference Sample description Origin and dating

Model samples

Archaeological

Model *The program is focused on the study of post-mortem delay and associated taphonomic signatures using proteomics.

Sample Preparation

We propose two sample preparation protocols, shown in Figure 4-1B and C and explained in detail in Section S4.2.1. and S4.2.2. Unless otherwise stated, all reagents were obtained from Merck (Darmstadt, Germany).

Samples were taken using hand drills and transferred to microcentrifuge tubes for extraction.

Sample amounts analysed are detailed in Table S4- fraction for analysis. Extracts were then digested using trypsin in a filter-aided approach, and evaporated using vacuum centrifugation, as described in Section S4.1.1. Each sample was then reconstituted in 10 µL 0.1% formic acid in water, the concentration was measured using the A280 method on a Nanodrop 2000 (ThermoFisher Scientific), and the sample diluted as needed.

Fractions A and B of each sample were diluted by the same dilution factor in order to semiquantitatively compare the extraction efficiency.

2.3.SDS-PAGE gel

. GuHCl and hydroxylamine extracts were visualised using a 10% SDS-PAGE gel, as described in Figure S4-1 in Section 0.

2.4.nanoLC-nanoESI-MS/MS analysis.

Tryptic digests were analysed using an Ultimate 300 nanoLC system coupled to an Orbitrap Eclipse mass spectrometer (ThermoFisher Scientific). Details regarding the LC separation and MS parameters are detailed in the Section S4.1.2.

2.5.Data Analysis.

Data were processed using PEAKS X (Bioinformatics Solutions Inc) as described in Section S4.1.3. Protein and peptide lists are available in Section S4.3. and S4.4. Protein number comparisons were performed using the 'Compare' function on PEAKS, proteins with a -10logP values of at least 20 were included. STRING (string-db.org) was used to identify protein functions.

Results and Discussion

Here

3.1.Solubilisation Efficiency and Hydrolysis Specificity

The action of the hydroxylamine extraction is demonstrated in Figure S4 In addition to this, complete solubilisation of the sample material was observed after the hydroxylamine extraction, while solid material remained after the GuHCl extraction. This was confirmed with higher concentration values in Fraction B versus A recorded with the Nanodrop; e.g. over 85% concentration increase for samples from World War 1 period, 80% for the Middle Age samples and 20% for the Bronze Age (Table S4-2).

Targeted Collagen Extraction

In shows that the increase in coverage corresponds to additional peptides that are detected in the C/N terminus telopeptide regions of both collagen 1α(i) and 2α(i), and that the combined analysis of Fractions A and B results in a higher number of peptide identifications across the whole sequence. Not only does the use of hydroxylamine result in a higher number of peptide identifications, but the overall intensity of collagen peptides and confidence in the peptide identifications are also higher in Fractions B and C, as demonstrated in Figure S4-4. When comparing peptides that were identified in both Fractions A and B, and A and C, the -10logP value, the measure of confidence reported by PEAKS, was statistically significantly higher in the fractions where hydroxylamine extraction was used (paired t-test, p>0.05, see Section S4.6.).

Compared to other studies targeting the insoluble part of the extracellular matrix in bone, (31) the use of the hydroxylamine extraction did not hamper or help in identification of any additional glycosylation sites (e.g. galactosylation, glucosylgalactosylation) that were not already confidently identified in the GuHCl fractions (Figure S4-5). The bulk deamidation of asparagine and glutamine in collagen type I was also considered. As shown in Figure S4

3.3.Simple Protocol Targeting Collagen

As we have shown, the incorporation of hydroxylamine into the proteomic sample preparation protocol can increase the yield of extracted collagen, but the quality of the resulting data is also improved. Therefore, in studies targeting collagen, we propose to directly apply the hydroxylamine extraction to the demineralise bone, as described in Section S4.2.2.

3.4.Increased Depth of Proteome Coverage

The utility of the described protocol stems from the fact that the hydroxylamine extraction step is performed on the insoluble fraction of the sample that is typically discarded, and thus acts as an additional source of information that does not deduct from the initial extraction. Therefore, by combining the data from Fractions A and B, an increase in the number of peptide and protein identifications was consistently observed across all sample types, in particular in archaeological samples as demonstrated in Table S4-3.

The heat map of the two model samples analysed in this study (Figure S4-7) shows that proteins are identified in Fraction B and C that are not identified in Fraction A. Figure 4-4 compares the number of protein identifications in each fraction across the different sample types analysed. In the fresh and forensic samples, proteins identified exclusively in Fraction B include minor collagens associated with fibril and bone formation, such as collagen α3(V) and α1(XXVII) (all identified with at least two peptides). Note that even if the number of proteins detected in Fractions B and C is smaller for these samples, the number of collagen peptides and their intensity were still higher than for Fraction A. In archaeological samples, the additional proteins identified in Fraction B consistently corresponded to proteins associated with the extracellular matrix, including minor collagens, such as collagen α3(V), α1(VII) and α1(XVI), and thromobospondin (identified with at least two peptides). Additional proteins associated with matrix formation were also identified in the hydroxylamine fractions, such as sialoproteins (identified with at least three peptides), which strongly bind to hydroxyapatite. One should note that this is also true for archaeological Sample 6, which was a dentine sample from tooth,

showing that this protocol can be used for all types of archaeological mineralised tissues and other extracellular matrix containing tissues, as previously demonstrated. ( 29

Conclusions

Recovering the maximum amount of protein information from bones is crucial in many studies in medical, forensic, biomaterial and archaeological research. Our results show that the incorporation of hydroxylamine into the proteomics workflow, or its use directly in a targeted protocol, results in a significant improvement of collagen type I sequence coverages and identification of additional proteins mainly associated with the extracellular matrix, as well as the applicability and repeatability of the protocol. We have also reported the step-by-step workflows in Section S4.2.1. and S4.2.2. for their straightforward implementation in other laboratories. One should note that this protocol can easily be adapted to the middle down proteomics approach by skipping the use of a trypsin hydrolysis step.

Introduction

Collagen is the most abundant structural protein in the body, and is the main component of the extracellular matrix, giving mechanical strength to the body's various connective tissues. An understanding of the formation and structure of the collagen network provides key insights into how it contributes to the mechanical strength of tissues and how the manifestation of a disease occurs on a molecular level. Such understanding is highly relevant to many fields, including food sciences, biomaterial research and health sciences. Consequently, many reviews have been published regarding the synthesis, structure and mechanics of the collagen protein, as well as its interaction with the mineralised portion of the bone matrix. (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11) In this review, we focus on the analysis of the enzymatic cross-links present in the collagen network. The biological role of such cross-links, as well as the mechanism of formation, in both mineralised tissues and the extracellular matrix has also been extensively covered in literature. (12)(13)(14)(15)(16)(17)(18)(19) Therefore, in this review, we will focus specifically on the analytical techniques used to analyse collagen cross-linking. To introduce the topic, below is a description of the key enzymes and reactions in the cross-linking mechanisms in collagen. Following this, the different methodologies used are separated into quantitative and qualitative approaches. It should be noted, that many applied studies of collagen cross-linking use a combination of at least two characterisation techniques in order to fully understand the impact of cross-linking in the context of their particular research question.

1.1.Collagen structure

Collagen is a challenging protein to study, due to its high heterogeneity and the complex systems that are involved in producing the collagen network. A structural protein, collagen adds Finally, the tertiary structure of collagens varies depending on the type of collagen and tissue studied, but in the case of fibrillar collagen, alpha helices are locked together in a staggered, highly cross-linked network that then forms long fibrils that strengthen the surrounding tissue, depicted in Figure 5-1A. (7) The collagen fibrils are wound very tightly, interlocking into a triple helix, straining the bond angles between the amino acids and resulting in a network that gives a high tensile strength to the tissues in which it is present. ( 23)

1.2.Collagen cross-linking mechanism

There are two main types of cross-links present in collagen networks, enzymatic and nonenzymatic. Non-enzymatic cross-links mostly take the form of advanced glycation end products (AGEs), and are typically age related, although they have also been linked to diabetes. (24) AGEs are present in tissues throughout the body, but accumulate in collagen-containing tissues, such as bone, due to the low turnover of collagen relative to other protein types. (25) AGEs are a result of a series of reactions consisting of the glycation of amino acid residues through the Maillard reaction. ( 14) Typically, AGEs are analysed using fluorescence detection, and studies are mostly focused on the analysis of pentosidine, the most well characterised AGE, as a representative value for the total AGE content of collagen. (26,27) Bone tissues with an abnormally high AGE content have been proven to be more brittle due to a combination of this uncontrolled cross-linking and the low bone collagen turnover; an issue that is becoming increasingly pressing as life expectancy increases. (28) This review will not focus on the analysis of these cross-links due to the high complexity of, and lack of literature on, the analysis, but readers are directed to several studies and reviews for further information. (18, 24, 25, 29-

31)

Table 5-1: The most important enzymes involved in collagen posttranslational modification (PTM). (10,32) As mentioned above, the structure and mechanism of formation of enzymatic cross-links has been well documented in previous reviews and research articles, and so only a brief introduction will be given here. Table 5-1 describes the enzymes involved in the key PTMs in collagen. It should be noted that there are several enzymes that produce the same PTM. It has been shown

that each enzyme targets different residues in the collagen sequence, and this in turn can affect the types of cross-links formed, and the physical properties of the tissue affected. (33,34) In short, there are two key enzyme families involved in the formation of cross-links, both targeting the lysine residues in collagen; lysyl hydroxylases (LH) and lysyl oxidases (LOX).

Lysyl hydroxylases hydroxylate the δ-carbon of lysine, as demonstrated in Figure 5 composed of a galactose and glucose subunits (GalGlu). First, a galactose unit is transferred to the 5-hydroxyl group of hydroxylysine, which is then followed by the addition of a glucose unit. (35) All these reactions are catalysed by the multifunctional enzyme lysyl hydroxylase 3 (coded by the gene PLOD 3). ( 36) Although the exact biological relationship between collagen glycosylation and lysine hydroxylation is not clear, it has been observed that the two modifications are strongly correlated to cross-linking in collagen. (37) When lysyl hydroxylases are inhibited, in particular LH3 which has roles in both the oxidation and glycosylation of lysine, cross-linking in collagen is severely inhibited. (38) Lysyl oxidases convert the primary amine of lysine into a highly reactive aldehyde, allysine, forming hydroxylysine, Figure 5-1B. (32,39) Following this reaction, the reactive allysines react with the primary amines of other lysine and hydroxylysine residues to form immine divalent bones, otherwise termed immature bonds. Since the immine bond is not chemically stable for long periods of time, these divalent cross-links undergo a second condensation reaction with a third lysine residue to produce a trivalent, mature, cross-link. These trivalent cross-links can have two main structures; they can be pyrrole (PYR) or pyridinoline (PYD) based, and are more chemically stable than the immine structures. The mechanism behind the dominance of one mature structure or the other is not clear, but various studies have indicated that it is tissue dependant. (40)(41)(42) As the formation of collagen cross-links is only partially enzymatically controlled, a variety of cross-link structures can be formed, which vary in the number and position of oxidations, glycosylation and, in the case of mature cross-links, the ring structure. his work will focus on the most commonly targeted cross-link structures in collagen cross-linking studies; cross-links at lysine residues, detailed in Figure 5-2.

The inclusion of histidine residues in cross-links is often considered when characterising the cross-link content of collagen, but recently it has been reported as a laboratory artefact. (43) Additionally, other cross-link structures have been proposed based on mass spectrometric data, but their structures have fully characterised, so they will not be considered in this review, but readers are directed to the papers of interest. (44, 45) Biomaterials The use of collagen-based scaffolds is of great interest to the medical community due to their inherent biological compatibility and physicochemical properties. The controlled introduction of artificial and natural cross-links into these systems can create a wide variety of materials with tailored mechanical and biological properties. (46) The presence of enzymatic cross-links can therefore interfere with the desired properties of the material, (46)(47)(48) or can be tailored to suit the requirements of the material. (49,50) Scurvy Resulting from a vitamin C deficiency, scurvy presents itself through tiredness, weakness and sore limbs. Vitamin C, also known as ascorbic acid, is a cofactor in a variety of enzymatic pathways due to its activity as a reducing agent, including prolyl and lysyl hydroxylase. (51,52) As such, various studies have demonstrated a negative effect on the concentration of collagen cross-links present in the urine of vitamin C deficient subjects, (53,54) as well as significant changes in bone porosity. (55)(56)(57) Cancer Tumours are characteristically surrounded by significant ECM deposition, which is highly cross-linked and thus inhibits drug delivery. The high growth rate of tumours can be detected through the presence of urinary cross-link metabolites, which can be used as a biomarker for certain cancers. (58) There is a strong correlation between the impenetrability of the ECM (i.e. the cross-linking density), and the patient's prognosis, (59, 60) therefore enzymes involved in cross-linking have been identified as potential drug targets. (61,62) Osteoporosis presents itself as bone fragility in the elderly, and commonly leads to fractures or broken bones from simple, everyday tasks. Its pathology is characterised by a decrease in bone density and strength, caused by low bone collagen turnover, as characterised using cross-link markers in urine. (63,64) Osteogenesis imperfecta Otherwise known as brittle bone disease, around 90% of osteogenesis imperfecta cases are caused by dominant mutations affecting the formation of collagen type I. Exercise Since the formation of, particularly, trivalent cross-links is not enzymatically controlled, it stands to reason that stress and strain on collagenous tissues can affect the crosslinking density. Studies have shown that increased mechanical strength can increase the concentration of mature cross-links in tissue, either over time as part of the aging process, or as a result of exercise. (13,67,68) Pregnancy The concentration of collagen cross-links in cervical tissues change during pregnancy, with a significant decrease observed in the days leading up to birth, resulting in an increase in elasticity of the tissue to allow for birth. (69, 70)

Quantitative Analysis

2.1.Fluorescence

Fluorescence is, by far, the most widely used detection method to quantify the presence of collagen cross-links, due to the fluorescent properties of the PYD-containing trivalent crosslink, which is most dominant across all tissue types. There are several advantages to using fluorescence as a quantification method; (i) fluorescence is linearly related to concentration, (ii) analysis using fluorescence detection has a very high sensitivity, and (iii), fluorescence has a relatively high chemical specificity, i.e. the excitation wavelength can be tuned specifically to target the fluorescent moiety of the analyte of interest. The fluorescent properties of PYDcontaining cross-links were vital to the discovery and elucidation of the structure by Fujimoto et al. (71)(72)(73) The distinctive fluorescence of the PYD moiety (λex: 330nm, λem: 390nm) is used for quantification using both liquid chromatography and ELISA assays (ELISA is discussed in Section 2.3.).

In most applied studies that use a change in the concentration of collagen cross-links as a marker of another physiological process, the fluorescent properties are used to calculate a quantitative change in the bulk cross-linking in a tissue. In many cases, these methods are applied to urine samples, where PYD cross-links are present as a marker for bone turnover. (66,74,75) In the case of collagenous tissues, cross-link concentration is typically reported as moles/moles of collagen, with this ratio based on the output of a hydroxyproline assay. ( 76)

The first step for all quantitative analysis of PYD cross-links by florescence detection is the complete hydrolysis of the proteins in the sample, through the hydrolysis of the amide bond that forms the backbone of the peptides involved in the cross-link, so that only the PYD component of the cross-link remains, resulting in the structures depicted in Figure 5-2B(i) and

(ii). ( 77) Typically, acid hydrolysis is performed, (78, 79) but base hydrolysis can also be used to preserve the glycosylation of the cross-links. (38,80) Lyophilised hydrolysates are then prepared with solid phase extraction (SPE), which can be done with a cellulose powder, or several available kits for the targeted SPE of collagen cross-links. (54,81) Hydrolysates are then resuspended for chromatographic analysis. PYD is positively charged at neutral or acidic pH and is therefore poorly retained using traditional reversed phase chromatography. Ion pairing or ion exchange chromatographic set ups are therefore used to increase the retention of the compound for its reproducible quantification. Quantification is then performed against authenticated cross-link standards to allows for absolute quantification. to determine the ratios of pentosidine and PYD in type 2 diabetes patients, as well as to correlate bone fracture toughness with collagen cross-link maturity. (67, 68, 91)

2.2.Radioactivity / Scintilation detection

Radioactivity is used similarly to fluorescence, as a marker, but for immature cross-links. It is often used in conjunction with quantification based on fluorescence described above. The simultaneous stabilisation and tagging of the unstable immine bond of immature crosslinks is performed through reduction with NaB 3 H4 (Figure 5-3). 3 H decays into 3 He through the beta decay of a neutron into a proton, which releases an electron that can be detected using a scintillation detector. The main advantage of this method is that is has a similarly high sensitivity as the fluorescence detection, but due to the specific laboratory requirements needed to perform experiments involving radioactivity, it is not as widely implemented.

Before any other chemical treatments, samples are first reduced using NaB 3 H4, stabilising the acid labile immine bond through the addition of two tritium ( 3 H) ions (Figure 5-3). Samples are then hydrolysed and prepared with SPE as described in Section 2.1. ( 77) Immunoaffinity chromatography has also been used to purify cross-linked collagen peptides for further downstream analysis using mass spectrometry. (42, 106) For example, immunoaffinity chromatography using antibodies for the N-telopeptide of collagen can be utilised to purify cross-links localised on these peptides, and then identify the sequence of the other peptides involved in the cross-link. (107, 108) A similar approach was used by Eriksen et al. for the purification of cross-linked peptides that include the collagen α1(i) C-terminal telopeptide, followed by the identification of the other peptides present in the cross-link. (99)

2.4.UV-Vis

UV-Vis detection is used for the targeted quantification of the PYR cross-link. Unlike PYD cross-links, the PYR structure is not fluorescent, and therefore, post-column derivatisation can be used in order to be able quantify this structure. This is predominantly performed through the use of Ehrlich's reagent, p-dimethylaminobenzaldehyde, which turns yellow in the presence of indoles. (109) The specificity of the PYR cross-link to Ehrlich's reagent was key to deciphering its structure. (109-111) A simple test can be performed on enzymatically digested samples (PYR is not stable to hydrolysis), where the reagent is dissolved in 4 M perchloric acid, added at a 6:1 ratio to the analyte, and the absorbance is measured at 572 nm. (110) This method can be used as an additional characteristic test during a variety of collagen cross-link investigations.

(67, 112)

2.5.Mass Spectrometry

Mass spectrometry has been used to quantify the cross-links present in collagen through the quantification of the reduced and hydrolysed moieties, as applied above for fluorescence and scintillation detection. An innate difference with using mass spectrometry for quantification in this way, in comparison to fluorescence measurements, is that the chemical specificity of mass spectrometry is significantly higher than that of fluorescence, and that multiple cross-link structures can be quantified in one analytical run. The high specificity of mass spectrometry means that the standards used in calibration must be chemically identical to those that will be quantified in the sample of interest. (113)

When quantifying cross-links using fluorescence, it is the fluorescent moiety that is the target of the analysis, but in mass spectrometry, it is the molar mass of the molecule of interest that is quantified. For highly sensitive quantification using mass spectrometry, strategies such as selected ion recording (SIR) or multiple reaction monitoring (MRM) are considered the gold standards. Such analyses are performed using triple quadrupole instruments. In MRM, ions corresponding to the analyte of interest are selectively fragmented, and the resulting fragment ions are monitored, therefore only compounds that have both the expected parent ion and fragment ion mass are counted in the quantification. SIR analysis significantly restricts the m/z window of the analysis to target a single ion, thereby significantly increasing the sensitivity of the MS. (114)

In these two analysis strategies, a mass list of ions that will be quantified is targeted, meaning that if there is inconsistency in the completeness of the hydrolysis of the cross-links in the sample, the quantification will not be wholly accurate. (78) Therefore, in order to ensure the accuracy of the method, the sample preparation and analysis method must be thoroughly validated by quantifying the reproducibility of the method. This can be done through the use of standard samples and internal standards, such as acetylated PYD and 18 O labelled PYD and DPD, that are included during the sample preparation phase in order to be aware of typical sample losses caused by the preparation protocol.

Qualitative Analysis

3.1.Spectroscopy

Spectroscopic analyses were key to the identification of the structures of the mature cross-links in collagen. The structure of the PYD cross-link was determined using 13 C NMR and mass spectrometry by Robin, after the large scale purification of bone hydrolysates. (122) However, since these first studies, little work has been done regarding the characterisation of collagen cross-links using NMR, likely due to difficulties related to the purification of such structures for accurate NMR characterisation.

3.2.Mechanical Tests

The high mechanical strength of bones is a result of the highly ordered and controlled structure of both the hydroxyapatite matrix and collagen fibril network. (9,123) In tendons and other connective tissues however, the high mechanical strength is primarily a result of the structure of the collagen network. For this reason, mechanical testing can be used as a macroscopic evaluation of the concentration and efficiency of the collagen cross-links present. The impact on the cross-linking network of various physiological processes can be studied using mechanical tests, for example, exercise, diabetes and gestation during pregnancy. (18,67,69) There have been many studies on the mechanical strength of bone and other collagenous tissues, including considerations of the direct impact of cross-linking on tissue strength, but the results of such studies do not always corroborate. In general, enzymatic cross-links have been shown

to cause higher elasticity in bones, while non-enzymatic cross-links decrease the elasticity.

However, there is some contradiction in the literature as to the exact impact of both enzymatic and non-enzymatic cross-links on the mechanical properties of bone, as a wide variety of mechanical tests, collagenous materials, and variables have been used and changed, and it is therefore difficult to make direct comparisons between different studies. (63) The two key properties leading to the high mechanical strength of bone is resistance to both applied stress and strain; stress defined as force per unit area, and strain as the change in length over the initial length, a measure of normalised deformation. (18) The most common mechanical test performed in cross-link studies is depicted in Figure 5-4A, whereby the sample is attached to two supports and is slowly pulled apart. The force applied (stress) and the distance between the two supports (strain) is constantly measured, either until the breaking point of the material, or over several cycles to determine the durability of the material. These two values are then plotted against each other as a stress-strain curve (Figure 5-4B), which can give values such as the Young's modulus (resistance to being non-permanently (elastically) deformed) or ultimate tensile strength (maximum stress reached before fracture). (18, 63)

3.4.Thermal stability

The thermal stability of tissues can be analysed with Differential scanning calorimetry (DSC) in order to determine the transition temperature (Tm), which in polymers, increases with increasing degrees of cross-linking. DSC has been used in the past to determine the effect of cross-linking on the Tm, which demonstrated that the thermal stability of collagen increase with age, and therefore presumably with an increase in mature cross-linking. (135, 136) However, bones, tendons and muscles are highly complex systems and more recent controlled experiments suggest that this is not directly related to cross-linking in collagen. It is instead more likely due to other ageing processes in the body. (116)

3.5.Mass Spectrometry

In recent years, the advancements in mass spectrometric resolution, sensitivity, speed, and mass range, as well as the widespread use of tandem mass spectrometry (MS2 and MS3 methodologies), have resulted in new methodologies that can be applied to (structural) biology, many of which are applicable to the analysis of cross-linking in collagen. Instruments, software, and sample preparation workflows have been developed specifically for the in-depth, targeted analysis of proteins. In order to better understand both the structures and mechanisms involved in collagen cross-linking, several aspects of these methodologies can be utilised, such as (a)

classical bottom up proteomics to quantify and localise the modifications associated with crosslinking, (b) the use of enzymes to enrich the sample in cross-linked peptides, and (c) the use of tandem mass spectrometry to sequence and perform structural characterisation of cross-linked peptides. The benefit of using such methods is that it is possible to gain information regarding the localisation of the cross-link on the collagen protein, which is lost through the hydrolysis step frequently used in cross-link quantification.

Proteomics

Through the use of classical bottom up proteomics, the amino acid sequence of peptides and proteins, and modifications of the amino acids themselves, can be deduced. The general bottom up proteomic workflow is described in Figure 5 The use of a bottom up proteomic-based approach for the analysis of the cross-links in collagen allows for the localisation of the crosslinks on the collagen sequence. In the case of diseases involved in collagen cross-linking, such as cancers or osteogenesis imperfecta, the variation in the hydroxylation of lysine residues in both the helix and telopeptide regions is normally the target of analysis. (138) Here, data are searched for the specific post translational modification hydroxylysine (+15.99 at K). Such studies can therefore be used to identify the effect of a knockout of a gene coding for a specific LH or LOX enzyme, and has allowed for the identification of specific sites in the collagen sequence that are targeted by different enzymes. (61,84,85,139) In addition to this, the use of proteomics to identify the modifications of key residues, combined with the bulk concentration of cross-links in the tissues studied can provide key information about the mechanisms occurring. (86,(140)(141)(142)(143) Other key modifications to target using traditional bottom up proteomics methodologies are glycosylation and glycation, with studies proving that non-enzymatic glycation is often localised on the same lysine residues that would normally be targeted by enzymatic crosslinking. (144) Additionally, it has been observed that the glycosidic bond of the GluGal units typically found in collagen are not cleaved during fragmentation, and remains largely on the fragment ion, which allows for an unambiguous localisation of the glycosylation modification.

(145, 146)

The studies described above follow a standard bottom up proteomics workflow. Crosslinking mass spectrometry however, uses chemical cross linkers to covalently link residues in close proximity to each other, revealing information about the dynamic structures of proteins and the structure of protein complexes. (147-150) In such studies, targeted data processing tools are used in order to identify cross links between two peptides and can also be used to study naturally occurring crosslinks. (151, 152) Such tools have not yet been applied to study cross linking in collagen but could aid in the analysis of immature cross links since a targeted enrichment is not necessary. Furthermore, such tools do not yet exist for the analysis of cross links between three peptides. The inherent heterogeneity of both the collagen protein and the crosslink structures significantly complicate mass spectrometric analysis of these peptides as the signal is spread over many peaks, while the sample preparation for the quantitative analysis discussed in Section 2.5 significantly reduces the analyte complexity.

Enzymatic digestion

A classical bottom up proteomics experiment uses trypsin in order to cleave a protein at K and R residues, into relatively equally sized peptides. However, due to the large size of collagenous proteins and the high complexity of the network, it can be advantageous to use alternative enzymes that will allow for the targeted enrichment of telopeptide regions (and by extension, cross-linked peptides).

One such enzyme that can be used to target telopeptide regions is bacterial collagenase, as this enzyme digests the helical sequence of the collagen chain, resulting in an enrichment of (crosslinked) telopeptides, which can be utilised to target both mature and immature cross-links in collagen. (41,42,141) for the following separation based on scintillation detection. A similar methodology was followed for the analysis of mature cross links, whereby a chymotryptic digest was fractionated using the same chromatographic workflow, using the fluorescence of PYD as a marker.

Following these enrichment procedures, characterisation was performed using a Q-ToF mass spectrometer via direct injection, with fragment peak assignment performed manually. (166)

Most recent developments using tandem mass spectrometry analysis are combined with the use of genetics procedures in order to identify abnormal cross-linking is genetic diseases. Here, gene knockout models are used in order to identify abnormal cross-linking through the use of targeted digestions (CNBr, bacterial collagenase) and SDS-PAGE. (65,139,(167)(168)(169) Following this, bands of interest are digested with trypsin, and then analysed using LC-MS/MS.

Due to the high molecular weight of, in particular, mature cross-links, often less hydrophobic LC columns are required, typically C4. Tandem mass spectrometry, both in the MS2 and MS3

dimensions, can then be used to resolve the structure, typically performed on linear ion trap instruments. (22,41,43,141,144,170)

Conclusions and Future Perspectives

The analysis of crosslinks in collagen is applicable to a wide variety of biological questions.

However, the structural complexity and heterogeneity of the collagen protein and the crosslink structures makes their characterisation challenging. In this review, various qualitative and quantitative analytical approaches were discussed, which allow for the quantification of crosslinks in various tissues and better characterision of the crosslinked peptides and their effect on the physical properties of various tissues, respectively. Figure 5-6 summarises the techniques discussed in this review, and the decision tree that one can use to select the type of analysis to perform depending on the analytical question.

As has been shown in this review, in order to relate the effect of cross linking in collagen to a specific biological question, multiple analysis strategies should be combined. Key challenges related to the analysis of crosslinking in collagen are related to the heterogeneity of both collagen and the cross-link structures, as well as the low concentration of cross links relative to the total collagen content. Frequently, mechanical testing is used in order to characterise the physical properties of a tissue, in combination with the quantification of cross links based on either fluorescence, scintillation or mass spectrometry analysis. In recent studies, proteomics is more frequently included in order to localise key sites in the collagen sequence that are involved in cross linking. Using mass spectrometry-based proteomics methodologies can provide more information regarding the physiological manifestation of a particular disease, for example, to

show if lysine oxidation is up or down regulated at particular sites in the collagen sequence.

Such methodologies effectively combine different analytical strategies to characterize multiple stages of the collagen cross linking pathway.

The most recent studies of collagen cross linking rely on mass spectrometry to deduce the peptide sequences of the peptides involved in the cross link. Future work in this field will undoubtably be based on the sequencing of these peptides using high resolution mass spectrometry and proteomics-based data analysis tools. The analysis of cross-linked peptides using this methodology will retain the information that is lost during the hydrolysis step of most quantification protocols, the localisation of the cross link on the collagen sequence, and the correlation of this information with the modifications on the cross link itself. This additional information will give significantly more insight into the various disease mechanisms that affect collagen cross linking, and thus give clearer drug targets to combat the physiological effects of such diseases.

hydroxylase is strongly associated with cross-linking. (10)(11)(12) The formation of these crosslinks is not enzymatically controlled, resulting in a wide range of possible cross-link structures, varying in terms of glycosylation, hydroxylation, and cyclic structure (5-or 6-membered rings), as demonstrated in Figure 6-2.

There are many analytical strategies which can be used to analyse cross-linking in collagen and typically more than one is applied to a sample in order to have a fuller overview of the effect of cross-linking in relation to a research question. One can use mechanical testing to evaluate the elasticity and tensile strength of collagenous tissues, which has been applied to study crosslinking in relation to pregnancy and exercise. (13)(14)(15)(16) The bulk cross-linking of a tissue is quantified by completely hydrolysing the tissue of interest, and then using either ion exchange or ion pairing chromatography coupled with fluorescence, scintillation or mass spectrometry (MRM mode) detection. (17,18) However, the analysis of the hydrolysed cross-links, results

in the loss of the localisation of the cross-links on the collagen protein, thus removing a significant portion of information. Thus, proteomics-based methodologies to localise the PTMs that are strongly associated with collagen cross-linking, and the analysis of cross-linked peptides using mass spectrometry are becoming more common strategies in the characterisation of the cross-linking mechanism. (6,(19)(20)(21). In this study, novel analysis methods for both divalent and trivalent cross-links in collagen were developed. This is of interest in the context of historical samples as it is highly likely that the type and degree of cross-linking in collagen is correlated to the preservation condition of such samples. In the analysis of archaeological samples, the analysis of cross-links in preserved collagen could be used as both an indicator of preservation state and to increase the sequence coverage of collagen in the sample, by allowing the sequencing of peptides that were otherwise not identified because they were involved in cross-linking.

Although both divalent and trivalent cross-links were targeted here, more focus was put on the targeted enrichment of trivalent peptides, as they are likely to be preserved in archaeological bone samples. The analysis of enzymatic cross links in collagen poses a significant challenge due to both (a) the high heterogeneity of the cross-link structures, and (b) the lack of bioinformatic support available to search for cross-links between three peptides. Therefore, in order to comprehensively characterise such structures, a targeted enrichment and analysis is required. The experiments described below were performed with modern bovine bone as a model sample in order to start with larger sample amounts. The full developed enrichment procedure is discussed, as well as the targeted mass spectrometry methods developed specifically for the analysis of trivalent cross-links.

Methods

All analysis was performed using a modern bovine femur as a model sample, which was obtained from a local butcher, and frozen at -80°C until use. Sampling was performed using a Dremmel 4000 hand drill, with shavings of the bone collected for further processing.

2.1.Analysis of PTMs associated with cross-linking

Data from a tryptic digest of an archaeological human bone were searched for modifications that are strongly correlated to collagen cross-linking. The sample was prepared according to the FASP-based protocol described in Section S4.2.1., but only the Fraction A was analysed.

Data were searched using PEAKS X (Bioinformatics Solutions Inc, Waterloo, Canada). Data were searched with a precursor ion tolerance of 10ppm, and a fragment ion tolerance of 0.02 Da. Along with a semi-tryptic digestion, the following modifications were specified: variable: oxidation (MPK), deamidation (NQ), galactosylation (K), glucosylgalactosylation (K) and fixed: carbamidomethylation (C). Data were searched using a Homo sapiens database, downloaded from Uniprot on 22/03/2021.

Analysis of Divalent Cross-links

Protein extraction and trypsin digestion

Unless otherwise stated, all reagents were obtained from Merck (Darmstadt, Germany).

For the analysis of divalent cross-links, the hydroxylamine protocol developed in Chapter 4 was followed, with the addition of a stabilising reduction step at the beginning of the sample preparation. For the reduction step, 200 µL of 10% NaBH4 in 0.01 M NaOH was added to 17 mg of fresh bone sample. Reduction was performed at room temperature for 1 hour, then samples were centrifuged at 13,200 g for 5 min. The supernatant was removed and 200 µL of water was added to wash the samples, which were vortexed and then centrifuged again at 13,200 g for 5 min. This step was performed three times. 200 µL 5% TFA was then added to demineralise the sample, which was performed at room temperature for 30min. Samples were then centrifuged again at 13,200 g for 5 min, and the supernatant was removed. Extraction was performed as described in Chapter 4, using a guanidine-based extraction, followed by a hydroxylamine digestion on the remaining pellet, then performing reduction and alkylation on the resulting extracts. For the FASP digestion, due to the larger sample amounts, a digestion volume of 200 µL of 50 mM ammonium bicarbonate was used, and 40 µL of 0.05 µg/µL of trypsin was added. Digestion was performed overnight at 37 °C, and the samples were cleaned and concentrated using an ethyl acetate extraction and vacuum centrifuge evaporation, as described in Chapter 4.

Peptide fractionation

Fractions A and B of the hydroxylamine extraction were fractionated in order to reduce sample complexity, allowing for the identification of low abundance cross-linked peptides.

Fractionation was performed using the Pierce high pH reversed-phase peptide fractionation kit (ThermoFisher Scientific). In short, the evaporated peptide mixture was resuspended in 300 µL of 0.1% TFA, and loaded onto conditioned spin columns by centrifuging at 3000 g for 2 min.

The columns were then washed with 300 µL of water, and the peptides were eluted into eight fractions with increasing percentage concentrations of acetonitrile in 0.1% triethylamine, with the following fraction gradients; 5, 7.5, 10, 12.5, 15, 17.5, 20, 50%. Each of the 300 µL fractions were then evaporated in a vacuum centrifuge, resuspended in 10 µL 0.1% formic acid, their concentration measured using a Nanodrop 3000 with the Protein A280 method, and diluted to a concentration of 700 pmol/µL.

LC-MS/MS analysis

Samples were analysed using a Dionex Ultimate 3000RSLCnano system coupled to an Orbitrap Eclipse mass spectrometer (ThermoFisher Scientific). 7), dynamic exclusion (30 s). MS2 fragmentation was performed with HCD at 28%, using the automated injection time.

Cross-link search

The high pH fractions 1-8 of Fractions A and B of the hydroxylamine extraction were searched using XlinkX (Proteome Discoverer version 2.5, ThermoFisher Scientific). (22,23) The following custom cross-links were input for the search: Lys-Lys (-17.0266, H(-3)N(-1)), Hlys-Lys (-1.0316,H(-3)N(-1)O( 1)), and Hlys-Hlys (+14.9633, H(-3)N(-1)O( 2)), accounting for the additional 2H resulting from the reduction step. Data were searched using a database containing the sequences of bovine collagen type I, with the following modifications: oxidation (MP, variable), deamidation (NQ, variable), and carbamidomethylation (C, fixed), allowing for 5 variable modifications, 3 missed tryptic cleavages, with an MS1 error of 10 ppm, an MS2 error of 0.02 Da, and an FDR of 5%. Identified cross-links were manually validated.

Enrichment of Trivalent Cross-links

2.3.1. Hydroxylamine digestion 1 g of fresh bovine bone was sampled using a Dremmel 4000 hand drill. Demineralisation was performed overnight in 25 mL of 5% TFA at room temperature in a 50 mL Falcon tube. The sample was then centrifuged at 13,200 g for 5 min to pellet the sample, the supernatant was discarded, and 25 mL of water was added to the sample for washing, by vortexing and then centrifuging at 13,200 g for 5 min. This step was performed three times. The remaining demineralised bone was then divided into aliquots and dried in 2 mL tubes in a vacuum centrifuge to remove any remaining water. 50 mg of dried demineralised bone was then extracted using the hydroxylamine digestion protocol described in Chapter 4, but on a larger scale, where 5 mL of hydroxylamine buffer was used. Extraction was performed overnight at 40 °C, and then the samples was centrifuged at 13,200 g for 5 min, and the supernatant was transferred to a 10 mL Amicron 10 kDa filter. Reduction and alkylation were then performed, as described in Chapter 4, but in 6 M GuHCl, using 1 mL of the reduction and alkyation buffers.

After alkylation was finished, the filter was flipped, and the sample was reconstituted in 5 mL of 6 M GuHCl for SEC separation.

SEC separation

Preparative scale SEC separation was performed using the ATKA PURE 25 separation system, with a HiLoad Superdex 200 16/600 column (120 mL, Mw range: 10-600 kDa (Cytiva Life Sciences)). 5 mL of 50 mg/mL hydroxylamine collagen digest in 6 M GuHCl was loaded onto the column, and the separation was performed in a 50 mM ammonium bicarbonate buffer at pH 8.8. A flow rate of 1 mL/min was used, and fractions of approximately 3 mL were collected.

Each fraction was then measured for fluorescence activity (λex:330 nm, λem: 390 nm), using an Agilent Cary Eclipse fluorescence spectrometer, and the concentration was measured using a Bradford assay. 10 µL of each fraction was also used for SDS-PAGE analysis, following the protocol described in Section S4.1.2. of Chapter 4.

Tryptic digestion

Fractions 12-16, the fractions with the most intense fluorescence activity, were digested with trypsin. Based on the measured concentration using the Bradford assay, trypsin was added to each fraction at a ratio of 20:1. Digestion was performed overnight at 37 °C in the dark, and SDS-PAGE analysis was used to verify that the digestion was complete. The digests were then evaporated to dryness in a vacuum centrifuge and reconstituted in 150 µL of 10 mM ammonium formate.

SCX separation

SCX separation was performed on Fraction 13 of the SEC separation. The SCX separation was performed using a PolySULFOETHYL A column (4.6 × 100 mm, 5 µm, 200 Å (PolyLC)), on an ATKA PURE 25 separation system. A 200 µL loop was used to load 150 µL of tryptic digest onto the column, and separation using a gradient of ammonium formate, where buffer A was 10 mM ammonium formate, pH 3, with 25% acetonitrile, and buffer B was 500 mM ammonium formate, pH 3, with 25% acetonitrile. A linear gradient was used, with an increase to 100 % B over 60 min with a flow rate of 0.2. Fractions of 200 µL were collected and then measured for fluorescence activity as described above.

LC-MS/MS(/MS) Analysis

LC-MS analysis of SCX fractions was performed using an Orbitrap Eclipse mass spectrometer coupled to an Ultimate300 nano-LC in backflush mode (ThermoFisher Scientific). The LC separation was performed using C4 PepMap trap column (0. During the analysis, the mass spectrometer was analysing in intact protein mode, with a resolution of 60,000 in MS1 and 120,000 in MS2. Precursor ions were filtered for intensity (1e6), and charge state (5)(6)(7)(8)(9)(10). EThcD fragmentation was used, with 15% supplemental collision energy. Four experiments were run over the course of the analysis, with 5, 10, 15 and 20 ms ETD reaction times.

Results

Analysis of PTMs associated with cross-linking iin archaeological bones

Data from a tryptic digest of an archaeological human bone were searched in order to determine if PTMs associated with cross-linking could be preserved on ancient samples. Three modifications associated with the lysyl hydroxylase enzyme, an enzyme shown to be strongly correlated to the activity of lysyl oxidase (Figure 6-1B), were identified; lysine hydroxylation (+15.99), and two lysine glycosylation modifications; galactosylation (+178.05) and glucosylgalactosylation (+340.10). Since the glucose units involved in collagen glycosylation typically remain on the peptide during fragmentation, they can be treated as a typical PTM. ( 24)

Figure 6-3 shows that numerous residue sites were identified with at least two of these modifications present, indicating that such modifications can be preserved over longer time periods. ( 25)

Analysis of divalent cross-linked peptides

The analysis of enzymatic cross-links in collagen was approached in two ways. The targeted enrichment of trivalent cross-links, discussed later, and the analysis of divalent cross-links using bioinformatic tools for the analysis of cross-linked peptides. Since there are several bioinformatic tools available that allow the user to search for cross-links between two peptides, designed for chemical cross-linking mass spectrometry studies, a targeted search for divalent cross-links in a tryptic digestion of bone collagen peptides was performed. However, since naturally occurring cross-linked peptides are present at significantly lower concentrations than cross-links resulting from chemical cross-linkers, it is necessary to fractionate the sample before analysis in order to obtain a greater depth of proteome coverage. Firstly, in order to stabilise the acid-labile immine bond present in the immature cross-links, a reduction using NaBH4 was necessary, as shown in Figure 6-4, thus making the cross-link stable for further sample processing. Having performed this reduction step, collagen was then extracted and digested with trypsin, using the hydroxylamine protocol described in Chapter 4.

