Jaison Présentée

Raj Rathina

Immanuel Arivalagan

Dr Arul Marie

Mme Melody

Clark Professeur

Présidente M Karim Benzerara

M Daniel

Professeur J Jackson

Rapporteur M Didier

Lutomski Professeur

M Frédéric

Mme Sophie Berland

M Arul

Dr Élise Marin

Dr Benjamin Dufour

Prof Philippe Grellier

Prof Adrien Gilles Luquet

Fabien Bussard

Charles Tredez

Jean Philippe Le Pabic

Zhilai Buffet

Ali Hong

Mcheik

Prof Sylvain Huchette Niclas

Dr Chunsheng

Jaison Arivalagan

Trystan Sanders

Corinna Breusing

Giovanni Chiapetta

Benjamin Marie

Joëlle Vinh

Heiko Stucks

Frank Melzner

Sophie Berland

Spécialité : Protéomique -Biominéralisation

Keywords: Shell Matrix Proteins (SMPs), biomineralization, calcite, aragonite, phenoloxidase, scaffold, carbonic anhydrase, Epidermal Growth Factor (EGF), fibronectin, phosphoprotein

The successful completion of this thesis wouldn't be possible

Résumé

L'association d'éléments minéraux à des molécules organiques sous la forme de structures biominéralisées, est largement développée dans la biosphère, par des organismes à différents degré de complexité: individu à cellule unique (tels que chez les foraminifères) ou métazoaire. La variabilité et la complexité des biominéraux émergent d'un processus d'orchestration par des molécules organiques et des processus biologiques, rassemblés sous le concept de 'contrôle biologique'. Les mollusques bivalves forment un taxon dans lequel une large gamme de structures composites de carbonate de calcium est élaborée dans la coquille, organisée par le jeu d'interactions du minéral et de molécules organiques. Parmi ces composants organiques qui constituent la matrice, les protéines (Shell matrix protein ou SMP), jouent un rôle clé quant à la nucleation et la croissance du morphe cristallin du carbonate de calcium.

La présente étude se fonde sur quatre espèces de bivalves, d'intérêt tant dans la filière aquacole que, pour la partie proprement biologique, la diversité de microstructure des coquilles et l'ancienneté de divergence de chacune des espèces modèles du point de vue phylogénétique : Crassostrea gigas (l'huître creuse du Pacifique), Mya truncata (la mye tronquée), Mytilus edulis (la moule comestible) and Pecten maximus (la coquille St Jacques).

Les protéines matricielles sont extraites des coquilles et analysées selon un processus standardisé de séparation et de 'pipeline' protéomique et bioinformatique. Ainsi, certains éléments protéiques fonctionnels clé, de type 'boite à outils', pour le processus de calcification, présents dans les composants matriciels de la coquille sont précisés. Corrélativement, l'étude comparative portant sur les quatre espèces a permis d'identifier des protéines matricielles spécifiques à la forme cristalline (calcitique ou aragonitique) des coquilles calcifiées. Parmi les protéines matricielles caractérisées, un nombre conséquent est spécifique à chacune des espèces, ce qui soutient l'hypothèse d'une forte vi réponse à la pression adaptative et au modus vivendi comparativement à celle des relations d'apparentement s'appliquant aux protéines de coquilles. Cette hypothèse est particulièrement soutenue par le fait que ces protéines spécifiques paraissent pour la plupart porter des fonctions de protection immune et s'agrègent dans la voie métabolique liée à la phénoloxidase et sa régulation, suggérant un rôle de défense contre les pathogènes.

Un autre volet d'étude comparative, porte sur le protéome de coquille d'une unique espèce modèle (Mytilus), pour différentes populations issues de la Mer du Nord et de la Baltique, mer intracontinentale et intérieure présentant naturellement un gradient de faible salinité par rapport au milieu marin ouvert.

L'étude du protéome par approche non-ciblée semi-quantitative (label-free) a permis de caractériser un set de protéines matricielles modulées selon les différents environnements. Ainsi, certaines protéines de controle de la biominéralisation, des protéines de liaison à la chitine (polysaccharide azoté, autre composant essentiel des matrices de coquilles) sont proportionnellement modulées dans les populations de Baltique. Les protéines portant des domaines fonctionnels tels que : anhydrase carbonique, facteurs de croissances de type EGF ou fibronectine n'apparaissent pas modulées en fonction de conditions environnementales potentiellement défavorables à la biominéralisation. En revanche, les protéines liées à l'immunité sont fréquentes dans le set de protéines matricielles modulées, ce qui est interprété comme le reflet d'une réponse à la pression biotique et abiotique inscrite dans le protéome de la coquille. Ces résultats sont lus comme autant de traits liés à la capacité particulièrement avancée chez Mytilus à l'adaptation à son environnement via la modulation de certains composants du protéome matriciel de la coquille. En conditions de faible salinité (en mer Baltique), le phénotype de la coquille est modifié avec un impact sur ses propriétés mécaniques et sur la barrière de défense biochimique.

L'ensemble de ce travail présente la diversité fonctionnelle des protéines de la matrice biominéralisée des coquilles chez les quatre espèces étudiées, et souligne en particulier les différents rôles liés à la protection biochimique vii s'ajoutant à ceux de contrôle de la biominéralisation sensu stricto dans les composants du protéome de la coquille. Ainsi, cette étude a donné lieu à l'identification de protéines liées à la protection immune et de phosphoprotéines, indiquant potentiellement des protéines de signalisation et souligne la complexité du contrôle biologique inscrit dans la matrice de biominéralisation.

Ces résultats viennent conforter la définiton de la coquille comme une structure fonctionnelle de protection physique et biochimique, dont la dynamique s'ancre dans le protéome.

List of abbreviations

Biomineralization

Mineral formation in the living organisms is a widespread phenomenon in nature.

Biomineralization is a process by which the organisms produce inorganic minerals under the control of organic macromolecules. All three domains of life involve in mineral formation and there are more than 60 such biominerals known in the biosphere (Lowenstam and Weiner 1989). These biomineral structures perform a range of function that includes mechanical support and mobility in vertebrates, protection of soft tissues in molluscs, corals, urchins etc., balance in fish otoliths, optic photonic effects in fish skin and insect elytra, detection of earth's magnetic field in magnetotactic bacteria etc. (Cusack and Freer 2008).

The study on biogenic minerals revealed more than 80% are crystalline and 20% are amorphous and nearly 50% of all known biominerals are calcium based (Lowenstam and Weiner 1989). Biological control via the organic components amazingly shape and arrange the minerals in a definite order providing extraordinary mechanical properties. For example, nacre (aragonite) structure formed by several mollusc species is a ceramic composed of 95% minerals and 5%

organic components yet it shows 3000 times higher fracture resistant than naturally formed aragonite (Currey, 1977).

Despite numerous studies to unravel the mechanism by which the organic components exert control over mineralization, the precise mechanism sill remains elusive. Nevertheless, the identification of several organic molecules from the shell by researchers might lead to the identification of the complete mechanism in near future. The next interrogation is whether there exists a common mechanism in biomineralization. In spite of using different minerals such as calcium carbonate or calcium phosphate or iron oxide, biomineralization is a multi-step process involving intracellular and extracellular components.