The resulting peptide mixture was fractionated using a high pH fractionation, and each fraction analysed using nanoLC-MS/MS, as shown in Figure 6-5, allowing for a greater proteome coverage to search for low intensity cross-linked peptides. Following the fractionation, data were searched using XlinkX in order to identify cross-linked peptides. Several cross-linked sites were identified, listed in Table S6-1, and an example of a spectrum identifying a cross-link is shown in Figure 6-6. Various forms of the divalent immature cross-link were searched, with none, one and two oxidation sites on the cross-link itself (noted as Lys-lys, Hlys-Lys, and Hlys-Hlys in Table S6-1). The sites identified by this cross-link search to be involved in cross-linking have not been previously reported, and are present throughout the collagen chain, not necessarily exclusively in the telopeptide regions as previously reported. In addition to this, on several occasions, the same cross-linking sites were identified with differing cross-links, in particular the cross-link between positions 236 and 227 in collagen α1(i), which was identified with no, one and two oxidations on the cross-link in different high pH fractions, as seen in Table S6-1. This reflects the heterogeneity of the crosslink structures in collagen, an additional challenge being that the heterogeneity in collagen is also a result of a high degree of proline oxidation, so it can be a challenge to identify the location of the oxidation on the peptide. Such an approach has not been used to analyse divalent collagen cross-links before. Previously, the targeted enrichment of divalent cross-linked peptides was performed by 'tagging' the crosslink using NaB 3 H4 in the reduction step and tracking the beta decay of 3 H using scintillation detection, requiring specialised lab equipment and additional safety measures. (21) Using this methodology has the advantage that a targeted enrichment is not necessary, since there is no natural 'marker' that can be used to enrich the peptides, such as the fluorescence that was used for the enrichment of trivalent collagen cross-links. The use of bioinformatic tools can additionally identify new sites of cross-linking which can then be used to identify trivalent cross-linked peptides. Having said this, the lack of targeted enrichment means that it is difficult to control for false positive hits; consequently, manual control of the MS/MS is required.

3.3.Analysis of trivalent cross-links

Unlike divalent cross-links in collagen, the pyridinoline moiety in trivalent cross-links has a very specific fluorescence activity, which can be used as a marker for a targeted enrichment, allowing a comprehensive characterisation of the structure, similar to the enrichment procedure described by Terajima et al. (21) The main difference between the enrichment procedure described by Terajima et al. and the procedure used in this study is that the enrichment was performed directly on a tryptic digest by Terajima et al., whereas here, the first SEC separation step was performed on a chemical digest, and the trypsin digestion was performed between the separation steps. A preparative scale aqueous SEC separation was then performed on the hydroxylamine digest, the collected fractions were screened for fluorescence activity, and their concentration was measured using a Bradford assay, as shown in Figure 678. It is worth nothing that while Fractions 13 and 14 were the most concentrated fractions, understandably as they are crosslinked, there is an efficient separation between the cross-linked fraction of the digest and the non-cross-linked fraction eluting mostly in Fractions 17-20. Fractions 12 to 16 were then digested with trypsin, resulting in peptide mixtures that are enriched in cross-linked peptides. A preparative scale SCX separation was performed on Fraction 13. Fractions of 200 µL were collected and controlled for fluorescence activity. The benefit of the SCX separation is that peptides with a higher charge elute later, so, while a normal tryptic peptide has a charge of 2+ at a low pH, a cross-linked trivalent tryptic peptide will have a much higher charge of 5+ or 6+, and therefore will elute much later. Finally, the SCX fractions with the most intense fluorescence were analysed using nanoRPLC-MS, an orthogonal separation mode. At first, a bottom up proteomics configuration was used for the analysis, with a C18 stationary phase and a smaller pore size of 100 Å, but only linear peptides with low charge states were identified, shown in Figure 6-10. Therefore, a top down configuration was used, with the mass spectrometer in intact protein mode, and a MAbPac polymer resin column with a larger pore size of 1500 Å. With this configuration, peptides with higher m/z values, and higher charge states were identified, corresponding to cross-linked peptides. The data obtained in these analyses demonstrate the complexity of the analytical question in this study. Figure 6-11 shows one analysis where the m/z is plotted on the x-axis, and retention time on the y-axis, and it can be seen that, for each peptide sequence, multiple forms are present (one peptide sequence is circled in red, for example), which vary in the number of oxidations on the peptide chain (change in m/z value), as well as the location of the oxidation (change in retention time). This significantly complicates the analysis due to the high heterogeneity of the peptides present, but also means that multiple spectra of the same peptide sequences are present in the analysis that can be used to aid in the characterisation of the crosslinked peptide. The MS2 spectra, as expected when using ETD fragmentation, are dominated by multiple charge states of the precursor ion, as shown in Figure 6-13, where the 6+, 5+ and 4+ charge state of the precursor ion are observed. In addition to this, the low intensity peaks present a highly complex fragmentation pattern which is challenging to interpret. The precursor ion at m/z: 1027.6499 (Figure 6-12 and Figure 6-13) was identified as a possible candidate for a cross-linked peptide because of the observed oxidation pattern characteristic of collagen peptides, its high m/z and z values, and because it was not possible to deconvolute it using tradition bioinformatic tools. In order to identify spectra that could correspond to crosslinked peptides, spectra were manually annotated in order to identify a sequence of several amino acids that could be attributed to a specific section of the collagen sequence. Following the identification of small sequences of amino acids, it was possible to construct a larger peptide sequence and confidently identify one of the peptides involved in the cross-link.

The annotation of one of the peptides in the cross-link at m/z: 1027.6499 is shown in Figure 6

Conclusions

In this study, novel enrichment and analysis methods for the characterisation of cross-linked peptides in collagen were developed. Cross-linking mass spectrometry approaches were used for the analysis of immature cross-links, including the use of an orthogonal fractionation in order to decrease the complexity of the sample for a deeper proteome coverage. This allowed for the identification of new cross-linking sites, which could then be used to inform the characterisation of the trivalent cross-links, as well as the cross-linking sites reported in previous literature. For the analysis of trivalent cross-links, a novel, targeted, multi-stage chromatographic enrichment procedure was developed for efficient isolation of the peptides. In addition to this, top down proteomics methods were applied to obtain a higher sensitivity for the high molecular weight cross-links, which revealed the high complexity of the different cross-link structures. The full characterisation of these cross-linked structures continues to be incredibly challenging due to the highly complex fragmentation spectra that are produced. With the identification of several candidate spectra for cross-linked peptides, the next steps would be to apply this analysis workflow to study the degradation mechanisms that involve cross-linking in collagen to better understand collagen preservation in historical samples.

Conclusions and Future Perspectives

This thesis aimed to advance the proteomics methodologies used in the analysis of cultural heritage objects. In particular this thesis focused on the analysis of collagens from mineralised tissues, predominantly bones, teeth and ivory, from both archaeological, forensic and museum collections.

The analytical methods developed were both intended for the applied analysis of precious objects using minimally invasive analysis techniques, and as a more exploratory study of the structure of the collagen network.

First, the introduction of this thesis defined the opportunities for improvement in the proteomic analysis of cultural heritage objects by considering the developments that have been made in the fields of clinical and structural proteomics. Key opportunities identified included utilising the high sensitivity of nanoLC setups typically used in proteomic applications for the analysis of trace levels of materials from precious museum and archaeological objects. In addition to this, the increased utilisation of high throughput analyses, resulting in more robust instrumentation and data analysis workflows, is a key opportunity for the analysis of large numbers of archaeological samples.

Finally, the more widespread use of proteomics and genomics in fields outside clinical settings has resulted in an increase in the number of annotated protein sequences available on online public data bases, as well as more robust de novo sequencing tools. Such advances are highly beneficial to the application of proteomics to cultural heritage analysis, as in many cases, the species of interest in various studies, are not fully sequenced in proteomic databases. Finally, opportunities in the field of structural proteomics were considered, as many of the techniques discussed could be applied with a high relevance to questions relating to cultural heritage in order to better understand the degradation mechanisms present in cultural heritage objects containing proteinaceous material.

Chapters 1, 2 and 3 detail the use of proteomics analysis applied to museum or archaeological samples in order to identify the species of origin of the object. This work focused on increasing the sensitivity, specificity and throughput of proteomics analysis in order to answer the question of species identification.

Having identified the opportunities in the field of proteomics analysis applied to cultural heritage, Chapter 1 focused on the analysis of several bone and ivory objects from the collection of the Metropolitan Museum of Art. Here, the goal of the study was to maximise the sensitivity of this analysis; in doing this, several developments were made, (a) an optimised sample preparation and analytical workflow for minimal sample amounts, (b) a data analysis methodology to confidently identify the species of origin of an object based on the identification of multiple informative peptides, (c) a novel data analysis procedure based on the use of peptide mass fingerprinting to extract species informative information from highly degraded materials. In addition to this, model samples of elephant and hippopotamus from the Department of Mammalogy at the American Museum of Natural History were analysed in order to construct homemade proteomic databases of these understudied species. This also included the development of a targeted MS3 method to discriminate between leucine and isoleucine for the analysis of samples from species that have not had their proteome fully sequenced. The development of this minimally invasive methodology will enable access to increasingly precious museum and archaeological collections to perform analysis that has never before been permitted.

Chapter 2 continued the analysis of elephant ivory that was started in Chapter 1, but with a focus on the analysis in the context of the CITES regulations. Here the goal of the analysis was to first identify the informative positions in the sequence that allow for the taxonomic distinction between elephant species. Following this, informative peptides were targeted to allow for the rapid identification of elephant species in future analyses. The significance of this work is related to CITES hearings wherein the species identification of seized ivory is often a key part of the prosecution, and current non-destructive methods are not fully conclusive, while DNA analysis requires large amounts of material and is very expensive.

Chapter 3 focused on the development of a more high throughput, streamlined workflow for the species identification of a large number of archaeological samples. This development was applied to a series of bone fragments from Le Piage, an archaeological site in southwest France, including a sample that had previously been identified a human based on morphological analysis. The key developments made in this chapter included (a) the development of more rapid LC-MS/MS analysis methods and (b) the development of a de novo-based data processing tool, Prometa, to be able to automate the data processing. Here, significant challenges were met in the data processing with Prometa, mostly related to the lack of annotation of the proteins of the species of interest in this site. The developments made in this chapter will significantly decrease the time spent on data analysis in future projects, where large numbers of samples are anticipated.

The first half of this thesis focused on the application of proteomics analysis to historical museum or archaeological objects. The developments made in this work, in particular the use of minimally invasive analysis, has already been applied to other objects at the Metropolitan Museum of Art, and has allowed access to far more precious collections. In addition to this, this work has laid the framework for future, large scale, projects in order to more rapidly analyse large cohorts of archaeological samples. This work has demonstrated the utility of proteomics for certain applications in the field of cultural heritage analysis, but that it can also be applied to other fields, such as forensic ivory analysis.

Chapters 4, 5 and 6 focused on a more exploratory analysis of the structure of the collagen network, in order to better understand how samples containing collagen are preserved over long periods of time. This was done by developing an analysis methodology to characterise cross-linked collagen peptides

Chapter 4 details the use of a hydroxylamine protocol to more efficiently solubilise cross-linked collagen to increase the efficiency of collagen extraction. This was of a high utility for the development of an analytical protocol for the analysis of collagen cross-links, but also was highly applicable to the proteomic analysis of collagen-containing tissues in order to have a deeper proteome coverage. This protocol gave a higher recovery of collagen in fresh, forensic and archaeological samples, and notably increased the number of proteins detected in archaeological samples.

Chapters 5 and 6 focused directly on the analysis of cross-linked collagen peptides. Chapter 5 consists of a detailed review into the analytical techniques used to characterise and quantify crosslinking in collagen. This review was vital to the experiments performed in Chapter 6, which detailed the use of various chromatographic separations and proteomic methodologies in order to characterise the cross-linking in collagen in model bones. Classical bottom-up proteomics was used in order to identify PTMs associated with cross-linking in archaeological bones, enabling the identification of candidate cross-linking sites. Secondly, cross-linking mass spectrometry methodologies were used in order to identify divalent cross-linking between collagen peptides.

Finally, a multi-step chromatographic separation was used to purify trivalent cross-links. Following this purification, top-down proteomics strategies were used in order to characterise the peptide sequences involved in the cross-link.

The developments made in this section of the thesis are the first steps in better understanding the cross-linking the behaviour of collagen using mass spectrometry-based proteomics. The characterisation of trivalent cross-links is of particular interest in the analysis of archaeological samples due to their stability over time. This work also serves to demonstrate the analytical questions concerning cultural heritage analysis that can be answered with high resolution mass spectrometry analysis. In the future, it is expected that the structural analysis of these complex systems can be utilised to answer questions about the degradation or preservation mechanisms of the proteins present in historical objects.

'Study of collagen cross-linking and associated modifications in bones using proteomics ' Peptides were detected using an Orbitrap Eclipse mass spectrometer in positive mode. Both MS1 and MS2 spectra were collected in the Orbitrap analyser, with a mass range of 375-1500 in MS1, and the automatic mass range mode in MS2. MS1 spectra were collected with a resolution of 120,000, AGC target of 100% and an automated injection time. MS2 spectra were acquired using a data dependent analysis method, including the following MS2 filters: MIPS (Peptide), intensity (2.5e4), charge state (2-7), dynamic exclusion (30 s). MS2 were collected with a HCD fragmentation at 28%, at a resolution of 30,000, with an AGC target of 100%, using the automated injection time mode for both model and the modern samples. For the lowcomplexity archaeological samples, the MS2 injection time was fixed at 100 ms.

During analysis, a tryptic digest of human archaeological bone was analysed at the beginning and end of the sequence in order to monitor the performance of the instrument during analysis.

In addition to this, blanks were placed between every sample in order to avoid carry over between the runs. S4.1.4. Data Analysis.

The collected data were analysed with PEAKS X (Bioinformatic Solutions). Mass tolerances of 10 ppm for parent and 0.2 Da for fragment ions were used. Data were searched against the Uniprot database for Homo sapiens or Bos taurus as necessary (databases were downloaded on 22/03/2021 and 06/07/2022 respectively). Carbamidomethylation (C) was included as a fixed modification, and allowed variable modifications were deamidation (NQ), oxidation (M), hydroxylation (P), galactosylation (K), glucosylgalactosylation (K) and water loss (S, Y). 7

variable PTMs and up to three missed cleavages were allowed per peptide, with an allowance for semi-specific cleavages per peptide. For samples extracted with GuHCl, tryptic digestion was chosen, for samples extracted with hydroxylamine, digestion with trypsin and hydroxylamine was specified (after N, before GX). An FDR of 1% was used. Fractions A and B were processed both separately and together. The online STRING database was used to determine the function and relationship of the proteins identified. The proteins.csv and peptide-proteins.csv files for each sample were compiled into master files, and data was sorted and analysed using these masters, which can be found in the Supplementary Files 4 and 5.

The data were also analysed using Proteome Discover 2.5 (ThermoFisher Scientific), in order to create protein heatmaps. The three replicates of the model modern and archaeological samples were grouped under Fractions A, B or C. The data files were searched together using Sequest HT, with mass tolerances of 10 ppm for parent and 0.2 Da for fragment ions. The same databases were used as in the PEAKS search, and the following modifications were searched: carbamidomethylation (C) as a fixed modification, deamidation (NQ), oxidation (M), and hydroxylation (P) as variable modifications. 5 variable PTMs and up to three missed cleavages were allowed per peptide. Results of the peptide search were filtered with an FDR of 1% both on the peptide and protein level using the peptide and protein validator nodes in Proteome Discoverer 2.5. Heat maps were then visualised based on the protein abundances using the visualisation tool in Proteome Discover 2.5.

S4.3. Master Proteins

Access to the file can be found here

S4.4. Master Peptides

Access to the file can be found here

S4.5. T-tests for coverage and peptides

Access to the file can be found here

  Chapitre 1 décrit une vaste étude des objets en ivoire de collections majeures du Metropolitan Museum of Art de New York. Des objets datant de 500 à 6000 ans des départements d'Art Egyptien, Art Médiéval, Instruments de Musique, et des collections des Iles Pacifiques ont été étudiés. L'objectif de cette analyse était d'identifier l'espèce biologique dont provenait chaque objet en ivoire. En raison des nombreux défis associés à cette analyse comme le fait que nous ciblions des espèces peu étudiées, que très peu d'échantillons étaient disponibles et que certains objets étaient considérablement dégradés, plusieurs développements ont été proposés au cours de cette étude : (a) le développement d'une méthode d'échantillonnage et d'analyse peu invasive afin d'effectuer des analyses protéomiques à partir de quantités ix minimales d'échantillons d'ivoire et d'os, (b) l'analyse d'échantillons d'ivoire modèles pour construire des bases de données protéomiques d'espèces non référencées dans les banques

  Chapitre 2 traite de l'analyse d'échantillons d'ivoire modèles afin de constituer des bases de données protéomiques d'Elephantidae. Alors que dans le chapitre 1, seul l'ivoire de Loxodonta africana (éléphant de savane d'Afrique) a été étudié en détails, ici, une analyse approfondie des séquences de collagènes provenant d'échantillons d'ivoire de plusieurs individus de trois espèces d'éléphants a été réalisée (éléphant d'Asie -Elephas maximus indicus, éléphant de savane d'Afrique -Loxodonta africana, éléphant de forêt d'Afrique -Loxodonta cyclotis). Cette étude est d'importance majeure à la fois pour l'analyse d'échantillons du patrimoine culturel et dans le contexte du CITES (Convention on International Trade in Endangered Species), un accord international qui vise à limiter le commerce de matériaux provenant de plantes et d'animaux menacés d'extinction. Le séquençage protéique des collagènes des espèces étudiées a permis l'identification de substitutions d'acides aminés clés permettant de différencier les espèces étudiées. Le Chapitre 3 illustre comment la procédure d'échantillonnage peu invasive développée dans le chapitre 1 a pu être déployée dans le cadre d'analyse d'échantillons datant de la période du Proto-Aurignacien, il y a environ 40 000 ans. Alors que dans le chapitre 1, les principaux développements et optimisations avaient pour objectifs d'accroître la sensibilité de l'analyse protéomique, ici, un objectif supplémentaire a été de développer une approche expérimentale permettant de traiter un nombre d'échantillons plus important. En effet, l'analyse d'un grand nombre d'échantillons archéologiques est souvent inhérent à l'étude de sites archéologiques. En particulier, le développement de gradients chromatographiques courts (quelques minutes à dizaines de minutes) permet de passer au crible un nombre élevé d'échantillons. Les développements de ce chapitre comprennent (a) le déploiement du protocole peu invasif développé au chapitre 1 à l'analyse d'échantillons archéologiques, (b) le développement de x méthodes d'analyses de type high throughput adaptées au screening d'échantillons, et (c) le développement d'un nouvel outil bioinformatique, Prometa, permettant une identification semiautomatisée des espèces d'échantillons sur la base des peptides de collagènes détectés.

S4. 1 . 3 .

 13 LC-MS/MS Analysis. ................................................................................................. 261 S4.1.4. Data Analysis. ............................................................................................................ 262 S4.2. Sample Preparation Protocols ............................................................................................ 264 S4.2.1. Successive hydroxylamine digestion protocol ........................................................... 264 S4.2.2. Direct hydroxylamine extraction protocol ................................................................. 265 S4.3. Master Proteins .................................................................................................................. 266 S4.4. Master Peptides ................................................................................................................. 266 S4.5. T-tests for coverage and peptides ...................................................................................... 266 S4.6. T-tests for score comparison ............................................................................................. 266 S4.7. Supplementary Figures and Tables .

1 :Figure 1 - 1 : 26 Figure 1 - 2 : 30 Figure 1 - 3 : 31 Figure 1 - 4 : 32 Figure 1 - 5 : 34 Figure 1 - 6 : 36 Figure 1 - 7 : 37 Figure 1 - 8 : 39 Chapter 2 :Figure 2 - 1 : 58 Figure 2 - 2 : 59 Figure 2 - 3 : 60 Figure 2 - 4 : 60 Figure 2 - 5 : 64 Figure 2 - 6 : 65 xx 3 :Figure 3 - 1 : 76 Figure 3 - 2 : 77 Figure 3 - 3 : 78 Figure 3 - 4 : 79 Figure 3 - 5 : 81 Figure 3 - 6 : 82 Figure 3 - 7 :

 1112612301331143215341636173718392215822592360246025642665331763277337834793581368237 Figure 1-1: The samples received for analysis. Pictured is the 6 µm diamond polishing film with a small amount of dust collected from the surface of the object. ................................................................................................................. 26 Figure 1-2: Peptide from collagen α2(i), unique to Bos genus identified in all three samples taken from object 11.176.1.; GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGPAGPAGPR, m/z: 1391.1682, z: 2, ppm: 1.2. .................................................................................................................................................................................. 30 Figure 1-3: Peptide from collagen α2(i), unique to Physeter macrocephalus, detected in object 1979.206.1587, but also identified in object 2012.346.; GAP(+15.99)GAVGAP(+15.99)GPAGN(+.98)GDRGEAGPAGAAGPAGSR, m/z: 1366.1423, z: 2, ppm: 0.1. ...................................................................................................................................... 31 Figure 1-4: A Peptide from collagen α2(i) unique to Bos, detected in sample S2 from object 40.66a,b GYP(+15.99)GNAGPVGAAGAP(+15.99)GPQGPVGPVGK, m/z: 1131.0693, z: 2, ppm: 2.5. B Peptide from collagen α2(i) used to identify the Cervidae family detected in samples S1 and S3 from object 40.66a,b; GSP(+15.99)GERGEVGPAGPN(+.98)GFAGPAGAAGQAGAK, m/z: 870.4167, z: 3, ppm: 2.3. ............................. 32 Figure 1-5: MS/MS spectrum showing the identification of the substitution P -> A at position #455 in collagen α2(i) peptide from Hippopotamus amphibious in exemplar 113816 (HT11D), Mammology collection, AMNH. GPP(+15.99)GESGAAGPTGAIGNRG, m/z: 791.3815, z: 2, ppm: 1.6. ....................................................................... 34 Figure 1-6: Peptide from collagen α2(i) unique to Hippopotamus amphibius, identified in object 66.99.1 HGN(+.98)RGEP(+15.99)GPAGVVGPTGAVGPR, m/z:686.3525, z:3, ppm: 3.3. ..................................................... 36 Figure 1-7: MS3 spectra to confirm isoleucine at position #865 of collagen α2(i) of Hippopotamus amphibius, identified in object 66.99.1. Residue identity was confirmed with three different peptides with the same sequence, through the fragmentation of the same z2 ion. ................................................................................................................ 37 Figure 1-8: Strategies for identifying species when a sample has poor collagen preservation. A: Peptide from collagen α2(i), HGN(+.98)RGEP(+15.99)GPAGVVGPTGAVGPR (m/z: 514.7659, z:4, ppm: 1.1), specific to Hippopotamus amphibius, detected in object 26.7.1299, shown is the spectrum that resulted from summing three individual MS2 spectra. B: LC MS PMF mapping of peptides unique to the Bos genus and subfamily Bovinae identified in sample 12.187.30 and sample 40;66a,b-S2 ................................................................................................................................. 39

84 Figure 3 - 9 : 86 Figure 3 -

 8439863 Figure 3-1: Visual impact of the minimally invasive sampling technique used for sampling in this analysis .............. 76 Figure 3-2: Reduction of the gradient length for the analysis of archaeological collagen peptides, A the effect on the TIC of the analysis, B the effect of changing the gradient on the number of peptides identified in the analysis and on the coverage of collagen α2(i), and C, the effect of the gradient slope. ......................................................................... 77 Figure 3-3: Mass spectrometer parameters adapted for shorter gradients, A the MS1 resolution, and B the MS2 acquisition strategies. ...................................................................................................................................................... 78 Figure 3-4: Identified amino acid substitutions in Rangifer tarandus when searched against a database for Odocoileus virignianus texanus, the closest relative that had collagen sequenced in the NCBI database. A: collagen α2(i), position 829; T(subS)GETGASGPP(+15.99)GFAGEKGPSG, m/z:888.9065, z:2, ppm:0.3; B: collagen α2(i), position 864; GPSGEP(+15.99)GTAGPP(+15.99)GTP(+15.99)GPQGF(subI)LGPP(+15.99)GFLGLP(+15.99)GSR, m/z:1032.1720, z:3, ppm:1.4. ................................................................................................................................................................... 79 Figure 3-5: Alignment of the collagen α2(i) sequences of Rangifer tarandus platyrhyncus (DNA reconstruction), Odocoileus virignianus texanus, and Rangifer tarandus (proteomic reconstruction) ..................................................... 81 Figure 3-6: Alignment of contigs produced using Prometa against a consensus contig that corresponds to the contig sequence ADELADHLGFQEAYRR. This alignment was constructed using the following parameters: kmers size: 7, kmers overlap: 6, contigs overlap: 5, gaps: 3. ................................................................................................................. 82 Figure 3-7: Workflow of the Prometa data processing tool. In red, the key data processing steps, in blue, file files generated, and in black, the parameters that can be input for the analysis. .................................................................... 83 Figure 3-8: A proposed illustration of the data using Prometa, where the number of peptides identified is plotted against the genes of different species. The top two plots correspond to model reindeer sample rang150, and the bottom two to rang1709. The colour of the bars in the histogram correspond to the species, in this case, we focused on the species in the Cervidae family that have collagen sequences available in the NCBI database. If there is more than one gene per species in each plot, this corresponds to an isoform. Assignment of the peptide sequences to a gene and species was performed using MMSeqs2.(35) ................................................................................................................. 84 Figure 3-9: Peptides specific to A Rangifer tarandus, and B Bos, identified in samples 1507 and 1509 respectively. * contamination .................................................................................................................................................................. 86 Figure 3-10: A The sequence coverage of collagen 1α(i) from Homo sapiens from sample LIP001, highlighted in grey, with two informative peptide sequences identified by blue rectangles. B and C show more detail regarding the identified peptide sequences with, i, the number of peptides identified for each sequence, ii, MS/MS spectra of the peptides, and iii, the species in which this peptide sequence is observed, ie; 32 species in the Primate order contain the peptide sequence B in their collagen 1α(i) sequence. *This peptide present as a polymorphism that is unique to Homo sapiens (discussed on the coming sections) .................................................................................................................... 88 Figure 3-11: A The sequence coverage of collagen 2α(i) from Homo sapiens from sample LIP001, highlighted in grey, with two informative peptide sequences identified by blue rectangles. B and C show more detail regarding the identified peptide sequences with, i, the number of peptides identified for each sequence, ii, MS/MS spectra of the peptides, and iii, the species in which this peptide sequence is observed. ...................................................................... 89 Figure 3-12: Spectrum identifying the peptide shown in Figure 3-10B, that was used as evidence to assign sample LIP001 as Homo sapiens................................................................................................................................................. 89 Figure 3-13: EICs of the two informative peptides identified collagen 1α(i) in Figure 3-10, A, GDAGP(+15.99)PGPAGPAGPP(+15.99)GPIGN(+0.98)VGAP(+15.99)GAK, and B, SGDRGETGPAGPTGPVGPVGAR, that were identified in both i, the LIP001 data, and ii, the data from the standard human collagen sample. .................................................................................................................................................. 90 Figure 3-14: EICs of the two informative peptides identified collagen 2α(i) in Figure 3-11, A GPSGPAGEVGKP(+15.99)GER, and B, GEN(+0.98)GVVGPTGPVGAAGPAGPN(+15.99 in both i, the LIP001 data, and ii, the data from the standard human collagen sample. A shows that the peptide identified in the LIP001 data was in fact not present in the standard human sample. ................................................................................................... 91 Figure 3-15: EICs of the peptides corresponding to the polymorphism at position 1075 in collagen α1(i) that allows for sample LIP001 to be confirmed as human. A: The EIC of the peptide with T at position 1075, B: EIC of the peptide with A at position 1075. One can see that the peptide shown in B is significantly higher in abundance than A, corresponding to the calculated frequencies discussed in (13). ...................................................................................... 92

88 Figure 3 - 11 : 89 Figure 3 - 12 : 89 Figure 3 - 13 : 90 Figure 3 - 14 : 91 Figure 3 -

 88311893128931390314913 Figure 3-1: Visual impact of the minimally invasive sampling technique used for sampling in this analysis .............. 76 Figure 3-2: Reduction of the gradient length for the analysis of archaeological collagen peptides, A the effect on the TIC of the analysis, B the effect of changing the gradient on the number of peptides identified in the analysis and on the coverage of collagen α2(i), and C, the effect of the gradient slope. ......................................................................... 77 Figure 3-3: Mass spectrometer parameters adapted for shorter gradients, A the MS1 resolution, and B the MS2 acquisition strategies. ...................................................................................................................................................... 78 Figure 3-4: Identified amino acid substitutions in Rangifer tarandus when searched against a database for Odocoileus virignianus texanus, the closest relative that had collagen sequenced in the NCBI database. A: collagen α2(i), position 829; T(subS)GETGASGPP(+15.99)GFAGEKGPSG, m/z:888.9065, z:2, ppm:0.3; B: collagen α2(i), position 864; GPSGEP(+15.99)GTAGPP(+15.99)GTP(+15.99)GPQGF(subI)LGPP(+15.99)GFLGLP(+15.99)GSR, m/z:1032.1720, z:3, ppm:1.4. ................................................................................................................................................................... 79 Figure 3-5: Alignment of the collagen α2(i) sequences of Rangifer tarandus platyrhyncus (DNA reconstruction), Odocoileus virignianus texanus, and Rangifer tarandus (proteomic reconstruction) ..................................................... 81 Figure 3-6: Alignment of contigs produced using Prometa against a consensus contig that corresponds to the contig sequence ADELADHLGFQEAYRR. This alignment was constructed using the following parameters: kmers size: 7, kmers overlap: 6, contigs overlap: 5, gaps: 3. ................................................................................................................. 82 Figure 3-7: Workflow of the Prometa data processing tool. In red, the key data processing steps, in blue, file files generated, and in black, the parameters that can be input for the analysis. .................................................................... 83 Figure 3-8: A proposed illustration of the data using Prometa, where the number of peptides identified is plotted against the genes of different species. The top two plots correspond to model reindeer sample rang150, and the bottom two to rang1709. The colour of the bars in the histogram correspond to the species, in this case, we focused on the species in the Cervidae family that have collagen sequences available in the NCBI database. If there is more than one gene per species in each plot, this corresponds to an isoform. Assignment of the peptide sequences to a gene and species was performed using MMSeqs2.(35) ................................................................................................................. 84 Figure 3-9: Peptides specific to A Rangifer tarandus, and B Bos, identified in samples 1507 and 1509 respectively. * contamination .................................................................................................................................................................. 86 Figure 3-10: A The sequence coverage of collagen 1α(i) from Homo sapiens from sample LIP001, highlighted in grey, with two informative peptide sequences identified by blue rectangles. B and C show more detail regarding the identified peptide sequences with, i, the number of peptides identified for each sequence, ii, MS/MS spectra of the peptides, and iii, the species in which this peptide sequence is observed, ie; 32 species in the Primate order contain the peptide sequence B in their collagen 1α(i) sequence. *This peptide present as a polymorphism that is unique to Homo sapiens (discussed on the coming sections) .................................................................................................................... 88 Figure 3-11: A The sequence coverage of collagen 2α(i) from Homo sapiens from sample LIP001, highlighted in grey, with two informative peptide sequences identified by blue rectangles. B and C show more detail regarding the identified peptide sequences with, i, the number of peptides identified for each sequence, ii, MS/MS spectra of the peptides, and iii, the species in which this peptide sequence is observed. ...................................................................... 89 Figure 3-12: Spectrum identifying the peptide shown in Figure 3-10B, that was used as evidence to assign sample LIP001 as Homo sapiens................................................................................................................................................. 89 Figure 3-13: EICs of the two informative peptides identified collagen 1α(i) in Figure 3-10, A, GDAGP(+15.99)PGPAGPAGPP(+15.99)GPIGN(+0.98)VGAP(+15.99)GAK, and B, SGDRGETGPAGPTGPVGPVGAR, that were identified in both i, the LIP001 data, and ii, the data from the standard human collagen sample. .................................................................................................................................................. 90 Figure 3-14: EICs of the two informative peptides identified collagen 2α(i) in Figure 3-11, A GPSGPAGEVGKP(+15.99)GER, and B, GEN(+0.98)GVVGPTGPVGAAGPAGPN(+15.99 in both i, the LIP001 data, and ii, the data from the standard human collagen sample. A shows that the peptide identified in the LIP001 data was in fact not present in the standard human sample. ................................................................................................... 91 Figure 3-15: EICs of the peptides corresponding to the polymorphism at position 1075 in collagen α1(i) that allows for sample LIP001 to be confirmed as human. A: The EIC of the peptide with T at position 1075, B: EIC of the peptide with A at position 1075. One can see that the peptide shown in B is significantly higher in abundance than A, corresponding to the calculated frequencies discussed in (13). ...................................................................................... 92

92 xxi 4 :Figure 4 - 1 :

 92441 Figure 3-1: Visual impact of the minimally invasive sampling technique used for sampling in this analysis .............. 76 Figure 3-2: Reduction of the gradient length for the analysis of archaeological collagen peptides, A the effect on the TIC of the analysis, B the effect of changing the gradient on the number of peptides identified in the analysis and on the coverage of collagen α2(i), and C, the effect of the gradient slope. ......................................................................... 77 Figure 3-3: Mass spectrometer parameters adapted for shorter gradients, A the MS1 resolution, and B the MS2 acquisition strategies. ...................................................................................................................................................... 78 Figure 3-4: Identified amino acid substitutions in Rangifer tarandus when searched against a database for Odocoileus virignianus texanus, the closest relative that had collagen sequenced in the NCBI database. A: collagen α2(i), position 829; T(subS)GETGASGPP(+15.99)GFAGEKGPSG, m/z:888.9065, z:2, ppm:0.3; B: collagen α2(i), position 864; GPSGEP(+15.99)GTAGPP(+15.99)GTP(+15.99)GPQGF(subI)LGPP(+15.99)GFLGLP(+15.99)GSR, m/z:1032.1720, z:3, ppm:1.4. ................................................................................................................................................................... 79 Figure 3-5: Alignment of the collagen α2(i) sequences of Rangifer tarandus platyrhyncus (DNA reconstruction), Odocoileus virignianus texanus, and Rangifer tarandus (proteomic reconstruction) ..................................................... 81 Figure 3-6: Alignment of contigs produced using Prometa against a consensus contig that corresponds to the contig sequence ADELADHLGFQEAYRR. This alignment was constructed using the following parameters: kmers size: 7, kmers overlap: 6, contigs overlap: 5, gaps: 3. ................................................................................................................. 82 Figure 3-7: Workflow of the Prometa data processing tool. In red, the key data processing steps, in blue, file files generated, and in black, the parameters that can be input for the analysis. .................................................................... 83 Figure 3-8: A proposed illustration of the data using Prometa, where the number of peptides identified is plotted against the genes of different species. The top two plots correspond to model reindeer sample rang150, and the bottom two to rang1709. The colour of the bars in the histogram correspond to the species, in this case, we focused on the species in the Cervidae family that have collagen sequences available in the NCBI database. If there is more than one gene per species in each plot, this corresponds to an isoform. Assignment of the peptide sequences to a gene and species was performed using MMSeqs2.(35) ................................................................................................................. 84 Figure 3-9: Peptides specific to A Rangifer tarandus, and B Bos, identified in samples 1507 and 1509 respectively. * contamination .................................................................................................................................................................. 86 Figure 3-10: A The sequence coverage of collagen 1α(i) from Homo sapiens from sample LIP001, highlighted in grey, with two informative peptide sequences identified by blue rectangles. B and C show more detail regarding the identified peptide sequences with, i, the number of peptides identified for each sequence, ii, MS/MS spectra of the peptides, and iii, the species in which this peptide sequence is observed, ie; 32 species in the Primate order contain the peptide sequence B in their collagen 1α(i) sequence. *This peptide present as a polymorphism that is unique to Homo sapiens (discussed on the coming sections) .................................................................................................................... 88 Figure 3-11: A The sequence coverage of collagen 2α(i) from Homo sapiens from sample LIP001, highlighted in grey, with two informative peptide sequences identified by blue rectangles. B and C show more detail regarding the identified peptide sequences with, i, the number of peptides identified for each sequence, ii, MS/MS spectra of the peptides, and iii, the species in which this peptide sequence is observed. ...................................................................... 89 Figure 3-12: Spectrum identifying the peptide shown in Figure 3-10B, that was used as evidence to assign sample LIP001 as Homo sapiens................................................................................................................................................. 89 Figure 3-13: EICs of the two informative peptides identified collagen 1α(i) in Figure 3-10, A, GDAGP(+15.99)PGPAGPAGPP(+15.99)GPIGN(+0.98)VGAP(+15.99)GAK, and B, SGDRGETGPAGPTGPVGPVGAR, that were identified in both i, the LIP001 data, and ii, the data from the standard human collagen sample. .................................................................................................................................................. 90 Figure 3-14: EICs of the two informative peptides identified collagen 2α(i) in Figure 3-11, A GPSGPAGEVGKP(+15.99)GER, and B, GEN(+0.98)GVVGPTGPVGAAGPAGPN(+15.99 in both i, the LIP001 data, and ii, the data from the standard human collagen sample. A shows that the peptide identified in the LIP001 data was in fact not present in the standard human sample. ................................................................................................... 91 Figure 3-15: EICs of the peptides corresponding to the polymorphism at position 1075 in collagen α1(i) that allows for sample LIP001 to be confirmed as human. A: The EIC of the peptide with T at position 1075, B: EIC of the peptide with A at position 1075. One can see that the peptide shown in B is significantly higher in abundance than A, corresponding to the calculated frequencies discussed in (13). ...................................................................................... 92

Figure 4 - 2 :

 42 Figure 4-1: A N-G cleavage sites for hydroxylamine in collagen α1(i) and α2(i), the blue and orange colours represent the produced peptide lengths; and the proposed protocol workflows including the hydroxylamine (HA) extraction B on the insoluble fraction, or C directly on the demineralized collagen. .............................................................................. Figure 4-2: Cleavage specificity of A hydroxylamine, and B trypsin, in both fresh and archaeological samples. .....Figure 4-3: The A coverage and B number of peptides identified in both i modern and ii archaeological peptides. ..Figure 4-4: The number of proteins identified in each sample type in the fresh, forensic and archaeological samples. Note: archaeological samples 2 and 5 were visibly degraded upon sampling. .............................................................

Figure 4 - 3 :

 43 Figure 4-1: A N-G cleavage sites for hydroxylamine in collagen α1(i) and α2(i), the blue and orange colours represent the produced peptide lengths; and the proposed protocol workflows including the hydroxylamine (HA) extraction B on the insoluble fraction, or C directly on the demineralized collagen. .............................................................................. Figure 4-2: Cleavage specificity of A hydroxylamine, and B trypsin, in both fresh and archaeological samples. .....Figure 4-3: The A coverage and B number of peptides identified in both i modern and ii archaeological peptides. ..Figure 4-4: The number of proteins identified in each sample type in the fresh, forensic and archaeological samples. Note: archaeological samples 2 and 5 were visibly degraded upon sampling. .............................................................

Figure 4 - 4 :Chapter 5 :Figure 5 - 1 :

 44551 Figure 4-1: A N-G cleavage sites for hydroxylamine in collagen α1(i) and α2(i), the blue and orange colours represent the produced peptide lengths; and the proposed protocol workflows including the hydroxylamine (HA) extraction B on the insoluble fraction, or C directly on the demineralized collagen. .............................................................................. Figure 4-2: Cleavage specificity of A hydroxylamine, and B trypsin, in both fresh and archaeological samples. .....Figure 4-3: The A coverage and B number of peptides identified in both i modern and ii archaeological peptides. ..Figure 4-4: The number of proteins identified in each sample type in the fresh, forensic and archaeological samples. Note: archaeological samples 2 and 5 were visibly degraded upon sampling. .............................................................

Figure 5 - 2 :

 52 Figure 5-1: A The structure of the collagen fibrillar network in bone, and B, the mechanism of formation of collagen cross-links. ....................................................................................................................................................................Figure 5-2: Structures of the most commonly targeted A, immature and B, mature cross-links that will be discussed in this review. ....................................................................................................................................................................Figure 5-3: The reduction of the immine bond in immature cross-links in order to stabilise it before acid hydrolysis ......................................................................................................................................................................................Figure 5-4: Methods of analysing the mechanical properties of collagen containing tissues. .....................................Figure 5-5: A standardised bottom up proteomics workflow. .....................................................................................Figure 5-6: Summary of the analytical techniques discussed in this review. ..............................................................

Figure 5 - 3 :

 53 Figure 5-1: A The structure of the collagen fibrillar network in bone, and B, the mechanism of formation of collagen cross-links. ....................................................................................................................................................................Figure 5-2: Structures of the most commonly targeted A, immature and B, mature cross-links that will be discussed in this review. ....................................................................................................................................................................Figure 5-3: The reduction of the immine bond in immature cross-links in order to stabilise it before acid hydrolysis ......................................................................................................................................................................................Figure 5-4: Methods of analysing the mechanical properties of collagen containing tissues. .....................................Figure 5-5: A standardised bottom up proteomics workflow. .....................................................................................Figure 5-6: Summary of the analytical techniques discussed in this review. ..............................................................

Figure 5 - 4 :

 54 Figure 5-1: A The structure of the collagen fibrillar network in bone, and B, the mechanism of formation of collagen cross-links. ....................................................................................................................................................................Figure 5-2: Structures of the most commonly targeted A, immature and B, mature cross-links that will be discussed in this review. ....................................................................................................................................................................Figure 5-3: The reduction of the immine bond in immature cross-links in order to stabilise it before acid hydrolysis ......................................................................................................................................................................................Figure 5-4: Methods of analysing the mechanical properties of collagen containing tissues. .....................................Figure 5-5: A standardised bottom up proteomics workflow. .....................................................................................Figure 5-6: Summary of the analytical techniques discussed in this review. ..............................................................

Figure 5 - 5 :

 55 Figure 5-1: A The structure of the collagen fibrillar network in bone, and B, the mechanism of formation of collagen cross-links. ....................................................................................................................................................................Figure 5-2: Structures of the most commonly targeted A, immature and B, mature cross-links that will be discussed in this review. ....................................................................................................................................................................Figure 5-3: The reduction of the immine bond in immature cross-links in order to stabilise it before acid hydrolysis ......................................................................................................................................................................................Figure 5-4: Methods of analysing the mechanical properties of collagen containing tissues. .....................................Figure 5-5: A standardised bottom up proteomics workflow. .....................................................................................Figure 5-6: Summary of the analytical techniques discussed in this review. ..............................................................

Figure 5 - 6 :

 56 Figure 5-1: A The structure of the collagen fibrillar network in bone, and B, the mechanism of formation of collagen cross-links. ....................................................................................................................................................................Figure 5-2: Structures of the most commonly targeted A, immature and B, mature cross-links that will be discussed in this review. ....................................................................................................................................................................Figure 5-3: The reduction of the immine bond in immature cross-links in order to stabilise it before acid hydrolysis ......................................................................................................................................................................................Figure 5-4: Methods of analysing the mechanical properties of collagen containing tissues. .....................................Figure 5-5: A standardised bottom up proteomics workflow. .....................................................................................Figure 5-6: Summary of the analytical techniques discussed in this review. ..............................................................

Figure 6 - 1 :

 61 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity). ............................................................Figure 6-2: Examples of possible cross link structures. ...............................................................................................Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model.Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 ......................... Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4. ....................Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion. ...........................................................Figure 6-6: Example of an immature crosslink between two peptides. .......................................................................Figure 6-7: Trivalent cross-link enrichment strategy. .................................................................................................. Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system. ................................................................ Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system. ................................................................Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states. ..........................................................................................................Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. ..........................................................................Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000. .....................................................................................................................................................Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a cross-linked peptide. ....Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 .................................................................................................................... Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. ..............................................................................................................................

Figure 6 - 2 :

 62 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity). ............................................................Figure 6-2: Examples of possible cross link structures. ...............................................................................................Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model.Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 ......................... Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4. ....................Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion. ...........................................................Figure 6-6: Example of an immature crosslink between two peptides. .......................................................................Figure 6-7: Trivalent cross-link enrichment strategy. .................................................................................................. Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system. ................................................................ Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system. ................................................................Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states. ..........................................................................................................Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. ..........................................................................Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000. .....................................................................................................................................................Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a cross-linked peptide. ....Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 .................................................................................................................... Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. ..............................................................................................................................

Figure 6 - 3 :

 63 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity). ............................................................Figure 6-2: Examples of possible cross link structures. ...............................................................................................Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model.Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 ......................... Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4. ....................Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion. ...........................................................Figure 6-6: Example of an immature crosslink between two peptides. .......................................................................Figure 6-7: Trivalent cross-link enrichment strategy. .................................................................................................. Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system. ................................................................ Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system. ................................................................Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states. ..........................................................................................................Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. ..........................................................................Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000. .....................................................................................................................................................Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a cross-linked peptide. ....Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 .................................................................................................................... Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. ..............................................................................................................................

Figure 6 - 4 :

 64 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity). ............................................................Figure 6-2: Examples of possible cross link structures. ...............................................................................................Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model.Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 ......................... Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4. ....................Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion. ...........................................................Figure 6-6: Example of an immature crosslink between two peptides. .......................................................................Figure 6-7: Trivalent cross-link enrichment strategy. .................................................................................................. Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system. ................................................................ Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system. ................................................................Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states. ..........................................................................................................Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. ..........................................................................Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000. .....................................................................................................................................................Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a cross-linked peptide. ....Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 .................................................................................................................... Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. ..............................................................................................................................

Figure 6 - 5 :

 65 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity). ............................................................Figure 6-2: Examples of possible cross link structures. ...............................................................................................Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model.Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 ......................... Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4. ....................Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion. ...........................................................Figure 6-6: Example of an immature crosslink between two peptides. .......................................................................Figure 6-7: Trivalent cross-link enrichment strategy. .................................................................................................. Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system. ................................................................ Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system. ................................................................Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states. ..........................................................................................................Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. ..........................................................................Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000. .....................................................................................................................................................Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a cross-linked peptide. ....Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 .................................................................................................................... Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. ..............................................................................................................................

Figure 6 - 6 :

 66 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity). ............................................................Figure 6-2: Examples of possible cross link structures. ...............................................................................................Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model.Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 ......................... Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4. ....................Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion. ...........................................................Figure 6-6: Example of an immature crosslink between two peptides. .......................................................................Figure 6-7: Trivalent cross-link enrichment strategy. .................................................................................................. Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system. ................................................................ Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system. ................................................................Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states. ..........................................................................................................Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. ..........................................................................Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000. .....................................................................................................................................................Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a cross-linked peptide. ....Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 .................................................................................................................... Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. ..............................................................................................................................

Figure 6 - 7 :

 67 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity). ............................................................Figure 6-2: Examples of possible cross link structures. ...............................................................................................Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model.Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 ......................... Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4. ....................Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion. ...........................................................Figure 6-6: Example of an immature crosslink between two peptides. .......................................................................Figure 6-7: Trivalent cross-link enrichment strategy. .................................................................................................. Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system. ................................................................ Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system. ................................................................Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states. ..........................................................................................................Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. ..........................................................................Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000. .....................................................................................................................................................Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a cross-linked peptide. ....Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 .................................................................................................................... Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. ..............................................................................................................................

Figure 6 - 8 :

 68 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity). ............................................................Figure 6-2: Examples of possible cross link structures. ...............................................................................................Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model.Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 ......................... Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4. ....................Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion. ...........................................................Figure 6-6: Example of an immature crosslink between two peptides. .......................................................................Figure 6-7: Trivalent cross-link enrichment strategy. .................................................................................................. Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system. ................................................................ Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system. ................................................................Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states. ..........................................................................................................Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. ..........................................................................Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000. .....................................................................................................................................................Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a cross-linked peptide. ....Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 .................................................................................................................... Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. ..............................................................................................................................

Figure 6 - 9 :

 69 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity). ............................................................Figure 6-2: Examples of possible cross link structures. ...............................................................................................Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model.Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 ......................... Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4. ....................Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion. ...........................................................Figure 6-6: Example of an immature crosslink between two peptides. .......................................................................Figure 6-7: Trivalent cross-link enrichment strategy. .................................................................................................. Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system. ................................................................ Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system. ................................................................Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states. ..........................................................................................................Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. ..........................................................................Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000. .....................................................................................................................................................Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a cross-linked peptide. ....Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 .................................................................................................................... Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. ..............................................................................................................................

Figure 6 - 10 :

 610 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity). ............................................................Figure 6-2: Examples of possible cross link structures. ...............................................................................................Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model.Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 ......................... Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4. ....................Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion. ...........................................................Figure 6-6: Example of an immature crosslink between two peptides. .......................................................................Figure 6-7: Trivalent cross-link enrichment strategy. .................................................................................................. Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system. ................................................................ Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system. ................................................................Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states. ..........................................................................................................Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. ..........................................................................Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000. .....................................................................................................................................................Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a cross-linked peptide. ....Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 .................................................................................................................... Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. ..............................................................................................................................

Figure 6 - 11 :

 611 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity). ............................................................Figure 6-2: Examples of possible cross link structures. ...............................................................................................Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model.Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 ......................... Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4. ....................Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion. ...........................................................Figure 6-6: Example of an immature crosslink between two peptides. .......................................................................Figure 6-7: Trivalent cross-link enrichment strategy. .................................................................................................. Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system. ................................................................ Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system. ................................................................Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states. ..........................................................................................................Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. ..........................................................................Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000. .....................................................................................................................................................Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a cross-linked peptide. ....Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 .................................................................................................................... Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. ..............................................................................................................................

Figure 6 - 12 :

 612 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity). ............................................................Figure 6-2: Examples of possible cross link structures. ...............................................................................................Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model.Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 ......................... Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4. ....................Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion. ...........................................................Figure 6-6: Example of an immature crosslink between two peptides. .......................................................................Figure 6-7: Trivalent cross-link enrichment strategy. .................................................................................................. Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system. ................................................................ Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system. ................................................................Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states. ..........................................................................................................Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. ..........................................................................Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000. .....................................................................................................................................................Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a cross-linked peptide. ....Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 .................................................................................................................... Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. ..............................................................................................................................

Figure 6 - 13 :

 613 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity). ............................................................Figure 6-2: Examples of possible cross link structures. ...............................................................................................Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model.Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 ......................... Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4. ....................Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion. ...........................................................Figure 6-6: Example of an immature crosslink between two peptides. .......................................................................Figure 6-7: Trivalent cross-link enrichment strategy. .................................................................................................. Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system. ................................................................ Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system. ................................................................Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states. ..........................................................................................................Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. ..........................................................................Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000. .....................................................................................................................................................Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a cross-linked peptide. ....Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 .................................................................................................................... Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. ..............................................................................................................................

Figure 6 - 14 :Figure 6 - 15 :

 614615 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity). ............................................................Figure 6-2: Examples of possible cross link structures. ...............................................................................................Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model.Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9 ......................... Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4. ....................Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion. ...........................................................Figure 6-6: Example of an immature crosslink between two peptides. .......................................................................Figure 6-7: Trivalent cross-link enrichment strategy. .................................................................................................. Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system. ................................................................ Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system. ................................................................Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states. ..........................................................................................................Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. ..........................................................................Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000. .....................................................................................................................................................Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a cross-linked peptide. ....Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 .................................................................................................................... Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. ..............................................................................................................................

29 Chapter 2 : 2 - 1 : 2 - 2 : 2 - 3 : 2 - 4 : 63 Chapter 3 : 3 - 1 : 3 - 2 : 87 Chapter 4 : 4 - 1 :Chapter 5 : 5 - 1 : 5 - 2 : 1 :Figure S1- 1 :Figure S1- 2 :Figure S1- 4 :Figure S1- 5 :Figure S1- 6 :

 2922122232463331328744155152112456 Figure S1-1: Alignment of the collagen α1(i) sequences of Loxodonta africana available on the NCBI and Uniprot protein databases, with MS/MS spectra showing the identification of the accurate sequence based on the data from the exemplar samples. ......................................................................................................................................................... 173 Figure S1-2: Most of the identified substitutions between the collagen α2(i) of Hippopotamus amphibius and Loxodonta africana, are between leucine and isoleucine. ............................................................................................ 173 Figure S1-3: A-E(i), peptides in collagen type I from Hippopotamus amphibius containing leucine or isoleucine residues, fragmented using EThcD fragmentation (27% supplemental HCD activation), A-E(ii) focus on the parts of the spectra showing the characteristic loss of either 29 or 43 Da, with the presence of the w-ion. .............................. 174 Figure S1-4: From left to right, the ETD-MS2 spectra of the 3+ charge state of the targeted peptide sequence followed by the corresponding MS3 spectra, showing a neutral loss of 29 Da to confirm isoleucine at residue 456 in collagen α2(i) of Hippopotamus amphibius. ............................................................................................................................... 175 Figure S1-5: From left to right, the ETD-MS2 spectra of the 3+ charge state of the targeted peptide sequence followed by the corresponding MS3 spectra, showing a neutral loss of 43 Da, confirming leucine at residue #515 in collagen α2(i) of Loxodonta africana. ......................................................................................................................................... 176 Figure S1-6: Peptide detected in object 12.187.30 that is not species specific, GSEGPQGVRGEP(+15.99)GPP(+15.99)GPAGAAGPAGNP(+15.99)GADGQP(+15.99)GAK, m/z:1061.8286, z:3, ppm:7.1 ......................................................................................................................................................................... 177

Chapter 3 :Figure S3- 1 :Chapter 4 :Figure S4- 2 :Figure S4- 3 :Figure S4- 4 :Figure S4- 5 :

 3142345 Figure S2-1:Sequence alignment of collagen A 1α(i), B 1α(ii), C 1α(iii), and D 2α(v), of Loxodonta africana and Elephas maximus. While some differences between the two species were identified in these proteins, they were not targeted as markers of proteotype because either (a), the portions of the sequences where the markers were identified were not detected in the microsamples (the case for B), or (b) the differences identified by the sequence alignment were in fact unknowns in the sequence from the DNA translation (A and C). ............................................................ 249

Chapter 3 : 1 : 2 :Chapter 4 : 1 : 2 : 3 :

 3124123 ........................................................................................ 213 Table S1-12: Peptides identified using MS/MS sequencing in the three exemplar samples from Loxodonta africana where residues 548-551 of collagen α1(i) were confirmed to be GPAG. ..................................................................... 230 Table S1-13: Peptides unique to Hippopotamus amphibius species identified in Tusk figure of a man (66.99.1, Egypt, ca. 3900-3500 B.C.) using MS/MS sequencing. .......................................................................................................... 235 Table S1-14: Peptides unique to Hippopotamus amphibius identified in sample Furniture leg fragment (26.7.1284, Egypt, ca. 2960-2649 B.C.), using MS/MS sequencing. .............................................................................................. 241 Table S1-15: Peptides unique to Bos genus identified in Figure of an Asiatic captive (66.99.50, Egypt, ca. 1295-1070 B.C.) using MS/MS sequencing. ................................................................................................................................... 242 Table S1-16: Peptides unique to Hippopotamus amphibius identified in Figurine or amulet of a hippo on a sled (26.7.1299, Egypt, ca. 2960-2649 B.C.) using MS/MS sequencing. ........................................................................... 246 Table S1-17: Examples of peptides unique to Hippopotamus amphibius identified in Figurine or amulet of a hippo on a sled (26.7.1299, Egypt, ca. 2960-2649 B.C.) and exemplar sample 113816 identified using a LC-MS-based PMF approach. ....................................................................................................................................................................... 247 Table S1-18: Examples of peptides identified in the Spoon (12.187.30, Egypt, ca. 3100-2649 B.C.) specific to the Bos genus using a LC-MS-based PMF approach. ................................................................................................................ 248 High throughput analysis of bone fragments from Le Piage Table S3-The samples analysed in this study originating from Le Piage archaeological site. Included information: the location at which the object was found, the initial fragment identification, pictures of the sampling location, before and after, and the species identification of the sample based on LC-MS/MS analysis with Prometa used for data analysis. ......................................................................................................................................................................... Table S3-Sequence coverage and number of collagen type I peptides identified in bone fragments originating from the Le Piage site. ........................................................................................................................................................... Successive protein extraction using hydroxylamine to increase the depth of proteome coverage in fresh, forensic and archaeological bones Table S4-Information regarding the samples amounts analysed. ............................................................................ Table S4-Nanodrop readings of the archaeological samples analysed in this study, demonstrating the consistently higher extraction yield in Fraction B. ........................................................................................................................... Table S4-Collagen coverage and number of peptides detected from the archaeological samples analysed in this study ..............................................................................................................................................................................

Figure 6 -

 6 13 and Figure 6-14. ...................................................

Figure 1 - 1

 11 Figure 1-1 This sequential sampling technique allowed for the removal of the topmost layer of contamination and access to potentially more well preserved material underneath the surface of the object.

Figure 1 - 1 :

 11 Figure 1-1:The samples received for analysis. Pictured is the 6 µm diamond polishing film with a small amount of dust collected from the surface of the object.

Figure 1 - 2 :

 12 Figure 1-2: Peptide from collagen α2(i), unique to Bos genus identified in all three samples taken from object 11.176.1.; GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGPAGPAGPR, m/z: 1391.1682, z: 2, ppm: 1.2.

Figure 1 - 3 :

 13 Figure 1-3: Peptide from collagen α2(i), unique to Physeter macrocephalus, detected in object 1979.206.1587, but also identified in object 2012.346.; GAP(+15.99)GAVGAP(+15.99)GPAGN(+.98)GDRGEAGPAGAAGPAGSR, m/z: 1366.1423, z: 2, ppm: 0.1.

Figure 1 - 4 :

 14 Figure 1-4: A Peptide from collagen α2(i) unique to Bos, detected in sample S2 from object 40.66a,b GYP(+15.99)GNAGPVGAAGAP(+15.99)GPQGPVGPVGK, m/z: 1131.0693, z: 2, ppm: 2.5. B Peptide from collagen α2(i) used to identify the Cervidae family detected in samples S1 and S3 from object 40.66a,b; GSP(+15.99)GERGEVGPAGPN(+.98)GFAGPAGAAGQAGAK, m/z: 870.4167, z: 3, ppm: 2.3.

Figure 1 - 5 :

 15 Figure 1-5: MS/MS spectrum showing the identification of the substitution P -> A at position #455 in collagen α2(i) peptide from Hippopotamus amphibious in exemplar 113816 (HT11D), Mammology collection, AMNH. GPP(+15.99)GESGAAGPTGAIGNRG, m/z: 791.3815, z: 2, ppm: 1.6.

Figure S1- 4 2 . 3 . 3 .

 4233 Figure S1-4 and Figure S1-5 show spectra from Mammalogy AMNH objects 52094 (Loxodonta africana) and 113816 (Hippopotamus amphibius).Figure S1-4 shows the fragmentation of the peptide sequence containing the substitution at position 455 in collagen α2(i) of Hippopotamus amphibius, in order to confirm that the I/L in position 456 is indeed isoleucine, thereby fully confirming the amino acid sequence of this section of the collagen α2(i) identified to be highly specific to Hippopotamus amphibius. Figure S1-5 shows the confirmation of leucine at position 515 of collagen α2(i) of Loxodonta africana.

Figure 1 - 6 :

 16 Figure 1-6: Peptide from collagen α2(i) unique to Hippopotamus amphibius, identified in object 66.99.1 HGN(+.98)RGEP(+15.99)GPAGVVGPTGAVGPR, m/z:686.3525, z:3, ppm: 3.3.

Figure 1 - 7 : 2 . 4 .

 1724 Figure 1-7: MS3 spectra to confirm isoleucine at position #865 of collagen α2(i) of Hippopotamus amphibius, identified in object 66.99.1. Residue identity was confirmed with three different peptides with the same sequence, through the fragmentation of the same z2 ion. Furniture leg fragment (26.7.1284, Egypt, ca. 2960-2649 B.C.) had lower collagen recovery than the Tusk figure of a man (66.99.1, Egypt, ca. 3900-3500 B.C.), but it was still possible to detect 68 and 50% of the collagen α1(i) and α2(i) proteins respectively. The species specific peptides of sample 26.7.1284 are shown in Table S1-14, which include the peptide HGN(+.98)RGEP(+15.99)GPAGVVGPTGAVGPR, identified in sample 66.99.1 and the exemplar samples as a marker for collagen from Hippopotamus amphibius.

Figure 1 - 8 :

 18 Figure 1-8: Strategies for identifying species when a sample has poor collagen preservation. A: Peptide from collagen α2(i), HGN(+.98)RGEP(+15.99)GPAGVVGPTGAVGPR (m/z: 514.7659, z:4, ppm: 1.1), specific to Hippopotamus amphibius, detected in object 26.7.1299, shown is the spectrum that resulted from summing three individual MS2 spectra. B: LC MS PMF mapping of peptides unique to the Bos genus and subfamily Bovinae identified in sample 12.187.30 and sample 40;66a,b-S2The low intensity of the collagen peptide peaks caused similar difficulties for Spoon (12.187.30; ca. 3100-2649 B.C.), where several uninformative collagen peptides were detected which were fully fragmented along the peptide backbone (an example is shown in FigureS1-6) but a confident taxonomic assignment based on the fragmentation spectra was not possible. Therefore, the LC-MS-based PMF approach was applied, where the m/z values of several taxon-specific peptides that were identified in other samples (exemplar and historic), including Hippopotamus amphibius and Loxodonta africana, were searched in the data from Spoon (12.187.30; ca. 3100-2649 B.C.). In

  many of the objects in this study due to the amount of sample required. Objects from the Department of Egyptian Art were of particular interest due to the specific cultural connotations associated with the use of hippopotamus or elephant ivory. In ancient Egypt, hippopotami were both feared and respected as dangerous creatures, often depicted in tombs showing their hunting and killing by kings, symbolising the royals' triumph over chaos and maintenance of order.(60, 61) Elsewhere, Taweret, a goddess who protected women and young children in ancient Egyptian religion, took the composite form incorporating a hippopotamus, crocodile, lion, and pregnant woman, making her a fierce protector.(60, 61) While hippopotami have not inhabited Egypt since the 19th century, the species was common during antiquity and was often hunted due to its symbolism, as well as its threat to domestic crops and danger to boats and people around the Nile River. (62) During the same period, elephants were not as prevalent in northern Africa, although their ivory was still a highly valued material that was traded from southern Africa.(63) The type of ivory, either elephant or hippopotamus, used to craft an object can be either an indication of the availability of raw materials or the symbolism in the ancient Egyptian culture. The figurine of a hippo on a sledge (26.7.1299, Egypt, ca. 2960-2649 B.C.) being made of actual hippopotamus ivory may serve as a bridge between materiality and symbolism or the overwhelming dominance of hippopotamus ivory in the objects studied may simply speak to the availability of species in ancient Egypt.(60, 61)

  ) First, demineralisation was performed by placing each microgrit in an Eppendorf tube and adding 200 µL of 5% trifluoroacetic acid (TFA). Samples were demineralised for 30 min, at which point the microgrit was removed using tweezers, and the TFA-based solution was evaporated in a vacuum centrifuge until dry. Following this, extraction, reduction, and alkylation were performed in a single step, whereby the samples were incubated in 200 µL extraction buffer at 60 °C for one hour in the dark. The extraction buffer consisted of 6 M guanidinium hydrochloride, 60 mM Tris hydrochloride, 5 mM Tris(2-carboxyethyl)phosphine and 15 mM chloroacetamide.

African(

  Loxodonta africana) elephants. (1) Genetic evidence has recently resulted in the acknowledgment of two separate species of African elephant, the Savannah (Loxodonta africana) and Forest (Loxodonta cyclotis) subspecies. (2) However, in the context of CITES, they are still considered as one species. (3) The Asian, African Savannah, and African Forest elephants are all classed as endangered, and their decline in population numbers is a direct result of the illegal ivory trade and extensive deforestation. (4) In 2020, a total of 601 elephant ivory specimens were seized in Europe in a variety of contexts, from fairs and auction houses, to highway stops.(5)

Figure 2 - 1 :

 21 Figure 2-1: TIC chromatogram of the tryptic digest of sample from individual S1.

Figure 2 - 2 :

 22 Figure 2-2:Sequence alignment of collagen α2(i) of Loxodonta africana and Elephas maximus, allowingfor the identification of two potential positions that can be used to discriminate between elephant proteotypes.

Figure 2 -

 2 3 shows the substitutions identified at position 384 in samples S1 and A2 corresponding to the two different proteotypes, while Figure2-4shows the differentiating substitution identified at position 993. It was possible to identify these substitutions across all samples with high quality spectra with fragments identified along the peptide backbone, as well as the corresponding m/z value and retention time. It was possible to identify key peptide markers that could be used to quickly differentiate between proteotypes, listed in Table 2-2.

Figure 2 - 3 :

 23 Figure 2-3: Spectra from data obtained from A, sample S1 and B, sample A2, identifying the differentiating substitution at position 384, A: GSSGEAGSAGPAGPP(+15.99)GLR, m/z:770.8704, ppm:-1.0, B: GSN(+0.98)GEAGSAGPAGPP(+15.99)GLR, m/z:784.8678, ppm:-1.0.

Figure 2 - 4 :

 24 Figure 2-4: Spectra from A, sample S1, and B, sample A2, identifying the differentiating substation at position 993, A: HGN(+0.98)RGEP(+15.99)GPAGSVGPVGAVGPR, m/z:681.3437, ppm:0.2, and B: HGNRGEP(+15.99)GPAGSVGPVGAIGPR, m/z:685.6874, ppm:-0.1.

Figure 2 - 5 :

 25 Figure 2-5: TIC of sample A3, analysed using an active gradient of 10 min

Figure 2 - 6 :

 26 Figure 2-6: EICs of the peptides that allow for the differentiation between elephant species, analysed using a 10 min active gradient. As in Table 2-2, P in bold indicates hydroxyproline, and N in bold indicates a deamidated asparagine. Residues in red are the informative substitutions identified. Two peaks are observed for the 784.8681 and 686.0125 ions, corresponding to the same peptide sequences but with a hydroxyproline modification in a different position.

  ) Here, peptides are analysed by MALDI-TOF-MS, a user friendly, high throughput analysis method where species are identified based on the comparison of experimental spectra to a large database of peptide markers for different species.(15) It has become the mainstream technique for the large scale screening of archaeological samples, revealing information such a population diets, trade routes and manufacturing processes.(16-

  centrifuge. Following this, 200 µL of extraction buffer (6 M GuHCl, 100 mM Tris HCl, 5 mM TCEP and 15 mM CAA) was added to each sample, and extraction was performed at 60 °C for one hour, in the dark. After extraction, the entirety of the extraction buffer was transferred to a 10 kDa Amicon centrifugal filter, and centrifuged at 13,200 g for 20 min. Following this, a buffer exchange was performed by adding 200 µL digestion buffer (50 mM ammonium bicarbonate, pH 8.8) and centrifuging at 13,200 g for 20 min. This step was performed three times. Finally, the Amicon filter was transferred to a new collection tube, 100 µL of digestion buffer was added to the filter, along with 10µL of 0.01 µg/µL trypsin. Digestion was performed at 37 °C overnight, in the dark, and digestion was stopped by centrifuging the sample at13,200

2. 4

 4 .1. PEAKS Data analysis was performed using PEAKS X. Data were searched with a mass tolerance of 10 ppm in MS1 and 0.02 Da in MS2. A semi-specific tryptic digestion was specified, with an allowance of 3 missed cleavages. The following modifications were specified, variable: oxidation (MP), deamidation (NQ) and fixed: carbamidomethylation (C). Data were searched against databases for the species Odocoileus virignianus texanus (downloaded from NCBI on 20/01/2023), Rangifer tarandus platyrhyncus (downloaded from NCBI on 21/04/2023) and a database for the order Artiodactyla (even-toed ungulates) (downloaded from NCBI on 10/05/2023) . Data were searched using the De Novo, PEAKS DB, PEAKS PTM and SPIDER algorithms.2.4.2. PrometaData analysis was also carried out using Prometa, developed by Benjamin Dartigues, CBiB, Université de Bordeaux in the context of the project selected by the bioinformatic committee dedicated to Ancient Proteins, MetaProteomics, coordinated by Caroline Tokarski. The Prometa workflow is as follows: raw LC-MS files are pre-processed using MSCONVERT in .mgf format, and are then used for de novo sequencing of MS2 spectra.Following file conversion, four de novo tools are used to search the .mgf files to identify peptide sequences; Novor, DeepNovo, CasaNovo, and PepNovo.(30)(31)(32)(33)(34) At this stage, search parameters such as resolution, error tolerances, cleavage specificity and PTMs are entered.

Figure 3 - 1 :

 31 Figure 3-1: Visual impact of the minimally invasive sampling technique used for sampling in this analysis

Figure 3 - 2 :

 32 Figure 3-2: Reduction of the gradient length for the analysis of archaeological collagen peptides, A the effect on the TIC of the analysis, B the effect of changing the gradient on the number of peptides identified in the analysis and on the coverage of collagen α2(i).

Figure 3

 3

Figure 3 - 3 :

 33 Figure 3-3: Mass spectrometer parameters adapted for shorter gradients, A the MS1 resolution, and B the MS2 acquisition strategies.

Figure 3 - 4 :

 34 Figure 3-4: Identified amino acid substitutions in Rangifer tarandus when searched against a database for Odocoileus virignianus texanus, the closest relative that had collagen sequenced in the NCBI database. A: collagen α2(i), position 829; T(subS)GETGASGPP(+15.99)GFAGEKGPSG, m/z:888.9065, z:2, ppm:0.3; B: collagen α2(i), position 864; GPSGEP(+15.99)GTAGPP(+15.99)GTP(+15.99)GPQGF(subI)LGPP(+15.99)GFLGLP(+15. 99)GSR, m/z:1032.1720, z:3, ppm:1.4.

Figure 3 - 5 :

 35 Figure 3-5: Alignment of the collagen α2(i) sequences of Rangifer tarandus platyrhyncus (DNA reconstruction), Odocoileus virignianus texanus, and Rangifer tarandus (proteomic reconstruction)In contrast to PEAKS SPIDER, Prometa, performs sequence reconstruction by producing contigs using kmer and De Bruijn graph methods. Figure3-6shows the alignment of several contigs to a consensus contig which corresponds to the peptide ADELADHLGFQEAYRR, constructed using the Ggmsa tool, which visualises sequence alignment data. (42) While sequence reconstruction solely based on de novo sequencing with Prometa requires more optimisation, it could be a valuable tool for the study of protein sequences from understudied species.

Figure 3 - 6 :

 36 Figure 3-6: Alignment of contigs produced using Prometa against a consensus contig that corresponds to the contig sequence ADELADHLGFQEAYRR. This alignment was constructed using the following parameters: kmers size: 7, kmers overlap: 6, contigs overlap: 5, gaps: 3.

Figure 3 - 9 :

 39 Figure 3-9: Peptides specific to A Rangifer tarandus, and B Bos, identified in samples 1507 and 1509 respectively. * contamination

Figure 3 -Figure 3 -Figure 3 -

 333 Figure 3-10: A The sequence coverage of collagen 1α(i) from Homo sapiens from sample LIP001, highlighted in grey, with two informative peptide sequences identified by blue rectangles. B and C show more detail regarding the identified peptide sequences with, i, the number of peptides identified for each sequence, ii, MS/MS spectra of the peptides, and iii, the species in which this peptide sequence is observed, ie; 32 species in the Primate order contain the peptide sequence B in their collagen 1α(i) sequence. *This peptide present as a polymorphism that is unique to Homo sapiens (discussed on the coming sections) Figure 3-10 and Figure 3-11 summarise the information that was extracted from the data from sample LIP001 in order to identify it as Homo sapiens. Three peptides that are almost exclusively present in collagen from the Primate family were identified (Figure 3-10A and Figure 3-11A and B) were identified. In addition to this, one peptide that is unique to humans, a polymorphism resulting from a single nucleotide polymorphism (SNP) previously identified

Figure 3 -

 3 Figure 3-11: A The sequence coverage of collagen 2α(i) from Homo sapiens from sample LIP001, highlighted in grey, with two informative peptide sequences identified by blue rectangles. B and Cshow more detail regarding the identified peptide sequences with, i, the number of peptides identified for each sequence, ii, MS/MS spectra of the peptides, and iii, the species in which this peptide sequence is observed.

Figure 3 - 12 :

 312 Figure 3-12: Spectrum identifying the peptide shown in Figure 3-11B, that was used as evidence to assign sample LIP001 as Homo sapiens.

Figure 3 -

 3 13 and Figure 3-14 show the EICs of the informative peptides, identified in Figure 3-10 and Figure 3-11, in (i) the data from sample LIP001, and (ii), the standard human collagen sample. The first observation to make from this data is that the intensity of the informative peptide identified in the data from sample LIP001 are close to the noise level.Figure 3-13A and B, and Figure 3-14B show that the peptides identified in the LIP001 data were also identified in the data from the model sample, but with an intensity up to three orders of magnitude higher in the model sample data than in the LIP001 data. Finally, Figure 3-14A shows the EIC of a peptide that was identified in the data from LIP001 (Figure 3-12), but not identified in the standard human collagen sample. Due to the low intensity of the peptides identifying sample LIP001 as Homo sapiens,

Figure 3 - 13 :

 313 Figure 3-13: EICs of the two informative peptides identified collagen 1α(i) in Figure 3-10, A, GDAGP(+15.99)PGPAGPAGPP(+15.99)GPIGN(+0.98)VGAP(+15.99)GAK, and B, SGDRGETGPAGPTGPVGPVGAR, that were identified in both i, the LIP001 data, and ii, the data from the standard human collagen sample.

Figure 3 - 14 :

 314 Figure 3-14: EICs of the two informative peptides identified collagen 2α(i) in Figure 3-11, A GPSGPAGEVGKP(+15.99)GER, and B, GEN(+0.98)GVVGPTGPVGAAGPAGPN(+15.99 in both i, the LIP001 data, and ii, the data from the standard human collagen sample. A shows that the peptide identified in the LIP001 data was in fact not present in the standard human sample. Finally, the peptide identified in Figure 3-10C is exclusively unique to Homo sapiens. However, it was detected at a very low intensity, which was in fact expected, based on the work performed by Welker et al. (13) Here, genetic analyses were performed to identify points of difference in the collagen protein sequences between Homo sapiens and Homo neanderthalensis, based on previously published genetic sequences. (43) The genetic analysis identified a polymorphism caused by an SNP at position 1075 in collagen 1α(i), where in Homo sapiens, an analine (A) residue is observed with a frequency of 98%, and a threonine (T) residue is observed at a 2% frequency. For Homo neanderthalensis, A is observed at position 1075 with a 100% frequency. Figure 3-15 shows the EICs of this informative peptide sequence with A, T in position 1075, and B, A in position 1075. The intensity difference of the two EICs correspond to the intensity difference expected based on the theoretical frequencies of this polymorphism, and allows for the confident confirmation that sample LIP001 is Homo sapiens.

Figure 3 - 15 :

 315 Figure 3-15:EICs of the peptides corresponding to the polymorphism at position 1075 in collagen α1(i) that allows for sample LIP001 to be confirmed as human. A: The EIC of the peptide with T at position 1075, B: EIC of the peptide with A at position 1075. One can see that the peptide shown in B is significantly higher in abundance than A, corresponding to the calculated frequencies discussed in(13).

Figure 4 - 1 :

 41 Figure 4-1: A N-G cleavage sites for hydroxylamine in collagen α1(i) and α2(i), the blue and orange colours represent the produced peptide lengths; and the proposed protocol workflows including the hydroxylamine (HA) extraction B on the insoluble fraction, or C directly on the demineralized collagen.

1 ,

 1 ranging from 180 µg to 330 µg, except for 2 samples. First, samples were demineralised by adding 200 µL of 5% TFA for 30 minutes.After demineralisation, the first extraction was performed by adding 200 µL 6 M GuHCl with 100 mM Tris, to the pellet remaining after demineralisation, and heating at 60 °C for one hour in the dark. Centrifugation was then performed at 14,000 g for 5 minutes to pellet the sample, and the supernatant was removed and stored as Fraction 'A'. The second extraction was then performed on the remaining pellet, where the hydroxylamine buffer was added (comprising of 0.5 M NH2OH, 0.2 M K2CO3, adjusted to pH 9 with NaOH) at a ratio of 10 µL:100 µg starting material, and the samples were incubated at 45 °C, in the dark, overnight. After this second extraction, the samples were again centrifuged, and the supernatant was removed and labelled as Fraction 'B.' For the model archaeological and modern bovine samples, the hydroxylamine extraction step was also performed without the initial GuHCl extraction step, hereby referred to as Fraction 'C'. It is worth noting that the acid soluble fraction could serve as an additional

  , we propose the use of a successive extraction protocol with fractions increasing in insolubility. Through the use of a second extraction step using a solid phase digestion, we have shown that we are able to (a) increase the yield of insoluble bone collagen in proteomic preparation, and (b) increase the depth of the proteome coverage in bone. For clarity, throughout the discussion, different fractions of the protocol are labelled A, B and C, detailed in Figure 4-1B and C. Fractions A and B originate from the same sample, where first an extraction was performed with GuHCl (Fraction A), and then a hydroxylamine extraction was performed on the remaining pellet (Fraction B). In addition to this, the hydroxylamine extraction was also performed directly on the demineralised extract, in order to compare the utility of the successive extraction protocol, hereby referred to as Fraction C. Fractions A, B and C are indicated by blue, orange, and black respectively throughout. The modern model samples are indicated by darker shades and the archaeological model samples are indicated by lighter shades.

  -1, wherein GuHCl and hydroxylamine extracts of bovine collagen were analysed with SDS-PAGE gel. In order to demonstrate the effect of the successive extraction protocol, the model archaeological and fresh bone samples were prepared and analysed in triplicate. The cleavage specificity of hydroxylamine was compared for the modern and archaeological model samples. Figure4-2Ashows the cleavage specificity of the hydroxylamine digestion, by considering peptides identified that result from an N-X cleavage. Hydroxylamine predominantly cleaves at N-G, at a consistent ratio of approximately 60% over both sample types. Other N-X cleavages were observed, but it should be noted that these occur at a consistent ratio, and that is has a limited impact on the following tryptic digestion, as demonstrated by the cleavage specificity in Figure4-2B, and the number of missed cleavages, shown in FigureS4-2.

Figure 4 - 2 :

 42 Figure 4-2: Cleavage specificity of A hydroxylamine, and B trypsin, in both fresh and archaeological samples.

  order to demonstrate the increased extraction yield of collagen, the % coverage and number of collagen type I peptides identified was compared for each extraction type. Firstly, it should be noted that Fractions A and B (successive extractions from the same sample) were diluted by the same volume in order to allow for a semi-quantitative comparison of the intensity of the signal. Fraction C was diluted to the same concentration as Fractions A and B based on the A280 Nanodrop concentration measurement. The data from Fractions A and B were processed both separately and together in order to demonstrate the gain in information with the addition of the second extraction step.

Figure 4 - 3 :

 43 Figure 4-3: The A coverage and B number of peptides identified in both i modern and ii archaeological peptides.

Figure 4 -

 4 Figure 4-3A(i) demonstrates that direct hydroxylamine extraction can increase the coverage of collagen type I in modern samples, a statistically significant increase is observed between Fraction A and Fractions B and C (paired t-test, p>0.05, see Section S4.5.). The number of collagen peptides identified (Figure 4-3B(i)) also increases, again with a statistically significant difference between Fraction A and Fractions B and C. In Figure S4-3A the number of times each amino acid residue was identified is plotted against the position in the sequence. This

Figure 4 -

 4  shows that the significant increase in coverage for Fractions B and C does not occur for archaeological samples, but the number of peptides identified in these samples still increases (Figure4-3B(ii)). While there is a statistically significant increase in the number of collagen peptides identified, this does not translate to a statistically significant increase in sequence coverage (paired t-test, p>0.05, see Section S4.5.). Figure S4-3B demonstrates that hydroxylamine extraction does not result in the detection of peptides in the C-and N-terminus of the protein. It is difficult at this stage to conclude if this may indicate that the telopeptide

  -6A, the deamidation of asparagine is highest in Fraction A of the fresh model sample, while Figure S4-6B shows that Fraction A has the lowest value in the archaeological sample. It was expected that the extended periods of heating required for the hydroxylamine extraction would result in increased levels of bulk deamidation for Fractions B and C (Figure S4-6B), but one must also consider the effect of the increased number of peptides identified in the hydroxylamine extracts.

  ) Several additional minor collagens were identified in Fraction B of this sample, such as collagen α2(VI), involved in cell adhesion in extracellular matrix tissues, α1(XXVII), which plays a role in the calcification of bone, and α5(VI), involved in extracellular matrix construction.

Figure 4 - 4 :

 44 Figure 4-4: The number of proteins identified in each sample type in the fresh, forensic and archaeological samples. Note: archaeological samples 2 and 5 were visibly degraded upon sampling.

22 )Figure 5 - 1 :

 2251 Figure 5-1: A The structure of the collagen fibrillar network in bone, and B, the mechanism of formation of collagen cross-links.

Figure 5 - 2 :

 52 Figure 5-2: Structures of the most commonly targeted A, immature and B, mature cross-links that will be discussed in this review.

1. 3 .

 3 Why study collagen cross-linking?Irregularities of collagen cross-linking in a variety of tissues are pathological markers/manifestations of many different classes of disease, with many occurring through irregular behaviour of the LOX and LH enzymes. Studying the quantity and type of collagen cross-links allows for a deeper understanding of the physical manifestations of these diseases and other physiological processes, shedding light on upstream events in the enzymatic pathway that are harder to identify. Listed below are a few examples of research areas where the analysis of collagen cross-linking is key.