Initially ions that form the crystal lattice should come together with the right orientation to form a stable structure. The nucleation is an important event in mineralization and is facilitated by increase in the ion concentration, which increases the probability of the ions to associate. Heterogeneous nucleation refers to nucleation on the surface that resembles the original crystal e.g. organic matrix formed in the mineralization of teeth, shell, bones etc. Once the initial crystal is formed it further expands by the influence of organics. Organic molecules also govern the inhibition of the crystal growth (Boskey 1998). This characteristic of organic molecules opened a new field of study in biomineralization (Wheeler et al.

1981).

Biomineralization is a multidisciplinary field and can be studied in different perspectives ranging from mechanical properties, mineralogy, and crystallography to biological control. The backbone of all these studies is ultimately to understand the biological control exerted via proteins, carbohydrates, lipids, etc. Biological control can be investigated in different level such as, inter-cellular interactions and extracellular matrix assembly and mineralization.

Evolution of biomineralization

Paleontological studies suggest that biomineralization process occurred as early as 3500 million years ago (mya) in microscopic stromatolites colonies. During the Cambrian era and later, two types of mineralization existed: biologically induced and biologically controlled mineralization. Biologically induced mineralization occurs at a distance from the organism. For example, in bacteria during sulfate reduction reaction, intracellular sulfide ions are released and which interacts with the surrounding metal ions to form insoluble precipitate (Cloud 1973). Similar mechanism was used by manganese-encrusting bacteria to form manganese deposits nearly 1.6 mya (Muir 1978). In contrast, Biologically controlled mineralization is an intercellular processes and the mineral thus formed is distinct with aspect to the inorganic counterparts. Initially, the prokaryotic organisms mineralized single crystal to aggregate near vesicle, spicules etc. A Magnetotactic bacterium is an example of this type, which formed magnetite chains within organic sheaths (Lovley et al. 1987). Metazoans that form complex mineralized structures also belong to biologically controlled mineralization class.

In organisms, a variety of mineral structures such as calcium carbonate, silica and calcium phosphate (Figure 1 Although it may appear that silica structures are not very common in organisms, it is present in six of the eight eukaryotic clades. In animals, it is limited to sponges and copepods (Sullivan et al. 1975). However, in plants it is found in algae (both marine and fresh water), grasses, sedges etc. Silica skeletons have evolved at least eight to ten times in eukaryotes. Calcium phosphate skeletons are limited to bony animals with lingulate branchiopod as exception. Their limited occurrence might be due to the cost of producing the biomineral even though it shares many similarities with carbonate skeletons in terms of organic components involved in mineral formation (Lopez et al. 1992; Kawasaki and Weiss 2006).

Simple biominerals

Simple biomineralized structures are constituted by single type of crystals and are usually found in unicellular organisms e.g. magnetotactic bacteria and coccoliths.

Magnetotactic bacteria produce magnetite inside a vesicle named magnetosome.

Magnetosomes are usually 35-120nm in size. However the size, shape and composition of the magnetite is species dependent (Bazylinski and Frankel 2004).

In case of magnetite, the crystal composition can vary, and can be composed of iron compounds such as iron oxide (magnetite, Fe304) or iron sulfide (greigite, Fe3S4; mackinawite, FeS) (Arakaki et al. 2003). Interestingly, even though magnetite is produced by the influence of organic molecules, so far no study has reported the presence of organics inside the magnetite crystals. The morphologies of the crystals can vary from cubo-octahedral, parallelepipedal, to bullet shaped (Meldrum and Mann 1993).

Microalgae, coccoliths produce an exoskeleton using calcite, a polymorph of calcium carbonate (Young et al. 1999). Coccolith can be classified as hetero or holo-coccoliths based on their species-specific shapes. Heterococccoliths are made up of complex crystalline structure (Figure 1.2) (interlocked calcite rhombohedra crystals) where as holococcoliths are composed of simple crystalline structures (Young et al. 1991). These unicellular microorganisms can perform high definition biominerals of controlled shapes and sizes.

Composite biominerals

Composite biomineralized structures are made of several layers of different crystal polymorphic forms. In addition, it might also possess amorphous inorganic phase, which interacts with the crystalline phase through organic components. Most of the multicellular organism produces composite biominerals and few examples of such structures are mollusk exoskeleton, avian eggshell, vertebrate bones, teeth etc. The nano crystals formed in these animals are arranged in a regular fashion to form a microstructure. Organic molecules play a significant role in the formation and arrangement of crystals (Lowenstam and Weiner 1989).

Organic components in biomineralization

Unique inorganic-organic composites synthesized are mediated by organic molecule, which provide scaffold, assist in nucleation and crystal growth and finally inhibit the crystal growth. Till date, several organic molecules have been reported in different mineralized structures. Out of which, proteins were suggested to control the biomineralization process (Wheeler et al. 1981; Giuseppe Falini et al. 1996).

Polysaccharides

Chitin is the second most abundant biopolymer after cellulose and it is found widely among protozoa, animal and plant kingdoms (Arias and Fernández 2008).

Chitin is a linear polymer of α or β (1-4)-2-acetamido-2-deoxy-D-glucapyranose and plays a significant role in the biomineral formation in several organisms such as molluscs, insects, crustaceans, fungus and was recently reported in sponges (Khor 2014)(Ehrlich 2011). α-chitin has the capacity to form linear polymer chains, which is water insoluble and is mostly preferred by marine organisms as the nucleation event is undisturbed by seawater. However some organisms such as barnacle, diatoms contain β chitin in their biomineralized structure (Fernandez et al. 2002). Chitin offers two or three-dimensional scaffolding for the mineral nucleation and intriguingly in vitro crystal growth revealed that in some cases chitin regulates crystallization (

Lipids

Lipids are water insoluble molecules and occur in a simple to very complex forms.

Types of biominerals

The remarkable properties of the biominerals cannot be attributed to single crystals but to the accumulation and organization of several nano-sized crystals. As indicated earlier, there exist a wide variety of mineralized structures that are brought into play by the organisms characterized by different shapes for specific roles. Next part describes about the most common biomineral systems such as magnetite, calcium phosphate and calcium carbonate.

Magnetite

Birds and fishes navigate precisely to a location thanks to magnetized biominerals.

Basic principle behind this is very similar to that of magnetic compass that orients the needle always in the north-south direction. The magnetite found in the ethmoid region above the beak in birds and head of the fish serves as the magnetoreceptor to find its location and direction (Wiltschko et al. 2002). Initially, the presence of magnetic iron oxide was found in teeth of marine invertebrates (Lowenstam 1962), which is used to scrap algae from substrates and later it was found in several organisms including honeybees. They evolved magnetic sensory system to enable them to move to specific location for spawning during a particular season of the year and also to identify their geographical location (Blakemore 1975). This mineralization is not restricted to only higher eukaryotes, even some magnetotactic bacteria uses these mechanism (Arakaki et al. 2008; Arakaki et al. 2015a). Bacteria uses the magnetic information to identify the depth of their habitat, as at higher depth food availability is limited and the dense population in shallow waters translates into heavy competition. Thus, magnetotactic bacteria use magnetic property to identify the proximal location for better survival.

Silica

Silica or silicon dioxide is the most abundant compound in the earth's crust and calcareous sponges serves as an important link between the silicifying and calcifying organisms.

Calcium carbonate

One of the most abundant biogenic minerals, calcium carbonate is found in many organisms ranging from unicellular foraminifera to biggest animal that exist in the biosphere. Otolith in the human ears is made of CaCO3, which helps in spatial orientation and upright position. During Cambrian, abundant elemental calcium and atmospheric CO2 precipitated excess CaCO3 in the ocean. This scenario is an important opportunity for eukaryote evolution, which favored several marine organisms to evolve CaCO3 biomineralization.

Sea urchin calcium carbonate biominerals

Sea urchin belongs to the phylum echinoderms and is characterized with spines that are made-up of calcium carbonate. In sea urchin larvae, more than 200 proteins were found to intercalate into micron-sized spicules that provide structure and shape to them (figure 1 In lieu of the above-mentioned mineralization steps, another theory called "nucleation-seeding" is proposed for the bone formation. This is based on the super saturation of calcium and phosphate at the mineral growth site (Strates and Neuman 1958). The collagen acts as an organic template and the space between the collagen templates is called 'hole zone' which exhibits high affinity for the calcium and phosphate ions and eventually mineralization takes place here (Hodge and Petruska 1963; Glimcher 1976; Weiner and Traub 1986). However, the inhibition of the crystal growth is controlled by the proteoglycans such as decorin and biglycan.

Bivalves and biomineralization

Mollusca are one of the morphologically and ecologically diverse metazoan phyla that comprise more than 200,000 living species (Runnegar 1996). A typical feature of majority of molluscs is their exoskeleton and which makes one of the driving forces for their evolutionary success Considering the richness of the species, bivalves are not distributed evenly across the globe; it varies with latitude, longitude and bathymetry. Globally there is a Ancestral bivalves utilized long labial palps for feeding even though numerous extant bivalves use ctenidium to filter feed (Taylor 1973). Although, a pair of gills is used for respiration, it also captures food particles from inhalant current and are transported to the mouth (Gainey 2010). The gill contains a special muscle called as water tube muscles that control and regulate feeding and respiration.

In most bivalves, a pair of mantle wraps the visceral mass and fused at the edges with openings anteriorly for the foot and posteriorly for the exhalant and inhalant siphons. Normally, sensory cells i.e. pallial eyes or tentacles are located in the mantle edge, which serves as the sensory organ in bivalves. The digestive tract of typical bivalves is made up of an esophagus, stomach, and intestine. The irregularly coiled intestine ends at anus near exhalent siphon through which the waste materials are excreted. A pair of kidney and gonad is also located near the exhalent siphon for excretion and reproduction. Heart of the bivalves consists of two auricles and one ventricle, which interact with both anterior and posterior aorta. Bivalves have the simple nervous system with three ganglia that extends the nerves to all the organs (Gosling 2004). Bivalves have a large, wedge-shaped foot and its movement is accomplished by changing blood pressure and muscle action (Figure 1.11). In burrowing species the foot is enlarged and is used as a powerful digging tool (Lee et al. 2012). The foot of mussels is adapted with byssus thread by which it can attach to the substrate.

Shell

Composition

The shells of bivalves are made up of 95% calcium carbonate mineral and between 1% to 5% organic components such as proteins, carbohydrates and lipids (Lowenstam and Weiner 1989). Interestingly, the CaCO3 mineral in mollusc can be of different polymorphs such as calcite, aragonite and to some extent vaterite.

Calcite and aragonite are the two predominantly occurring polymorphs of calcium carbonate in the shells of marine organisms and have very similar crystal structures. The calcium ion is located almost in the same position in lattice (001) layers alternative to carbonate ion layers; however, the difference is in the organization and orientation of the carbonate ions. Calcite nucleation is spontaneous in the marine environment and whereas the presence of magnesium ions in the sea water along with organic molecules secreted by the animals favors the formation of aragonite (Kitano et al. 1969). Each polymorph has a unique property, for example calcite is less prone to dissolution and aragonite is more resistant to mechanical stress. Only very few bivalve shells are completely made of either of these polymorph but in majority of the bivalves these polymorphs coexist and imparts unique physio-chemical property to the shell (Harper 2000). It is considered that aragonite is the primitive form, since the ancient seawater favored the formation of aragonite due to its higher magnesium content. The organisms that evolved later, adapted to the subsequent calcitic seas (Wood and Zhuravlev 2012) in which the magnesium to calcium ratio is lesser. Contrarily, modern bivalves exhibit both aragonite and calcite minerals distribution in their shell along a clear polyphyletic distribution, which makes the evolution of shell mineral composition debatable (Harper 2016).

Microstructure

Microstructure of the shell is defined as the arrangement of basic structural unit i.e. CaCO3 polymorphs as granules, tablets, fibers, laths, rods or blades (Figure 1.12). Bivalve shell consists of periostracum, prismatic and nacreous layer. Below are the definitions for these three layers suggested in "Illustrated glossary of bivalvia" (Carter et

Proposed mechanism for bivalve biomineralization

The key step in understanding the complex biomineralization process is to learn about the different constituents such as minerals, scaffolding molecules, other organic components and the interactions among them. So far, several models have been proposed for the mechanism of shell formation. Weiner Revenue from blue mussels production is approximately 231 millions euros (179,934 tones) and that of oyster is about 295million euros (142,000 tones). Soft shell clam (M. truncata) was chosen for its conservatory significance as it has been notified as the species of highest conservation need by wild action plan in the year 2012. In addition, the four species differ in their evolutionary lineage, M. truncata belongs to the taxon Heteroconchia and diverged from the other three species The outer layer of both the valves are composed of simple prismatic calcite structures, foliated calcite structure is the most ubiquitous, the curved area in the shells are covered by chalky calcite and in some rare cases several chambers are formed in the valves where foliated structures are replaced by complex cross foliated calcite (Carter 1980; Higuera-Ruiz and Elorza 2009).

Blue mussel -Mytilus edulis

Blue mussels are one of the widely distributed bivalve species, extending from Russian White Sea to Atlantic Ocean of southern France. This species is both euryhaline and eurythermal; therefore, it can be found in marine as well as brackish waters and are well acclimated to the temperature range 5°C-20°C.

Moreover, it is one of the bivalve species with high tolerance to wide fluctuations in temperature, salinity, pH etc. It can be found in high intertidal region to sub-

King scallop -Pecten maximus

Pecten maximus are found in sub-littoral areas extending from the east coast of Mya truncata is a filter feeder and burrower, which lives 1-2 feet below the sand or mud. Adult reaches the maximum length of 8 cm. The shells are completely made of aragonite with outer granular and inner cross-lamellar structure.