( 40 )

 40 Of the remaining ~10% of recessive cases, many of the mutated genes code for enzymes involved in both the formation of the collagen helix and cross-links.(40, 65) The presence of urinary PYD cross-links can be used as a non-invasive biomarker. (66) Characterising the defects in the cross-links present in the bones of these patients can indicate the specific mutation that causes their condition and has helped to identify drug targets.(41)

  Sims et al. suggest the use of a weak cation exchange column with a citrate buffer system in order to quantify the cross-links against authenticated cross-link standards, (82) which has been applied to determine the point of maturity of the digital flexor tendon of young thoroughbred horses. (83) Yamauchi et al. also used cation exchange chromatography,(78, 79) in order to study enzymatic cross links in cancerous tissues, the cross-linking in patients suffering from osteogenesis imperfect, and to determine the role of bone morphogenetic protein (BMP) signalling pathways in skeletal development and bone formation.(33,(84)(85)(86)(87) Saito et al. also propose using ion exchange chromatography, to simultaneously quantify the immature cross-links present in the sample. This was performed using the addition of a postcolumn derivatisation using o-phthalaldehyde, which reacts with primary amines forming a fluorescent product. (88) Alternatively, Avery et al. recommend the use of ninhydrin as a postcolumn derivatising agent, which also reacts with primary and secondary amine groups, that can be quantified using UV detection at 440nm.(81) Finally, Bank et al., and Sroga and Vashishth propose similar methods wherein reverse phase chromatography is used with the addition of an ion pairing agent, heptafluorobutyric acid (HFBA), to the mobile phase in order to increase the retention of PYD. This method allows for the highly sensitive detection of both PYD and the AGE pentosidine, through a switch of the fluorescence excitation wavelength halfway through the analysis.(89, 90) It has been applied

Figure 5 - 3 :

 53 Figure 5-3: The reduction of the immine bond in immature cross-links in order to stabilise it before acid hydrolysis The use of radioactive tagging for the analysis of immature cross-links was first presented by Yamauchi et al. (78) For the quantification of radioactively tagged immature cross-links, the

( 98 )

 98 Antibodies specific to both C-and N-terminal peptide sequences known to be involved in crosslinking have also been used in ELISAs.(22, 42, 99) Both the C-and N-terminal cross-linked telopeptides are known to be markers for bone turnover, (100) the β-CTX assay in particular is a widely used screening assay. It has also been identified by the International Federation of Clinical Chemistry and laboratory medicine (IFCC) committee on bone metabolism as a candidate become a standardised assay, and as such it is widely used in large cohort studies.(101-103) This assay uses the CrossLaps antibodies, which are specific to the EKAHDGGR amino acid sequence in the C-terminal telopeptide region of collagen 1α(i). (104) The β-CTX assay is based on a sandwich ELISA, using two copies of the same antibody, since C-terminal cross-linked collagen peptides typically contain two copies of the EKAHDGGR peptide sequence. (103) A significant advantage of such an assay is that it can be used for larger population studies, such as in Diemar et al.'s study where the β-CTX assay, and several other bone turnover biomarkers were used in order to establish reference intervals for bones turnover markers in children.(105)

Table 5 - 2 :

 52 (78, 115) Sections 2.1 and 2.2 describe the use of ion exchange chromatography in order to have sufficient retention during the chromatographic separation and therefore allow for accurate quantification of the hydrolysed cross-links, as under normal reversed phase conditions, the hydrolysed moieties are too hydrophilic to be retained in the column. Since buffers with high salt concentrations are not compatible with mass spectrometry detection, the analysis methods discussed below use ion pairing chromatography, wherein the volatile ion pairing agent HFBA is included in the buffer composition to allow for sufficient chromatographic retention. (113, 115) A comparison of the sensitivity of the various MS methodologies suggested are presented in Table5-2. Gineyts et al. used reverse phase ion pairing chromatography with SIR to quantify both immature (DHLNL, HLNL, LNL) and mature (PYD DPD) cross-links. (113) This method has been applied in a clinical setting to track the change in type and density of collagen cross-links in cervical tissues during pregnancy. (69, 70) Takaoka et al. used a similar methodology, but also suggested the use of a post-column splitter that splits the LC flow between an MS and fluorescence detector, for further confirmation of the MS detection. (116) This SIR method has been applied to the quantification of collagen cross-link density in a variety of collagenous tissues, including tendon and corneal tissues. (116, 117) Alternatively, Tang et al. use MRM as a confirmation strategy to measure PYD and DPD in urine. (115) This method has been successfully applied to the analysis of PYD and DPD in urine to study the relationship between high vitamin D diets and bone loss, and the breed-specific high bone turnover of Friesian horses. (64, 118) A similar method has been applied to perform quantitative analysis of cross-links in cervical tissue in order to relate their increase in concentration to tensile strength.(69, 119) The widely cited Yamauchi et al. method for quantification using fluorescence and radioactivity detection also suggests using isotopically labelled standards during MRM analysis, so that the internal standards are chemically identical to the analytes.(78) The use of HFBA as an ion pairing agent allows for reproducible chromatography, in turn enabling accurate and sensitive quantification. However, such a system could also have ion suppression effects during the ESI ionisation, possibly reducing the MS sensitivity. As an alternative, Naffa et al. suggested the use of a silica hydride column, which required a far simpler solvent system of water and acetonitrile. (45, 120) Alternatively, Arakawa et al. suggest the use of a Hydrophilic interaction liquid chromatographic separation (HILIC), which also removes the need for ion pairing agents. (121) Limits of detection and quantification of various MS-based analysis strategies for crosslinks in collagen.

Figure 5 - 4 :

 54 Figure 5-4: Methods of analysing the mechanical properties of collagen containing tissues. Both Stammers et al. and Svensson et al. used such a test to evaluate the mechanical importance of immature (immine) cross-links, through reduction using NaBH4 to inhibit the formation of mature cross-links. There were significant differences in the behaviour of the plastic phase (the phase in which deformation is an irreversible change) between the reduced and non-reduced tendons, which demonstrated the importance of the reversibility of cross-link formation. (124, 125) Miguez et al. used a similar experimental set-up to determine the ultimate tensile strength of crown and root dentin, finding that, while the collagen content was comparable between the two types, the cross-linking density was higher in the root, and this was reflected in the ultimate tensile strength of the demineralised material. (126) Similarly, Yoshida et al. measured the fracture point of mouse cervices at different points during gestation, finding a strong correlation between the decrease in cross-link density and the decrease in mechanical strength. (119) However, Thorpe et al. performed a similar experiment aiming to correlate the mechanical strength of equine tendons to the cross-linking density and found only a weak correlation between cross-link concentration and mechanical strength, indicating that the mechanical impact of cross-linking density is highly tissue dependant. (127)

- 5 .Figure 5 - 5 :

 555 Figure 5-5: A standardised bottom up proteomics workflow.

  In a similar vein, pepsin is commonly used in the food industry in the production of gelatin to remove cross-links in collagen, as it cleaves at the C-terminal of aromatic amino acids (tyrosine, phenylanaline and tryptophan), which are more abundant in the telopeptide region of collagen. (153) To that end, pepsin can also be used to isolate the telopeptide regions in collagen. (154, 155) Additionally, pepsin has been used to target crosslinks involving collagen type IV, a globular collagen that covalently attaches to fibrillar collagen. (156) Cathepsin K digestion can be used in a similar way for selective analysis of Ntelopeptide-containing cross-links.(22, 107) Solid phase digestion can also be used to increase the solubility of the cross-linked collagen network. Solid phase trypsin digestion has previously shown an increase in the observed glycosylation of collagen. (157) The examples above used solid phase enzymatic digestion methodologies, but chemical digestion can also be used as a pre-solubilisation step. Key examples include chemical digestion with CNBr (cleaves after M) and hydroxylamine (cleaves at NG). (158-162) In comparison to trypsin, these chemical digestion agents cleave collagen into relatively large peptides and can be followed by a second digestion with trypsin to reduce the size of the peptides for mass spectrometric analysis. (22, 159)3.5.3. Tandem Mass SpectrometryTandem mass spectrometry, meaning the use of MS2 or MS3 fragmentation, can be used both to identify the amino acid sequence of the peptides involved in the cross-link, and for structural analysis of the cross-link itself. In particular, the structures of immature cross-links were originally deduced from the mass spectra of purified and derivatised reduced immature crosslinks. (163) Such methodologies use the same fragmentation principles as those used in bottom up proteomics in order to identify the amino acid sequences of the peptides themselves. However, the characterisation of cross linked collagen peptides using tandem mass spectrometry has predominately been performed on highly purified samples. (164, 165) The most comprehensive characterisation of crosslinked collagen peptides was performed through a targeted multi-step chromatographic enrichment procedure. Here, Terajima et al. enriched immature crosslinked peptides, reduced with NaB 3 H4, by fractionating a tryptic peptide mixture first with size exclusion, then ion exchange, and finally reversed phase chromatography, selecting the fraction

Figure 5 - 6 :

 56 Figure 5-6: Summary of the analytical techniques discussed in this review.

Figure 6 - 1 :

 61 Figure 6-1: The structure of the collagen network in bone, and B The mechanism of formation of collagen cross-links. (This figure is identical to Figure 5-1, but has been repeated here for clarity).

Figure 6 - 2 :

 62 Figure 6-2: Examples of possible cross link structures.

Figure 6 - 3 :

 63 Figure 6-3: A Coverage of human collagen α1(i) obtained from a tryptic digest of an archaeological model. Highlighted in blue circles are residues that were identified to have modifications corresponding to cross-linking in collagen. The modifications identified at lysine residues are: lysine hydroxylation (+15.99), galactosylation (+178.05) and glucosylgalactosylation (+340.10). B An example of a spectrum of a peptide that contains a galactosylation modification, the peptide sequence is highlighted with a red square in A, GLTGPIGPP(+15.99)GPAGAPGDK(+178.05)GESGPSGPAGPTGAR, m/z: 1011.1593, ppm: 1.9

Figure 6 - 4 :

 64 Figure 6-4: Stabilisation of the immine bond in immature cross-links through reduction with NaBH4.

Figure 6 - 5 :

 65 Figure 6-5: High pH fractionation of an NaBH4 stabilised collagen digestion.

Figure 6 - 6 :

 66 Figure 6-6: Example of an immature crosslink between two peptides.

Figure 6 - 7 :

 67 Figure 6-7: Trivalent cross-link enrichment strategy.

Figure 6 -

 6 Figure 6-7 demonstrates the enrichment strategy used in this work. First, the hydroxylamine digestion protocol described in Chapter 4 was applied to fresh bovine bone collagen. This protocol allows for the efficient solubilisation of the highly cross-linked, insoluble, parts of the collagen matrix that are the target of this enrichment procedure. In addition to this, it creates larger peptides than a trypsin digestion, which are an ideal size for well-resolved, preparative scale SEC.

Figure 6 - 8 :

 68 Figure 6-8: Preparative scale SEC separation of a hydroxylamine digest of bovine bone collagen, using a HiLoad Superdex 200 16/600 column and the ATKA PURE 25 separation system.

Figure 6 - 9 :

 69 Figure 6-9: SCX separation of the tryptic digest of Fraction 13 of the preparative scale SEC fractionation, using a PolySULFOETHYL A column on an ATKA PURE 25 separation system.

Figure 6 -

 6 9 shows that the fluorescent peaks in the SCX chromatogram do not correspond to the bulk of the material eluting, shown by the UV absorbance, indicating that the sample was effectively enriched in cross-linked peptides.

Figure 6 - 10 :

 610 Figure 6-10: RP-LC-MS/MS analysis of the SCX fractions, using a C18 column, the peptides identified were predominately linear and had low charge states.

Figure 6 - 11 :

 611 Figure 6-11: Retention time plotted against m/z value of one of the SCX fractions, analysed using a top-down LC-MS configuration, demonstrating the complexity of the purified sample. The two peaks highlighted in Figure 6-11 correspond to the same peptide but in different charge states. It is again possible to see the added complexity due to the high heterogeneity of the collagen protein, as demonstrated in Figure 6-12 which shows the MS1 spectra of the peptide containing five different forms of the peptide with a different number of modifications. For the MS2 fragmentation, an isolation window of 3 m/z was used, with EThCD fragmentation with fragmentation times calibrated to the charge of the precursor ion, with 12% supplemental HCD fragmentation.

Figure 6 - 12 :

 612 Figure 6-12: MS1 spectra of peaks identified that correspond to cross-linked collagen peptides, demonstrating the heterogeneity of the enriched fractions, m/z:1024.6504, with increasing additional oxidations. Spectra are at a resolution of 60,000.

Figure 6 - 13 :

 613 Figure 6-13: MS2 spectrum of the m/z:1028.3188 precursor ion, which was identified as a crosslinked peptide.

- 14 .

 14 Due to the high complexity of the fragmentation, Figure6-14 shows the spectrum in three separate mass ranges. Here, it was possible to assign fragments corresponding to a large part of cross-linked peptide, only band c-ions were observed until the lysine residue involved in the cross-linking, and only yand z-ions were observed following this lysine residue. The theoretical values of the fragments are detailed in TableS6-2, and those identified are highlighted in green.

Figure 6 - 14 :

 614 Figure 6-14: Zoomed annotated spectra of the peak at m/z: 1026.9822, attributed to a cross-linked peptide, A: m/z: 150-600, B: m/z: 600-1000, C:1000-1360 Following the identification of this peptide sequence in the fragmentation spectrum of the m/z: 1026.9822 precursor, the large mass of this peptide indicates that the other two peptides involved in the cross-link are much smaller. The deconvoluted mass of the precursor is 6155.893 Da, while the mass of the peptide already identified is 4275.944 Da, leaving around

Figure 6 - 15 :

 615 Figure 6-15: EICs of MS2 intensities of fragments corresponding to the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR (reported in Table S6-2), indicating potentially two series of cross-linked peptides. The identification of this cross-link corresponds to other studies in the literature characterising collagen cross-linking using mass spectrometry. Terajima et al. identified trivalent crosslinking between two known sequence motifs involved in cross-linking in collagen type I, at MKGHR (collagen 1α(i), position 264 and 1107) and EKAHD (collagen 1α(i), position 1207), the position identified in this work. (21)

Figure S1- 3 :

 3 Figure S1-3:A-E(i), peptides in collagen type I from Hippopotamus amphibius containing leucine or isoleucine residues, fragmented using EThcD fragmentation (27% supplemental HCD activation), A-E(ii) focus on the parts of the spectra showing the characteristic loss of either 29 or 43 Da, with the presence of the w-ion.

Figure S1- 4 :

 4 Figure S1-4: From left to right, the ETD-MS2 spectra of the 3+ charge state of the targeted peptide sequence followed by the corresponding MS3 spectra, showing a neutral loss of 29 Da to confirm isoleucine at residue 456 in collagen α2(i) of Hippopotamus amphibius.

Figure S1- 5 :

 5 Figure S1-5: From left to right, the ETD-MS2 spectra of the 3+ charge state of the targeted peptide sequence followed by the corresponding MS3 spectra, showing a neutral loss of 43 Da, confirming leucine at residue #515 in collagen α2(i) of Loxodonta africana.

Figure S1- 6 :

 6 Figure S1-6: Peptide detected in object 12.187.30 that is not species specific, GSEGPQGVRGEP(+15.99)GPP(+15.99)GPAGAAGPAGNP(+15.99)GADGQP(+15.99)GAK, m/z:1061.8286, z:3, ppm:7.1

S4. 6 .Figure S4- 1 :

 61 Figure S4-1: SDS-PAGE gel (10%) of 200 µg of bovine collagen extracts using GuHCl (Fraction A, as referred in the protocol described in the Figure 4-1B), and hydroxylamine extraction (Fraction B). Not only is the extraction yield visibly higher in the hydroxylamine extract, but the size of the peptides into which the protein is cleaved are visible.

Figure S4- 2 :

 2 Figure S4-2: Proportion of missed cleavages in each sample and fraction type, demonstrating the hydroxylamine digestion does not have a significant impact of the missed cleavages, and it is more related with the sample type.

Figure S4- 3 :

 3 Figure S4-3: The coverage of A, the modern model sample, and B, the model archaeological sample when using the different protocols, demonstrating the effect of the extraction method on the coverage of the protein. 'Counts' corresponds to the number of times a peptide was detected at a given position.

Figure S4- 4 :

 4 Figure S4-4: Total area of collagen type I peptides identified in the model A, archaeological and B, modern bones, giving an indication of abundance of collagen in different extraction protocols, as calculated by PEAKS. Note the logarithmic scale in A(ii).

Figure S4- 5 :

 5 Figure S4-5: Sequence coverage of collagen α2(i) of the model bovine sample when Fractions A and B are processed together (93%). Highlighted in blue are the glycosylation sites (galactosylation, glucosylgalactosylation) that were identified in both fractions. The following modifications are respectively noted h, o, d, c: hydroxylation, oxidation, deamidation and carbamidomethylation.

Table 1 - 1 :

 11 The analysis performed in this research, including accession number, description of the objects, sample analysed, sampling location, notes on condition or treatment, and taxonomic identification. ....................................... 27

Table 1 -2: Sequence

 1

Table S1 - 1 :

 S11 Details of the samples analysed in this research that resulted in identification of collagens type I without taxon identification. The table details ascension number, name of the objects sampled, sampled analyzed, sampling location, notes on condition or treatment, and method used to identify collagens type I. ............................................ 178

Table S1 - 2 :

 S12 Effect of the successive sampling procedure performed on exemplar human archaeological bone. Sequence coverages and numbers of peptides identified for collagens α1(i) and α2(i) are shown. Settings used for data processing are described in the Materials and Methods section. .................................................................................................... 179

Table S1 - 3 :

 S13 Peptides unique to Bos genus identified across the three samples, each from a single white key, from the Muselar Virginal (11.176.1, Johannes Ruckers, 1622), using MS/MS sequencing. ..................................................... 180

Table S1 - 4 :

 S14 Peptides unique to Physeter microcephalus identified in the Queen chess piece (2012.346, Scandinavian, 13 th c.)using MS/MS sequencing. ................................................................................................................................. 188

Table S1 - 5 :

 S15 Peptides unique to Physeter microcephalus identified in sample Hawaii bone pendant (1979.206.1587, 18-19 th c.) using MS/MS sequencing. ................................................................................................................................ 193

Table S1 - 6 :

 S16 Peptides unique to Bos genus identified in sample S2 from the central European saddle decorated with bone plaques (40.66.a,b, ca. 1400-1420) using MS/MS sequencing. ................................................................................... 199

Table S1 - 7 :

 S17 Peptides unique to Cervidae and Elephantidae families identified in S1 from the central European saddle decorated with bone plaques (40.66.a,b, ca. 1400-1420)using MS/MS sequencing. ................................................... 204

Table S1 - 8 :

 S18 Peptides unique to Cervidae and Elephantidae families identified in S3 from the central European saddle decorated with bone plaques (40.66.a,b, ca. 1400-1420)using MS/MS sequencing. ................................................... 206

Table S1 - 9 :

 S19 Sequences coverages and numbers of peptides identified for collagens α1(i) and α2(i) from exemplars in the Mammology collection at AMNH. ......................................................................................................................... 208 Table

Table S6 - 1 :

 S61 Divalent cross-linked identified in a fractionated tryptic digest of bovine bone collagen using XlinkX.

Table S6 - 2 :

 S62 Fragment values identified for the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR, P indicates hydroxyproline. Values in bold correspond to fragments identified in the spectrum in

Table 1 -1: The

 1 analysis performed in this research, including accession number, description of the objects, sample analysed, sampling location, notes on condition or treatment, and taxonomic identification.

MET Accession number Title Sample Location description Treatment / condition notes Identification Family Genus Species Samples from The Metropolitan Museum of Art

	11.176.1	Muselar Virginal by Johannes Ruckers	2 nd & 3 rd rubbings 2 nd & 3 rd rubbings 2 nd & 3 rd rubbings	S16, D5 key S17, A4 key S18, B2 key	---		Bos Bos Bos
	2012.346	Queen Chess Piece, Scandinavia,13 th c.	2 nd rubbing	Base of figure at rear	-		Physeter macrocephalus
	1979.206.1587 Pendant, Hawaii, 18-19 th c.	4 th rubbing	Inside of hole	-		Physeter macrocephalus
			2 nd rubbing	S1, Decorative trim	-	Cervidae
	40.66.a,b	Saddle, Central Europe, ca. 1400-1420	2 nd rubbing 2 nd rubbing	S2, Body of the saddle S3, Decorative trim (second sampling location)	--	Cervidae	Bos
	66.99.1	Tusk figure of a man, Egypt, ca. 3900-3500 B.C.	2 nd rubbing	Reverse rim, interior surface	Good condition with glossy surface		Hippopotamus amphibius
	26.7.1284	Furniture leg fragment, Egypt, ca. 2960-2649 B.C.	2 nd rubbing	Lower edge opposite of perforations, lighter area within break edge	Lighter area appears to be a recent break		Hippopotamus amphibius
	66.99.50	Figure of an Asiatic captive, Egypt, ca. 1295-1070 B.C.	2 nd & 3 rd rubbings	Reverse of waist, along break area	Break previously joined		Bos
	26.7.1299	Figurine or amulet of a hippo on a sled, Egypt, ca. 2960-2649 B.C.	1 st rubbing	Ground below loss of proper right front leg	Darkened surface with potential coating		Hippopotamus amphibius
	12.187.30	Spoon, ca. 3100-2649 B.C.	2 nd rubbing	Bottom edge, below perforation	Previously consolidated with Paraloid B-72		Bos

Table 1 -1 (continued) Mammology AMNH Accession number Title Sample Location description Treatment/condition notes Identification Family Genus Species

 1

	Samples from the American Museum of Natural History

Table 1 -2: Sequence

 1 coverage and the number of peptide identifications for samples where a species assignment was possible.

	Accession

number Description Identification Collagen α1(i) Collagen α2(i) # Taxon - specific peptides identified Family Genus Species % Coverage # Peptides % Coverage # Peptides

	2.2. Taxonomic analysis of ancient collagens from represented species in reference databases
	2.2.1. Single Component Studies
		Virginal D5 key	Bos*	70	993	73		53
	11.176.1	Virginal A4 key	Bos*	71	1060	72		92
		Virginal B2 key	Bos*	70	837	71		76
	1979.206.1587	Pendant	Physeter macrocephalus	69	593	66		68
	2012.346	Queen Chess Piece	Physeter macrocephalus	63	259	56		66
		Saddle S1	Cervidae *	62	213	54		16
	40.66.a,b	Saddle S2	Bos*	67	493	66		85
		Saddle S3	Cervidae *	70	740	69		30
	66.99.1	Tusk figure of a man, ca. 3900-3500 B.C.	Hippopotamus amphibius	92	534	78		111
		Figure of an
	66.99.50	Asiatic captive, ca. 1295-1070	Bos*	68	474	69		67
		B.C.
	26.7.1284	Furniture leg fragment, ca. 2960-2649 B.C.	Hippopotamus amphibius	68	132	51	89	68
		Figurine or
	26.7.1299	amulet of a hippo on a sled, ca.	Hippopotamus amphibius	59	101	51	82	14**
		2960-2649 B.C.
	12.187.30	Spoon, ca. 3100-2649 B.C.	Bos*	55	79	46	60	17**
	* Identification was only possible at higher taxonomic levels due to the high conservation of the
	collagen sequence between species within Bos genus and Cervidae family respectively.
	**Identification confirmed using peptide mass fingerprints and retention time of the peptides.

  Another example is the Hawai'ian pendant (1979.206.1587, United States, Hawai'i, 18-19 th century), which was also identified to be made from whale ivory, reflecting the nautical history of Hawai'i. (66) The Figure of an Asiatic captive (66.99.50, Egypt, ca. 1295-1070 B.C.) and Spoon (12.187.30, Egypt, ca. 3100-2649 B.C.) highlights one of the greatest challenges of analysing art that has a

long history of intervention. Both objects were characterized as ivory (hippopotamus or elephant)

Table 2 -

 2 Accession numbers shown are those of Loxodonta africana, as there are no collagen sequences for Loxodonta cyclosis available on NCBI as of 11/06/2023.

	Species	Gene	NCBI Accession number	%Coverage	#Peptides
		COL1A1	XP_049716279.1	71.7 ±0.9	1061 ±121
		COL1A2	XP_049749558.1	74.0 ±0.8	729 ±104
	Elephas maximus	COL2A1	XP_049738899.1	22.3 ±3.7	71 ±12
		COL3A1	XP_049743614.1	16.3 ±1.9	27 ±3
		COL5A2	XP_049743615.1	17.0 ±1.6	27 ±2
		COL1A1	XP_010592644.1	72.0 ±0.8	979 ±67
		COL1A2	XP_010585560.1	74.0 ±0.8	708 ±36
	Loxodonta africana	COL3A1	XP_023398556	54.3 ±6.1	189 ±93
		COL2A1	XP_003405792.1	20.3 ±2.1	72 ±3
		COL5A2	XP_003406222.1	23.3 ±2.9	37 ±6
		COL1A1	XP_010592644.1	73.0 ±0.8	1080 ±68
		COL1A2	XP_010585560.1	74.3 ±0.5	770 ±40
	Loxodonta cyclosis*	COL3A1	XP_023398556	20.0 ±2.4	74 ±5
		COL2A1	XP_003405792.1	24.3 ±6.3	38 ±11
		COL5A2	XP_003406222.1	21.3 ±3.1	28 ±3
	*

1: Proteins identified in standard samples

Table 2 - 2 :

 22 Peptide markers that can be used to identify the phenotype of elephant ivory. P in bold indicates hydroxyproline, and N in bold indicates a deamidated asparagine. Residues in red are the informative substitutions previously identified.

	Elephas maximus			Loxodonta africana
	Sequence	m/z*	z	Sequence	m/z*	z
		collagen α2(i) position 993
	HGNRGEPGPAGSVGPVGAIGPR	685.6859 3	HGNRGEPGPAGSVGPVGAVGPR	681.0140	3
	HGNRGEPGPAGSVGPVGAIGPR	686.0125 3	HGNRGEPGPAGSVGPVGAVGPR	681.3407	3
	HGNRGEPGPAGSVGPVGAIGPR 691.3425 3	HGNRGEPGPAGSVGPVGAVGPR	686.6707	3
	HGNRGEPGPAGSVGPVGAIGPR 691.0159 3	HGNRGEPGPAGSVGPVGAVGPR	686.3440	3
		collagen α2(i) position 384
		784.3742	2		770.8687	2
	GSNGEAGSAGPAGPPGLR			GSSGEAGSAGPAGPPGLR
		523.2519	3		514.2483	3
		792.3692	2		778.8637	2
	GSNGEAGSAGPAGPPGLR			GSSGEAGSAGPAGPPGLR
		528.5819	3		519.5783	3
		784.8642	2		1034.4889	2
	GSNGEAGSAGPAGPPGLR			GSSGEAGSAGPAGPPGLRGGPGSR
		523.5786	3		689.9950	3

*Measurement errors in m/z are Δm < ±1.3ppm

Table 2 -

 2 4, with the justification of this identification shown in Table 2-3.

Table 2 -

 2

3:

Identified amino acids at key positions in collagen α2(i)

Sample

AA@384 AA@993

Table 2 - 4 :

 24 Samples analysed in this study

	AMNH Assigned Species	Sample Reference (# samples)	AMNH Reference	Collection information	Proteomics assigned taxon
	Elephas	A2(x1)	39085	No collection information (collected by Carl Akeley)	Elephas maximus
	maximus	A3(x2)	3819	Zoo	Elephas maximus
				Collected in Democratic Republic of
		S1(x3)	51949	Congo, Upper Uele, Yakuluku, 3 Oct 1911 (collected by Herbert Lang and	Loxodonta
				James Chapin)
				Collected in Democratic Republic of
	Loxodonta africana	S2(x2)	51950	Congo, Upper Uele, Yakuluku, 3 Oct 1911 (collected by Herbert Lang and	Loxodonta
				James Chapin)
				Collected in Democratic Republic of
		S3(x2)	52094	Congo, Upper Uele, Faradje, 1 Dec 1911, (collected by Herbert Lang and	Loxodonta
				James Chapin)
				Collected in Democratic Republic of
		F1(x1)	119510*	Congo, Mai-Ndombe, 1 Nov 1933	Loxodonta
				(Collected by George F. Ryan)
	Loxodonta cyclosis	F2(x2)	119510 *	Collected in Democratic Republic of (Collected by George F. Ryan) Congo, Mai-Ndombe, 1 Nov 1933	Loxodonta
				Collected in Liberia, Cavalla
		F3(x2)	89453	Region, 1 Jan 1938 (collected by	Loxodonta
				Dienkel)

* Two tusks from the same animal or collected at the same time and location

3.4.Short method for rapid species discrimination

  Le Piage is situated in the Aquitaine basin in southwest France, not so far from the border between this basin and the Massif central. Le Piage site is made up of a series of caves and covered shelters at the base of a Cretaceous limestone cliff. It was first excavated in the 1960s by Champagne and Espitalié, who recognised it as a key early Upper Palaeolithic site as it was supposed to deliver interstratified archaeological layers related to the transitional period between Middle Palaeolithic and Upper Palaeolithic times. During this period, Neandertals were being replaced by Anatomically Modern Humans throughout Western Eurasia. (26) At this time, there were two key cultures the Proto-Aurignacian (43-40 Ky cal BP), and

	3. Results and Discussion
	3.1.Le Piage
	7/8/9, a primary kmer overlap of 6/7/8 was used, a contig over lap of 7/9/11, and 3/1 gaps
	authorised. To search the data, the Cervidae RefSeq database of NCBI was used, downloaded
	15/06/2023.

2 Da, carbamidomethylation (C, +57.09 Da) as a fixed modification, oxidation (MP, +15.99 Da), and deamidation (NQ, +0.98 Da) as variable modifications, 3 missed cleavages allowed. For the contig assembly, the kmer size was set as Châtelperronian (45-42 Ky cal BP). (

37

) One should note that that the early Aurignacian, which succeeds the Proto-Aurignacien, is widely considered to result from the activity of Anatomically Modern Humans, while the Châtelperronian (preceding the Proto-Aurignacien)

Table 3 - 1 :

 31 Number of peptides and coverage of collagen α2(i) when using PEAKS DB searching with different databases. (Focus on collagen α2(i) as this chain has more variation between species)

	Sample	Database (date downloaded)	Collagen α2(i) %Coverage #Peptides
		Odocoileus virignianus texanus (14/02/2023)	75	995
	Rang_150	Odocoileus virignianus texanus with T@829, F@846	77	1055
		Rangifer tarandus platyrhyncus (27/04/2033)	77	1157
		Odocoileus virignianus texanus (14/02/2023)	84	955
	Rang_1709	Odocoileus virignianus texanus with T@829, F@846	87	1064
		Rangifer tarandus platyrhyncus (27/04/2033)	87	1135

Reindeer are part of the Cervidae family of deer, which has 251 members listed on NCBI, with only 6 having collagen sequenced. Protein sequences for the Svalbard reindeer, Rangifer tarandus platyrhyncus, were added 31 st March 2023, which had been translated from genetic

Table 3 -

 3 2, with details on the coverage of collagen and number of collagen peptides identified shown in TableS3-1.

Table 4 - 1 .

 41 In particular, the reference material consists of adult human bone and tooth samples collected from archaeological specimens derived from different archaeological assemblages of different time periods including Bronze Age (3000 BCE to 1100 BCE), Post Medieval (16th-18th centuries) and Contemporary Era (World War I -early 20th century) from excavations conducted in northern France. The fresh adult human bone samples were collected from two human specimens from the body donation program for health education and research studies in accordance with French Law (Ethic agreement 2021-473-S91, CER U-Lille, ProtOsIPoM protocol, ProTaphOs Program). In order to demonstrate the versatility of the extraction procedure, animal material consisting of fresh bovine bone samples obtained at a local butcher were included in the study.

Table 4 - 1 :

 41 Information regarding the samples analysed.

FPS (Club Jeunes de la Société Française de la Proteomique) : Chargé de communications

  1-4 th June 2021, ISBA9, Toulouse (online), Catherine Gilbert, Francesca Galluzzi, Katell Bathany, Stéphane Claverol, Quentin Scanvion, Benoit Bertrand, Valery Hedouin, Caroline Tokarski • Organisation of Journées du CJ-FPS, Bordeaux • Mag d'été 2022, youth club edition • Mag d'hiver 2022, recounting experience at HUPO • Organisation of Journées du CJ-FPS 2023, Strasbourg • Management of CJ-FPS LinkedIn Page and group

	3. Outreach
	ARCHE LinkedIn page and Website management
	Bordeaux Proteome LinkedIn page management
	Declics 17 th November 2021, Lycée Magendie
	Teaching, Bordeaux Summer School/MaSC Workshop 26-28/09/2022, Bottom up proteomics
	applied to cultural heritage
	4. Awards:
	Best junior presentation at the CJ-FPS Days in Bordeaux, €1000 to attend HUPO2022 in Cancun
	5. ADoC (Association des Doctorants en Chimie à Bordeaux): Présidente
	• Presentation of ADoC at Rentrée EDSC 19/11/21
	• Apéro 23/11/21
	• Badminton 27/01/22
	• Ice skating 24/02/22
	• Interlab tournament 20/03/2022
	• Climbing 07/06/2022
	• Apéro 30/06/2022
	• Weekend Plage 23-25/09/2022
	• Ninja Warrior 02/11/2022
	• Board game night 8/12/22
	• Weekend Ski 03-05/03/2023
	• WES FSDIE (university funding) presentation 27/03/2023
	• Presentation of ADoC at EDSC day 13/04/2023
	6. CJ-

Table S1 - 2 :

 S12 Effect of the successive sampling procedure performed on exemplar human archaeological bone. Sequence coverages and numbers of peptides identified for collagens α1(i) and α2(i) are shown. Settings used for data processing are described in the Materials and Methods section.

		Collagen α1(i)	Collagen α2(i)
	Sampling No.
		%Coverage #Peptides % Coverage #Peptides
	1	31	27	29	22
	2	70	636	72	575
	3	69	620	72	626

Table S1 - 3 :

 S13 Peptides unique to Bos genus identified across the three samples, each from a single white key, from the Muselar Virginal (11.176.1, Johannes Ruckers, 1622), using MS/MS sequencing.