Aim of this study

The objective of my research is two-fold:

1. To improve the knowledge about the shell matrix proteins in the context of shell formation in four selected bivalve models using proteomic approach.

To achieve this goal mantle transcriptome data generated by the collaborators of the consortium was used to identify the SMPs. In fact, this work attempts to identify the key proteins involved in the shell formation in addition to the proteins that are specific to different CaCO3 polymorphs. • Arakaki A, Nakazawa H, Nemoto M, Mori T, Matsunaga T. 2008. Formation of magnetite by bacteria and its application. J. R. Soc. Interface 5:977-999.

• Arakaki A, Shimizu K, Oda M, Sakamoto T, Nishimura T, Kato T. 2015a. Biomineralizationinspired synthesis of functional organic/inorganic hybrid materials: organic molecular control of self-organization of hybrids. Org. Biomol. Chem. 13:974-989.

• Arakaki A, Shimizu K, Oda M, Sakamoto T, Nishimura T, Kato T. 2015b. Biomineralizationinspired synthesis of functional organic/inorganic hybrid materials: organic molecular control of self-organization of hybrids. Org. Biomol. Chem. 13:974-989.

• Arakaki A, Webb J, Matsunaga T. 2003. A novel protein tightly bound to bacterial magnetic particles in Magnetospirillum magneticum strain AMB-1. J. Biol. Chem. 278:8745-8750.

•

Shotgun Proteomics

Shotgun proteomics refers to the direct analysis of complex protein mixture to rapidly generate a global profile of the protein in the sample. It is a gel-free approach often termed as "Multidimensional Protein Identification Technology"

(MudPIT) where high-pressure liquid chromatography (HPLC) is used to separate extensively the peptides before fragmentation in mass spectrometry (Wu and MacCoss 2002). In bottom-up approach, the proteins in the samples are identified by analyzing thousands of peptides resulting form enzymatic digestion of the proteins (described later in this chapter). This approach is used to characterize proteins from complex mixture (Dirk A. Wolters et al. 2001; Yates 2004). On the other hand, top-down approach is used to characterize intact proteins and has the advantage of determining protein isoforms and post translation modification (PTM) (figure 2.3). One major disadvantage of this approach is that during mass spectrometry the intact proteins are difficult to fragment and requires considerable sample purification and concentration.

Middle-down approach is a hybrid of the other two approaches offering larger peptides and providing insight about PTM (Wu et al. 2012).

Peptide separation

Peptides are separated or pre-fractionated using different chromatography techniques such as ion exchange (IEX), chromatofocusing, gel filtration, reverse phase (RPC), affinity, etc. (Haynes and Yates III 2000). IEX separate proteins or peptides based on their charge and can separate peptides that differ by even single amino acid. In affinity chromatography, the separation is based on affinity of the peptides to the immobilized functional groups such as chemical ligands coupled to the chromatography matrix. Affinity chromatography is generally used to enrich a particular class of peptides with high specificity. In RPC, the separation is based on the hydrophobic interaction between the stationary phase and the peptides. Increase in the percentage of the organic solvent results in the dissociation of this interaction resulting in elution of the solute according to its hydrophobicity. In proteomics, the separation of the peptides is carried out using nano-HPLC technique, which uses nano-HPLC column and operates at very low flow rates (nanoliter/minute) for peptide separation leading to higher sensitivity and requires lower amount of sample for analysis.

Mass spectrometry

Both gel-free and gel-based proteomic approaches use mass spectrometry approach. In de novo approach, peptide sequences are directly predicted from the MS/MS spectra without inferring the database (Liska and Shevchenko 2003).

In contrast, database search approach correlates the MS/MS spectra with theoretical peptides that are generated in-silico from the protein sequence database (figure 2.8). Database search approach is best suited for the protein identification only if the database contains all the sequences of the proteins that are present in the samples. Nd is number of matches to decoy sequences and N is the number of peptides qualifies the cut-off. The main advantage of target decoy approach is it uses minimal distribution assumptions. However, performing the search against decoy database increases the search time by two folds. iTRAQ can be used for both relative and absolute quantification. In this method, the tags are labeled in the N-termini and lysine residues of the peptide. During Label free approach overcomes the drawbacks in the labeling method such as cost of the labeling reagents, inefficient labeling, difficulty in the analysis of less abundant peptides and limited sample numbers. Label free quantification is performed in two different ways: spectral counting and spectral feature based approach (figure 2.12). In spectral counting method, the frequency of the peptide spectral match of the respective protein is correlated with their amount. This method use simple normalization and statistical analysis i.e. protein abundance index (PAI), which is defined as the number of identified peptide divided by the number of theoretically observable peptide for each protein (Cutillas and ). However, our work aims to decalcify shell powder and using strong acid like HCl would hydrolyze the shell matrix proteins. EDTA is weak organic acid and is a chelating agent and bonds to the metal ions. In fact, it binds to the calcium ion leading to the dissolution of the CaCO3 molecule. On the other hand, EDTA also has strong affinity towards magnesium ions, which is a key cofactor found in many bivalve shell proteins. Recent works on shell matrix proteins in CaCO3 biominerals employ HOAc, since it is a weak organic acid and less destructive to the matrix proteins. Also, proteins are extracted at lower temperature (usually at 4°C), which reduces the risk of hydrolysis and other chemical reactions.

Quantitative proteomics

Sample preparation in biomineralization

Shell cleaning and decalcification

Sample preparation for mass spectrometry analysis

Although many enzymes are available for protein digestion, serine protease trypsin is preferred often in proteomics experiments due to its high cleavage specificity (carboxyl terminal side of arginine and lysine) and could generate peptides of optimal size ideal for fragmentation in the mass spectrometer.

In case of shell matrix proteins, many of them have unusual amino acid composition and low complexity domains, which are characterized often by lack of trypsin cleavage sites leading to the generation of fewer peptides. Bédouet et al, suggested acid hydrolysis using TFA along with trypsin digestion to increase the sequence coverage of proteins with complex repetitive motifs. However, it resulted in generating peptides that corresponds to 4% of the total matrix proteins (Bédouet et al. 2012) and for complex samples, the usage of acid hydrolysis may not be feasible. • Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R. 1999. Quantitative analysis of complex protein mixtures using isotope-codedaffinity tags. Nat. Biotechnol. 17:994-999.

Introduction

This chapter deals with the identification of the shell matrix proteins from the four bivalve species that were introduced earlier in chapter 1. A comparative study of the SMPs from these species offers interesting perspectives. First, the microstructures of the four species differ in their construction and as well as the calcium carbonate polymorph that composes the different shell layers. Second, identification of SMPs using a standardized analytical workflow enables to discover the conserved proteins in the four species in the context of evolution. In this work, we could identify few proteins that are potentially part of the "basic biomineralization tool kit" at least in bivalves and also ones that are polymorph specific. This study permits us for the first time to propose categorically that shell not only offers mechanical protection but also biochemical defense based on the identification of proteins containing immunity related domains. A pertinent question is how the presence of these proteins affects the biomineralization mechanism and their contribution to the total organic content.