	Peptide R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GF PGAVGAKGEGGPQ(+.98)GP(+15.99)R.G* R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)PGF P(+15.99)GAVGAKGEGGPQGPR.G* R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)PGF PGAVGAKGEGGP(+15.99)QGPR.G* R.GNDGATGAAGPPGP(+15.99)TGPAGPP(+15.99)GF P(+15.99)GAVGAKGEGGP(+15.99)Q(+.98)GPRG.S* R.GNDGATGAAGPPGP(+15.99)TGPAGPP(+15.99)GF P(+15.99)GAVGAKGEGGP(+15.99)QGP(+15.99)RG.S* R.GNDGATGAAGPPGP(+15.99)TGPAGPP(+15.99)GF P(+15.99)GAVGAKGEGGPQGPR.G* R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRG.E** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGE.A** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEA.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGPAGPAG.P** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGPAGPAGP(+15.99)R.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGPAGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRG EAGPAGPAGPA.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRG VGPVGK.H* P.GNAGPVGAAGAP(+15.99)GPQ(+.98)GPVGPVGK.H* A.AGAP(+15.99)GPQ(+.98)GPVGPVGK.H* A.AGAP(+15.99)GPQGPVGPVGK.H* EAGPAGPAGPAGP.R** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRG EAGPAGPAGPAGPR.G** P.GNAGPVGAAGAP(+15.99)GPQGPVGPVGK.H* G.N(+.98)AGPVGAAGAP(+15.99)GPQ(+.98)G PVGPVGK.H* A.GAP(+15.99)GPQ(+.98)GPVGPVGK.H* A.GAP(+15.99)GPQGPVGPVGK.H*	Mass 3383.56 3382.58 3382.58 3456.57 3471.58 3382.58 1594.72 1723.77 1794.8 2527.16 2796.3 2780.31 2469.15 2623.23 1927.97 1304.67 1303.68 2779.33 1926.99 1871.93 1233.63 1232.65	Length 39 39 39 40 40 39 19 20 21 31 33 33 30 32 23 15 15 33 23 22 14 14	|ppm| 4.1 5 4.6 6.7 2.5 4.2 6.2 1.4 4.6 5 5 3.1 4.8 4.9 3.4 3.4 3.2 3.4 2.4 3.1 3.3 3.3	m/z 1128.9 1128.5 1128.5 1153.2 1158.2 846.66 798.37 862.89 898.41 1264.6 933.11 696.09 1235.6 1312.6 965 653.34 652.85 695.84 964.5 936.98 617.82 617.33	z 3 3 3 3 3 4 2 2 2 2 3 4 2 2 2 2 2 4 2 2 2 2	RT 48.37 46.77 47.32 50.23 52.09 44.69 27.69 29.94 31.4 44.1 36.71 37.05 40.25 44.32 43.21 31.38 29.78 35.25 41.44 49.11 30.62 29.01	Range 322-360 322-360 322-360 322-361 322-361 322-360 674-692 674-693 674-694 674-704 674-706 674-706 674-703 674-705 950-972 958-972 958-972 674-706 950-972 951-972 959-972 959-972	PTM Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Deamidation (NQ); Hydroxyprolination Hydroxyprolination	Identified in Samples (-10logP value shown) Sample S16:D Sample S17:A Sample S18:B --57.73 89.31 ----83.2 57.24 ---54.44 ---46.57 72.96 80.66 63.95 64.31 80.25 -61.47 70.03 -93.3 62.05 76.47 71.41 73.7 -134.81 129.14 90.59 59.4 66.83 56.36 ---58.93 41.91 -80.73 52.32 -71.28 59.45 49.31 106.88 101.25 72.66 -87.41 60.06 -64.95 50.45 -61.83 47.98 -75.36 57.05
	R.GN(+.98)DGATGAAGPP(+15.99)GPTGPAGP(+15.99) PGFP(+15.99)GAVGAKGEGGP(+15.99)QGPR.G* R.GN(+.98)DGATGAAGPP(+15.99)GPTGPAGP(+15.99) PGFP(+15.99)GAVGAKGEGGPQ(+.98)GPR.G* R.GN(+.98)DGATGAAGPP(+15.99)GPTGPAGP(+15.99) PGFP(+15.99)GAVGAKGEGGPQ(+.98)GPRG.S* R.GN(+.98)DGATGAAGPP(+15.99)GPTGPAGP(+15.99) PGFP(+15.99)GAVGAKGEGGPQGPR.G* R.GN(+.98)DGATGAAGPP(+15.99)GPTGPAGPP(+15.99) GFP(+15.99)GAVGAKGEGGP(+15.99)QGPR.G* R.GN(+.98)DGATGAAGPP(+15.99)GPTGPAGPP(+15.99) GFP(+15.99)GAVGAKGEGGPQ(+.98)GPR.G* R.GN(+.98)DGATGAAGPP(+15.99)GPTGPAGPP(+15.99) GFP(+15.99)GAVGAKGEGGPQ(+.98)GPRG.S* R.GN(+.98)DGATGAAGPP(+15.99)GPTGPAGPP(+15.99) GFP(+15.99)GAVGAKGEGGPQGPR.G* R.GN(+.98)DGATGAAGPPGP(+15.99)TGPAGP(+15.99) PGFP(+15.99)GAVGAKGEGGPQGPR.G* R.GN(+.98)DGATGAAGPPGPTGP(+15.99)AGP(+15.99) PGFPGAVGAKGEGGP(+15.99)QGPR.G* R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGF P(+15.99)GAVGAKGEGGPQ(+.98)GPRG.S* R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)G FP(+15.99)GAVGAKGEGGP(+15.99)Q(+.98)GPRG.S* R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKGEGGP(+15.99)Q(+.98)GPR.G* R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKGEGGP(+15.99)Q(+.98)GPRG.S* R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKGEGGP(+15.99)QGPR.G* R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKGEGGPQ(+.98)GPR.G* R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKGEGGPQ(+.98)GPRG.S* R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKGEGGPQGPR.G* R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF PGAVGAKGEGGP(+15.99)QGPR.G* R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF PGAVGAKGEGGPQGP(+15.99)R.G* R.GNDGATGAAGPPGP(+15.99)TGPAGP(+15.99) P(+15.99)GFP(+15.99)GAVGAKGEGGPQGPR.G* R.GNDGATGAAGPPGP(+15.99)TGPAGP(+15.99)PGF P(+15.99)GAVGAKGEGGP(+15.99)QGP(+15.99)RG.S* R.GNDGATGAAGPPGPTGPAGP(+15.99)P(+15.99)GF P(+15.99)GAVGAKGEGGPQGPR.G* K.GEGGPQ(+.98)GPRGSEGPQ(+.98)GVR.G* K.GEGGPQ(+.98)GPRGSEGPQ(+.98)GVRGEP(+15.99) G.P* K.GEGGPQ(+.98)GPRGSEGPQGVR.G* K.GEGGPQ(+.98)GPRGSEGPQGVRGE.P* K.GEGGPQ(+.98)GPRGSEGPQGVRGEP(+15.99)G.P* K.GEGGPQGPRGSEGPQ(+.98)GVRGEP(+15.99)GP P(+15.99)GPAGAAGPAGNP(+15.99)GADGQP(+15.99)GAK.G* A.RGAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GD RGEAGPAGPAGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GP(+15.99)AGA N(+.98)GDRGEAGPAGPAGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GP(+15.99)AGA NGDRGEAGPAGPAGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GP(+15.99)AGA NGDRGEAGPAGPAGPAGPRG.S** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGP(+15.99)AGPAGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGP.A** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPA.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAG.P** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGP(+15.99)AGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGP.A** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGPA.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGPAG.P** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGPAGP(+15.99)AGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGPAGP.A** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRGEA.G** GEAG.P** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRG EAGP(+15.99)AGPAGPAGPRG.S** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRG EAGP.A** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRG EAGPA.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRG EAGPAG.P** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRG EAGPAGP(+15.99)AGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRG EAGPAGP.A** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRG EAGPAGPA.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRG EAGPAGPAG.P** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRG EAGPAGPAGP.A** Y.P(+15.99)GNAGPVGAAGAP(+15.99)GPQGP G.AAGAP(+15.99)GPQGPVGPVGK.H* Y.P(+15.99)GNAGPVGAAGAP(+15.99)GPQ(+.98) GPVGPVGK.H* G.AAGAP(+15.99)GPQ(+.98)GPVGPVGK.H* Y.P(+15.99)GN(+.98)AGPVGAAGAP(+15.99)GPQ GPVGPVGK.H* V.GAAGAP(+15.99)GPQGPVGPVGK.H* V.GAAGAP(+15.99)GPQ(+.98)GPVGPVGK.H* GPVGPVGK.H* P.VGAAGAP(+15.99)GPQGPVGPVGK.H* G.YP(+15.99)GNAGPVGAAGAP(+15.99)GPQ GP(+15.99)VGPVGK.H* P.VGAAGAP(+15.99)GPQ(+.98)GPVGPVGK.H* R.GYP(+15.99)GNAGPVGAAGAP(+15.99)GPQ R.GAP(+15.99)GAIGAPGPAGAN(+.98)GDRGEA GPAGPAGPAGPR.G** R.GYP(+15.99)GNAGP(+15.99)VGAAGAPG G.PVGAAGAP(+15.99)GPQGPVGPVGK.H* P(+15.99)QGPVGPVGK.H* G.PVGAAGAP(+15.99)GPQ(+.98)GPVGPVGK.H* A.GPVGAAGAP(+15.99)GPQGPVGPVGK.H* EAGPAGPAGPAGPR.G** A.GPVGAAGAP(+15.99)GPQ(+.98)GPVGPVGK.H* R.GAP(+15.99)GAIGAPGP(+15.99)AGANGDRG R.GAP(+15.99)GAIGAPGP(+15.99)AGANGDRG R.GAP(+15.99)GAIGAPGP(+15.99)AGANGDRG N.AGPVGAAGAP(+15.99)GPQGPVGPVGK.H* EAGPAGPAG.P** N.AGPVGAAGAP(+15.99)GPQ(+.98)GPVGPVGK.H* EAGPAGPA.G** G.NAGPVGAAGAP(+15.99)GPQGPVGPVGK.H* R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDR R.GAP(+15.99)GAIGAPGP(+15.99)AGANGDRG EAGP(+15.99)AGPAGPAGPR.G** G.NAGPVGAAGAP(+15.99)GPQ(+.98)GPVGPVGK.H* R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKGEG.G* R.GNDGATGAAGPPGPTGP(+15.99)AGP(+15.99)PGF PGAVGAKGEGGPQ(+.98)GPR.G* R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGP(+15.99)AGP(+15.99)AGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRG.E** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRGE.A** R.GAP(+15.99)GAIGAPGP(+15.99)AGAN(+.98) GDRGEAGPAGPAGPAGPR.G** G.N(+.98)AGPVGAAGAPGP(+15.99)QGPVGPVGK.H* R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)PGF PGAVGAKGEGGPQGPR.G* R.GNDGATGAAGPPGPTGP(+15.99)AGP(+15.99)PGF P(+15.99)GAVGAKGEGGPQGPR.G* R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAG.P** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGPAGPAGPRG.S** R.GAP(+15.99)GAIGAPGP(+15.99)AGAN(+.98) GDRGEAGPAGPAGPAG.P** G.N(+.98)AGPVGAAGAP(+15.99)GPQGPVGPVGK.H* *specific to the Bovinae subfamily, **specific to the Bos genus 3399.55 3384.55 3441.57 3383.56 3399.55 3384.55 3441.57 3383.56 3383.56 3383.56 3440.58 3456.57 3399.55 3456.57 3398.57 3383.56 3440.58 3382.58 3382.58 3382.58 3398.57 3471.58 3382.58 1722.79 2078.92 1721.81 1907.87 2077.94 4018.82 2936.41 2796.3 2795.32 2852.34 2796.3 1948.88 2019.91 2076.94 2796.3 2173.99 2245.03 2302.05 2796.3 2399.1 1793.82 1850.84 2852.34 1947.89 2018.93 2075.95 2795.32 2173 2244.04 2301.06 2398.12 2040.03 1374.72 2041.01 1375.7 2041.01 1431.74 1432.73 2203.09 1530.81 2276.1 1531.79 2764.32 1627.86 2276.1 1628.85 1684.88 2779.33 1685.87 1755.92 2301.06 1756.91 2244.04 1869.96 2795.32 1870.95 2790.27 3367.57 2812.29 1593.74 1722.78 2780.31 1870.95 3366.59 3382.58 1851.82 2837.33 2527.16 1870.95	39 39 40 39 39 39 40 39 39 39 40 40 39 40 39 39 40 39 39 39 39 40 39 18 22 18 20 22 45 34 33 33 34 33 23 24 25 33 26 27 28 33 29 21 22 34 23 24 25 33 26 27 28 29 24 16 24 16 24 17 17 25 18 26 18 33 19 26 19 20 33 20 21 28 21 27 22 33 22 33 39 33 19 20 33 22 39 39 22 34 31 22	P02453|CO1A1_BOVIN 5.7 1134.2 3.4 1129.2 7.9 1148.2 3.2 1128.9 5.7 1134.2 6.7 1129.2 11.1 1148.2 5.1 1128.9 4.7 1128.9 3.6 1128.9 3.6 1147.9 5.6 1153.2 7.2 1134.2 5.6 1153.2 5.9 1133.9 4.7 1128.9 3.9 1147.9 4.1 1128.5 4.4 1128.5 4.6 846.66 1.4 1133.9 4.3 1158.2 4.1 1128.5 0.2 575.27 8.9 693.99 0.6 574.94 1.2 636.96 6.9 693.66 4.7 1005.7 P02465|CO1A2_BOVIN 3.3 735.11 5.1 933.11 4.9 932.78 4 951.79 4.4 933.11 5.1 975.45 5.3 1011 4.5 1039.5 5.3 933.11 4.6 1088 4.4 1123.5 4 1152 4.9 933.11 1.5 1200.6 5.4 897.92 4.6 926.43 1.1 951.79 4.6 974.96 4.5 1010.5 2.8 1039 5 932.78 4.5 1087.5 4.8 1123 3.8 1151.5 5.3 1200.1 4.4 681.02 4.1 688.37 5 1021.5 3.4 688.86 3.8 1021.5 4.2 716.88 3.6 717.37 7.6 735.38 2.9 766.41 6.6 759.71 3.3 766.91 0.2 922.45 3.7 814.94 5.7 759.71 2.7 815.43 3 843.45 7.5 927.44 3 843.94 1.8 878.97 4.5 768.03 2.2 879.46 4.1 749.02 3 935.99 3.1 932.78 2.4 936.48 5.3 931.1 3 1123.5 7.2 938.44 7 797.88 4.7 862.4 2.6 1391.2 3.4 936.48 3.8 1123.2 8 1128.5 5.9 926.93 2.4 946.79 3.9 1264.6 3.1 936.48	3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 4 4 3 3 3 3 2 2 2 3 2 2 2 3 2 2 2 3 2 2 2 3 2 2 2 2 3 2 2 2 2 2 2 3 2 3 2 3 2 3 2 2 3 2 2 3 2 3 2 3 2 3 3 3 2 2 2 2 3 3 2 3 2 2	48.03 49.17 52.64 49.35 48.03 47.39 50.89 48.28 45.74 49.04 52.83 50.7 45.34 50.7 44.24 45.74 50.95 44.64 47.18 47.19 55.34 52.03 53.56 22.67 23.19 21.59 22.22 22.43 29.76 32.53 37.28 33.88 41.74 37.19 34.65 36.3 36.35 35.85 40.69 40.49 40.45 34.5 44.49 29.39 29.34 42.47 31.24 34.55 32.42 32.9 36.99 36.6 36.45 40.84 41.74 30.42 43.45 32.13 44.14 31.04 32.38 45.39 34.75 42.48 36.25 41.54 37 45.25 38.75 38.9 39.95 40.69 40.45 36.35 42.28 36.45 41.24 36.6 43.06 49.28 48.86 37.52 25.46 26.82 38.56 47.28 47.67 45.95 31.53 36.81 44.13 47.14	322-360 322-360 322-361 322-360 322-360 322-360 322-361 322-360 322-360 322-360 322-361 322-361 322-360 322-361 322-360 322-360 322-361 322-360 322-360 322-360 322-360 322-361 322-360 352-369 352-373 352-369 352-371 352-373 352-396 673-706 674-706 674-706 674-707 674-706 674-696 674-697 674-698 674-706 674-699 674-700 674-701 674-706 674-702 674-694 674-695 674-707 674-696 674-697 674-698 674-706 674-699 674-700 674-701 674-702 949-972 957-972 949-972 957-972 949-972 956-972 956-972 948-972 955-972 947-972 955-972 674-706 954-972 947-972 954-972 953-972 674-706 953-972 952-972 674-701 952-972 674-700 951-972 674-706 951-972 322-354 322-360 674-706 674-692 674-693 674-706 951-972 322-360 322-360 674-695 674-707 674-704 951-972	Deamidation (NQ); Deamidation (NQ); Deamidation (NQ); Hydroxyprolination Deamidation (NQ); Hydroxyprolination Deamidation (NQ); Hydroxyprolination Deamidation (NQ); Hydroxyprolination Deamidation (NQ); Hydroxyprolination Deamidation (NQ); Hydroxyprolination Deamidation (NQ); Hydroxyprolination Deamidation (NQ); Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Deamidation (NQ) Deamidation (NQ); Hydroxyprolination Deamidation (NQ) Deamidation (NQ) Deamidation (NQ); Hydroxyprolination Deamidation (NQ); Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Deamidation (NQ) Deamidation (NQ) Hydroxyprolination; Hydroxyprolination; Hydroxyprolination; Hydroxyprolination Deamidation (NQ) Deamidation (NQ) Hydroxyprolination; Hydroxyprolination Hydroxyprolination Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Hydroxyprolination Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination; Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Hydroxyprolination Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Hydroxyprolination Hydroxyprolination Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Deamidation (NQ); Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Deamidation (NQ); Hydroxyprolination	--85.96 75.35 -104.19 ----106.4 -123.9 131.21 79.17 145.11 -----66.89 -76.5 45.37 48.38 90.39 -74.37 --46.1 63.6 79.28 90.94 85.89 72.84 74.8 78.9 70.27 46.41 90.22 81.86 58.28 81.93 80.54 67.39 58.89 74.83 71.54 -70.62 --------------69.89 --------52.75 -96.92 ---71.53 81.02 60.39 ----87.45 87.98 --	-61.03 111.52 103.58 51.19 94.6 --49.99 -95.44 81.11 113.32 117.28 77.45 132.34 103.55 62.38 55.94 -46.23 65.43 47.78 72.11 -51.93 -86.56 75.92 56.38 56 56.9 63.29 79.95 83.71 81.16 -72.64 68.32 79.01 57.34 78.06 75.85 -64.32 75.73 80.45 59.95 69.53 67.32 -58.42 97.46 78.25 78.85 77.52 81.88 78.03 66.07 57.35 80.15 43.97 75.82 83.72 49.38 80.19 96.07 78.1 -77.51 86.16 50.24 80.16 48.93 82.06 -66.35 -62.35 54.64 59.65 71.43 75.6 --46.76 88.05 -82.72 83.81 68.58 77.56	47.05 -81.65 ---66 44.88 -52.43 81.92 65.08 87.13 71.41 75.31 87.88 --46.99 52.24 -43.81 -55.07 -------52.94 60 66.17 72.02 62.99 64.58 62.67 67.36 58.96 -68.67 -57.7 57.06 59.11 70.65 50.42 67.32 59.23 61.18 63.38 55.68 --64.94 54.75 --52.28 58.67 -62.68 53.76 67.12 64.06 44.82 65.53 57.44 73.82 -61.08 -74.94 59.53 -46.17 ---46.31 58.61 56.22 85.31 61.86 59.91 -45.61 68.32 56.15 --
											181 182 183 184 186

Table S1 - 4 :

 S14 Peptides unique to Physeter microcephalus identified in the Queen chess piece (2012.346, Scandinavian, 13 th c.)using MS/MS sequencing.

	Peptide A.N(+.98)GDRGEAGPAGAAGPAGSR.G N.GDRGEAGPAGAAGPAGSR.G R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSA GAAGQPGAK.G R.GSEGPQGVRGEP(+15.99)GP(+15.99)PGPAGA AGPAGNP(+15.99)GTDGQAGAK.G R.GPTGAAGPP(+15.99)GPR.G	-10lgP 30.06 27.74 25.9 35.61 XP_007108110.1 collagen alpha-1(III) chain [Physeter macrocephalus] Mass Length |ppm| m/z z 1667.7611 19 0.1 556.9277 3 1552.7341 18 0.2 518.5852 3 2624.2166 30 1.6 875.7476 3 3170.4465 36 2.3 1057.8252 3 32.48 1049.5204 12 3.3 525.7692 2 19.87 RT 22.88 19.24 37.33 30.07	Scan 9898 7031 20925 15364 7493	Range 690-708 691-708 709-738 361-396 684-695	PTM Deamidation (NQ) Hydroxyprolination, Deamidation (NQ) Hydroxyprolination Hydroxyprolination
	R.GAP(+15.99)GAVGAP(+15.99)GP(+15.99)AGA N(+.98)GDRGEAGPAGAAGPAGSR.G R.GAP(+15.99)GAVGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGAAGP(+15.99)AGSR.G R.GAP(+15.99)GAVGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGAAGPAGSR.G R.GAP(+15.99)GAVGAP(+15.99)GPAGANGDRG EAGP(+15.99)AGAAGPAGSR.G R.GAP(+15.99)GAVGAP(+15.99)GPAGANGDRG EAGPAGAAGPAGSR.G R.GAP(+15.99)GAVGAPGP(+15.99)AGAN(+.98) GDRGEAGPAGAAGPAGSR.G R.GAP(+15.99)GAVGAPGP(+15.99)AGANGDRG EAGPAGAAGPAGSR.G R.GAP(+15.99)GAVGAPGPAGAN(+.98)GDRGE AGPAGAAGPAGSR.G R.GAP(+15.99)GAVGAPGPAGANGDRGEAGP(+ 15.99)AGAAGPAGSR.G A.VGAP(+15.99)GPAGAN(+.98)GDRGEAGPAG AAGPAGSR.G V.GAP(+15.99)GPAGAN(+.98)GDRGEAGPAGA AGPAGSR.G G.PAGAAGPAGSR.G R.GSP(+15.99)GERGEVGPAGP(+15.99)NGFAGS AGAAGQ(+.98)P(+15.99)GAKGER.G AGAAGQP(+15.99)GAKGER.G R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSA. G R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSA G.A R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSA GA.A R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSA GAA.G R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSA GAAG.Q R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSA GAAGQ(+.98).P 5.99)GPAGPSGGG.Y R.GSQGSQGPAGPP(+15.99)GPP(+15.99)GPP(+1 5.99)GPAGPSGG.G R.GSQGSQGPAGPP(+15.99)GPP(+15.99)GPP(+1 5.99)GPAGPSG.G R.GSQGSQGPAGPP(+15.99)GPP(+15.99)GPP(+1 R.GSQGSQGPAGPP(+15.99)GP(+15.99)PGPP(+1 5.99)GPAGPSGG.G R.GEPGPP(+15.99)GP(+15.99)AGAAGPAGNP(+1 5.99)GTDGQ(+.98)AGAK.G R.GSQGSQGPAGPP(+15.99)GP(+15.99)PGPP(+1 5.99)GPAGP.S 5.99)GTDGQAGAK.G R.GEP(+15.99)GPP(+15.99)GPAGAAGPAGNP(+1 R.GSQGSQGPAGP(+15.99)PGPP(+15.99)GPP(+1 5.99)GPAGPSGGG.Y R.GEP(+15.99)GPP(+15.99)GPAGAAGPAGNP(+1 5.99)GTDGQ(+.98)AGAK.G R.GSP(+15.99)GERGEVGPAGP(+15.99)NGFAGS R.GSQGSQ(+.98)GPAGP(+15.99)PGP(+15.99)PG PP(+15.99)GPAGPSGG.G 5.99)GTDGQAGAK.G R.GEP(+15.99)GP(+15.99)PGPAGAAGPAGNP(+1 R.GSQ(+.98)GSQGPAGPP(+15.99)GPP(+15.99)G PP(+15.99)GPAGPSGGG.Y AGPAGNP(+15.99)GTDGQAGAK.G R.GSEGPQGVRGEP(+15.99)GPPGP(+15.99)AGA E.AGPAGAAGPAGSR.G A.GPAGAAGPAGSR.G R.GSQ(+.98)GSQGPAGPP(+15.99)GPP(+15.99)G P(+15.99)PGPAGPS.G AGPAGNP(+15.99)GTDGQAGAK.G R.GSEGPQGVRGEP(+15.99)GPP(+15.99)GPAGA R.GEAGPAGAAGPAGSR.G G.EAGPAGAAGPAGSR.G R.GSQ(+.98)GSQGPAGPP(+15.99)GP(+15.99)PG PP(+15.99)GPAGPSG.G R.GSEGPQGVRGEP(+15.99)GPP(+15.99)GPAGA AGPAGNP(+15.99)GTDGQ(+.98)AGAK.G G.DRGEAGPAGAAGPAGSR.G D.RGEAGPAGAAGPAGSR.G R.GSP(+15.99)GERGEVGPAGPNGFAGSAGAAG QP(+15.99)GAK.G R.GSEGPQGVRGEP(+15.99)GPP(+15.99)GPAGA AGPAGN(+.98)P(+15.99)GTDGQ(+.98)AGAK.G K.SGDRGETGPVGPSGAP(+15.99)GPVGSR.G K.SGDRGETGPVGPSGAPGPVGSR.G	XP_007105361.1 collagen alpha-2(i) chain [Physeter macrocephalus] 25.8 2746.2507 33 5.3 916.429 3 33.18 22.71 2746.2507 33 4.3 916.4281 3 30.75 48.07 2730.2607 33 4.2 911.098 3 32.64 22.82 2745.2666 33 5.7 916.1013 3 28.79 33.16 2729.2766 33 1.6 910.7676 3 31.16 37.08 2730.2607 33 3.8 683.5751 4 31.6 26.02 2729.2766 33 3.4 683.3241 4 31.55 31.24 2714.2708 33 1.7 905.7657 3 35.43 32.89 2729.2766 33 3.4 683.3241 4 31.55 29.86 2361.1008 28 1 788.0417 3 29.86 24.12 2262.0322 27 1.8 755.0193 3 27.05 21.13 910.462 11 0.9 456.2379 2 15.5 30.97 2998.3616 33 4.2 750.6008 4 30.81 23.99 2997.3777 33 3.8 750.3546 4 29.56 31.35 1886.8345 21 2.1 944.4265 2 41.86 24.26 1943.856 22 2.2 972.9374 2 41.06 22.69 2014.8931 23 0.6 1008.4544 2 43 26.02 2085.9302 24 0.5 1043.9729 2 43.7 27.23 2142.9517 25 1.9 1072.4851 2 43.2 21.22 2271.9941 26 1.8 1137.0063 2 44.79 XP_007128607.1 collagen alpha-1(i) chain [Physeter macrocephalus] 35.9 2225.979 27 7.8 1114.0055 2 38.52 29.95 2168.9575 26 7.2 723.9983 3 38.77 37.48 2111.936 25 5.8 1056.9814 2 38.97 39.18 2168.9575 26 6.5 1085.493 2 38.72 29.37 2304.0105 27 6.3 769.0156 3 29.81 30.67 1967.8824 23 5.6 984.954 2 40.81 44.23 2303.0266 27 5.9 1152.5273 2 28.58 27.15 2225.979 27 7 743.0055 3 38.52 43.23 2304.0105 27 5.2 1153.0186 2 29.8 34.5 2169.9414 26 7 1085.9856 2 40.07 40.28 2303.0266 27 5.9 768.6874 3 28.58 40.99 2226.9629 27 7.9 1114.4976 2 39.82 31.93 3170.4465 36 4.1 793.6222 4 30.23 21.01 1038.5206 13 0.5 520.2673 2 18.8 21.78 967.4835 12 0.7 484.7487 2 17.03 26.1 2055.8984 24 9.8 1028.9666 2 40.41 59.46 3170.4465 36 5.1 1057.8281 3 30.16 39.22 1224.5846 15 0.8 613.2991 2 21.35 26.7 1167.5632 14 0.9 584.7884 2 20.9 34.59 2112.9199 25 6.1 1057.4736 2 40.26 45.22 3171.4304 36 5 1058.156 3 31.5 24.87 1495.7126 17 0.5 499.5779 3 20.17 28.03 1380.6858 16 1.9 461.235 3 18.21 29.04 2639.2224 30 5 880.7524 3 33.53 36.84 3172.4146 36 4.1 1058.4832 3 32.34 32.89 2008.9513 22 1.7 670.6588 3 27.85 36.06 1992.9613 22 0.4 665.328 3 31.06	17633 16124 17361 14694 16239 16536 16536 19421 16474 15201 13020 4187 15781 14973 24316 23909 25165 25723 25311 26561 21670 21848 21986 21815 15099 23497 14177 21724 15112 22819 14125 22683 15471 6705 5441 23264 15429 8729 8319 23024 16199 7820 6267 18016 16926 13638 16086	676-708 676-708 676-708 676-708 676-708 676-708 681-708 682-708 698-708 709-741 709-734 1081-1107 709-733 1081-1106 709-732 1081-1105 709-731 1081-1106 370-396 709-730 1081-1103 370-396 709-729 1081-1107 370-396 1081-1106 370-396 709-741 1081-1107 361-396 676-708 696-708 697-708 1081-1104 361-396 676-708 694-708 695-708 1081-1105 361-396 676-708 692-708 693-708 709-738 1061-1082 361-396 1061-1082	Hydroxyprolination Hydroxyprolination Hydroxyprolination, Deamidation (NQ) Hydroxyprolination Hydroxyprolination, Deamidation (NQ) Hydroxyprolination Hydroxyprolination, Deamidation (NQ) Hydroxyprolination, Deamidation (NQ) Deamidation (NQ) Deamidation (NQ) Deamidation (NQ) Deamidation (NQ) Deamidation (NQ) Deamidation (NQ) Deamidation (NQ) Hydroxyprolination, Hydroxyprolination Hydroxyprolination, Hydroxyprolination Hydroxyprolination, Hydroxyprolination Hydroxyprolination, Hydroxyprolination Deamidation (NQ) Hydroxyprolination, Hydroxyprolination, Hydroxyprolination Hydroxyprolination Hydroxyprolination, Hydroxyprolination Deamidation (NQ) Hydroxyprolination, Hydroxyprolination Deamidation (NQ) , Hydroxyprolination Hydroxyprolination Hydroxyprolination, Deamidation (NQ) , Hydroxyprolination Hydroxyprolination Deamidation (NQ) Hydroxyprolination, Deamidation (NQ) , Hydroxyprolination Hydroxyprolination Deamidation (NQ) Hydroxyprolination, Deamidation (NQ) , Hydroxyprolination Deamidation (NQ) Hydroxyprolination, Deamidation (NQ) Hydroxyprolination, Hydroxyprolination, Hydroxyprolination Hydroxyprolination, Hydroxyprolination Deamidation (NQ)
	G.AP(+15.99)GPAGAN(+.98)GDRGEAGPAGAA GPAGSR.G R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSA GAAGQ.P R.GSEGPQ(+.98)GVRGEP(+15.99)GPP(+15.99)G PAGAAGPAGNP(+15.99)GTDGQAGAK.G	24.25 43.69 26.92	2205.011 2271.0103 3171.4304	26 26 36	1.4 2 3.6	736.012 1136.5146 1058.1545	3 2 3	26.56 43.3 31.96	12670 25420 16812	683-708 709-734 361-396	Hydroxyprolination, Deamidation (NQ) Hydroxyprolination, Deamidation (NQ) Deamidation (NQ) , Hydroxyprolination
	P.GPAGAN(+.98)GDRGEAGPAGAAGPAGSR.G G.PAGAN(+.98)GDRGEAGPAGAAGPAGSR.G R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSA GAAGQP(+15.99)G.A R.GSEGPQGVRGEP(+15.99)GP(+15.99)PGP(+15. 99)AGAAGPAGNP(+15.99)GTDGQAGAK.G	38.09 29.01 21.46 33.44	2020.931 1963.9095 2441.0745 3186.4365	24 23 28 36	0.9 0.7 3.3 6	674.6503 655.6433 1221.5486 1063.1592	3 3 2 3	24.74 23.67 41.61 28.24	11259 10425 24139 14622	685-708 686-708 709-736 361-396	Deamidation (NQ) Deamidation (NQ) Hydroxyprolination, Deamidation (NQ) Hydroxyprolination
	P.AGAN(+.98)GDRGEAGPAGAAGPAGSR.G A.GAN(+.98)GDRGEAGPAGAAGPAGSR.G R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSA GAAGQP(+15.99)GAK.G R.GSEGPQGVRGEP(+15.99)GP(+15.99)PGPAGA AGP(+15.99)AGNP(+15.99)GTDGQAGAK.G	22.61 24.76 45.53 26.09	1866.8568 1795.8197 2640.2065 3186.4365	22 21 30 36	1.9 1.3 3.9 6	623.2917 599.613 881.0796 1063.1592	3 3 3 3	22.43 21.05 35.19 28.24	9598 8508 19269 14013	687-708 688-708 709-738 361-396	Deamidation (NQ) Deamidation (NQ) Hydroxyprolination, Deamidation (NQ) Hydroxyprolination
											191 192

Table S1 - 5 :

 S15 Peptides unique to Physeter microcephalus identified in sample Hawaii bone pendant (1979.206.1587, 18-19 th c.) using MS/MS sequencing.

	Peptide R.GEP(+15.99)GPP(+15.99)GPAGAAGP(+15.99)AGNP(+15.99)G TDGQAGAK.G A.P(+15.99)GAVGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGA AGPAGSR.G R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSAGAA.G	-10lgP 26.56 52.46 45.49	Mass 2319.017 2602.202 2085.93	Length 27 31 24	|ppm| 7.2 4.3 2.4	m/z 774.0073 868.4117 1043.975	z 3 3 2	RT 25.23 31.64 43.56	Scan 16896	Range 370-396 678-708 709-732	PTM Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ)
	XP_007128607.1 collagen alpha-1(i) chain [Physeter macrocephalus] R.GSEGPQGVRGEP(+15.99)GPP(+15.99)GPAGAAGPAGN P(+15.99)GTDGQAGAK.G 81.55 3170.447 36 4.6 1057.828 3 R.GSEGPQGVRGEP(+15.99)GPP(+15.99)GPAGAAGPAGN(+.98) P(+15.99)GTDGQAGAK.G 74.96 3171.43 36 5 1058.156 3 R.GSEGPQGVRGEP(+15.99)GPP(+15.99)GPAGAAGP(+15.99)AGN P(+15.99)GTDGQAGAK.G 59.28 3186.437 36 7.6 1063.161 3 XP_007105361.1 collagen alpha-2(i) chain [Physeter macrocephalus] G.ARGAP(+15.99)GAVGAP(+15.99)GPAGAN(+.98)GDRGEAGPA GAAGPAGSR.G 33.12 2957.399 35 4.7 740.3605 4 G.AVGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGAAGPAGSR.G 50.94 2432.138 29 1.7 811.7213 3 R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSAG.A 42.38 1943.856 22 3.1 972.9383 2 R.GAP(+15.99)GAVGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGA AGPAGSR.G 67.4 2730.261 33 5.3 911.099 3 R.GAP(+15.99)GAVGAP(+15.99)GPAGANGDRGEAGPAGAAGPA GSR.G 60.51 2729.277 33 3.9 910.7697 3 G.AVGAP(+15.99)GPAGANGDRGEAGPAGAAGPAGSR.G 35.46 2431.154 29 8.7 811.3989 3 A.VGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGAAGPAGSR.G 46.02 2361.101 28 2.1 788.0425 3 R.GSP(+15.99)GERGEVGPAGPNGFAGSAGAAGQ 40.01 2639.222 30 4.1 880.7516 3 P(+15.99)GAK.G V.GAP(+15.99)GPAGAN(+.98)GDRGEAGPAGAAGPAGSR.G 45.38 2262.032 27 3.3 755.0205 3 R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSAGAAGQ P(+15.99)G.A 36.04 2441.075 28 6.4 814.704 3 R.GSP(+15.99)GERGEVGPAGPNGFAGSAG.A 32.45 1942.872 22 3.6 972.4468 2 R.GSEGPQGVRGEP(+15.99)GP(+15.99)PGPAGAAGP(+15.99)AGN P(+15.99)GTDGQAGAK.G 45.4 3186.437 36 4.4 1063.158 3 R.GSEGPQGVRGEP(+15.99)GPP(+15.99)GPAGAAGPAGNP(+15.99) GTDGQ(+.98)AGAK.G 45.27 3171.43 36 4.6 1058.156 3 R.GAP(+15.99)GAVGAPGP(+15.99)AGAN(+.98)GDRGEAGPAGA AGPAGSR.G 58.38 2730.261 33 4.2 683.5753 4 G.AP(+15.99)GPAGAN(+.98)GDRGEAGPAGAAGPAGSR.G 41.71 2205.011 26 2.5 736.0128 3 R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSAGAAGQ P(+15.99).G 32.35 2384.053 27 5.8 1193.041 2 R.GSEGPQ(+.98)GVRGEP(+15.99)GPP(+15.99)GPAGAAGPAGNP (+15.99)GTDGQAGAK.G 42.35 3171.43 36 7.5 793.8708 4 R.GAP(+15.99)GAVGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGA AGPAGSRG.S 53.93 2787.282 34 4.7 930.1057 3 A.P(+15.99)GPAGAN(+.98)GDRGEAGPAGAAGPAGSR.G 55.35 2133.974 25 2.9 712.334 3 R.GSP(+15.99)GERGEVGPAGP(+15.99)NGFAGSAGAAGQ 30.01 2997.378 33 3.6 750.3544 4 P(+15.99)GAKGER.G R.GSEGPQ(+.98)GVRGEP(+15.99)GP(+15.99)PGPAGAAGPAGNP (+15.99)GTDGQ(+.98)AGAK.G 36.34 3172.415 36 5.7 1058.485 3 R.GAP(+15.99)GAVGAPGPAGAN(+.98)GDRGEAGPAGAAGPAGSR.G 44.7 2714.271 33 2.3 905.7662 3 P.GPAGAN(+.98)GDRGEAGPAGAAGPAGSR.G 57.07 2020.931 24 0.4 674.6512 3 P.GPAGANGDRGEAGPAGAAGPAGSR.G 33.86 2019.947 24 6.6 674.3185 3 R.GSP(+15.99)GERGEVGPAGP(+15.99)N(+.98)GFAGSAGA AGQP(+15.99)GAKGER.G 29.91 2998.362 33 5.9 750.6021 4 R.GSEGPQ(+.98)GVRGEP(+15.99)GPP(+15.99)GPAGAAGPAGNP (+15.99)GTDGQ(+.98)AGAK.G 33.13 3172.415 36 7.1 794.1165 4 R.GAP(+15.99)GAVGAP(+15.99)GPAGAN(+.98)GDRGEAG P(+15.99)AGAAGPAGSR.G 44.44 2746.251 33 5.2 916.429 3 G.PAGAN(+.98)GDRGEAGPAGAAGPAGSR.G 53.51 1963.91 23 0.6 655.6442 3 P.AGAN(+.98)GDRGEAGPAGAAGPAGSR.G 49.12 1866.857 22 0.8 623.2934 3 R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSAGAAG Q(+.98)P(+15.99)GAKGERG.T 29.45 3040.377 34 3.8 1014.47 3 G.PQGVRGEP(+15.99)GPP(+15.99)GPAGAAGPAGNP(+15.99)GT 28.19 2840.329 32 5.6 947.7888 3 R.GAP(+15.99)GAVGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGA AGP(+15.99)AGSR.G 42.3 2746.251 33 5.2 916.429 3 A.GAN(+.98)GDRGEAGPAGAAGPAGSR.G 46.49 1795.82 21 0.4 599.6141 3 G.AN(+.98)GDRGEAGPAGAAGPAGSR.G 43.74 1738.798 20 0.3 580.6069 3 R.GSP(+15.99)GERGEVGPAGP(+15.99)N(+.98)GFAGSAG AAGQ(+.98)P(+15.99)GAKGER.G 27.31 2999.346 33 5.4 750.8477 4 DGQAGAK.G G.VRGEP(+15.99)GPP(+15.99)GPAGAAGPAGNP(+15.99)GTDGQ AGAK.G 35.97 2558.196 29 5.3 853.7438 3 V.RGEP(+15.99)GPP(+15.99)GPAGAAGPAGNP(+15.99)GTDGQA GAK.G 33.02 2459.128 28 5.3 820.7208 3 R.GEP(+15.99)GPP(+15.99)GPAGAAGPAGNP(+15.99)GTDGQAG AK.G 64.1 2303.027 27 7.3 1152.529 2 R.GEP(+15.99)GPP(+15.99)GPAGAAGPAGNP(+15.99)GTDG Q(+.98)AGAK.G 53.24 2304.011 27 7 1153.021 2 R.GEP(+15.99)GPP(+15.99)GPAGAAGPAGN(+.98)P(+15.99)GTD GQ(+.98)AGAK.G 38.46 2304.995 27 8.1 769.345 3 R.GAP(+15.99)GAVGAP(+15.99)GPAGAN(+.98)GDRGEAGPAG AAG.P 40.95 2262.016 28 5.4 1132.022 2 A.N(+.98)GDRGEAGPAGAAGPAGSR.G 36.54 1667.761 19 1.7 556.9267 3 R.GSP(+15.99)GERGEVGPAGPNGFAGSA.G 26.57 1885.851 21 3.9 943.9362 2 R.GAP(+15.99)GAVGAP(+15.99)GP(+15.99)AGAN(+.98)GDRGE AGPAGAAGPAGSR.G 37.39 2746.251 33 5.3 916.4291 3 N.GDRGEAGPAGAAGPAGSR.G 43.7 1552.734 18 0.9 777.3751 2 G.DRGEAGPAGAAGPAGSR.G 37.88 1495.713 17 0.5 499.5784 3 R.GSQGSQGPAGPP(+15.99)GPP(+15.99)GPP(+15.99)GPA 51.33 2225.979 27 7.7 1114.005 2 GPSGGG.Y R.GAP(+15.99)GAVGAP(+15.99)GPAGANGDRGEAGPAGAAG 32.83 2745.267 33 5.8 916.1014 3 R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSAGAAG Q(+.98)P(+15.99)GAK.G 74.19 2641.19 30 4.6 881.4081 3 R.GSQGSQ(+.98)GPAGPP(+15.99)GPP(+15.99)GP P(+15.99)GPAGPSGG.G 51.22 2169.941 26 7.9 1085.987 2 P(+15.99)AGSR.G R.GAP(+15.99)GAVGAP(+15.99)GPAGANGDRGEAGP(+15.99)A 29.21 2745.267 33 5.9 916.1016 3 R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSAGAAG Q(+.98)P(+15.99)GAKGER.G 73.62 2983.356 33 5.1 995.4643 3 R.GSQGSQ(+.98)GPAGPP(+15.99)GPP(+15.99)GP P(+15.99)GPAGPSG.G 48.9 2112.92 25 7.7 1057.475 2 GAAGPAGSR.G G.AP(+15.99)GAVGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGA AGPAGSR.G 48.09 2673.239 32 3.4 892.09 3 R.GSP(+15.99)GERGEVGPAGPN(+.98)GFAGSAGAAG QP(+15.99)GAK.G 72.03 2640.207 30 4.1 881.0797 3 R.GSQGSQGPAGPP(+15.99)GPP(+15.99)GP 45.8 2168.958 26 6.8 1085.493 2 P(+15.99)GPAGPSGG.G R.GSQGSQGPAGPP(+15.99)GPP(+15.99)GP 45.47 2111.936 25 6.8 1056.982 2	29.77 30.86 27.56 27.72 30.41 40.84 32.41 29.43 27.71 29.43 32.81 25.45 42.42 38.1 28.45 32.48 32.41 26.18 41.43 31.64 32.11 25.69 28.55 32.79 35.1 24.33 24.23 29.97 31.92 29.09 23.21 22.03 37.35 26.82 29.09 21.44 21.25 31.05 24.53 23.32 28.2 29.47 30.95 35.03 22.59 38.35 31.64 18.74 19.69 38.75 27.68 35.77 40.34 29.07 31.37 40.64 32.01 34.63 39 39.2	15949 13687 15225 12169 12730 12392 11306 11306 10414 9523 9054 8955 9988 7006 7813 20058 16659 19156	361-396 361-396 361-396 680-708 709-730 674-708 676-708 676-708 680-708 709-738 681-708 709-736 682-708 709-730 361-396 676-708 683-708 709-735 361-396 676-709 684-708 709-741 361-396 676-708 685-708 709-741 685-708 361-396 676-708 686-708 687-708 709-742 361-396 365-396 676-708 688-708 689-708 709-741 368-396 369-396 370-396 370-396 370-396 690-708 709-729 676-703 691-708 692-708 1081-1107 676-708 676-708 709-738 1081-1106 676-708 709-741 1081-1105 709-738 1081-1106 677-708 1081-1105	Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination; Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination; Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Hydroxyprolination; Deamidation (NQ) Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Deamidation (NQ); Hydroxyprolination Deamidation (NQ); Hydroxyprolination; Deamidation (NQ) Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Deamidation (NQ); Hydroxyprolination; Deamidation (NQ) Deamidation (NQ) Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Deamidation (NQ) Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Deamidation (NQ); Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Deamidation (NQ); Hydroxyprolination Hydroxyprolination Hydroxyprolination; Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Deamidation (NQ) Hydroxyprolination

Table S1 - 6 :

 S16 Peptides unique to Bos genus identified in sample S2 from the central European saddle decorated with bone plaques (40.66.a,b, ca. 1400-1420) using MS/MS sequencing.

	Peptide R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)PGF P(+15.99)GAVGAKGEGGPQGPR.G* R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKGEGGPQGPR.G* R.GAP(+15.99)GAIGAP(+15.99)GPAGA N(+.98)GDRGEAGPAG.P** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGD RGEAGPAGPAGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GPAG AN(+.98)GDRGE.A** R.GAP(+15.99)GAIGAP(+15.99)GPAGA N(+.98)GDRGEAGPA.G** R.GAP(+15.99)GAIGAP(+15.99) GPAGAN(+.98)G.D** PAGANGDRGEAGPAGP.A** AGANGDRGEAGPAGPAGPAGPR.G** R.GYP(+15.99)GNAGPVGTAGAP(+15.99)GP G.AIGAP(+15.99)GPAGAN(+.98)GDR GEAGPAGPAGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GPA GANGDRGEAGPAGPAGPA.G** R.GAP(+15.99)GAIGAPGPAGAN(+.98) GDRGEAGP(+15.99)AGPAGPA.G** QGPVG.P R.GYP(+15.99)GNAGPVGTAGAP(+15.99)GP R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGPAGPAGPRG.S** R.GAP(+15.99)GAIGAP(+15.99)G PAGAN(+.98)GDRGEA.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGP(+15.99)AGP(+15.99)AGPAGPR.G** Q(+.98)GPVG.P R.GYP(+15.99)GNAGPVGTAGAP(+15.99)GP R.GAP(+15.99)GAIGAP(+15.99)GPAGA N(+.98)GDRGEAGPAGPA.G** G.AAGAP(+15.99)GPQGPVGPVGK.H* R.GAP(+15.99)GAIGAP(+15.99) GPAGAN(+.98).G** D.GRIGQP(+15.99)GAVGPAGIR.G* Q(+.98)GPVGPTG.K QGPVGPT.G R.GAP(+15.99)GAIGAP(+15.99)G PAGANGDRGEAGPAGPAGP.A** R.GYP(+15.99)GNAGPVGTAGAP(+15.99)GP	-10lgP 67.48 54.32 58.87 58.25 57.85 56.49 50.94 68.35 50.88 45.41 38.53 56.51 50.77 45.32 37.61 46.09 50.39 45.3 44.17 36.7 37.36 55.74	Mass collagen alpha-1(i) chain precursor [Bos] Length |ppm| 3382.578 39 5.2 1128.539 m/z 3382.578 39 5.2 1128.539 2076.936 25 6.2 1039.482 2779.329 33 3.7 927.4536 1723.766 20 6.1 862.8957 2019.915 24 4.2 1010.969 1266.574 16 8.3 634.2996 1908.887 22 6 955.4564 2482.19 29 1 828.4048 2469.153 30 3.2 1235.588 2470.138 30 4 824.3897 1909.871 22 5.8 955.9482 2837.334 34 2.9 946.7881 1794.803 21 10 898.418 2812.293 33 4.7 938.4426 2106.987 24 3.4 1054.504 2245.026 27 4.3 1123.525 1374.721 16 3.4 688.3699 1209.553 15 8.6 605.7888 1420.785 15 2.4 474.6034 2398.116 29 5.2 1200.072 2164.993 25 6.1 1083.51	2 2 2 2	z 3 3 2 3 2 2 2 3 2 3 3 2 3 2 2 2 3 2	RT 45.5 45.5 35.54 36.44 31.48 37.15 36.33 50.63 37.88 42.92 44.9 52.37 39.3 31.29 38.33 53.79 41.23 31.59 36.43 29.57 41.63 55.43	Scan 19350 19954 16260 20584 19920 947-968 21120 24957 947-968 22189 16116 947-970 23699 16343 19993 947-971	Range 322-360 322-360 674-698 674-706 674-693 674-697 Hydroxyprolination Hydroxyprolination PTM Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Cervidae 674-689 Deamidation (NQ) Hydroxyprolination; 674-703 Hydroxyprolination Hydroxyprolination; 674-703 Deamidation (NQ) Hydroxyprolination; Cervidae 678-706 Deamidation (NQ) Hydroxyprolination; 674-694 Hydroxyprolination; Deamidation (NQ) Deamidation (NQ) Hydroxyprolination; 674-706 Deamidation (NQ) Hydroxyprolination Cervidae 674-707 Deamidation (NQ) 674-700 Hydroxyprolination; Deamidation (NQ) 957-972 Hydroxyprolination Deamidation (NQ) 1064-1078 Hydroxyprolination Deamidation (NQ) 674-688 Hydroxyprolination; 674-702 Hydroxyprolination Hydroxyprolination; Cervidae
	R.GN(+.98)DGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGF P(+15.99)GAVGAKGEGGPQGPR.G* K.GEGGPQ(+.98)GPRGSEGPQGVR.G* R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99) P(+15.99)GFP(+15.99)GAVGAKGEGGPQGPR.G* R.GYP(+15.99)GNAGPVGAAGAP(+15.99) GPQGPVGPVGK.H* R.GYP(+15.99)GN(+.98)AGPVGAAGAP(+15.99) GPQGPVGPVGK.H* R.GYP(+15.99)GNAGPVGAAGAP(+15.99)GP Q(+.98)GPVGPVGK.H* R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)G DRGEAGPAGPAGPAGPR.G* R.GYP(+15.99)GN(+.98)AGPVGAAGAPGP(+15.99) Q(+.98)GPVGPVGK.H* R.GAP(+15.99)GAIGAP(+15.99)GPAG AN(+.98)GDR.G** R.GAP(+15.99)GAIGAP(+15.99)GP AGAN(+.98)GDRG.E** R.GAP(+15.99)GAIGAP(+15.99)GPA GANGDRGEAG.P** R.GAP(+15.99)GAIGAP(+15.99)GPAG ANGDRGEAGPAGPA.G** R.GAP(+15.99)GAIGAP(+15.99)GP AGANGDRGEAGPAG.P** A.GAP(+15.99)GPQGPVGPVGK.H* R.GAP(+15.99)GAIGAPGP(+15.99) AGANGDRGEA.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGP(+15.99)AGPAGPAGPR.G** A.AGAP(+15.99)GPQGPVGPVGK.H* R.GYP(+15.99)GNAGPVGTAGAP(+15.99)GP G.AP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGPAGPAGPR.G** D.GRIGQ(+.98)P(+15.99)GAVGPAGIR.G* QGPVGPTG.K R.GYP(+15.99)GNAGPVGTAGAP(+15.99)GP R.GAP(+15.99)GAIGAP(+15.99)GPAGAN.G** QGPVGPTGK.H R.GAP(+15.99)GAIGAPGP(+15.99) AGANGDRGEAGPAG.P** R.GYP(+15.99)GNAGPVGTAGAP(+15.99)GP R.GAP(+15.99)GAIGAP(+15.99)GPAG AN(+.98)GDRGEAG.P** A.IGAP(+15.99)GPAGAN(+.98)G DRGEAGPAGPAGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GP AGANGDRGEAGPAGPAG.P** N.AGPVGAAGAPGP(+15.99)Q(+.98) GPVGPVGK.H* Q(+.98)GPVGPTGK.H R.GYP(+15.99)GN(+.98)AGPVGTAGAP(+15.99) R.GAP(+15.99)GAIGAPGP(+15.99)AGANG DRGEAGPAGPAGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GD RGEAGPAGP(+15.99)AGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)G PAGANGDRG.E** R.GAP(+15.99)GAIGAP(+15.99)GP AGAN(+.98)GD.R** R.GYP(+15.99)GN(+.98)AGPVGAAGAP GP(+15.99)QGPVGPVGK.H* R.GAP(+15.99)GAIGAP(+15.99) GPAGANGDRGEAGP.A** G.AP(+15.99)GPQGPVGPVGK.H* R.GEP(+15.99)GPVGAVGPAGAVGPR.G* GPQ(+.98)GPVGPTGK.H K.DGRIGQ(+.98)P(+15.99)GAVGPAGIR.G* A.P(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGPAGPAGPR.G** R.GYP(+15.99)GN(+.98)AGPVGTAGAP(+15.99) *specific to the Bovinae subfamily, **specific to the Bos genus 46.74 44.64 36.14 collagen alpha-2(i) chain precursor [Bos] 3383.562 39 9.2 1128.872 1721.808 18 0.3 574.9435 3398.568 39 5 1133.869 78.38 2260.114 26 4.6 1131.069 77.32 2261.098 26 5 1131.562 68.4 2261.098 26 5 754.7103 68.18 2780.313 33 4.9 927.7828 64.52 2262.082 26 3.6 1132.052 63.64 1537.702 18 6 769.863 55.74 1594.724 19 6.5 798.3743 55.68 1850.841 22 4.5 926.4319 54.3 2244.042 27 4.9 1123.034 50.24 2075.952 25 3.3 1038.987 49.87 1232.646 14 3.5 617.3326 49.24 1793.819 21 4.5 897.921 44.12 2796.303 33 4.7 933.1125 36.58 1303.684 15 3.7 652.8514 41.25 2164.009 25 5.3 1083.017 43.36 2723.291 32 2.7 908.7734 36.36 1421.769 15 3.3 474.9318 82.61 2292.104 26 5 1147.065 42.87 1208.569 15 7.4 605.2961 36.08 2075.952 25 5.2 1038.989 82.58 2293.088 26 5.5 1147.557 54.26 1851.825 22 4.5 926.9238 49.11 2411.153 28 2.4 804.7268 42.86 2301.064 28 4.5 1151.544 35.77 1756.906 21 2.8 879.4626 81.47 2294.072 26 6 1148.05 53.49 2779.329 33 2.9 695.8414 52.86 2796.303 33 6.8 933.1146 52.76 1593.74 19 6.5 797.8823 52.43 1381.601 17 7.5 691.813 52.31 2261.098 26 4.2 754.7097 48.88 1947.894 23 5.3 974.9592 42.83 1175.625 13 3.9 588.822 45.46 1559.801 18 3.7 780.9105 48.26 1536.796 16 2.5 513.2739 42.67 2652.254 31 3.3 885.0949 80.6 2293.088 26 4.6 1147.556 GPQGPVGPTGK.H P.GAIGAP(+15.99)GPAGAN(+.98)G DRGEAGPAGPAGPAGPR.G** 48.09 2539.211 30 0.1 847.4111 G.RIGQ(+.98)P(+15.99)GAVGPAGIR.G* 42.12 1364.747 14 2.3 455.9241 Y.P(+15.99)GNAGPVGTAGAP(+15.99)GPQ(+.98) 62.67 2073.003 24 4.2 1037.513 GPVGPTGK.H R.GAP(+15.99)GAIGAP(+15.99)G PAGAN(+.98)GDRGEAGP.A** 47.63 1948.878 23 4.9 975.4508 R.GAP(+15.99)GAIGAPGPAGAN(+.98) GDRGEAGPAGPAGPAGPR.G** 41.93 2764.323 33 1.9 922.45 Y.P(+15.99)GNAGPVGTAGAP(+15.99)GPQGPVGPTGK.H 62.24 2072.019 24 5.3 1037.022 G.NAGPVGTAGAP(+15.99)GPQGPVGPTGK.H 64.01 1901.955 22 3.3 951.9877 R.GAP(+15.99)GAIGAP(+15.99)G PAGANGDRGEAGPA.G** 47.43 2018.931 24 4.9 1010.478 P.VGAAGAP(+15.99)GPQGPVGPVGK.H* 47.32 1530.81 18 3.2 766.4149 N.AGPVGTAGAP(+15.99)GPQGPVGPTGK.H 77.91 1787.912 21 3 894.9658 R.GYP(+15.99)GNAGPVGAAGA P(+15.99)GPQGPVGPVG.K* 40.08 2132.019 25 4.5 1067.022 R.GAP(+15.99)GAIGAPGP(+15.99)AGA N(+.98)GDRGEAGPAGPAGPAG.P** 40.03 2527.159 31 3.1 843.3961 64.42 1788.896 21 2.7 895.4575 N.AGPVGTAGAP(+15.99)GPQ(+.98)GPVGPTGK.H G.PVGTAGAP(+15.99)GPQGPVGPTGK.H 68.27 1659.853 19 3.6 830.9368	2 2 2 2 2 2 2 2 2 2 2	3 3 3 2 2 3 3 2 2 2 2 2 2 2 2 3 2 3 3 2 2 2 3 2 2 4 3 2 2 3 2 2 2 3 3 3 3 2 3 2 2 2 3	45.44 22.69 45.15 46.74 49.17 48.28 39.57 50.76 29.57 29.67 30.27 37.64 34.91 30.12 30.77 40.13 30.91 52.37 39.3 31.43 42.95 32.92 33.63 44.39 32.36 36.48 38.91 43.32 46.75 36.38 35.49 27.58 38.9 51.19 32.31 29.62 44.36 36.53 39.1 45.24 38.63 31.29 40.06 37.2 42.32 38.53 38.17 35.44 35.88 37.43 59.19 44.66 38.98 33.79	9495 14917 15334 20946 18872 15226 15715 22746 947-971 22195 947-972 17361 947-972 16860 20074 21902 947-972 20000 20372 13234 21858 31279 16837 14868 20138 22036 947-972 21686 16124 949-972 20602 24534 949-972 951-972 19184 19626 952-972 37129 26250 952-972 954-972	322-360 352-369 322-360 947-972 947-972 947-972 674-706 947-972 674-691 674-692 674-695 674-700 674-698 959-972 674-694 958-972 Hydroxyprolination Deamidation (NQ); Hydroxyprolination Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination; Cervidae Hydroxyprolination 674-706 Deamidation (NQ) Hydroxyprolination; Deamidation (NQ); 1064-1078 Hydroxyprolination Cervidae Hydroxyprolination 675-706 Deamidation (NQ) 674-688 Hydroxyprolination 674-698 Hydroxyprolination Hydroxyprolination; Cervidae Hydroxyprolination Hydroxyprolination; 679-706 Hydroxyprolination; Deamidation (NQ) 674-701 Hydroxyprolination Deamidation (NQ) Hydroxyprolination; 952-972 Deamidation (NQ) Hydroxyprolination; Cervidae 674-695 Deamidation (NQ) 674-706 Hydroxyprolination 674-706 Hydroxyprolination; Deamidation (NQ) 674-692 Hydroxyprolination 674-690 Hydroxyprolination; Deamidation (NQ) 947-972 Hydroxyprolination; Deamidation (NQ) 674-696 960-972 Hydroxyprolination 977-994 Deamidation (NQ) Hydroxyprolination Hydroxyprolination Deamidation (NQ); Hydroxyprolination; Hydroxyprolination; Cervidae 676-706 Deamidation (NQ) Deamidation (NQ) 1063-1078 Hydroxyprolination Hydroxyprolination; Hydroxyprolination; Cervidae Deamidation (NQ); 1065-1078 Deamidation (NQ) Hydroxyprolination 677-706 Deamidation (NQ) Hydroxyprolination; Hydroxyprolination Cervidae Hydroxyprolination; 674-706 Deamidation (NQ) Hydroxyprolination Cervidae 674-696 Deamidation (NQ) 674-697 Hydroxyprolination 955-972 Hydroxyprolination Hydroxyprolination Cervidae 947-971 Hydroxyprolination Hydroxyprolination; Cervidae Deamidation (NQ) Hydroxyprolination; 674-704 Deamidation (NQ) Hydroxyprolination Cervidae
	R.GAP(+15.99)GAIGAP(+15.99)GPAGA N(+.98)GDRGEAGPAGPAG.P** R.GAP(+15.99)GAIGAP(+15.99) GPAGANG.D** R.GAP(+15.99)GAIGAP(+15.99) GPAGANGDRGEA.G** R.GAP(+15.99)GAIGAP(+15.99)GP(+15.99)A GAN(+.98)GDRGEAGPAGPAGPAGPR.G**	62.97 51.7 47.3 39.49	2302.047 1265.59 1793.819 2796.303	28 16 21 33	5.1 8.4 4.8 6.8	1152.037 633.8076 897.9213 933.1146		2 2 2 3	41.13 33.92 29.03 35.49	18007 14329 19423	674-701 674-689 674-694 674-706	Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ)
	N.AGPVGAAGAP(+15.99)GPQGPVGPVGK.H* R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDR.G** R.GAP(+15.99)GAIGAP(+15.99) GPAGANGDRGE.A** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98) GDRGEAGPAGPAGP(+15.99)AGPR.G** R.GAP(+15.99)GAIGAPGP(+15.99)AGAN(+.98) GDRGEAGPAGP(+15.99)AGPAGPR.G**	62.17 60.16 51.41 45.83 39.33	1755.922 1536.718 1722.782 2796.303 2796.303	21 18 20 33 33	3.3 6.8 5.4 4.6 5.6	878.971 769.3716 862.403 933.1125 933.1134		2 2 2 3 3	41.63 27.58 29.03 37.83 36.84	14385 21346 20585	952-972 674-691 674-693 674-706 674-706	Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ)
	R.GAP(+15.99)GAIGAP(+15.99)GPAGANGD.R** R.GAP(+15.99)GAIGAP(+15.99)G R.GAP(+15.99)GAIGAP(+15.99)GP(+15.99)	59.08 45.41 39.13	1380.617 2173.005 2795.319	17 26 33	6.7 5 4.9	691.3204 1087.515 932.7847		2 2 3	35.84 36.23 33.97	19879 18090	674-690 674-699 674-706	Hydroxyprolination Hydroxyprolination Hydroxyprolination
												200 201 202 203 208

Table S1 - 9 :

 S19 Sequences coverages and numbers of peptides identified for collagens α1(i) and α2(i) from exemplars in the Mammology collection at AMNH.

	Accession number	Description	Species	Collagen α1(i) % Coverage #Peptides % Coverage #Peptides Collagen α2(i)
	51949	Collected in Democratic Republic of Congo, Upper
		Uele, Yakuluku, 3 Oct 1911 (collected by Herbert	Loxodonta africana	73	1293	76	991
	(S11D)	Lang and James Chapin)
	51950	Collected in Democratic Republic of Congo, Upper
		Uele, Yakuluku, 3 Oct 1911 (collected by Herbert	Loxodonta africana	73	1360	78	1013
	(S21D)	Lang and James Chapin)
	52094	Collected in Democratic Republic of Congo, Upper
		Uele, Faradje, 1 Dec 1911, (collected by Herbert Lang	Loxodonta africana	75	1311	77	1042
	(S31D)	and James Chapin)
						P@455	P@455
						A@455	A@455
	113816 (HT11D)	Collected in Africa, Fan Awaing, 4 Sep 1938 (collected by William D. Campbell)	Hippopotamus amphibius	96	774	86 87	487 515
	90302					82	409
		Zoo, collected 28 Apr 1929	Hippopotamus amphibius	94	663
	(HT21D)					82	430
	81856			95		83	451
		Collected in Africa, no collection date	Hippopotamus amphibius		705
	(HT31D)					83	481

Table S1 - 10 :

 S110 Peptides identified using MS/MS sequencing in the three exemplar samples from Hippopotamus amphibius where the amino acid substitution from P to A at position 455 of collagen α2(i) was identified.

	R.GERGPP(+15.99)GESGAAGPTGAIGN.R GVVGAP(+15.99)GTAGPSGPSGIP(+15.99)GER.G	1766.813	20	884.416	2	30.13	439-458	Hydroxyprolination	58.1	58.48	83.54
	R.GERGPP(+15.99)GESGAAGPTGAIGNR.G R.GPP(+15.99)GESGAAGP(+15.99)TGAIGNR.G	1922.914 1596.739	21 18	641.9802 799.3821	3 2	24.74 27.48	439-459 442-459	Hydroxyprolination Hydroxyprolination	63.15 60.71	72.33 69.67	96.84 70.61
	R.GERGPP(+15.99)GESGAAGPTGAIGNRG.P R.GPP(+15.99)GESGAAGPT.G	1979.936 1012.441	22 12	660.9874 507.2301	3 2	24.98 21.4	439-460 442-453	Hydroxyprolination Hydroxyprolination	Identified in samples 58.26 70.31 --51.79 -
	Peptide R.GERGPP(+15.99)GESGAAGPTGAIGNRGPS.G R.GPP(+15.99)GESGAAGPTG.A	Mass 2164.021 1069.463	Length 24 13	m/z 722.3467 535.741	z 3 2	RT 28.53 21.84	Range 439-462 442-454	PTM Hydroxyprolination Hydroxyprolination	(-10logP value shown) 47.27 --45.13 49.18 -
	R.GERGPP(+15.99)GESGAAGPTGAIGNRGPSGPAGPD.G R.GPP(+15.99)GESGAAGPTGA.I	2658.233 1140.5	30 14	887.0881 571.2592	3 2	33.95 24.6	439-468 442-455	Hydroxyprolination Hydroxyprolination	113816 --	90302 -45.27	81856 85.26 54.17
	A.AGPTGAIGNR.G R.GERGPP(+15.99)GESGAAGPTGAIGNRGPSGPAGPDGNKGE R.GPP(+15.99)GESGAAGPTGAIG.N	912.4777 5089.391 1310.605	10 57 16	457.2467 1018.891 656.3129	2 5 2	20.79 42.29 38.5	450-459 439-495 442-457	Hydroxyprolination Hydroxyprolination	37.09 64.78 53.96	--55.31	50.2 -63.09
	A.GPTGAIGNR.G P(+15.99)GVVGAP(+15.99)GTAGPSGPSGIP(+15.99)GER.G R.GPP(+15.99)GESGAAGPTGAIGN(+.98).R	841.4406 1425.632	9 17	421.7271 713.827	2 2	18.66 42.15	451-459 442-458	Hydroxyprolination	44.43 55.31	49.34 -	52.42 -
	E.SGAAGPTGAIGNR.G R.GP(+15.99)P(+15.99)GESGAAGPTGAIGNR.G	1127.568 1596.739	13 18	564.7911 799.3812	2 2	23.27 26.55	447-459 442-459	Hydroxyprolination ; Deamidation (NQ)	59.78 65.14	64.29 75.2	73.93 81.61
	G.AAGPTGAIGNR.G R.GP(+15.99)PGESGAAGP(+15.99)TGAIGNR.G R.GPP(+15.99)GESGAAGPTGAIGN(+.98)R.G	983.5148 1596.739 1581.733	11 18 18	492.7645 799.3817 791.8761	2 2 2	22.29 28.73 29.88	449-459 442-459 442-459	Hydroxyprolination Hydroxyprolination	50.18 40.14 63.29	54.95 45.69 67.45	62.76 -79.93
	G.ERGPP(+15.99)GESGAAGPTGAIGNR.G R.GP(+15.99)PGESGAAGPTGAI.G	1865.893 1253.584	20 15	622.9724 627.8018	3 2	24.93 40.89	440-459 442-456	Hydroxyprolination Hydroxyprolination ; Deamidation (NQ)	60.64 -	--	68.31 53.08
	G.ESGAAGPTGAIGNR.G R.GP(+15.99)PGESGAAGPTGAIGN(+.98).R R.GPP(+15.99)GESGAAGPTGAIGN(+.98)RG.P	1256.611 1425.632 1638.755	14 17 19	629.3118 713.8274 820.3864	2 2 2	24.98 41.8 29.98	446-459 442-458 442-460	Hydroxyprolination Hydroxyprolination	60.8 45.51 -	67.93 -59.37	79.47 -60.92
	G.PP(+15.99)GESGAAGPTGAIGNR.G	1523.728	17	762.8738	2	27.48	443-459	Hydroxyprolination ; Deamidation (NQ) ; Deamidation (NQ)	60.95	65.73	76.72
	P.GESGAAGPTGAIGNR.G R.GP(+15.99)PGESGAAGPTGAIGN(+.98)R.G R.GPP(+15.99)GESGAAGPTGAIGN(+.98)RGP(+15.99)SGPAGPD	1313.632 1581.733 4732.22	15 18 54	657.8226 791.8776 1184.069	2 2 4	24.98 31.36 49.42	445-459 442-459 442-495	Hydroxyprolination Hydroxyprolination	61.37 52.63 59.95	64.51 --	76 --
	P.P(+15.99)GESGAAGPTGAIGNR.G GNK	1426.675	16	714.3467	2	25.86	444-459	Hydroxyprolination ; Deamidation (NQ) ; Deamidation (NQ)	68.71	76.82	86.91
	R.GERGP(+15.99)P(+15.99)GESGAAGPTGAIGNR.G R.GP(+15.99)PGESGAAGPTGAIGN(+.98)RGPSGPAGPD.G GEPGVVGAP(+15.99)GTAGPSGPSGIPGER.G	1938.904 2317.052	21 27	647.3124 773.3583	3 3	23.93 42.92	439-459 442-468	Hydroxyprolination Hydroxyprolination	62.31 60.17	70.77 54.96	60.43 -
	R.GERGP(+15.99)PGESGAAGPTGAIGN(+.98)R.G R.GPP(+15.99)GESGAAGPTGAIGN(+.98)RGPSGPAG.P	1923.898 2104.972	21 25	642.309 1053.502	3 2	26.13 38.93	439-459 442-466	Hydroxyprolination ; Deamidation (NQ) Hydroxyprolination	--	62.51 54.09	--
	R.GP(+15.99)PGESGAAGPTGAIGN(+.98)RGPSGPAGPDGNKGE	4748.21	54	1188.067	4	46.53	442-495	; Deamidation (NQ) Hydroxyprolination ; Deamidation (NQ)	-	77.42	-
	R.GERGP(+15.99)PGESGAAGPTGAIGN(+.98)RGPSGPAGPDGN P(+15.99)GVVGAP(+15.99)GTAGPSGPSGIP(+15.99)GER.G R.GPP(+15.99)GESGAAGPTGAIGN(+.98)RGPSGPAGPDGNKGEP	5090.375 4748.21	57 54	1019.089 1188.066	5 4	43.4 47.32	439-495 442-495	Hydroxyprolination ; Deamidation (NQ) Hydroxyprolination	69.52 47.04	--	--
	KGE R.GP(+15.99)PGESGAAGPTGAIGN.R (+15.99)	1424.648	17	713.3347	2	38.89	442-458	; Deamidation (NQ) Hydroxyprolination ; Deamidation (NQ)	56.29	-	-
	P(+15.99)GVVGAP(+15.99)GTAGPSGPSGIP(+15.99)GER.G R.GP(+15.99)PGESGAAGPTGAIGNR.G GVVGAP(+15.99)GTAGPSGPSGIP(+15.99)GER.G	1580.749	18	527.9253	3	27.78	442-459	Hydroxyprolination	-	-	57.74
	R.GERGP(+15.99)PGESGAAGPTGAIGNR.G R.GP(+15.99)PGESGAAGPTGAIGNRGPSGPA.G R.GPP(+15.99)GESGAAGPTGAIGN.R	1922.914 2046.967 1424.648	21 24 17	641.9795 683.3317 713.3336	3 3 2	25.5 35.35 36.37	439-459 442-465 442-458	Hydroxyprolination Hydroxyprolination Hydroxyprolination	50.77 -37.12	--56.4	57.26 60.53 46.89
	R.GERGP(+15.99)PGESGAAGPTGAIGNRG.P R.GP(+15.99)PGESGAAGPTGAIGNRGPSGPAGPD.G R.GPP(+15.99)GESGAAGPTGAIGNR.G	1979.936 2316.068 1580.749	22 27 18	660.9865 773.0322 791.3833	3 3 2	24.68 39.39 28.32	439-460 442-468 442-459	Hydroxyprolination Hydroxyprolination Hydroxyprolination	--74.24	-62.12 84.97	70.74 69.27 98.91
	R.GERGP(+15.99)PGESGAAGPTGAIGNRGPS.G R.GP(+15.99)PGESGAAGPTGAIGNRGPSGPAGPDG.N R.GPP(+15.99)GESGAAGPTGAIGNRG.P	2164.021 2373.09 1637.771	24 28 19	722.348 792.0384 819.8945	3 3 2	27.15 38.85 28.12	439-462 442-469 442-460	Hydroxyprolination Hydroxyprolination Hydroxyprolination	43.14 -56.2	--75.3	57.08 53.38 89.71
	R.GERGP(+15.99)PGESGAAGPTGAIGNRGPSGPAGPD.G R.GP(+15.99)PGESGAAGPTGAIGNRGPSGPAGPDGN(+.98)KGE R.GPP(+15.99)GESGAAGPTGAIGNRGP.S	2658.233 4748.21 1734.824	30 54 20	887.0879 1188.066 868.4233	3 4 2	35.42 45.6 31.19	439-468 442-495 442-461	Hydroxyprolination Hydroxyprolination Hydroxyprolination	68.47 56.15 62.65	-56.44 52.31	--73.44
	R.GERGPP(+15.99)GESGAAGP(+15.99)TGAIGNR.G P(+15.99)GVVGAP(+15.99)GTAGPSGPSGIP(+15.99)GER.G R.GPP(+15.99)GESGAAGPTGAIGNRGPS.G	1938.904 1821.856	21 21	647.3123 911.9435	3 2	21.68 32.29	439-459 442-462	Hydroxyprolination ; Deamidation (NQ) Hydroxyprolination	48.23 54.56	59.4 -	60.52 -
	R.GERGPP(+15.99)GESGAAGPTG.A R.GP(+15.99)PGESGAAGPTGAIGNRGPSGPAGPDGN.K R.GPP(+15.99)GESGAAGPTGAIGNRGPSG.P	1411.628 2487.133 1878.877	16 29 22	706.8228 830.0558 940.4465	2 3 2	19.43 38.7 31.66	439-454 442-470 442-463	Hydroxyprolination Hydroxyprolination Hydroxyprolination	--58.96	---	54.39 54.67 82.9
	R.GERGPP(+15.99)GESGAAGPTGAIG.N R.GP(+15.99)PGESGAAGPTGAIGNRGPSGPAGPDGNKGEP(+15. R.GPP(+15.99)GESGAAGPTGAIGNRGPSGPA.G	1652.77 3410.594 2046.967	19 39 24	827.3943 1137.884 1024.49	2 3 2	30.66 39.04 35.35	439-457 442-480 442-465	Hydroxyprolination Hydroxyprolination Hydroxyprolination	54.3 39.39 58.23	45.56 --	65.77 -68.61
	R.GERGPP(+15.99)GESGAAGPTGAIGN(+.98)R.G 99) R.GPP(+15.99)GESGAAGPTGAIGNRGPSGPAG.P	1923.898 2103.988	21 25	642.3085 702.3375	3 3	25.86 35.68	439-459 442-466	Hydroxyprolination Hydroxyprolination	58.31 -	--	71.64 46.53
	GVVGAP(+15.99).G R.GPP(+15.99)GESGAAGPTGAIGNRGPSGPAGPD.G	2316.068	27	1159.043	2	39.39	442-468	; Deamidation (NQ) Hydroxyprolination	63.67	78.22	100.54
	R.GERGPP(+15.99)GESGAAGPTGAIGN(+.98)RGPSGPAGPD.G R.GP(+15.99)PGESGAAGPTGAIGNRGPSGPAGPDGNKGEP(+15. R.GPP(+15.99)GESGAAGPTGAIGNRGPSGPAGPDGNK.G	2659.217 4747.226 2615.227	30 54 30	887.4146 1187.813 872.751	3 4 3	35.89 45.36 31.41	439-468 442-495 442-471	Hydroxyprolination Hydroxyprolination Hydroxyprolination	44.17 --	-56.36 79.94	65.51 -94.73
	99)							; Deamidation (NQ)
											211

Table S1 - 11 :

 S111 Peptides identified using MS/MS sequencing in the three exemplar samples from Loxodonta africana where the G was identified at residue 326 of collagen α1(i).