The first publication 'Shell matrix proteins of the clam, Mya truncata: Roles beyond shell formation through proteomic study' is one among the multi-disciplinary works contributed by the CACHE PhDs to a special issue "cells to shells" in Marine Genomics journal. This publication deals with the different shell matrix proteins identified in M. truncata. In addition to shell proteomics, mantle proteomics was also carried out to make a comparative study on shared proteins between them. In fact, only few identified proteins are common to both shell and mantle tissue. Indepth functional analysis of shell proteomic results revealed many number of proteins including biomineralization proteins, immunity domain containing

Mussel adaptation to low salinity leads to altered shell proteomes 4.1 Introduction

In chapter-3, the diversity of SMPs from four bivalve models was reported, in which special emphasis was given to the proteins with domains related to immunity functions. Moreover, based on the SMPs it was evidenced that among the four bivalves M. edulis has high adaptive potential. In this chapter, the M.

edulis from North Sea is compared with two brackish Baltic Sea populations to identify the potential protein candidates that are modulated with respect to abiotic and biotic components associated with their environment and in particular salinity.

It has been already reported that the Baltic mussels are relatively fragile and smaller in size compared to North Sea mussels. Although it depends on the difference in calcium saturation, which is influenced by fluctuations in salinity, it also depends on the other factors including biotic components. Baltic Sea is characterized with fewer amounts of predators and pathogens, which also makes a reason for the Baltic mussels to produce fragile and thin shells.

Here we expect the basic biomineralization tool kit proteins to be modulated among these mussel populations. Apart from biomineralization proteins, we analyzed the modulation of proteins containing domains related to immunity and in particular proteins involved in phenoloxidase pathway. This is the first work in biomineralization studies to identify the modulation of SMPs and correlate it to the shell phenotype.

Interestingly, proteins with immunity domains are also modulated. Our findings indicate that successful adaptation of mussels to low salinity requires modulation of the different types of shell matrix proteins resulting in shell phenotype

distinctions with altered mechanical and biochemical defense properties.

The Baltic Sea is characterized by a continuous decrease in the salinity gradient from the West to the East, which is negatively associated to a decline in biodiversity of benthic marine invertebrates. Apart from the challenges that reduced salinity imposes on osmoregulatory systems 1 correlated reductions in calcium and inorganic carbon further limit organisms that form calcified shells 2- 4 . Mussels (Mytilus spp.) can dominate benthic habitats in Baltic coastal regions, and can make up more than 80% of the benthic macro fauna biomass 5 when salinity is down to around 5 psu. Baltic Mytilus achieve competitive dominance despite growing slower and reaching smaller final sizes when compared to North Sea populations 6 . Recent findings indicate that Baltic mussels can cope better with reduced inorganic carbon availability characteristic for the Baltic Sea than

North Sea animals [START_REF] Thomsen | Naturally acidified habitat selects for ocean acidification-tolerant mussels[END_REF] . The underlying mechanisms that enable local adaptation are unknown, but modifications of shell organic compounds in charge of calcified shell layer edification might have been a primary target of selection.

In bivalves, shell formation involves the elaboration of an organic framework, constituted of proteins, glycoproteins, lipids, carbohydrates, which act as a template for the CaCO3 minerals to nucleate [START_REF] Lowenstam | Minerals formed by organisms[END_REF] . While calcification rate is governed by the calcium carbonate saturation state at the site of shell formation 9,[START_REF] Waldbusser | Saturation-state sensitivity of marine bivalve larvae to ocean acidification[END_REF] , shell organic compounds may increase rates of mineral precipitation, even under low calcium carbonate saturation state conditions as typically encountered in the Baltic Sea [START_REF] Thomsen | Food availability outweighs ocean acidification effects in juvenile Mytilus edulis : laboratory and field experiments[END_REF] . The different CaCO3 polymorphs and their associated organics (proteins, glycoproteins, lipids and carbohydrates) are arranged in distinctive patterns, resulting in a vast number of shell microstructures [START_REF] Carter | Guide to bivalve shell microstructures[END_REF] . The composite organic-inorganic material imparts unique physio-chemical properties to the bivalve shell impacting shell strength, shape and ultimately the fitness of the organisms [START_REF] Harper | Are calcitic layers an effective adaptation against shell dissolution in the Bivalvia?[END_REF] .

Although shell matrix proteins (SMPs) that are entrapped within the shell exert control over calcium carbonate crystal nucleation, growth and polymorphism, the nature and quantity of SMP components is affected by the organism physiological processes, which are intimately influenced by environmental pressures. In order to understand how Mytilus can form shells under the extremely low calcium carbonate saturation states typical of the Eastern Baltic Sea, it is important to learn more about the specific composition of the shell organic matrix, so called the calcifying matrix. We hypothesized that the different Baltic Mytilus populations modulate their shell matrix protein compositions and that the lowered calcium carbonate saturation state observed in the Eastern Baltic Sea is noticeably counterbalanced by a higher total amount and a population variation in the relative composition of shell protein matrices.

Results and discussion

Studying shell matrix proteins through proteomics offers a unique possibility not only to identify proteins in the shell matrix space but also their modulation and provide new insight about the intricate mechanism by which the organism maintains a balance between the environment (e.g. salinity) and shell formation.

Often, proteins secreted by the mantle epithelium undergo maturation and modifications before their integration in the shell and consequently, transcriptomic expression data might not correlate with that of shell matrix protein contents [START_REF] Joubert | Transcriptome and proteome analysis of Pinctada margaritifera calcifying mantle and shell: focus on biomineralization[END_REF] . In the current work, 300 transcripts (SMPs) could be matched by searching the M. edulis transcriptome database. Based on the presence of characteristic functional domains, majority of the proteins could be grouped in 5 main categories including chitin related proteins, metal ion binding, calcium transport, proteases ou protease inhibitors, and enzymes related to oxidation-reduction.

So far, the effects of different parameters such as temperature, salinity and pH have been correlated to shell phenotype responsivness [START_REF] Fitzer | Biomineral shell formation under ocean acidification: a shift from order to chaos[END_REF][START_REF] Welladsen | The effects of exposure to near-future levels of ocean acidification on shell characteristics of Pinctada fucata (Bivalvia: Pteriidae)[END_REF][START_REF] Gizzi | Shell properties of commercial clam Chamelea gallina are influenced by temperature and solar radiation along a wide latitudinal gradient[END_REF][START_REF] Tunnicliffe | Survival of mussels in extremely acidic waters on a submarine volcano[END_REF] . Often, these data are obtained under controlled laboratory conditions, lacking the complexity of the interplay between different parameters that characterize in the habitat of the