	Peptide A.AGP(+15.99)P(+15.99)GPTGPAGP(+15.99)PGFP(+15.99) GAVGAK.G A.AGPP(+15.99)GPTGPAGP(+15.99)PGFPGAVGAK.G A.AGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)GAVGA K.G A.AGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)GAVGA KGEAGPQGAR.G A.AGPP(+15.99)GPTGPAGPP(+15.99)GFPGAVGAK.G A.GP(+15.99)P(+15.99)GP(+15.99)TGPAGPP(+15.99)GF P(+15.99)GAVGAK.G A.GPP(+15.99)GPTGPAGP(+15.99)PGFP(+15.99)GAVGA KGEAGPQ(+.98)GAR.G A.GPP(+15.99)GPTGPAGP(+15.99)PGFP(+15.99)GAVGA KGEAGPQGAR.G A.GPP(+15.99)GPTGPAGP(+15.99)PGFPGAVGAK.G A.GPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)GAVGA K.G A.GPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)GAVGA KGEAGP(+15.99)QGAR.G A.GPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)GAVGA KGEAGPQ(+.98)GAR.G A.GPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)GAVGA KGEAGPQGAR.G A.GPP(+15.99)GPTGPAGPP(+15.99)GFPGAVGAK.G A.RGNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAK.G A.TGAAGPP(+15.99)GPTGPAGP(+15.99)PGFP(+15.99)G AVGAKGEAGPQGAR.G A.TGAAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)G AVGAK.G A.TGAAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)G AVGAKGEAGP(+15.99)QGAR.G A.TGAAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)G AVGAKGEAGPQGAR.G A.TGAAGPP(+15.99)GPTGPAGPP(+15.99)GFPGAVGAK.G A.TGAAGPPGP(+15.99)TGPAGPP(+15.99)GFP(+15.99)G AVGAK.G D.GATGAAGP(+15.99)P(+15.99)GP(+15.99)TGPAGP P(+15.99)GFP(+15.99)GAVGAK.G D.GATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFPGAVG AK.G D.GATGAAGPP(+15.99)GPTGPAGP(+15.99)PGFP(+15.99) GAVGAKGEAGP(+15.99)QGAR.G D.GATGAAGPP(+15.99)GPTGPAGP(+15.99)PGFPGAVGA KGEAGP(+15.99)QGAR.G D.GATGAAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99) GAVGAK.G D.GATGAAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99) GAVGAKGEAGP(+15.99)Q(+.98)GAR.G D.GATGAAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99) GAVGAKGEAGP(+15.99)QGAR.G D.GATGAAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99) GAVGAKGEAGPQGAR.G D.GATGAAGPP(+15.99)GPTGPAGPP(+15.99)GFPGAVGA K.G D.GATGAAGPPGP(+15.99)TGPAGPP(+15.99)GFP(+15.99) GAVGAK.G D.GATGAAGPPGPTGPAGPP(+15.99)GFP(+15.99)GAVGA K.G D.GATGAAGPPGPTGPAGPP(+15.99)GFPGAVGAK.G G.AAGP(+15.99)P(+15.99)GPTGPAGP(+15.99)P(+15.99) GFPGAVGAK.G G.AAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)GAV GAK.G G.AAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)GAV GAKGEAGP(+15.99)QGAR.G G.AAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)GAV GAKGEAGPQGAR.G G.AAGPP(+15.99)GPTGPAGPP(+15.99)GFPGAVGAK.G G.ARGNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)G FP(+15.99)GAVGAK.G G.ATGAAGPP(+15.99)GPTGPAGP(+15.99)PGFPGAVG AK.G G.ATGAAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99) GAVGAK.G G.ATGAAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99) GAVGAKGEAGP(+15.99)QGAR.G G.ATGAAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99) GAVGAKGEAGPQGAR.G G.ATGAAGPP(+15.99)GPTGPAGPP(+15.99)GFPGAVGAK. G G.N(+.98)DGATGAAGP(+15.99)PGPTGPAGP(+15.99)PG FP(+15.99)GAVGAK.G G.N(+.98)DGATGAAGP(+15.99)PGPTGPAGPP(+15.99)G FP(+15.99)GAVGAK.G G.N(+.98)DGATGAAGPP(+15.99)GPTGPAGP(+15.99)PG FP(+15.99)GAVGAK.G N.DGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GFPGAVGA K.G N.DGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GFPGAVGA KGEAGP(+15.99)QGAR.G N.DGATGAAGPPGP(+15.99)TGPAGPP(+15.99)GFP(+15.99) GAVGAKGEAGPQGAR.G P.GP(+15.99)TGPAGPP(+15.99)GFP(+15.99)GAVGAK.G P.GPTGPAGPP(+15.99)GFP(+15.99)GAVGAK.G P.GPTGPAGPP(+15.99)GFP(+15.99)GAVGAKGEAGP(+15.9 9 )QGAR.G P.GPTGPAGPP(+15.99)GFP(+15.99)GAVGAKGEAGPQGAR. G P.GPTGPAGPP(+15.99)GFPGAVGAK.G P.P(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)GAVGAK.G P.P(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)GAVGAKGEA G P(+15.99)Q(+.98)GAR.G R.GN(+.98)DGATGAAGPPGPTGPAGPP(+15.99)GFP(+15.99 ) GAVGAK.G R.GN(+.98)DGATGAAGPPGPTGPAGPP(+15.99)GFP(+15.99 ) GAVGAKGEAGP(+15.99)QGAR.G R.GN(+.98)DGATGAAGPPGPTGPAGPP(+15.99)GFPGAVG A K.G R.GNDGATGAAGP(+15.99)P(+15.99)GP(+15.99)TGPAGP P(+15.99)GFP(+15.99)GAVGAK.G R.GNDGATGAAGP(+15.99)P(+15.99)GPTGPAGP(+15.99)PG FP(+15.99)GAVGAKGEAGP(+15.99)QGAR.G R.GNDGATGAAGP(+15.99)P(+15.99)GPTGPAGPP(+15.99)G FP(+15.99)GAVGAK.G R.GNDGATGAAGP(+15.99)PGP(+15.99)TGPAGPP(+15.99)G FP(+15.99)GAVGAK.G R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFP(+15. 99) GAVG.A R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFP(+15. 99) GAVGA.K R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFP(+15. 99) GAVGAK.G R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFP(+15. 99) GAVGAKGEAGP(+15.99)Q(+.98)GARG.S R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFP(+15. 99) GAVGAKGEAGP(+15.99)QGAR.G R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFP(+15. 99) GAVGAKGEAGPQ(+.98)GAR.G R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFP(+15. 99 )GA.V R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFP(+15. 99) GAV.G R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFP(+15. 99) GAVG.A R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFP(+15. 99) GAVGA.K R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFP(+15. 99) GAVGAK.G R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFP(+15. 99) GAVGAKG.E R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFP(+15. 99) GAVGAKGEAGPQGAR.G R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFPGAV G.A R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFPGAV GAK.G R.GNDGATGAAGP(+15.99)PGPTGPAGPPGFP(+15.99)GAV GAK.G R.GNDGATGAAGP(+15.99)PGPTGPAGPPGFPGAVGAK.G R.GNDGATGAAGPP(+15.99)G.P R.GNDGATGAAGPP(+15.99)GP(+15.99)TGPAGP(+15.99)PG F P(+15.99)GAVGAK.G R.GNDGATGAAGPP(+15.99)GP(+15.99)TGPAGPP(+15.99)G F P(+15.99)GAVGAK.G R.GNDGATGAAGPP(+15.99)GP(+15.99)TGPAGPP(+15.99)G R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)P(+15.99)G FP(+15.99)GAVGAKG.E R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)P(+15.99)G FP(+15.99)GAVGAKGEA.G R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)P(+15.99)G FP(+15.99)GAVGAKGEAG.P R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)P(+15.99)G FP(+15.99)GAVGAKGEAGP(+15.99)QGAR.G R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)P(+15.99)G FP(+15.99)GAVGAKGEAGPQGAR.G R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)P(+15.99)G FP(+15.99)GAVGAKGEAGPQGARG.S R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)PGF P(+15.99)G.A R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)PGF P(+15.99)GAVG.A R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)PGF P(+15.99)GAVGAK.G R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)PGF PGAVGAKGEAGP(+15.99)Q(+.98)GAR.G R.GNDGATGAAGPP(+15.99)GPTGPAGP.P R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99).G R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)G.F R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF.P R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99).G R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)G.A R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GA.V R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAV.G R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVG.A R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGA.K R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKGEAGPQG.A R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKGEAGPQGAR.G R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P.G R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF PG.A R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF PGA.V R.GNDGATGAAGPPGP(+15.99)TGPAGPP(+15.99)GF P(+15.99)GAVGAK.G R.GNDGATGAAGPPGP(+15.99)TGPAGPP(+15.99)GF P(+15.99)GAVGAKGEAG.P R.GNDGATGAAGPPGP(+15.99)TGPAGPP(+15.99)GF P(+15.99)GAVGAKGEAGPQ(+.98)GARG.S R.GNDGATGAAGPPGP(+15.99)TGPAGPP(+15.99)GF P(+15.99)GAVGAKGEAGPQ.G R.GNDGATGAAGPPGP(+15.99)TGPAGPP(+15.99)GF P(+15.99)GAVGAKGEAGPQG.A R.GNDGATGAAGPPGP(+15.99)TGPAGPP(+15.99)GF P(+15.99)GAVGAKGEAGPQGAR.G R.GNDGATGAAGPPGP(+15.99)TGPAGPPGFP(+15.99) GAVGAK.G R.GNDGATGAAGPPGP(+15.99)TGPAGPPGFPGAVGA K.G R.GNDGATGAAGPPGPTGP(+15.99)AGP(+15.99)P(+15.99) GFP(+15.99)GAVGAKGEAGP(+15.99)QGAR.G R.GNDGATGAAGPPGPTGPAGPP(+15.99)GF.P K.TGPP(+15.99)GPAGQ(+.98)DGRP(+15.99)G PP(+15.99)G.P 1562.696 1919.938 Mass 1887.948 1903.943 2727.336 1887.948 1864.895 2657.283 2656.299 1816.911 1832.906 2672.294 2657.283 2656.299 1816.911 2703.3 2956.443 2133.049 2972.438 2956.443 2117.054 2133.049 2293.097 2245.113 3100.496 3084.501 2261.107 3101.48 3100.496 3084.501 2245.113 2261.107 2245.113 2229.118 1990.975 1974.98 2814.368 2798.374 1958.985 2774.337 2188.091 2204.086 3043.475 3027.48 2188.091 2491.161 2491.161 2491.161 2360.14 3199.528 3199.528 1581.779 1565.784 2405.172 2389.177 1549.789 1678.831 2519.204 2532.188 3371.576 2516.193 2579.189 3402.582 2563.194 2563.194 2348.067 2419.104 2547.199 3444.593 3386.587 3371.576 2191.977 2291.045 2348.067 2419.104 2547.199 2604.22 3370.593 2332.072 2531.204 2531.204 2515.209 1056.447 2563.194 2563.194 2620.215 2820.295 2877.316 3402.582 3386.587 3443.609 2120.94 2348.067 2547.199 3371.576 1633.733 1746.781 1803.802 1950.871 2063.918 2120.94 2191.977 2291.045 2348.067 2419.104 3143.454 3370.593 2047.924 2104.945 2175.982 2547.199 2861.322 3428.598 3086.433 3143.454 3370.593 2531.204 2515.209 3402.582 1934.876 17 K.TGPP(+15.99)GPAGQDGRP(+15.99)GP(+15.99) 1771.812 19 K.TGPP(+15.99)GPAGQDGRP(+15.99)GPPGPPGAR.G 2023.982 22 PGP(+15.99)P.G K.TGPP(+15.99)GPAGQDGRPG.P 1278.595 14 R.GNDGATGAAGPPGPTGPAGPP(+15.99)GFP(+15.99) GAVGAK.G 2531.204 R.GNDGATGAAGPPGPTGPAGPP(+15.99)GFP(+15.99) GAVGAKGEAGPQGAR.G 3354.598 R.GNDGATGAAGPPGPTGPAGPP(+15.99)GFPGAVGAK.G 2515.209 K.TGPP(+15.99)GPAGQ(+.98)DGRP(+15.99)GP P(+15.99)GP(+15.99)PGAR.G 2056.956 22 K.TGPP(+15.99)GPAGQ(+.98)DGRP(+15.99)GP P(+15.99)GP.P 1659.749 18 K.TGPP(+15.99)GPAGQDGRP(+15.99)GP(+15.99) 2055.972 22 K.TGPP(+15.99)GPAGQDGRPGP(+15.99).P 1391.643 15 PGP(+15.99)PGAR.G K.TGPP(+15.99)GPAGQDGRP(+15.99)GP(+15.99) 1771.812 19 K.TGPP(+15.99)GPAGQDGRPGP(+15.99)P(+15.99)G 1771.812 19 P(+15.99)P.G PGPP(+15.99).G K.TGPP(+15.99)GPAGQDGRPGP(+15.99)PG.P 1545.717 17 R.GNDGATGAAGPPGPTGPAGPP(+15.99)GFPGAVGA KGEAGP(+15.99)QGAR.G 3354.598 K.TGPP(+15.99)GPAGQ(+.98)DGRP(+15.99)GP P(+15.99)GPP(+15.99)G.A 1829.818 20 K.TGPP(+15.99)GPAGQDGRP(+15.99)GP(+15.99) 2112.994 23 K.TGPP(+15.99)GPAGQDGRPGP(+15.99)PGPPGAR.G 2023.982 22 PGPP(+15.99)GARG.Q R.PGAP(+15.99)GP(+15.99)AGARGNDGATGAAGPPGPT GPAGPPGFP(+15.99)GAVGAK.G 3378.634 K.TGPP(+15.99)GPAGQ(+.98)DGRP(+15.99)GP P(+15.99)GPP(+15.99)GAR.G 2056.956 22 K.TGPP(+15.99)GPAGQDGRP(+15.99)GP.P 1391.643 15 K.TGPP(+15.99)GPAGQDGRPGPP(+15.99)G.P 1545.717 17 K.TGPP(+15.99)GPAGQ(+.98)DGRP(+15.99)GP PGPP(+15.99)GAR.G 2040.961 22 K.TGPP(+15.99)GPAGQDGRP(+15.99)GPP(+15.99).G 1504.691 16 K.TGPP(+15.99)GPAGQDGRPGPP(+15.99)GP(+15.99) 2039.977 22 PGAR.G K.TGPP(+15.99)GPAGQDGRP(+15.99)GPP(+15.99) 1561.712 17 K.TGPP(+15.99)GPAGQDGRPGPP(+15.99)GPPGAR.G 2023.982 22 G.P T.GAAGP(+15.99)P(+15.99)GP(+15.99)TGPAGPP(+15.99) GFP(+15.99)GAVGAK.G 2063.991 T.GAAGPP(+15.99)GPTGPAGP(+15.99)PGFPGAVGAK.G 2016.006 T.GAAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)G 2032.001 K.TGPP(+15.99)GPAGQ(+.98)DGRPGP(+15.99).P 1392.627 15 K.TGPP(+15.99)GPAGQ(+.98)DGRPGP(+15.99) PGPPGAR.G 2024.966 22 K.TGPP(+15.99)GPAGQDGRPGPPGPP(+15.99)GAR.G 2023.982 22 K.TGPP(+15.99)GPAGQDGRP(+15.99)GPP(+15.99) GP(+15.99)P.G 1771.812 19 K.TGPPGP(+15.99)AGQDGRP(+15.99)GPPGPP(+15.99) 2039.977 22 GAR.G K.TGPP(+15.99)GPAGQDGRP(+15.99)GPP(+15.99) GP(+15.99)PG.A 1828.834 20 K.TGPPGPAGQDGRPGP(+15.99)P(+15.99)GPPGAR.G 2023.982 22 AVGAK.G T.GAAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)G AVGAKGEAGPQGAR.G 2855.395 K.TGPP(+15.99)GPAGQ(+.98)DGRPGPPGP P(+15.99)GAR.G 2024.966 22 K.TGPP(+15.99)GPAGQDGRP(+15.99)GPP(+15.99) 2055.972 22 P.DGKTGPP(+15.99)GPAGQDGRP(+15.99)GPPG 2356.116 25 P(+15.99)P(+15.99)GAR.G GP(+15.99)PGAR.G T.GAAGPP(+15.99)GPTGPAGPP(+15.99)GFPGAVGAK.G 2016.006 K.TGPP(+15.99)GPAGQ.D 796.3715 9 K.TGPP(+15.99)GPAGQD.G 911.3984 10 K.TGPP(+15.99)GPAGQDGRP(+15.99)GPP(+15.99) GP.P 1658.765 18 P.DGKTGPP(+15.99)GPAGQDGRPGPP(+15.99)G 2356.116 25 P(+15.99)P(+15.99)GAR.G P.P(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)GAVGAKGEA G P(+15.99)QGAR.G 2518.22 P.P(+15.99)GPTGPAGPP(+15.99)GFP(+15.99)GAVGAKGEA G PQGAR.G 2502.225 P.P(+15.99)GPTGPAGPP(+15.99)GFPGAVGAK.G 1662.836 P.P(+15.99)GPTGPAGPP(+15.99)GFPGAVGAKGEAG P(+15.99)QGAR.G 2502.225 P.PGPTGPAGPP(+15.99)GFP(+15.99)GAVGAK.G 1662.836 R.GN(+.98)DGATGAAGP(+15.99)P(+15.99)GPTGPAGP P(+15.99)GFP(+15.99)GAVGAK.G 2564.178 R.GNDGATGAAGP(+15.99)PGP(+15.99)TGPAGPP(+15.99)G FP(+15.99)GAVGAKGEAGP(+15.99)QGAR.G 3402.582 R.GNDGATGAAGP(+15.99)PGPTGP(+15.99)AGPP(+15.99)G FP(+15.99)GAVGAK.G 2563.194 R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)P(+15.99)G FP(+15.99)GAVGAK.G 2563.194 R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)P(+15.99)G FP(+15.99)GAVGAKG.E 2620.215 R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)P(+15.99)G FPGAVGAKGEAGPQGAR.G 3370.593 R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFP(+15. 99) GAV.G 2291.045 R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFP(+15. 99) GAVGAKGEAGPQGAR.G 3370.593 R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFPGA.V 2175.982 R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFPGAV. G 2275.05 R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFPGAV G.A 2332.072 R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFPGAV GA.K 2403.109 R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFPGAV GA K.G 2531.204 R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFP(+15. 99) GAVGAKGEA.G 2804.3 R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFP(+15. 99) GAVGAKGEAGP(+15.99)QG.A 3159.449 R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFP(+15. 99) GAVGAKGEAGP(+15.99)QGAR.G 3386.587 R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFP(+15. 99) GAVGAKGEAGPQ(+.98)GAR.G 3371.576 F P(+15.99)GAVGAKGEAGP(+15.99)QGAR.G 3402.582 R.GNDGATGAAGPP(+15.99)GP.T 1153.5 R.GNDGATGAAGPP(+15.99)GPT.G 1254.548 R.GNDGATGAAGPP(+15.99)GPTG.P 1311.569 R.GNDGATGAAGPP(+15.99)GPTGP(+15.99)AGPP(+15.99)G F P(+15.99)GAVGAK.G 2563.194 R.GNDGATGAAGPP(+15.99)GPTGP.A 1408.622 R.GNDGATGAAGPP(+15.99)GPTGPA.G 1479.659 R.GNDGATGAAGPP(+15.99)GPTGPAG.P 1536.68 R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)P(+15.99)G FP(+15.99)GAVGAK.G 2563.194 R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)PGF P(+15.99)GAVGAKGEAGP(+15.99)Q(+.98)GAR.G 3387.571 R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)PGF P(+15.99)GAVGAKGEAGP(+15.99)QGAR.G 3386.587 R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)PGF P(+15.99)GAVGAKGEAGPQ(+.98)GAR.G 3371.576 R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)PGF P(+15.99)GAVGAKGEAGPQGAR.G 3370.593 R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)PGF PGAVG.A 2332.072 R.GNDGATGAAGPP(+15.99)GPTGPAGP(+15.99)PGF PGAVGAK.G 2531.204 R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF 2547.199 R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF PGAVG.A 2332.072 R.GNDGATGAAGPPGPTGP(+15.99)AGPPGFP(+15.99) GAVGAK.G 2531.204 K.TGPP(+15.99)GPAGQDG.R 968.4199 11 K.TGPP(+15.99)GPAGQDGR.P 1124.521 12 K.TGPP(+15.99)GPAGQDGRP(+15.99)GPP(+15.99) GPP(+15.99).G 1771.812 19 P.GPAGQDGRP(+15.99)GPP(+15.99)GPP(+15.99) 1687.803 18 GAR.G P(+15.99)GAVGAK.G R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKG.E 2604.22 R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKGE.A 2733.263 R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKGEA.G 2804.3 R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKGEAG.P 2861.322 R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF P(+15.99)GAVGAKGEAGP(+15.99)Q(+.98)GAR.G 3387.571 R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF PGAVGAK.G 2531.204 R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF PGAVGAKGEAGP(+15.99)QGAR.G 3370.593 R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF PGAVGAKGEAGPQ(+.98)GAR.G 3355.582 R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GF PGAVGAKGEAGPQGAR.G 3354.598 R.GNDGATGAAGPP(+15.99)GPTGPAGPPGFP(+15.99) GAVGAK.G 2531.204 R.GNDGATGAAGPPGP(+15.99)TGPAGPP(+15.99)GF.P 1950.871 R.GNDGATGAAGPPGPTGPAG.P 1520.686 R.GNDGATGAAGPPGPTGPAGP(+15.99)PGFP(+15.99) G.A 2104.945 GAVGAK.G 2531.204 GAVGAKGEAGP(+15.99)QGAR.G 3370.593 R.GNDGATGAAGPPGPTGPAGP(+15.99)PGFP(+15.99) GAVGAKGEAGPQGAR.G 3354.598 R.GNDGATGAAGPPGPTGPAGP(+15.99)PGFPGAVGAK.G 2515.209 R.GNDGATGAAGPPGPTGPAGP.P 1617.738 P(+15.99)GPPGAR.G 2055.972 22 P.G 1504.691 16 PG.P 1561.712 17 GAR.G 2039.977 22 K.TGPP(+15.99)GPAGQDGRP(+15.99)GPPGPP(+15.99) K.TGPP(+15.99)GPAGQDGRP(+15.99)GP(+15.99) P(+15.99)GAR.G 2055.972 22 K.TGPP(+15.99)GPAGQDGRP(+15.99)GPPGP(+15.99) K.TGPP(+15.99)GPAGQDGRP(+15.99)GP(+15.99) P(+15.99).G 1771.812 19 K.TGPP(+15.99)GPAGQDGRP(+15.99)GPPGP(+15.99) K.TGPP(+15.99)GPAGQDGRP(+15.99)GP(+15.99) K.TGPP(+15.99)GPAGQDGRP(+15.99)GPPGP(+15.99).P 1658.765 18 R.GNDGATGAAGPPGPTGPAGP(+15.99)PGFP(+15.99) K.TGPP(+15.99)GPAGQDGRP(+15.99)GP(+15.99) P(+15.99)GPP(+15.99)GAR.G 2071.967 22 GPP(+15.99)GAR.G 2055.972 22 K.TGPP(+15.99)GPAGQDGRP(+15.99)GPP(+15.99) R.GNDGATGAAGPPGPTGPAGP(+15.99)PGFP(+15.99) K.TGPP(+15.99)GPAGQDGRP(+15.99).G 1237.569 13 K.TGPP(+15.99)GPAGQDGRP(+15.99)G.P 1294.59 14 K.TGPP(+15.99)GPAGQDGRP(+15.99)GPP(+15.99) 1828.834 20 P.GPAGQDGRPGP(+15.99)P(+15.99)GP(+15.99)P 1687.803 18 GAR.G GPP(+15.99)G.A K.TGPP(+15.99)GPAGQDGRP(+15.99)GP(+15.99) P(+15.99)GP(+15.99)PGAR.G 2071.967 22 K.TGPP(+15.99)GPAGQDGRP(+15.99)GPP(+15.99) GPP(+15.99)GA.R 1899.871 21 P.P(+15.99)GPAGQDGRP(+15.99)GPP(+15.99)G P(+15.99)PGAR.G 1800.85 19	Lengt h 22 22 22 31 22 21 30 30 21 21 30 30 30 21 31 34 25 34 34 25 25 27 27 36 36 27 36 36 36 27 27 27 27 23 23 32 32 23 32 26 26 35 35 26 29 29 29 28 37 37 18 18 27 27 18 19 28 30 39 30 30 39 30 30 28 29 30 40 39 39 26 27 28 29 30 31 39 28 30 30 30 13 30 30 31 33 34 39 39 40 25 28 30 39 20 21 22 23 24 25 26 27 28 29 37 39 24 25 26 30 34 40 36 37 39 30 30 39 23 782.3625 960.9777 2 63.31 322-343 m/z z RT Range 944.9816 2 50.72 322-343 952.9855 2 50.16 322-343 910.1201 3 40.49 322-352 944.9828 2 49.19 322-343 933.4579 2 74.08 323-343 886.768 3 40.92 323-352 886.4398 3 39.57 323-352 909.463 2 49.62 323-343 917.46 2 43.83 323-343 891.7719 3 38.92 323-352 886.7715 3 40.69 323-352 886.4397 3 39.46 323-352 909.4617 2 48.27 323-343 902.1078 3 41.04 313-343 986.4922 3 42.72 319-352 1067.533 2 46.16 319-343 991.8168 3 40.89 319-352 986.4873 3 41.34 319-352 706.6921 3 50.34 319-343 712.0248 3 47.46 319-343 765.3741 3 74.52 317-343 749.3779 3 50.84 317-343 1034.506 3 41.85 317-352 1029.175 3 44.69 317-352 1131.561 2 46.85 317-343 1034.836 3 44 317-352 1034.505 3 41.43 317-352 1029.174 3 41.83 317-352 1123.564 2 50.89 317-343 754.7098 3 48.31 317-343 749.3784 3 49.38 317-343 744.0461 3 53.34 317-343 996.4956 2 64.12 321-343 988.4972 2 45.49 321-343 939.1311 3 40.25 321-352 933.7992 3 40.7 321-352 980.4999 2 49.72 321-343 925.7858 3 40.69 312-343 1095.054 2 51.59 318-343 1103.051 2 46.31 318-343 1015.499 3 40.99 318-352 1010.165 3 41.51 318-352 730.3707 3 50.39 318-343 1246.59 2 60.94 315-343 831.3947 3 61.35 315-343 1246.591 2 61.35 315-343 1181.078 2 61.47 316-343 1067.516 3 52.02 316-352 800.8896 4 49.11 316-352 791.8969 2 62.18 326-343 783.8994 2 40.99 326-343 802.7323 3 36.53 326-352 797.3997 3 37.08 326-352 775.9022 2 45.72 326-343 840.423 2 42.37 325-343 840.7407 3 40.46 325-352 845.0759 3 63.62 314-343 1124.867 3 50.3 314-352 839.7382 3 61.32 314-343 860.7369 3 74.26 314-343 1135.201 3 43.7 314-352 855.3978 3 74.3 314-343 855.4063 3 49.16 314-343 1175.043 2 64.55 314-341 1210.556 2 66.33 314-342 1274.607 2 60.88 314-343 1149.206 3 49.95 314-353 1129.869 3 51.48 314-352 1124.866 3 56.78 314-352 731.6671 3 58.36 314-339 1146.529 2 66.7 314-340 783.6975 3 64.51 314-341 807.3755 3 63.14 314-342 850.0737 3 65.95 314-343 869.0818 3 49.77 314-344 843.6559 4 47.51 314-352 1167.045 2 67.35 314-341 844.7402 3 67.6 314-343 1266.608 2 55.33 314-343 839.4058 3 58.55 314-343 529.2311 2 19.65 314-326 1282.604 2 45.97 314-343 855.4031 3 47.01 314-343 874.4137 3 48.42 314-344 941.1061 3 49.4 314-346 960.1125 3 49.08 314-347 1135.203 3 43.46 314-352 1129.867 3 44.08 314-352 1148.88 3 44.1 314-353 707.9871 3 55.31 314-338 783.6975 3 64.73 314-341 1274.607 2 60.88 314-343 1124.865 3 49.22 314-352 817.8755 2 40.25 314-333 874.3975 2 38.77 314-334 902.9087 2 37.78 314-335 976.4434 2 58.37 314-336 1032.967 2 57.03 314-337 1061.478 2 55.12 314-338 1096.996 2 58.17 314-339 1146.531 2 63.81 314-340 1175.043 2 61.52 314-341 1210.56 2 63.33 314-342 1048.826 3 50.88 314-350 1124.538 3 45.19 314-352 1024.969 2 63.94 314-337 1053.481 2 60.43 314-338 1089 2 62.61 314-339 1274.606 2 51.17 314-343 954.7819 3 50.65 314-347 1143.875 3 50.24 314-353 1029.817 3 51.03 314-349 1048.825 3 50.74 314-350 843.6575 4 46.28 314-352 1266.607 2 54.98 314-343 839.4113 3 57.14 314-343 1135.204 3 42.8 314-352 968.4451 2 61.94 314-336 2 21.91 544-560 886.9158 2 25.07 544-562 675.6681 3 27.35 544-565 640.3049 2 20.34 544-557 30 844.7422 3 51.48 314-343 39 1119.206 3 45.68 314-352 30 839.4109 3 57.68 314-343 686.6608 3 23.06 544-565 830.8821 2 26.88 544-561 1028.993 2 22.26 544-565 696.8296 2 22.58 544-558 886.9141 2 26.45 544-562 886.9153 2 24.22 544-562 773.8678 2 24.78 544-560 39 1119.208 3 49.52 314-352 915.9238 2 26.1 544-563 705.339 3 22.25 544-566 675.6689 3 28.45 544-565 40 845.6683 4 61.27 304-343 1029.486 2 23.72 544-565 696.829 2 21.65 544-558 773.8658 2 23.8 544-560 681.3281 3 26.99 544-565 753.3527 2 21.31 544-559 681.001 3 25.22 544-565 781.8638 2 21.14 544-560 675.668 3 26.37 544-565 24 1033.004 2 73.97 320-343 24 1009.01 2 51.25 320-343 24 1017.008 2 45.82 320-343 697.323 2 23.62 544-558 675.9991 3 28.34 544-565 675.6675 3 27.18 544-565 886.9155 2 24.05 544-562 1020.996 2 24.19 544-565 915.4261 2 24.54 544-563 675.6685 3 26.15 544-565 33 952.8059 3 41 320-352 675.9987 3 27.36 544-565 686.3317 3 22.25 544-565 590.0357 4 21.85 541-565 24 1009.01 2 50.02 320-343 399.1929 2 20.23 544-552 456.7067 2 21.22 544-553 830.3907 2 25.81 544-561 590.0367 4 22.84 541-565 28 840.4138 3 37.63 325-352 28 835.0825 3 38.18 325-352 19 832.4255 2 46.99 325-343 28 835.0808 3 41.09 325-352 19 832.4268 2 44.92 325-343 30 855.7386 3 61.07 314-343 39 1135.204 3 42.8 314-352 30 1282.607 2 47.22 314-343 30 855.4071 3 47.63 314-343 31 1311.117 2 49.42 314-344 39 1124.53 3 45.85 314-352 27 1146.53 2 66.77 314-340 39 1124.529 3 55.43 314-352 26 1089 2 63.36 314-339 27 1138.534 2 69.68 314-340 28 1167.043 2 66.89 314-341 29 802.0439 3 68.47 314-342 30 844.7433 3 68.06 314-343 33 935.7761 3 50.8 314-346 37 1054.156 3 51.26 314-350 39 847.6527 4 45.91 314-352 39 1124.866 3 54.8 314-352 39 1135.203 3 43.46 314-352 14 577.7576 2 27.46 314-327 15 628.2814 2 26.84 314-328 16 656.7914 2 27.51 314-329 30 855.4057 3 45.92 314-343 17 705.3186 2 34.26 314-330 18 740.8384 2 34.21 314-331 19 769.3485 2 34.11 314-332 30 1282.605 2 48.67 314-343 39 1130.203 3 49.34 314-352 39 1129.869 3 51.48 314-352 39 1124.871 3 51.93 314-352 39 1124.54 3 47.51 314-352 28 1167.044 2 66.71 314-341 30 1266.611 2 56.2 314-343 30 1274.606 2 49 314-343 28 1167.044 2 66.71 314-341 30 844.7418 3 54.97 314-343 485.2182 2 20.76 544-554 563.2677 2 17.35 544-555 886.9146 2 25.38 544-562 563.6077 3 18.24 548-565 31 869.0814 3 48.62 314-344 32 912.0962 3 49.87 314-345 33 935.7748 3 49.77 314-346 34 954.781 3 49.56 314-347 39 1130.199 3 44.42 314-352 30 1266.608 2 53.04 314-343 39 1124.538 3 46.36 314-352 39 1119.534 3 48.05 314-352 39 1119.206 3 46.81 314-352 30 844.7415 3 52.17 314-343 23 976.4435 2 62.91 314-336 19 761.3494 2 38.52 314-332 25 1053.48 2 59.16 314-338 30 1266.612 2 53.85 314-343 39 1124.539 3 46.98 314-352 39 1119.201 3 47.26 314-352 30 1258.612 2 58.62 314-343 20 809.8776 2 44.37 314-333 686.3297 3 36.87 544-565 753.3523 2 20.27 544-559 781.8633 2 20.86 544-560 681.0009 3 24.19 544-565 686.3311 3 20.58 544-565 886.9158 2 25.07 544-562 830.3914 2 26.94 544-561 691.6627 3 21.21 544-565 1028.993 2 23.23 544-565 619.7915 2 17.3 544-556 648.3022 2 17.6 544-557 915.4297 2 25.18 544-563 844.9082 2 18.23 548-565 1036.992 2 20.58 544-565 950.9438 2 27.71 544-564 601.2896 3 19.99 547-565	PTM Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Deamidation (NQ); Oxidation (P) Deamidation (NQ); Oxidation (P) Deamidation (NQ); Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ); Oxidation (P) Deamidation (NQ); Oxidation (P) Deamidation (NQ); Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Deamidation (NQ); Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P); Deamidation (NQ) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P) Oxidation (P)	Identified in samples (-10logP value shown) 51949 51950 52094 36.31 --35.65 --61.34 65.62 69.96 -67.26 ---74.81 --48 -57.39 -55.18 60.03 -50.99 62.73 -64.85 74.54 80.22 61.43 62.22 59.08 --47.02 --74.09 -57.18 68.3 70.09 -87.96 41.68 --66.67 76.14 79.77 --68.23 -78.41 81.91 66.38 50.91 74.39 42.36 ---44.9 -49.14 -41.37 -43.67 ---63.45 67.3 80.83 85.43 56.44 --63.93 77.66 83.21 54.55 86.46 84.59 67.84 75.89 81.95 34.62 --48.15 -48.75 --57.6 -41.13 -66.75 77.4 81.06 62.01 73.53 79.66 -79.31 89.98 60.16 51.13 79.26 --69.58 64.42 --66.03 75.42 78.41 55.01 -69.77 --67.3 -63.18 75.53 -56.37 -64.23 --60.08 --65.42 70.42 86.71 --50.71 -60.51 --55.22 66.35 -66.11 72.95 -65.54 72.05 -75.96 78.12 -69.42 76.26 61.74 73.49 75.05 45.7 --72.98 81.88 66.18 50.68 --76.15 52.01 76.8 -42.09 40.99 55.36 --35.06 60.08 -37.57 69.17 55.23 43.57 -49.3 39.44 40.19 -53.06 60.44 52.41 -44.57 ---54.03 34.78 ---49.38 ---42.48 59.16 52.55 40.24 --53.6 62.32 58.55 49.26 42.66 --43.23 --52.14 --56.17 42.92 49.08 53.27 ----41.37 36.42 44.22 45.31 44.86 -59.94 --41.55 -51.55 --49.94 ---52.68 56.86 --53.54 63.56 -60.48 ---49.57 --42.78 -36.88 82.08 71.07 --59.41 58.75 62.43 69.5 57.14 64.66 66.05 -52.75 53 59.33 71.91 76.74 -66.9 47.13 60.3 73.26 74.14 -72.79 64.79 -69.46 -52.08 69.38 68.47 -68.97 -55.17 -63.7 -71.6 -85.78 106.08 113.63 --63.26 38.39 51.9 57.49 --52.44 65.47 --52.23 ---48.82 --48.73 --63.99 57.48 58.5 --54.85 --59 --43.84 --58.03 60.56 -60.67 58.69 58.37 41.93 -48.78 ---37.72 -56.39 66.07 93.56 92.96 -87.54 102.65 71.16 86.9 52.77 -57.46 55.32 65.2 60.36 --54.13 54.8 -56.18 52.42 --39.7 --43.31 --88.08 69.85 -53.74 59.14 53.84 -46.43 49.43 --54.23 ---41.73 55.47 60.78 -63.22 -66.14 65.22 62.24 59.79 53.11 64.87 56.71 57.06 --39.77 --61.57 65.18 -55 -54.53 43.16 --64.11 --64.64 79.07 53.12 --50.03 ----55.31 --43.3 --55.42 -41.34 ---59.22 81.37 ---46.19 -54.15 -56.39 -52.25 54.04 75.52 ----42.9 41.32 51.92 53.57 -56.83 55.58 41.8 --77.38 64.21 77.45 74.98 55.84 79.33 85.42 63.09 76 80.89 --63.62 56.77 --38.47 --41.16 ---62.11 -58.78 51.43 -40.73 --48.88 --42.8 -43.29 -43.07 41.24 39 --39.72 --38.01 53.99 --53.9 -43.15 --46.21 --51.19 --53.42 ---64.57 70.99 38.38 --50.46 54.7 56.26 52.8 66.03 67.96 60.09 68.13 68.7 56.76 75.9 76.99 57.45 68.48 67.16 -42.56 54.81 59.74 70.07 71.89 60.4 73.05 72.72 60.17 ---57.29 62.95 -55.31 43.2 44.93 ----50.85 66.67 --76.88 91.36 96.47 --65.47 45.05 ---40.65 40.2 39.96 60.41 54.4 -61.06 60.21 --49.85 67.58 74.47 77.12 48.77 79.75 78.76 -81.27 82.79 -69.51 68.42 81.22 99.13 106.69 71.71 91.26 94.27 75.39 94.09 103.32 --102.24 65.81 102.17 104.9 76.93 66.77 91.22 49.99 --52.41 -61.52 37.78 ---44.73 ---58.83 55.76 55.51 -42.17 ----41.36 -47.49 51.03 49.94 46.64 51.61 --57.58 46.01 --47.78 52.13 42.91 ---50.59 --45.51 52.48 -56.86 43.83 -56.29 59.35 52.85 54.38 62.92 51.18 65.14 69.78 -48.8 46.91 58.31 --41.44 -57.55 62.44 35.4 --
				215 222 223 224 225 226 228 229 233 234

Table S1 - 13 :

 S113 Peptides unique to Hippopotamus amphibius species identified in Tusk figure of a man (66.99.1, Egypt, ca. 3900-3500 B.C.) using MS/MS sequencing.

	R.GPSGPQ(+.98)GPSGPSGP(+15.99)KG.N R.GIP(+15.99)GADGRAGVM(+15.99)GIP(+15.99)GSR.G R.GERGPP(+15.99)GESGAAGPTGAIGNRGPSGPAGPD.G R.GERGP(+15.99)PGESGAAGPTGAIGNRGP.S R.GPP(+15.99)GESGAAGPTGAIG.N R.GPP(+15.99)GESGAAGPTGAIGNRGP.S K.HGN(+.98)RGEP(+15.99)GPAGVVGPTGA.V	47.21 56.12 57.1 56.62 54.8 54.7 52.44	1379.627 1714.832 2658.233 2076.989 1310.605 1734.824 1645.776	16 18 30 23 16 20 18	7.5 9.7 4.1 5 7.8 4.6 7.8	690.8258 572.6235 887.0886 693.3403 656.3151 868.423 549.6035	2 3 3 3 2 2 3	24.06 44.62 44.18 36.96 47.9 40.62 36.13	8531 22406	208-223 286-303 439-468 439-461 442-457 442-461 761-778	Deamidation (NQ); Hydroxyprolination Hydroxyprolination; Oxidation (M) Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Deamidation (NQ); Hydroxyprolination
	Peptide R.GPSGPQ(+.98)GPSGPSGPKGN(+.98)SGEP(+15.99)GAP(+15.99)GSK G.D I.P(+15.99)GADGRAGVM(+15.99)GIP(+15.99)GSR.G R.GERGPP(+15.99)GESGAAGPTGAIGN(+.98)RGPS.G R.GPP(+15.99)GESGAAGPTGAIGN(+.98)R.G K.HGN(+.98)RGEP(+15.99)GPAGVVGPT.G	-10lgP 46.92 57.8 56.26 54.5 49.02	Mass C0HJN5|CO1A1_HIPAM Length 2435.074 28 1544.727 16 2165.005 24 1581.733 18 1517.717 16	|ppm| 7.7 10 7.6 6.6 7.4	m/z 812.7048 515.9213 722.681 791.8792 506.9168	z 3 3 3 2 3	RT 24.51 44.67 37.3 37.97 32.45	Scan 8833 22524	Range 208-235 288-303 439-462 442-459 761-776	PTM Deamidation (NQ); Hydroxyprolination Hydroxyprolination; Oxidation (M) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Deamidation (NQ); Hydroxyprolination
	R.GPSGPQGARGIP(+15.99)G.T G.P(+15.99)AGAAGAN(+.98)GAP(+15.99)G IAGAP(+15.99)GFP(+15.99)GAR.G G.PAGAAGAN(+.98)GAP(+15.99)GIAGAP(+15.99)GFP(+15.99)GAR.G P.AGAAGAN(+.98)GAP(+15.99)GIAGAP(+15.99)GFP(+15.99)GAR.G A.GAAGAN(+.98)GAP(+15.99)GIAGAP(+15.99)GFP(+15.99)GAR.G R.GPSGPQ(+.98)GPSGP(+15.99)SGPKGN(+.98) SGEP(+15.99)GAP(+15.99)GSKGDTGAK.G R.GPSGPQ(+.98)GPSGPSGPKGN(+.98)SGEP(+15.99) GAP(+15.99)GSKGDTG.A R.GP(+15.99)SGPQGPSGPSGPKGN(+.98)SGEP(+15.99)GAP(+15.99)G SK.G R.GPSGPQGP(+15.99)SGPSGP(+15.99)KGN(+.98)S GEPGAP(+15.99)GSKGDTGAK.G G.ADGRAGVM(+15.99)GIP(+15.99)GSR.G A.DGRAGVM(+15.99)GIP(+15.99)GSR.G D.GRAGVM(+15.99)GIP(+15.99)GSR.G R.GERGP(+15.99)PGESGAAGPTGAIGNRGPS.G R.GERGP(+15.99)PGESGAAGPTGAIGN(+.98)RGPSG.P R.GPP(+15.99)GESGAAGPTGAIGN(+.98)RG.P K.HGN(+.98)RGEPGP(+15.99)AGVVGPTGAVGPR.G R.GERGPP(+15.99)GESGAAGPTGAIGN(+.98)RGP.S R.GPP(+15.99)GESGAAGPTGAIGNRGPS.G R.GP(+15.99)PGESGAAGPTGAIGN(+.98)RGPSGPA.G H.GN(+.98)RGEP(+15.99)GPAGVVGPTGAVGPR.G H.GNRGEP(+15.99)GPAGVVGPTGAVGPR.G R.AGVM(+15.99)GIP(+15.99)GSRGATGPAGVR.G R.GERGPP(+15.99)GESGAAGPTGAIGN(+.98)RGPSGPAGPDG.N R.GERGPP(+15.99)GESGAAGPTGAIG.N R.GPP(+15.99)GESGAAGPTGAIGN.R R.GPP(+15.99)GESGAAGPTGAIGN(+.98)RGP.S G.N(+.98)RGEP(+15.99)GPAGVVGPTGAVGPR.G G.NRGEP(+15.99)GPAGVVGPTGAVGPR.G G.ARGPSGPQ(+.98)GPSGPSGPK.G R.GPSGPQ(+.98)GPSGPSGPKGN(+.98)SGEP(+15.99) GAP(+15.99)GSKGDTGAK.G R.GPSGPQGPSGPSGPKGNSGEP(+15.99)GAP(+15.99)GSKGDTGAK. G R.VGPP(+15.99)GPSGN(+.98)AGPP(+15.99)GPP(+15.99)GPVGKRGET .G I.P(+15.99)GQ(+.98)RGERGFPGIPGPSGEP(+15.99)GKQ(+.98)GPSGTS GER.G R.GFP(+15.99)GIP(+15.99)GPSGEPGKQ(+.98)GPSGTSGER.G R.AGVM(+15.99)GIP(+15.99)GSRG.A G.VM(+15.99)GIP(+15.99)GSR.G R.GERGP(+15.99)PGESGAAGPTGAIGN(+.98)RGPSGPAG.P R.GP(+15.99)PGESGAAGPTGAIGNRGPSGPAGPDG.N G.N(+.98)RGEPGPAGVVGPTGAVGPR.G R.GERGPP(+15.99)GESGAAGPTGAIGN.R R.GP(+15.99)PGESGAAGPTGAIGN(+.98)RGPSGPAGPDG.N N.RGEP(+15.99)GPAGVVGPTGAVGPR.G N.RGEPGP(+15.99)AGVVGPTGAVGPR.G R.GERGPP(+15.99)GESGAAGPTGAIGNR.G R.GERGP(+15.99)PGESGAAGPTGAIGN(+.98)RGPSGPAGPD.G R.GPP(+15.99)GESGAAGPTGAIGNRGPSG.P R.GEP(+15.99)GPAGVVGPTGAVGPR.G G.PP(+15.99)GESGAAGPTGAIGNR.G R.GEPGPAGVVGPTGAVGPR.G R.GERGPP(+15.99)GESGAAGPTGAIGNRGPSGPA.G R.GERGP(+15.99)PGESGAAGPTGAIGNR.G P.P(+15.99)GESGAAGPTGAIGNR.G G.EP(+15.99)GPAGVVGPTGAVGPR.G	46.26 57.44 48.79 64.4 39.64 44.54 42.54 42.04 41.86 39.75 50.98 52.94 55.27 54.69 54.15 45.6 52.25 53.27 52.33 71.15 64.21 50.11 51.35 49.44 50.29 48.95 75.81 56.95 53.88 66.76 64.81 64.37 53.23 68.72 46.11 44.54 47.74 43.32 50.53 47.73 42.64 69.61 46.75 70.02 46.35 41.85 66.63 44.31 47.55 68.38 43.46 58.41 66.58	1165.579 1968.909 1952.919 1855.867 1784.829 2923.292 2708.17 2393.058 2922.308 1374.663 1303.625 1188.598 2164.021 2222.026 1638.755 2055.02 2077.973 1821.856 2047.951 1917.961 1916.977 1741.884 2716.239 1652.77 1424.648 1735.808 1860.939 1859.955 1533.754 2907.302 2905.334 2284.094 3066.429 2285.046 1032.497 847.4172 2447.138 2373.09 1844.949 1766.813 2374.074 1745.912 1745.912 1922.914 2659.217 1878.877 1589.811 1523.728 1573.821 2389.132 1922.914 1426.675 1532.79	13 23 23 22 21 33 31 27 33 14 13 12 24 25 19 22 23 21 24 21 21 19 31 19 17 20 20 20 17 33 33 25 31 24 11 8 28 28 20 20 28 19 19 21 30 22 18 17 18 27 21 16 17	7.3 9.1 9.4 9.4 9.8 9.4 8.2 10 9.2 6.8 6.2 7.1 4.3 6.3 7.3 14.6 6.5 7.4 5.3 7.1 5.8 6.9 4.8 6.8 7 6.8 6 5.9 4.5 7.7 5.9 5.1 4.1 7.5 7.8 8.4 6.2 6.4 4.4 7 6.1 7.1 7.1 6.7 5.2 4 7.2 7 3.9 5.4 6.7 6.6 6.7	583.801 985.4747 651.9877 928.951 893.4307 731.8372 903.738 798.7024 731.5909 459.2312 435.5518 397.2095 722.3506 741.6873 820.3906 514.7697 693.6693 911.9418 683.6612 640.332 640.0032 581.6394 906.4246 827.3981 713.3364 868.917 621.3241 620.996 512.2607 727.8384 727.3451 762.3757 767.6115 762.6951 517.2599 424.7195 816.7249 1187.56 615.993 884.4202 792.3699 582.9822 582.9822 641.983 887.4176 940.442 795.9186 762.8766 787.921 797.3889 641.983 714.3495 767.4072	2 2 3 2 2 4 3 3 4 3 3 3 3 3 2 4 3 2 3 3 3 3 3 2 2 2 3 3 3 4 4 3 4 3 2 2 3 2 3 2 3 3 3 3 3 2 2 2 2 3 3 2 2	25.78 54.66 58.58 53.57 54.11 22.24 26.43 22.63 21 28.81 30.46 22.38 35.15 37.72 38.16 39.04 39.1 40.48 48.55 50.23 44.28 37.77 46.03 39.24 45.91 45.12 50.67 44.48 20.9 22.59 20.6 40.28 47.25 50.52 29.16 30.09 42.35 49.98 56.8 38.01 52.34 44.97 44.97 30.82 46.42 43.1 55.29 34.73 58.43 40.33 30.82 32.21 55.58	9716 29406 32148 28687 29075 7244 10165 7527 6380 11769 12822 7321 17877 6342 7509 6141 19541 24378 26688 12085 12623 13635 19549	61-73 185-207 185-207 186-207 208-240 208-238 208-234 208-240 290-303 291-303 439-462 442-460 761-782 439-463 439-461 442-462 762-782 442-465 762-782 292-303 294-312 439-469 439-457 442-458 763-782 442-461 763-782 187-207 206-222 208-240 208-240 665-689 740-770 294-304 442-469 763-782 439-466 439-458 764-782 442-469 764-782 296-303 439-459 442-463 765-782 439-468 443-459 765-782 747-770 439-465 439-459 444-459 766-782	Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Deamidation (NQ); Hydroxyprolination Deamidation (NQ); Hydroxyprolination Deamidation (NQ); Deamidation (NQ); Hydroxyprolination Deamidation (NQ); Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Oxidation (M); Hydroxyprolination Oxidation (M); Hydroxyprolination Oxidation (M); Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Deamidation (NQ); Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Deamidation (NQ); Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Oxidation (M); Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Deamidation (NQ); Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Deamidation (NQ) Deamidation (NQ); Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Oxidation (M); Hydroxyprolination Oxidation (M); Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination; Hydroxyprolination Hydroxyprolination Deamidation (NQ) Hydroxyprolination Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination
	R.GPSGPQ(+.98)GPSGPSGPKGN(+.98)SGEP(+15.99)GAP(+15.99)GSK .G R.GPSGPQGPSGPSGPKGN(+.98)SGEP(+15.99)GAP(+15.99)GSKGDT GAK.G R.GFP(+15.99)GIP(+15.99)GPSGEPGKQGPSGTSGER.G R.GFP(+15.99)GIP(+15.99)GPSGEP(+15.99)GKQ(+.98)GPSGTSGER.G R.GERGPP(+15.99)GESGAAGPTGAIGN(+.98)RGPSGPA.G R.GP(+15.99)PGESGAAGPTGAIGNRGPSGPAGPD.G P.GESGAAGPTGAIGNR.G E.P(+15.99)GPAGVVGPTGAVGPR.G R.GERGP(+15.99)PGESGAAGPTGAIGNRGPSG.P R.GPP(+15.99)GESGAAGPTGAIGN(+.98)RGPSGPA.G E.SGAAGPTGAIGNR.G P.GPAGVVGPTGAVGPR.G S.GAAGPTGAIGNR.G G.PAGVVGPTGAVGPR.G R.GFP(+15.99)GIP(+15.99)GPSGEP(+15.99)GKQGPSGTSGER.G R.GERGPP(+15.99)GESGAAGPTGAIGNRGP.S R.GPP(+15.99)GESGAAGPTGAIGNR.G G.AAGPTGAIGNR.G R.GPAGPSGPVGKDGRTGHP(+15.99)GAVGPAGIR.G	60.69 60.14 62.94 61.86 66.06 70 62.95 63.72 64.26 65.88 54.89 64.02 51.69 62.77 59.5 64.06 64.73 53.5 70.87	2378.052 2906.318 2284.062 2301.036 2390.116 2316.068 1313.632 1403.747 2221.042 2047.951 1127.568 1290.704 1040.536 1233.683 2300.052 2076.989 1580.749 983.5148 2437.252	27 33 24 24 27 27 15 16 25 24 13 15 12 14 24 23 18 11 27	7.1 5.8 8.7 9.6 6.2 5.4 4.3 7.8 6 5.1 3.2 4.4 3.3 3.8 9.2 6.8 5.1 4.2 5.7	793.697 727.5909 762.3679 768.0275 797.7175 1159.048 657.8262 702.8864 741.3591 1024.988 564.7933 646.3623 521.2771 617.8511 767.6991 693.3416 527.9264 492.7668 610.3239	3 4 3 3 3 2 2 2 3 2 2 2 2 2 3 3 3 2 4	24.55 21.82 48.7 47.05 42.4 50.37 30.87 52.44 35.57 48.5 28.1 49.29 27.52 47.05 45.31 35.48 35.97 26.27 34.43	8786 7023 25193 24314 21001 16365 23258 16268	208-234 747-770 747-770 442-468 445-459 767-782 439-465 439-463 447-459 768-782 442-465 448-459 769-782 208-240 747-770 439-461 442-459 449-459 840-866	Deamidation (NQ); Hydroxyprolination Deamidation (NQ); Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Hydroxyprolination Hydroxyprolination Deamidation (NQ) Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination
	R.GPSGPQ(+.98)GPSGPSGPKG.N R.GPSGPQ(+.98)GPSGPSGPKGN(+.98)SGE.P S.GTSGERGP(+15.99)PGP(+15.99)MGPP(+15.99)GIAGPP(+15.99)GES GR.E R.GERGP(+15.99)PGESGAAGPTGAIGN(+.98)R.G R.GPP(+15.99)GESGAAGPTGAIGN(+.98)RGPSG.P K.HGN(+.98)RGEP(+15.99)GPAGVVGPTGAVGPR.G	56.79 51.73 51.98 62.46 62.54 84.25	1363.637 1751.76 2435.094 1923.898 1879.861 2055.02	16 20 26 21 22 22	3.8 2 9.3 6.9 5.4 6.7	682.8282 876.8889 812.7152 642.3112 940.9429 686.0184	2 2 3 3 2 3	24.65 27.77 41.02 33.04 43.1 40.18	8934 11053 20067 14552	208-223 208-227 765-790 439-459 442-463 761-782	Deamidation (NQ) Deamidation (NQ) Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Deamidation (NQ); Hydroxyprolination
	R.GPSGPQGPSGPSGPKG.N R.GPSGPQ(+.98)GPSGPSGPKGNSGEP(+15.99)GAP(+15.99)GSK.G R.GPSGPQ(+.98)GPSGPSGPKGNSGEP(+15.99)GAP(+15.99)GSKGDT GAK.G R.GPSGPQ(+.98)GPSGPSGPKGN(+.98).S R.GAAGPQGPRGDKGETGEQ(+.98)GDRGIK.G R.GAAGPQ(+.98)GPRGDKGETGEQ(+.98)GDRGIK.G R.GAAGPQ(+.98)GP(+15.99)RGDKGETGEQ(+.98)GDRGIK.G R.GAAGPQ(+.98)GPRGDKGETGEQGDRGIK.G R.GERGP(+15.99)PGESGAAGPTGAIGN(+.98)RGPSGPAGPDG.N R.GERGP(+15.99)PGESGAAGPTGAIGN(+.98)RGPS.G R.GPP(+15.99)GESGAAGPTGAIGN(+.98)RGPSGPAG.P K.HGNRGEP(+15.99)GPAGVVGPTGAVGPR.G R.GP(+15.99)PGESGAAGPTGAIGNRGPSGPA.G K.HGN(+.98)RGEP(+15.99)GPAGVVGPTGAVGPRG.P R.GERGPP(+15.99)GESGAAGPTGAIGN(+.98)RG.P R.GP(+15.99)PGESGAAGPTGAIGN(+.98)RGPSGPAGPD.G K.HGN(+.98)RGEPGPAGVVGPTGAVGPR.G R.GP(+15.99)P(+15.99)GESGAAGPTGAIGNR.G K.HGN(+.98)RGEP(+15.99)GP(+15.99)AGVVGPTGAVGPR.G	51.57 49.79 49.62 47.54 58.53 55.92 46.32 40.22 59.15 58.59 62.22 79.36 61.43 60.79 57.99 61.35 60.41 59.45 59.59	1362.653 2377.068 2906.318 1478.664 2338.126 2339.11 2355.1 2338.126 C0HJN6|CO1A2_HIPAM 16 27 33 17 24 24 24 24 2716.239 31 2165.005 24 2104.972 25 2054.036 22 2046.967 24 2112.041 23 1980.92 22 2317.052 27 2039.03 22 1596.739 18 2071.01 22	4.1 8.3 7.4 3.2 3.9 3.8 6 5.3 5.1 7.6 4.2 6.4 4 6 6.5 6.5 4.1 9.1 8.2	682.3364 793.37 727.5921 740.3415 468.6343 468.8311 589.7858 585.5419 906.4248 722.681 1053.498 514.5195 1024.495 529.0207 661.3182 1159.541 510.7668 799.3842 518.7639	2 3 4 2 5 5 4 4 3 3 2 4 2 4 3 2 4 2 4	23.36 23.12 22.58 26.38 19.83 20.7 20.11 19.83 45.87 37.3 48.4 37.47 45.87 40.1 33.24 52.73 43.1 34.82 40.97	7957 7946 7509 10260 5498 6135 5762 5454 23435 17549 14762	208-223 208-234 208-240 208-224 861-884 861-884 861-884 861-884 439-469 761-782 442-466 442-465 761-783 439-462 439-460 761-782 442-468 442-459 761-782	Deamidation (NQ); Hydroxyprolination Deamidation (NQ); Hydroxyprolination Deamidation (NQ) Deamidation (NQ) Deamidation (NQ) Deamidation (NQ); Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Hydroxyprolination; Hydroxyprolination Deamidation (NQ) Hydroxyprolination Deamidation (NQ); Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Deamidation (NQ) Hydroxyprolination Deamidation (NQ); Hydroxyprolination
	R.GERGPP(+15.99)GESGAAGPTGAIGNRGPSGPAG.P R.GPP(+15.99)GESGAAGPTGAIGNRGPSGPAG.P	57.56 56.51	2446.154 2103.988	28 25	6.1 4.5	816.3968 1053.006	3 2	40.33 45.96	19621	439-466 442-466	Hydroxyprolination Hydroxyprolination
											237 239 240

Table S1 - 14 :

 S114 Peptides unique to Hippopotamus amphibius identified in sample Furniture leg fragment (26.7.1284, Egypt, ca. 2960-2649 B.C.), using MS/MS sequencing.