Modulation of biomineralization proteins

What constitutes the basic biomineralization toolkit in bivalves is yet to be defined. However, the evolutionarily conserved toolkit for shell formation, crystal specific and species-specific SMPs from four highly divergent bivalves including M. edulis was reported [START_REF] Arivalagan | Insights from the shell proteome: biomineralization to adaptation[END_REF] . Recently, Feng and is co-workers observed that proteins containing domains such as carbonic anhydrase, sushi, Von willebrand factor A (VWA) and chitin binding domains were conserved in the shell matrix of many bivalve species [START_REF] Feng | Identification of conserved proteins from diverse shell matrix proteome in Crassostrea gigas: characterization of genetic bases regulating shell formation[END_REF] . In this work, proteins such as blue mussel shell protein (BMSP), sushi like protein, chitinase-3 and tyrosinase, which are part of the basic tool kit were found to be modulated (Fig. 1 andsupplementary table. 1), testifying for the occurrence of alteration in biomineralization processes. In fact, proteins containing domains such as VWA and chitin binding domain-2 (ChBt-2) contribute to the silk-like protein complexes [START_REF] Weiner | Aspartic acid-rich proteins: major components of the soluble organic matrix of mollusk shells[END_REF] and act as glue assembling β-chitin sheets and minerals [START_REF] Suzuki | Identification and characterisation of a calcium carbonate-binding protein, blue mussel shell protein (BMSP), from the nacreous layer[END_REF] . The relatively higher modulation of the above proteins in Baltic mussel shells points out that scaffolding β-chitin in the shell matrix space may be altered. Glycine and alanine rich (GA-rich) proteins are hydrophobic and sandwich β-chitin supporting its β-sheet conformation. In addition, serine and aspartic acid rich (SD-rich) proteins are believed to surround the above complex and interacts with mineral phase aiding in crystal formation [START_REF] Weiner | Aspartic acid-rich proteins: major components of the soluble organic matrix of mollusk shells[END_REF][START_REF] Falini | Control of Aragonite or Calcite Polymorphism by Mollusk Shell Macromolecules[END_REF][START_REF] Weiner | X-ray diffraction study of the insoluble organic matrix of mollusk shells[END_REF][START_REF] Nakahara | Fine structure and amino acid composition of the organic envelope in the prismatic layer of some bivalve shells[END_REF] . Intriguingly, these two chitin interacting proteins show relatively lower modulation in the Baltic mussels and thus it can be inferred that decrease in the relative amounts of both GA and SD-rich proteins might lead to impaired microstructural organization of the shell as indicated by the qualitative observation of the shells under scanning electron microscopy (supplementary fig. 2). In case of Baltic mussels, due to lower salinity and calcium under-saturated conditions, crystallization is less favored and imposes considerable remodeling of the shell. Relative increase in enzyme chitinase-3 in Baltic mussel shells concords with this idea. In fact, chitinase-3 plays a key role in the metabolism of chitin polymers and provides the template for mineral nucleation. Its expression was found to substantially increase in the outer mantle epithelial cells of P.

fucata during shell regeneration experiment [START_REF] Li | Molecular characterization and expression analysis of chitinase from the pearl oyster Pinctada fucata[END_REF] and also during shell remodeling in the freshwater snail L. stagnalis [START_REF] Yonezawa | Molecular cloning and functional analysis of chitinases in the fresh water snail, Lymnaea stagnalis[END_REF] . Increase in the total shell organic content in Baltic mussels with respect to North Sea mussels could reflect this remodeling process (Fig. 3). Contrastingly, under acid stress the chitinase-3 gene expression was found to be down regulated in mussels [START_REF] Hüning | Impacts of seawater acidification on mantle gene expression patterns of the Baltic Sea blue mussel: Implications for shell formation and energy metabolism[END_REF] indicating that chitin metabolism may vary in response to the abiotic factors. We analyzed shell morphometric patterns of N=3 equally sized animals collected at three sampling locations (Sylt, North Sea, average salinity >30 psu;

Nynashamn and Usedom, average salinity < 10 psu). We found that the thickness of the periostracum, an uncalcified chemically resistant cuticle covering the shell mineral layer, what clearly similar between organism of the same populations originating from the different location, with thicker layers in the Baltic populations (Fig. 3a). Increase in the periostracum thickness can be associated with the potential to calcify to a greater extent and protects shells from external dissolution when calcium carbonate saturation state is low [START_REF] Thomsen | Calcifying invertebrates succeed in a naturally CO2-rich coastal habitat but are threatened by high levels of future acidification[END_REF] . Interestingly, tyrosinase and byssal peroxidase like proteins levels were elevated in Baltic mussel shells following the removal of the periostracum (Fig. 1, supplementary table. 1 and Fig. 2) suggesting that processes that are important for periostracum maturation (i.e. oxidation of tyrosine to L-Dopa and L-Dopa to O-quinone [START_REF] Qin | In-depth proteomic analysis of the byssus from marine mussel Mytilus coruscus[END_REF][START_REF] Zhang | A novel putative tyrosinase involved in periostracum formation from the pearl oyster (Pinctada fucata)[END_REF] are also vital for shell matrix formation, particularly those of the more external calcitic prism layer, under low calcium carbonate saturation states encountered in the Baltic Sea. Tyrosinases have also been extracted from shells of other Mytilus species [START_REF] Gao | Layer-by-Layer Proteomic Analysis of Mytilus galloprovincialis Shell[END_REF] and acclimation of Baltic mussels to ocean acidification (which goes along with dramatic reduction in calcium carbonate saturation state) resulted in very strong (>100 fold) increase in mRNA expression of a mantle tissue specific tyrosinase isoform [START_REF] Hüning | Impacts of seawater acidification on mantle gene expression patterns of the Baltic Sea blue mussel: Implications for shell formation and energy metabolism[END_REF] . Generally, higher organic content, together with reduced calcium and inorganic carbon availability lead to increased energy demand and this may explain the dwarfism in Mytilus spp. in the low saline Baltic Sea [START_REF] Kautsky | Genotypic and phenotypic differences between Baltic and North Sea populations of Mytilus edulis evaluated through reciprocal transplantations. I Gorwth and morphology[END_REF] .

Several proteins appears not modulated in our data, comprising proteins containing domains such as carbonic anhydrase (CA), epidermal growth factor (EGF) and fibronectin-3 (FN3) (Fig. 2), which are part of the basic biomineralization tool kit and play a critical role in shell formation. CA converts carbon dioxide to bicarbonate ions [START_REF] Miyamoto | A carbonic anhydrase from the nacreous layer in oyster pearls[END_REF] , EGF might interact with calcium ions and while fibronectin bring anions and cations to proximity to enhance nucleation as proposed for calcium phosphate biomineralization [START_REF] Ba | Complementary effects of multi-protein components on biomineralization in vitro[END_REF] . Due to this apparently unchanged regulation in CA, it can be inferred that bicarbonate for nucleation might be here not a decisive factor for shell alteration process by low salinity. On the other hand, it is also possible that the shell does not reflect the actual expression of different isoforms of CA in the mantle tissue, as it has been proposed earlier [START_REF] Ivanina | Interactive effects of elevated temperature and CO2 levels on energy metabolism and biomineralization of marine bivalves Crassostrea virginica and Mercenaria mercenaria[END_REF][START_REF] Roy | Carbonic anhydrase and metazoan biocalcification : a focus on molluscs[END_REF] . Calcite specific EGF containing protein seems not modulated but matrix protein-1 containing lam-G domain is modulated (supplementary fig. 1 and supplementary table. 1) even though lam-G is similar to EGF in function and structure [START_REF] Panayotou | Domains of laminin with growthfactor activity[END_REF] , which binds to aragonite crystals [START_REF] Suzuki | The Molecular Evolution of the Pif Family Proteins in Various Species of Mollusks[END_REF] . matrix maturation [START_REF] Marie | Deep conservation of bivalve nacre proteins highlighted by shell matrix proteomics of the Unionoida[END_REF] . Similarly, C-lectin domain containing protein perlucin is predominantly believed to be involved in nucleation processes of calcium carbonate crystals [START_REF] Blank | The nacre protein perlucin nucleates growth of calcium carbonate crystals[END_REF] . However, C-lectins in animals are always described so far in the context of immunity-related process [START_REF] Mcgreal | Divergent roles for C-type lectins expressed by cells of the innate immune system[END_REF] . Thus, the presence of these groups of proteins, not specific to calcifed matrices, is in contrast to the role of other known and "more classical" biomineralization proteins. Interestingly, two SMPs identified in this work show homology to shell mytilin and mytimycin precursor both of which are known to be involved in innate immunity [START_REF] Charlet | Innate immunity. Isolation of several cysteine-rich antimicrobial peptides from the blood of a mollusc, Mytilus edulis[END_REF] . Curiously, many Mytilus SMPs that are not considered to be part of the exclusive shell protein sets are also clearly modulated in Baltic mussels raising question about their function in the shell formation or conservation processes. Interestingly, many of these SMPs could be associated to the phenol oxidase (PO) pathway [START_REF] Arivalagan | Insights from the shell proteome: biomineralization to adaptation[END_REF] .

POs are copper containing class of enzymes, which include tyrosinase, catecholase and laccase [START_REF] Luna-Acosta | Differential tissue distribution and specificity of phenoloxidases from the Pacific oyster Crassostrea gigas[END_REF] . In case of insects and crustaceans PO participates in innate immunity, pigmentation (melanization) and repair of damaged exoskeleton including hardening (sclerotization) [START_REF] Cerenius | The prophenoloxidase-activating system in invertebrates[END_REF] . Activity and expression of POs are observed in hemocytes and other tissues of bivalves as immunity and stress responses [START_REF] Luna-Acosta | Differential tissue distribution and specificity of phenoloxidases from the Pacific oyster Crassostrea gigas[END_REF][START_REF] Kuchel | Phenoloxidase activity as an indicator of stress in the silver-lip pearl oyster, Pinctada maxima[END_REF][START_REF] Moullac | Effect of hypoxic stress on the immune response and the resistance to vibriosis of the shrimpPenaeus stylirostris[END_REF] but only tyrosinase is suggested to be also involved in the periostracum and calcified shell layer formation [START_REF] Zhang | A novel putative tyrosinase involved in periostracum formation from the pearl oyster (Pinctada fucata)[END_REF] . The identification and modulation of many known proteins and enzymes belonging to the PO cascade suggests that they play a certain role in shell biomineralization or alternatively maturation processes. In addition, the presence of laccase like protein and its associated molecules in the shells of bivalves might be part of an efficient biochemical defense system, and seems here not to be exported to the shell just as simple cellular debris [START_REF] Marie | Proteomics of CaCO3 biomineral-associated proteins: How to properly address their analysis[END_REF] . In fact, the inactivated form of PO in the lobster cuticle was found to be activated by proteases [START_REF] Ferrer | Phenoloxidase from the Cuticle of Florida Spiny Lobster (Panulirus argus): Mode of Activation and Characterization[END_REF] .

Generally, PO activation starts with aggregation of several proteins and enzymes, which convert proPO to PO (Fig. 4). Proteinases (peptidase) are key molecules activating the PO cascade and are not modulated in both North Sea and Baltic mussels. However, serine protease inhibitors containing domains such as kunitz, serpins and TIMP (tissue inhibitor of metalloproteinase), which are negative regulator of the PO pathway, are highly modulated in Baltic mussel shells even though proPO (Laccase-4 like protein) is not modulated implying that PO pathway could not favored in Baltic mussels. The final outcome of PO activation leads to immune responses such as melanization, opsonization etc. [START_REF] Jiravanichpaisal | Cell-mediated immunity in arthropods: Hematopoiesis, coagulation, melanization and opsonization[END_REF] In crustaceans, antimicrobial peptide synthesis (AMPs) are initiated by PO through an unknown mechanism [START_REF] Amparyup | Prophenoloxidase system and its role in shrimp immune responses against major pathogens[END_REF] and a similar mechanism involving PO might be operating in Baltic mussels as evidenced by the down regulation of AMP mytilin (Fig. 1 and supplementary table. 1). It should be noted that mytilin gene upregulated in response to shell damage stress in clam [START_REF] Sleight | Transcriptomic response to shell damage in the Antarctic clam, Laternula elliptica: Time scales and spatial localisation[END_REF] . Mussels that have impaired PO activation could be susceptible to predators, pathogens, shell boring organisms, endoliths etc. In fact, the fauna and flora found in Baltic Sea is less diverse compared to the North Sea [START_REF] Johannesson | Life on the margin: genetic isolation and diversity loss in a peripheral marine ecosystem, the Baltic Sea[END_REF][START_REF] Kautsky | On the trophic role of the blue mussel (Mytilus edulis L.) in a Baltic coastal ecosystem and the fate of the organic matter produced by the mussels[END_REF]

Proteomic analysis

Peptides were desalted on a C 18 cartridge (Acclaim PepMap100, 5µm particles, 300µm i.d. x 5 mm length, Thermo Scientific) at 15 µl/min flow rate in buffer A, 0.05% aq.TFA/ACN 98:2 (v/v) for 5 min, before being eluted to a C 18 column (Acclaim PepMap100, C 18 , 3 µm particles, 75 µm i.d. x 50 cm length, ThermoFisher Scientific) at a flow rate of 220 nL/min using a gradient of buffer B, ACN/0.1% aq. The PSM score can be improved by taking into account mutation and posttranslational modification of many peptides that are retrieved from EST database or protein database. For example, in case of M. edulis, a number of MS/MS spectra could be matched to the peptides when mutation or substitution of amino acid is taken into consideration. However, it might be possible that both peptides (normal and mutated forms) are generated from the proteins (figure Although, it seems like all these sequences belong to the single mRNA, the sequence level variation confirms them as homologous proteins. Unlike mammals, the sequences in the molluscs (especially shell matrix protein sequences) are not conserved between species, which narrows the possibility to identify proteins only with the EST database of the organism. Table 1 shows very few to no protein matches when search was performed using M. truncata shell peptides against other bivalve EST databases. In addition, the diverse sequences in shell proteins hampers the functional annotation. Often, homologous protein search against NCBInr database results in high number of matches, which includes unknown, unnamed or hypothetical proteins. In such cases, the the bone matrix exemplifying the functional significance of phosphorylated proteins in biomineralization (Alvares 2014). In vertebrates, the proteins such as phosphophoryn and dentin matrix protein1 play a crucial role in crystal nucleation, binding (Sfeir et al. 2011) and transforming amorphous calcium phosphate to apatite (He et al. 2005). In addition, phosphophoryn activates morphogenesis pathways (Jadlowiec et al. 2006). This is the first work to report many phosphorylated SMPs (from bivalves) with diverse functions. This finding reveals the complex mechanism operating behind shell formation involving several signaling molecules, protein trafficking and protein-protein interaction, which reinforces our idea that shell is a dynamic system.