	Peptide	-10lgP	Mass	Length	|ppm|	m/z	z	RT	Range	PTM
		C0HJN5|CO1A1_HIPAM
	R.GPSGPQ(+.98)GPSGPSGPKGN(+.98)SGEP(+15.99)GAP(+15.99)GSK.G	54.98	2378.0522	27	5.8	793.6959	3	22.74	208-234	Deamidation (NQ); Hydroxyprolination
	R.GP(+15.99)SGPQ(+.98)GARGIPGT.A	43.1	1267.6107	14	4.4	634.8154	2	28.33	61-74	Hydroxyprolination; Deamidation (NQ)
	R.GFP(+15.99)GIP(+15.99)GPSGEPGKQ(+.98)GPSGTSGER.G	43.32	2285.0461	24	5.1	762.6932	3	47.49	747-770	Hydroxyprolination; Deamidation (NQ)
	R.GFP(+15.99)GIP(+15.99)GPSGEP(+15.99)GKQ(+.98)GPSGTSGER.G	60.39	2301.0361	24	7.4	768.025	3	44.02	747-770	Hydroxyprolination; Deamidation (NQ)
	K.HGN(+.98)RGEP(+15.99)GPAGVVGPTGAVGPR.G	54.7	2055.0195	22	3.1	514.7637	4	37.87	761-782	Deamidation (NQ); Hydroxyprolination
		C0HJN6|CO1A2_HIPAM
	R.GSTGEIGPAGPP(+15.99)GP(+15.99)PGIR.G	56.34	1647.8118	18	6.1	824.9182	2	52.16	262-279	Hydroxyprolination
	P.GPAGVVGPTGAVGPR.G	52.88	1290.7043	15	0.6	646.3599	2	46.18	768-782
	G.N(+.98)RGEP(+15.99)GPAGVVGPTGAVGPR.G	52.06	1860.9392	20	2.5	621.3219	3	50.99	763-782	Deamidation (NQ); Hydroxyprolination
	R.GEPGP(+15.99)AGVVGPTGAVGPR.G	49.52	1589.8112	18	4	795.916	2	52.57	765-782	Hydroxyprolination
	R.GSTGEIGPAGP(+15.99)P(+15.99)GPPGIR.G	48.32	1647.8118	18	6.1	824.9182	2	52.16	262-279	Hydroxyprolination
	K.RGSTGEIGPAGPPGP(+15.99)P(+15.99)GIR.G	48.02	1803.9128	19	5.8	602.3151	3	41.59	261-279	Hydroxyprolination
	R.AGVM(+15.99)GIP(+15.99)GSR.G	47.44	975.4758	10	4.7	488.7475	2	27.47	294-303	Oxidation (M); Hydroxyprolination
	G.PAGVVGPTGAVGPR.G	47.35	1233.6829	14	0.2	617.8488	2	43.87	769-782
	R.GATGPAGVR.G	43.02	784.4191	9	3	393.218	2	17.64	304-312

Table S1 - 15 :

 S115 Peptides unique to Bos genus identified in Figure of an Asiatic captive (66.99.50, Egypt, ca. 1295-1070 B.C.) using MS/MS sequencing.

	Peptide R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRGEAGPAG.P** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGPA.G** G.PVGAAGAP(+15.99)GPQGPVGPVGK.H* V.GAAGAP(+15.99)GPQGPVGPVGK.H*	-10lgP 64.22 43.26 51 42.81	Mass 2076.936 2245.0261 1627.8632 1431.7421	Length |ppm| 25 6 27 6.4 19 5 17 2.4	m/z 1039.4816 1123.5275 814.9429 716.88	z 2 2 2 2	RT 43.82 48.68 44.61 36.24	Scan	Range 674-698 954-972 674-700 956-972	PTM Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Hydroxyprolination Deamidation (NQ) Hydroxyprolination
	R.GNDGATGAAGPP(+15.99)GPTGPAGPP(+15.99)GFP(+15.99) R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDR.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)G.D** G.AAGAP(+15.99)GPQGPVGPVGK.H* A.GAP(+15.99)GPQGPVGPVGK.H* GAVGAKGEGGPQGPR.G* R.GNDGATGAAGP(+15.99)PGPTGPAGPP(+15.99)GFP(+15.99) R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGPAG.P** R.GAP(+15.99)GAIGAPGPAGAN(+.98)GDRGEAGPAGPAGPAGPR.G** G.AP(+15.99)GPQGPVGPVGK.H* R.GPAGPSGPAGKDGRIGQP(+15.99)GAVGPAGIR.G* GAVGAKGEGGP(+15.99)QGPR.G* R.GNDGATGAAGPPGP(+15.99)TGPAGPP(+15.99)GFP(+15.99) R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRGEA.G** R.GAPGAIGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGPAGPAGPR.G** R.GPAGPSGPAGKDGRIGQ(+.98)P(+15.99)GAVGPAGIR.G* GAVGAKGEGGP(+15.99)QGPR.G* R.GNDGATGAAGPPGP(+15.99)TGPAGPP(+15.99)GFP(+15.99) GAVGAKGEGGPQGPR.G* R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFP(+15.99) GAVGAKGEGGPQGPR.G* R.GN(+.98)DGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFP(+15.99) GAVGAKGEGGP(+15.99)QGPR.G* R.GN(+.98)DGATGAAGPP(+15.99)GP(+15.99)TGPAGPPGFP(+15.99) GAVGAKGEGGPQGPR.G* R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFP(+15.99) R.GNDGATGAAGP(+15.99)P(+15.99)GPTGPAGPPGFP(+15.99) R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRG EAGPAGPAGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRGEAG.P** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRGEA GPAGPAGPAGPRG.S** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGD.R** R.GYPGNAGP(+15.99)VGAAGAP(+15.99)GPQGPVGPVGK.H* R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98).G** R.GYPGN(+.98)AGP(+15.99)VGAAGAP(+15.99)GPQGPVGPVGK.H* R.GAP(+15.99)GAIGAP(+15.99)GPAGANG.D** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRGEAGPAGP.A** R.GYP(+15.99)GN(+.98)AGPVGAAGAPGP(+15.99)Q(+.98)GPVGPVGK.H * R.GAP(+15.99)GAIGAP(+15.99)GP(+15.99)AGAN(+.98)GDRGEA GPAGPAGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRGEAGPAGPA.G** R.GYP(+15.99)GN(+.98)AGPVGAAGAP(+15.99)GPQ(+.98)GPVGPVGK.H * R.GYP(+15.99)GNAGPVGAAGAP(+15.99)GPQGPVGPVGK.H* GAVGAKGEGGPQGPR.G* R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRG.E** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN.G** R.GYP(+15.99)GN(+.98)AGPVGAAGAP(+15.99)GPQGPVGPVGK.H* GAVGAKGEGGP(+15.99)QGPR.G* R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRGE.A** R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDR.G** K.GERGYP(+15.99)GN(+.98)AGPVGAAGAP(+15.99)GPQGPVGPVGK.H* R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRGEA.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRG.E** R.GAP(+15.99)GAIGAPGP(+15.99)AGANGDRGEAGPAGPAGPAGPR.G* D.GRIGQP(+15.99)GAVGPAGIR.G* * G.RIGQP(+15.99)GAVGPAGIR.G* R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRGEAGP(+15.99) AGPAGPAGPR.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRGE.A** R.GSQGSQGPAGPP(+15.99)GPP(+15.99)GPPGPAG.P* R.GSQGSQGPAGPP(+15.99)GPP(+15.99)GPPGPAGPSG.G* R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRGEAGPA.G** R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGPA GPAG.P** R.GSQGSQGPAGPP(+15.99)GPP(+15.99)GPPGPAGPSGGG.Y* R.GSQGSQGPAGP(+15.99)PGP(+15.99)PGPPGPAGPSGG.G* R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRGEAGP.A** G.AIGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGPAGPAGPR.G** R.GAP(+15.99)GAVGAP(+15.99)GPAGATGDRGEAGPAGSAGPAGPR.G* R.GAP(+15.99)GAVGAPGPAGATGDRGEAGPAGSAGPAGPR.G*	99.32 63.37 43.17 47.32 45.11 69.68 62.04 42.33 49.74 54 67.84 60.41 41.58 48.97 48.12 44.6 43 38.84 86.62 67.63 67.12 47.89 50.67 48.76 54.14 50.33 48.87 72.8 50.76 50.72 73.09 76.62 37.23 51.43 51.04 78.27 38.24 57.82 52.26 57.7 60.32 59.22 41.57 46.27 43.46 59.17 36.26 59 51.71 58.81 35.94 42.88 40.06 58.03 43.61 68.8 43.72	P02453|CO1A1_BOVIN 3382.5779 39 1537.7021 18 1266.5741 16 1374.7206 16 1232.6464 14 3398.5679 39 2302.0474 28 2764.3228 33 1175.6249 13 2412.2571 27 3398.5679 39 1794.8033 21 2764.3228 33 2413.2412 27 3382.5779 39 3382.5779 39 3399.5518 39 3383.5618 39 P02465|CO1A2_BOVIN 2780.3127 33 1851.8248 22 2837.3342 34 1380.6171 17 2260.1138 26 1209.5527 15 2261.0977 26 1265.5901 16 2173.0049 26 2262.0818 26 2796.3027 33 2244.042 27 2262.0818 26 2260.1138 26 3382.5779 39 1593.7396 19 1208.5687 15 2261.0977 26 3398.5679 39 1722.7822 20 1536.7181 18 2603.2629 29 1793.8193 21 1594.7236 19 2779.3286 33 1420.7849 15 1363.7634 14 2796.3027 33 1723.7662 20 1854.8397 22 2095.946 25 2019.9147 24 2527.1587 31 2209.989 27 2152.9675 26 1947.8936 23 2482.1899 29 2742.2971 33 2726.3071 33	6.5 6.7 8.4 5.8 6.8 7.2 5.3 1.4 5.4 1.9 7.2 7.4 1 1.7 6.6 8.2 5.6 4.7 7.5 5.7 5 12.3 1.3 9.2 9.4 8.4 6 6 6.5 6.6 5.8 5 2.8 6.7 7.8 5.9 0.5 7.4 7.5 4.7 6.2 5.3 5.6 3.6 4.8 9.7 8.3 5.5 6.3 6.2 6.7 6.8 7.9 6.7 9.8 5.8 2	1128.5405 769.8635 634.2996 688.3715 617.3347 1133.8713 1152.0371 922.4495 588.8229 604.0727 1133.8713 898.4156 922.4473 604.3186 846.6573 1128.5425 1134.1975 1128.8665 927.7852 926.9249 946.79 691.3243 754.3776 605.7892 754.7144 633.8076 1087.5162 1132.0549 933.1143 1123.0356 1132.0547 1131.0698 1128.5364 797.8824 605.2963 1131.5627 1133.8638 862.4048 769.3721 868.7656 897.9225 798.3733 927.4449 474.604 455.5973 933.1172 862.8976 928.4323 1048.9869 1010.9708 843.3992 1106.0093 1077.4995 974.9606 828.4128 915.1116 909.7781	3 2 2 2 2 3 2 3 2 4 3 2 3 4 4 3 3 3 3 2 3 2 3 2 3 2 2 2 3 2 2 2 3 2 2 2 3 2 2 3 2 2 3 3 3 3 2 2 2 2 3 2 2 2 3 3 3	57.35 32.8 41.34 35.34 33.24 56.81 48.51 51.76 32.41 41.01 56.81 36.71 49.99 42.77 57.11 55.35 60.02 60.02 46.97 37.68 47.43 39.44 60.81 41.1 62.13 37.98 44.31 61.64 46.75 46.2 64.25 56.48 61.05 30.48 37.69 59.6 59.92 31.97 31.08 52.88 35.04 32.67 50.06 32.06 31.58 44.47 35.14 38.76 43.31 42.96 55.2 43.02 43.33 38.56 44.86 38.56 39.58		322-360 674-691 957-972 674-689 959-972 322-360 674-701 960-972 674-706 1052-1078 322-360 674-694 674-706 1052-1078 322-360 322-360 322-360 674-690 947-972 674-707 674-688 947-972 674-695 674-689 674-699 947-972 674-706 674-700 947-972 674-706 947-972 322-360 674-692 674-688 947-972 322-360 674-693 674-691 944-972 322-360 674-694 674-706 1064-1078 1065-1078 674-692 1081-1102 674-693 1081-1105 674-706 1081-1107 674-704 1081-1106 674-697 674-696 676-708 678-706 676-708	Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Hydroxyprolination Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Hydroxyprolination Deamidation (NQ) Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Deamidation (NQ); Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Deamidation (NQ); Hydroxyprolination Hydroxyprolination Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Deamidation (NQ); Deamidation (NQ) Hydroxyprolination; Hydroxyprolination Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination; Deamidation (NQ) Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Hydroxyprolination; Hydroxyprolination Hydroxyprolination Hydroxyprolination Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Hydroxyprolination Hydroxyprolination Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Hydroxyprolination Deamidation (NQ); Hydroxyprolination Hydroxyprolination; Hydroxyprolination Hydroxyprolination Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Hydroxyprolination; Hydroxyprolination Deamidation (NQ) Hydroxyprolination Deamidation (NQ) Hydroxyprolination; Hydroxyprolination; Hydroxyprolination Deamidation (NQ) Hydroxyprolination Deamidation (NQ) Hydroxyprolination Hydroxyprolination; Hydroxyprolination Deamidation (NQ) Hydroxyprolination
	R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRGEAGPAGPAGPAGPR.G* * R.GAP(+15.99)GAIGAPGP(+15.99)AGAN(+.98)GDRGEAGPAGPAG.P** N.AGPVGAAGAP(+15.99)GPQGPVGPVGK.H* A.GPVGAAGAP(+15.99)GPQGPVGPVGK.H*	66.46 44.45 61.86 57.68	2779.3286 2302.0474 1755.9218 1684.8846	33 28 21 20	6 5 4.4 3.6	927.4446 768.3602 878.972 843.4526	3 3 2 2	47.71 49.54 49.3 47.19		674-706 952-972 674-701 953-972	Hydroxyprolination Hydroxyprolination Hydroxyprolination; Deamidation (NQ) Hydroxyprolination
	R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRGEAGPA.G** A.GPVGAAGAPGP(+15.99)QGPVGPVGK.H*	64.63 47.95	2018.9307 1684.8846	24 20	6.1 3.6	1010.4788 843.4526	2 2	40.86 47.19		674-697 953-972	Hydroxyprolination Hydroxyprolination
											243 244 245

*specific to the Bovinae subfamily, **specific to the Bos genus

Table S1 - 16 :

 S116 Peptides unique to Hippopotamus amphibius identified in Figurine or amulet of a hippo on a sled (26.7.1299, Egypt, ca. 2960-2649 B.C.) using MS/MS sequencing.

	Peptide	-10lgP	Mass	Length	|ppm|	m/z	z	RT	Range	PTM
				C0HJN6|CO1A2_HIPAM
	R.GERGP(+15.99)PGESGAAGPTGAIGNR.G	45.31	1922.919	21	0.5	641.9801	3	25.61	439-459	Hydroxyprolination
	R.GP(+15.99)P(+15.99)GESGAAGPTGAIGNR.G	38.14	1596.749	18	0.1	799.382	2	28.07	442-459	Hydroxyprolination
	G.AAGPTGAIGNR.G	46.96	983.5148	11	1	492.7642	2	22.91	449-459
	K.HGN(+.98)RGEP(+15.99)GPAGVVGPTGAVGPR.G	56.81	2055.024	22	1.3	514.7627	4	31.58	761-782	Deamidation (NQ);
										Hydroxyprolination
	G.N(+.98)RGEP(+15.99)GPAGVVGPTGAVGPR.G	41	1860.944	20	0.4	621.3217	3	39.3	763-782	Deamidation (NQ);
										Hydroxyprolination
	R.GEP(+15.99)GPAGVVGPTGAVGPR.G	46.3	1589.816	18	0.6	795.9149	2	42.88	765-782	Hydroxyprolination
	G.EP(+15.99)GPAGVVGPTGAVGPR.G	39.31	1532.795	17	0.1	767.4047	2	43.35	766-782	Hydroxyprolination
	E.P(+15.99)GPAGVVGPTGAVGPR.G	38.52	1403.752	16	0.2	702.8832	2	40.47	767-782	Hydroxyprolination
	P.GPAGVVGPTGAVGPR.G	49.87	1290.704	15	0.5	646.3597	2	38.15	768-782
	G.PAGVVGPTGAVGPR.G	39.06	1233.683	14	0.5	617.8484	2	36.47	769-782
	P.AGVVGPTGAVGPR.G	42.38	1136.63	13	1.4	569.3215	2	34.02	770-782

Table S1 - 17 :

 S117 Examples of peptides unique to Hippopotamus amphibius identified in Figurine or amulet of a hippo on a sled (26.7.1299, Egypt, ca. 2960-2649 B.C.) and exemplar sample 113816 identified using a LC-MS-based PMF approach.

	Peptide	Mass	Length	ppm	m/z	z	RT	Range	Present 26.7.1299?
		C0HJN5|CO1A1_HIPAM
	R.GPSGPQ(+.98)GPSGPSGP(+15.99)KG.N	1379.627	16	7.5	690.8258	2	19.46	208-223	Y
	R.GPSGPQGPSGPSGPKG.N	1362.653	16	4.1	682.3364	2	21.38	208-223	Y
	R.GPSGPQGARGIP(+15.99)G.T	1165.579	13	7.3	583.801	2	22.46	61-73	N
	R.GPSGPQ(+.98)GPSGPSGPKGN(+.98).S	1478.664	17	3.2	740.3415	2	32.5	208-224	Y
		C0HJN6|CO1A2_HIPAM
	G.AAGPTGAIGNR.G	983.5148	11	4.2	492.7668	2	20.46	449-459	Y
	D.GRAGVM(+15.99)GIP(+15.99)GSR.G	1188.598	12	7.1	397.2095	3	22.6	292-303	Y
	S.GAAGPTGAIGNR.G	1040.536	12	3.3	521.2771	2	24.27	448-459	N
	E.SGAAGPTGAIGNR.G	1127.568	13	3.2	564.7933	2	24.61	447-459	N
	P.P(+15.99)GESGAAGPTGAIGNR.G	1426.675	16	6.6	714.3495	2	25.63	444-459	Y
	P.GESGAAGPTGAIGNR.G	1313.632	15	4.3	657.8262	2	26.37	445-459	N
	R.GPP(+15.99)GESGAAGPTGAIGNR.G	1580.749	18	5.1	527.9264	3	29.75	442-459	Y
	R.AGVM(+15.99)GIP(+15.99)GSRG.A	1032.497	11	7.8	517.2599	2	29.96	294-304	Y
	R.AGVM(+15.99)GIP(+15.99)GSRGATGPAGVR.G	1741.884	19	6.9	581.6394	3	30.06	294-312	N
	R.GPP(+15.99)GESGAAGPTGAIGN(+.98)R.G	1581.733	18	6.6	791.8792	2	31.36	442-459	Y
	N.RGEP(+15.99)GPAGVVGPTGAVGPR.G	1745.912	19	7.1	582.9822	3	35.52	764-782	Y
	R.GIP(+15.99)GADGRAGVM(+15.99)GIP(+15.99)GSR.G	1714.832	18	9.7	572.6235	3	36.34	286-303	Y
	G.PAGVVGPTGAVGPR.G	1233.683	14	3.8	617.8511	2	37.68	769-782	Y
	P.GPAGVVGPTGAVGPR.G	1290.704	15	4.4	646.3623	2	39.39	768-782	Y
	E.P(+15.99)GPAGVVGPTGAVGPR.G	1403.747	16	7.8	702.8864	2	41.07	767-782	Y
	R.GEP(+15.99)GPAGVVGPTGAVGPR.G	1589.811	18	7.2	795.9186	2	44.21	765-782	Y
	G.EP(+15.99)GPAGVVGPTGAVGPR.G	1532.79	17	6.7	767.4072	2	44.73	766-782	Y
									248

Table S1 - 18 :

 S118 Examples of peptides identified in the Spoon (12.187.30, Egypt, ca. 3100-2649 B.C.) specific to the Bos genus using a LC-MS-based PMF approach.

	Peptide	Mass	Length	ppm	m/z	z	RT	Range	Present in 12.187.30*
	collagen alpha-1(i) chain precursor
	K.GEGGPQ(+.98)GPRGSEGPQGVR.G	1721.8	18	0.3	574.944		22.69	352-369	Y
	R.GN(+.98)DGATGAAGP(+15.99)PGPTGPAGP(+15.99)PGFP(+15.99)GAVGAKGEGGPQGPR.G	3383.6	39	9.2	1128.87		45.44	322-360	Y
	R.GNDGATGAAGP(+15.99)PGPTGPAGP(+15.99)P(+15.99)GFP(+15.99)GAVGAKGEGGPQGPR.G 3398.6	39	5	1133.87		45.15	322-360	Y
	collagen alpha-2(i) chain precursor
	G.AP(+15.99)GPQGPVGPVGK.H	1175.6	13	3.9	588.822		29.62	960-972	Y
	R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98).G	1209.6	15	8.6	605.789		36.43	674-688	Y
	N.AGPVGAAGAP(+15.99)GPQGPVGPVGK.H	1755.9	21	3.3	878.971		41.63	952-972	Y
	R.GYP(+15.99)GN(+.98)AGPVGAAGAP(+15.99)GPQGPVGPVGK.H	2261.1	26	5	1131.56		49.17	947-972	Y
	R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRGEAGPAGPAGPAGPR.G	2780.3	33	4.9	927.783		39.57	674-706	Y
	A.GAP(+15.99)GPQGPVGPVGK.H	1232.6	14	3.5	617.333		30.12	959-972	Y
	G.RIGQ(+.98)P(+15.99)GAVGPAGIR.G	1364.7	14	2.3	455.924		31.29	1065-1078	N
	R.GAP(+15.99)GAIGAP(+15.99)GPAGAN.G	1208.6	15	7.4	605.296		32.92	674-688	Y
	D.GRIGQP(+15.99)GAVGPAGIR.G	1420.8	15	2.4	474.603		29.57	1064-1078	N
	A.AGAP(+15.99)GPQGPVGPVGK.H	1303.7	15	3.7	652.851		30.91	958-972	Y
	R.GAP(+15.99)GAIGAP(+15.99)GPAGANG.D	1265.6	16	8.4	633.808		33.92	674-689	N
	G.AAGAP(+15.99)GPQGPVGPVGK.H	1374.7	16	3.4	688.37		31.59	957-972	Y
	R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GD.R	1381.6	17	7.5	691.813		38.9	674-690	Y
	R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDR.G	1537.7	18	6	769.863		29.57	674-691	Y
	R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDR.G	1536.7	18	6.8	769.372		27.58	674-691	N
	P.VGAAGAP(+15.99)GPQGPVGPVGK.H	1530.8	18	3.2	766.415		35.88	955-972	N
	R.GAP(+15.99)GAIGAP(+15.99)GPAGAN(+.98)GDRG.E	1594.7	19	6.5	798.374		29.67	674-692	N
	R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRGEAG.P	1850.8	22	4.5	926.432		30.27	674-695	Y
	R.GYP(+15.99)GNAGPVGAAGAP(+15.99)GPQGPVGPVG.K	2132	25	4.5	1067.02		59.19	947-971	N
	R.GYP(+15.99)GNAGPVGAAGAP(+15.99)GPQGPVGPVGK.H	2260.1	26	4.6	1131.07		46.74	947-972	Y
	R.GAP(+15.99)GAIGAP(+15.99)GPAGANGDRGEAGPAGPAGPAGPR.G	2779.3	33	3.7	927.454		36.44	674-706	Y
	*Y is Yes, N is No

Table S3 - 1 :

 S31 The samples analysed in this study originating from Le Piage archaeological site. Included information: the location at which the object was found, the initial fragment identification, pictures of the sampling location, before and after, and the species identification of the sample based on LC-MS/MS analysis with Prometa used for data analysis. washed in backflush mode before being injected onto a PepMap RSLC C18 column (2 µm, 100 Å, 75 µm x 50 cm, ThermoFisher Scientific), with a gradient of 146 min and a flow rate of 3 nL/min. Where buffer A was 0.1% formic acid in water, and buffer B was 20% water/80% acetonitrile with 0.1% formic acid, the following gradient was used: 0-3 min, 4% B, 3-69 min, increase to 40% B, 69-121 min, increase to 90% B, 121-125 min, hold at 90% B, 125-126 min, decrease to 4% B, 126-146 min, hold at 4% B.

			coxal
	n° object; site and year of excavation, square, level 1507; PG18, I5C, b8(1) 1515; PG18, I5C, b8(1) 1462; PG18, I5C, b8(1) PG18, I5C, b8(1) b8(1) PG18, I5C, PG18, I5C, 1517; 1509; 1467; b8(1) 1468; PG18, I5C, 1513; PG18, I5C, b8(1) LIP001	Object identification long bone long bone diaphysis diaphysis fragment fragment fragment Tooth fragment fragment diaphysis diaphysis long bone long bone diaphysis fragment long bone long or flat bone diaphysis Morphologically long bone identified as diaphysis fragment human	Additional fragment, scapula, vertebra, taxonomically Information identifiable at level taxonomic Reindeer, In two pieces determinable	Before Sampling	After Sampling
	b8(1)	fragment
	1465;	long bone
	PG18, I5C, b8(1) 1470; PG18, I5C, b8(1) 1471; PG18, I5C, b8(1) 1510: PG18, I5C, b8(1) 1514; PG18, I5C, b8(1)	diaphysis fragment long bone diaphysis fragment tarsus or carp bone long or flat bone diaphysis fragment joint-end fragments	complete, definable at taxonomic level
	1466; PG18, I5C, b8(1)	long or flat bone fragment
					252 253 254 255 257 258

then

Table S4 - 3 :

 S43 Collagen coverage and number of peptides detected from the archaeological samples analysed in this study

			Collagen α1(i)	Collagen α2(i)
	Sample		% Coverage	#Peptides	% Coverage	# Peptides
		A	65	470		500
	Archaeological bone 1 WW1	B	70	789		657
		A+B	73	1235		985
		A	11	11	7	9
	Archaeological bone 2* WW1	B	6	8		11
		A+B	24	22		22
		A	71	655		625
	Archaeological bone 3 Post Medieval Period	B	73	718		668
		A+B	76	1105		984
		A	71	769		739
	Archaeological bone 4 Post Medieval Period	B	72	794		709
		A+B	73	1158		1048
		A	10	13		14
	Archaeological bone 5* Bronze Age	B	14	18		15
		A+B	19	21		16
		A	70	843		761
	Archaeological tooth 6 Post Medieval Period	B	72	780		721
		A+B	75	1276		1075
	Forensic A	A B	70 72	755 857		643 708
	Forensic B	A B	72 72	899 952		756 744
	* indicates samples that were visibly degraded upon sampling.

Table S6 - 2 :

 S62 Fragment values identified for the peptide TGDAGPAGPPGPPGPPGPPGPPSGGYDLSFLPQPPQEKAHDGGR, P indicates hydroxyproline. Values in bold correspond to fragments identified in the spectrum in Figure 6-13 and Figure 6-14.

	b++	c++	b+	c+		AA		y+	z+
	51.531	60.044	102.055	119.082	1	T	44
	80.042	88.555	159.076	176.103	2	G	43	4175.903	4158.877
	137.555	146.069	274.103	291.130	3	D	42	4118.882	4101.855
	173.074	181.587	345.140	362.167	4	A	41	4003.855	3986.829
	201.585	210.098	402.162	419.188	5	G	40	3932.818	3915.791
	250.111	258.624	499.215	516.241	6	P	39	3875.796	3858.770
	285.630	294.143	570.252	587.278	7	A	38	3778.744	3761.717
	314.140	322.654	627.273	644.300	8	G	37	3707.707	3690.680
	362.667	371.180	724.326	741.353	9	P	36	3650.685	3633.659
	419.188	427.701	837.369	854.395	10	P	35	3553.632	3536.606
	447.699	456.212	894.390	911.417	11	G	34	3440.590	3423.563
	496.225	504.738	991.443	1008.470	12	P	33	3383.568	3366.542
	552.747	561.260	1104.486 1121.512	13	P	32	3286.515	3269.489
	581.257	589.771	1161.507 1178.534	14	G	31	3173.473	3156.446
	629.784	638.297	1258.560 1275.587	15	P	30	3116.451	3099.425
	686.305	694.818	1371.603 1388.629	16	P	29	3019.398	3002.372
	714.816	723.329	1428.624 1445.651	17	G	28	2906.356	2889.329
	763.342	771.855	1525.677 1542.704	18	P	27	2849.334	2832.308
	819.864	828.377	1638.720 1655.746	19	P	26	2752.281	2735.255
	848.374	856.888	1695.741 1712.768	20	G	25	2639.239	2622.212
	896.901	905.414	1792.794 1809.821	21	P	24	2582.217	2565.191
	953.422	961.935	1905.837 1922.863	22	P	23	2485.164	2468.138
	996.938	1005.451 1992.869 2009.895	23	S	22	2372.122	2355.095
	1025.449 1033.962 2049.890 2066.917	24	G	21	2285.090	2268.063
	1053.960 1062.473 2106.912 2123.938	25	G	20	2228.068	2211.042
	1135.491 1144.004 2269.975 2287.002	26	Y	19	2171.047	2154.020
	1193.005 1201.518 2385.002 2402.029	27	D	18	2007.983	1990.957
	1249.547 1258.060 2498.086 2515.113	28	L	17	1892.956	1875.930
	1293.063 1301.576 2585.118 2602.145	29	S	16	1779.872	1762.846
	1366.597 1375.110 2732.187 2749.213	30	F	15	1692.840	1675.814
	1423.139 1431.652 2845.271 2862.297	31	L	14	1545.772	1528.745
	1471.665 1480.179 2942.323 2959.350	32	P	13	1432.688	1415.661
	1535.695 1544.208 3070.382 3087.408	33	Q	12	1335.635	1318.609
	1584.221 1592.734 3167.435 3184.461	34	P	11	1207.577	1190.550
	1640.742 1649.256 3280.477 3297.504	35	P	10	1110.524	1093.497
	1704.772 1713.285 3408.536 3425.563	36	Q	9	997.481	980.454
	1769.293 1777.806 3537.579 3554.605	37	E	8	869.422	852.396
	1833.340 1841.854 3665.674 3682.700	38	K*	7	740.380	723.353
	1868.859 1877.372 3736.711 3753.737	39	A	6	612.285	595.258
	1937.388 1945.902 3873.770 3890.796	40	H	5	541.248	524.221
	1994.902 2003.415 3988.797 4005.823	41	D	4	404.189	387.162
	2023.413 2031.926 4045.818 4062.845	42	G	3	289.162	272.135
	2051.923 2060.437 4102.839 4119.866	43	G	2	232.140	215.114
					44	R	1	175.119	158.092

*Cross-link occurs at this residue

Acknowledgements

. Ina Reiche, Julie Arslanoglu and Prof. Bruno Maureille. This work would not have been possible without the European Union's Horizon 2020 research and innovation programme, in particular the Marie Sklodowska-Curie ETN programme, which funded my position under the grant agreement No. 861389, Palaeoproteomics to Unleash Studies on Human History (PUSHH). Wildlife Fund

Dry Human Bone Sample Femur sample taken from archaeological assemblage Post Medieval Period (16th-18th centuries) Saint-Amé collegiate church, Douai, Northern France Modern Model Fresh Animal Bone Sample Local Butchery Bordeaux Fresh bovine bone samples, (2021) Forensic samples Forensic A Fresh Human Bone Sample Rib sample, F261H donor, Ref 261H-H2G0 (27/05/21) Ethic agreement 2021-473-S91, CER U-Lille, ProtOsIPoM protocol, ProTaphOs Program* Forensic B Fresh Human Bone Sample Rib sample, F280H donor, Ref 280H-H2G0 (22/08/21) Ethic agreement 2021-473-S91, CER U-Lille, ProtOsIPoM protocol, ProTaphOs Program* Archaeological samples Archaeological 1 Dry Human Bone Sample Sample taken from archaeological assemblage Contemporary Era, WWI (20 th Century) Saint Laurent Blangy, North of France Archaeological 2 Dry Human Bone Sample Sample taken from archaeological assemblage Contemporary Era, WWI (20 th Century) Saint Laurent Blangy, North of France Archaeological 3 Dry Human Bone Sample Sample taken from archaeological assemblage Post Medieval Period (16th-18th centuries) Saint Jacques church, Douai, Northern France Archaeological 4 Dry Human Bone Sample Sample taken from archaeological assemblage Post Medieval Period (16th-18th centuries) Saint Jacques church, Douai, Northern France Archaeological 5 Dry Human Bone Sample Sample taken from archaeological assemblage Bronze Age (3000 BCE-1100 BCE) Lambres-lez-Douai, Northern France

Methods

2.1.Samples

Samples were obtained from the Mammalogy Department at the American Museum of Natural History. The samples analysed in this study are detailed in Rangifer tarandus species

*Poor collagen preservation **Specific to Rangifer tarandus and Odocoileus virginianus texanus only.

***Specific at the family level of Bovidae, because there is one species outside of the Bos genus where this peptide sequence is also found.

Of the 13 samples analysed from Le Piage, 8 were identified as Rangifer tarandus while 4 were classified as Bos, based on a database search using PEAKS DB in PEAKS X, the details of which can be found in Table S3-2. These identifications were expected, as the humans in the Proto-Aurignacian period were known to be hunter-gatherers of reindeer, and Bovidae were known to be present in their living environment, the identification of such remains, highly fragmented as the ones discovered during the recent fieldworks, indicate the presence of intensive human site occupation. Le Piage can be considered as a residential prehistoric place.

Identification of human remains at Le Piage

Sample LIP001 was prepared and analysed separately to the 12 samples prepared using the minimally invasive procedure. This sample had previously been identified as potential human perinate tibia based on morphological analysis, therefore was prepared separately, and a longer, more sensitive gradient was used for the analysis. (29) Additionally, a larger sample amount was used, as previous DNA analysis had not yielded results, so it was known that the sample was more degraded in comparison to the other samples in this study.

Chapter 4: Successive protein extraction using hydroxylamine to increase the depth of proteome coverage in fresh, forensic and archaeological bones Abstract Chapter 4 describes the development of a novel sample preparation procedure that targets collagen in archaeological bones. Here, the target of the protocol is to gain a deeper proteome coverage and in particular, access the proteins from the insoluble fraction of the material.

This study proposes the use of hydroxylamine as an (additional) extraction step.

Hydroxylamine, a chemical digestion reagent, can be used to perform a solid phase digestion, resulting in the efficient solubilisation of proteinaceous material that is not soluble with a typical guanidine-based extraction. This hydroxylamine protocol can be used to:

• Increase the yield of collagen extracted from modern and archaeological samples.

• Increase the sequence coverage of collagen in modern samples.

• Increase the depth of proteome coverage in archaeological samples, through the identification of additional protein groups in the hydroxylamine extract.

This chemical digestion procedure efficiently solubilises insoluble proteinaceous materials in both modern and archaeological samples. This therefore allows for the analysis of the insoluble fraction of the extracellular matrix in bone, allowing access to information that is lost in the discarded material in a typical proteomics workflow.

This chapter has been written in the format of Analytical Chemistry and was submitted as a

Technical Note in June 2023.

Abstract

Chapter 6 discusses the analysis of cross-linked collagen peptides using mass spectrometry.

Following the extensive literature review in Chapter 5, and the development of a chemical digestion protocol targeting collagen in Chapter 4, targeted analysis strategies for both divalent and trivalent cross-links were developed. This chapter details:

• Targeted analysis of PTMs associated with cross-linking in archaeological bones

• A cross-linking mass spectrometry approach for the identification of divalent cross-links in bone

• A targeted enrichment procedure for the purification of trivalent cross-linked peptides in bone

• Top down mass spectrometry approaches for the analysis of trivalent peptides, in addition with targeted MS3 fragmentation for easier sequence identification.

This chapter details the analysis of highly complex structures, with the analytical complexity originating from both the heterogeneity of the cross-link structures and from the collagen protein itself. Here, the biggest challenge was the analysis of the mature collagen cross-links, requiring adapted fragmentation methods and the manual sequencing of the peptide sequence.

Introduction

Collagen is the most abundant protein in the body, and the mechanical strength of bone can, in part, be attributed to the extensive and highly controlled network of cross-links present in bone collagen. (1) The collagen network in bone is mostly made up of fibrillar collagen, in particular collagen type I. Such fibrillar collagens have a characteristic G-X-Y repeating motif, where G is Glycine, X is proline, and Y is hydroxyproline. (2) This triplet repeating motif allows for three collagen chains to form a triple helix, making up a collagen fibril. These fibrils are then arranged in a staggered network, as shown in Figure 6-1A, which are held in place by enzymatic cross-links. (3)

The analysis of cross-linking in collagen is relevant to a wide range of biological questions, from studying cancer and tumour architecture, to understanding genetic bone diseases such as osteogenesis imperfecta. (4-7) The formation of collagen cross-links is initiated by the enzymatic action of lysyl oxidase, which transforms lysine residues into reactive allysine, as shown in Figure 6-1B. Following this, the cross-links are formed by non-enzymatic condensation reactions; first into divalent, immature, cross-links, and then into trivalent, mature cross-links. (8,9) In addition to this, the hydroxylation and glycosylation of lysine by lysyl

S2. Supplementary information Chapter 2

Figure S2-1: Sequence alignment of collagen A 1α(i), B 1α(ii), C 1α(iii), and D 2α(v), of Loxodonta africana and Elephas maximus. While some differences between the two species were identified in these proteins, they were not targeted as markers of proteotype because either (a), the portions of the sequences where the markers were identified were not detected in the microsamples (the case for B), or (b) the differences identified by the sequence alignment were in fact unknowns in the sequence from the DNA translation (A and C).

S3. Supplementary Information Chapter 3

Figure S3-1: The effect of different injection times in either MS1 or MS2, analysis performed with a 145 min gradient on an archaeological bone collagen tryptic digest. Archaeological bones were sampled using a small hand drill (Velleman VTHD02). The powder was collected using wax paper and transferred to an Eppendorf tube for analysis. In the case of the bovine femur model, sampling was performed using a higher power Dremel 4000 hand drill. For the modern human forensic samples, sampling from the rib was performed using a single-use file at the diaphysis. Forensic samples were kept at -80°C after sampling.

The two forensic samples analysed consisted of both bone and muscle tissue. In order to optimise the solubility of these tissues, 100 µL of water was added to approx. 25 mg of tissue, before homogenising the tissue using five 2.4 mm metal beads in a 2 mL vial, using a Bead

Ruptor Elite bead mill homogeniser (Omni Inc). The samples were homogenised at 5 m/s for 30 s at a time with a 1 min 30 s pause between three homogenising periods. 10 µL of the resulting homogenate was then sampled for preparation, resulting in a starting sample size of approx. 2.5 mg. From this point, the protocol described in the main paper was followed.