Fast evolving shell proteome leads to efficient adaptation

Rapid evolution of proteins is noted in molluscan shell matrix and is likely to

Repeated sequences and functional properties

Shell matrix proteins associated along with crystal can partly improve the mechanical properties of the shell. The relation between the repeating motifs and mechanical strength are well studied in the spider silk fiber where the repeated structures (AVSTH/GLY-rich) forms a 'β-sheet with a turn', which

Matrix proteins and calcium management in the changing environment

Vast number of studies and meta-analyses aimed to determine the impact of ocean acidification (OA) on marine and estuarine organisms concluded that OA imposes severe consequences on these organisms, especially molluscs. Although it is described that OA affects molluscs, it does not act solitarily. The increased oceanic CO2 concentration increases the temperature and in estuarine habitats OA fluctuates salinity and oxygen levels. The reduction in salinity in turn reduces calcium and inorganic carbon availability, which is very essential for shell forming molluscs to build their exoskeleton. In fact, the multiple stressors increase constrain of molluscs both during the early life and adult stages impacting shell formation, fertilization and maintaining acid-base balance etc. It is also hypothesized that the exposure of adults to adverse abiotic conditions would produce offspring with increased resilience.

Recently, Gazeau et al reported the summary of the previous works related to the impacts of OA on different molluscan species (Gazeau et al. 2013). Almost in all the species studied, the stressors affect the shell growth and calcification with most of the species acclimating to the conditions with a trade-off between metabolism and shell growth. For example, M. edulis mussels showed good resilience to low pH condition and acclimatize by decreasing shell size (Kautsky et al. 1990), altered material and microstructural properties (Fitzer et al. 2014; Fitzer et al. 2016). In fact, the mussels from Baltic Sea follow the same strategy to overcome multiple stressors. However this is not the case with oysters, although there is no significant change in growth, the shell hardness and fracture resistance is adversely affected and might reduce its capacity to withstand strong currents and predators (Dickinson et al. 2012).

The amount of organic content in the shells depends on the shell characteristics, which in turn depends on the environment of the organism. In four bivalves, organic content (calculated by summing up AIM and ASM) vary significantly between species (M. edulis-0.47%, C. gigas-0.41%, M. truncata-0.2%, P. maximus-0.22%). Relative changes in shell organic content among the species is an indicative of calcium management, which is described in chapter-4 where M.

edulis with impaired microstructure and calcium management showed relatively higher organic content. In addition, the change in organic content points out the modulation of the matrix proteins, which determines the shell characteristics including their mechanical properties.

Very limited number of studies has focused on the effect of abiotic stressors via matrix proteins and moreover these works primarily depends on mantle gene expression. Study using P. margaritifera subjected to temperature and food stress showed that the expression of two proteins Pif-177 and shematrin- Mussels from Baltic and North Sea revealed differential expression of several SMPs, which includes proteins that were already reported to be modulate under stressed conditions and also some newly identified proteins. The higher expression of proteins related to periostracum and mineral-chitin interface in stressed Baltic mussels is congruent with the idea of Parker and co-workers, that the protective organic matrix surrounding the shell is an important factor for the resilience of the species (Parker et al. 2013). However, the amount of carbonic anhydrase and other CaCO3 specific proteins clearly implies the impaired calcium management in Baltic mussels. The above discussion supports the idea A significant number of proteins with immunity related functions is found in all the four models and are species-specific. It seems that high diversity in such proteins might be associated with the potential pathogens in their environment.

Moreover, the high complexity of SMPs may underpin the evolutionary process, which leads to the successful diversification of the bivalves.

Comparative study of the mussel populations from low saline Baltic Sea and full marine environment North Sea revealed that many SMPs are modulated.

Increase in chitin related proteins and chitin-crystal interacting proteins along with non-modulated crystal nucleating proteins in Baltic mussels points to altered crystal nucleation and scaffold resulting in impaired shell microstructure.

A correlation may exist between the modulation of immunity related shell proteins and the pathogen. The data suggests that successful adaptation of Baltic mussel imposes a tradeoff between mechanical and biochemical defense properties of the shell, which in turn is reflected in the shell proteome.

Interestingly, North Sea mussels show relatively higher abundance in two proteins that are characterized by unusual amino acid sequence repeats. These proteins may adopt unique structural conformation and orient the crystal organization and thus modify the mechanical properties of the shell (e.g. tensile strength). In fact, the modulation of these proteins was observed between the mussel phenotypes characterized by thick, highly calcified shells and thinner with thicker periostracum shells. This indicates the potential of proteomics to investigate the shell phenotypes in relation with the environment.

Significant number of phosphopeptides identified in the mussel shell ensures the presence of phosphoproteins in the shell matrix. Skeletal and immunity related and few unknown proteins contain at least one phosphopeptides. However, the existence of phosphorylation/dephosphorylation mechanism in the shell matrix needs to be probed to confirm the role of the molecules involved in signaling pathway.

Previous studies from our group reported small molecules from the watersoluble fraction of nacre of Pinctada margaritifera. They were principally peptides of size less than 1kDa and were suggested to have a functional role The enzymes such as carbonic anhydrase, chitinase and tyrosinase are well known to the scientific community working on biomineralization. However, more often, the shell proteomics works result in the identification of some new enzymes, which is often considered as the byproducts of cellular secretion process and are accumulated in shell matrix along with other cellular debris (Mann et al. 2008). Our data from shells of M. truncata is rich in such new enzymes, which includes the enzymes such as glyceraldehyde 3-phosphate dehydrogenase (GPDH) and phosphoenol pyruvate carboxykinase (PEPCK) belonging to gluconeogenesis pathway. Our publication in marine genomics suggested that these enzymes as cellular debris. However, the identification of GPDH in coral shell matrix (Drake et al. 2013) and phosphoenol pruvate (PEP) in crustacean shell is quite intriguing. In fact, PEP is suggested to play a significant role in stabilizing amorphous calcium carbonate (Sato et al. 2011). Hence the newly identified enzymes in the shell proteomes from the present studies should not be neglected and these enzymes might possess functions implying biomineralization process.

Our work is the first to report several immunity related proteins in shell matrix and to discuss them in the context of immunity. The diversity of immunity proteins identified in four bivalve species is remarkable. Future studies may be oriented to characterize these immunity related proteins in shell matrix, which might lead to the discovery of antimicrobial peptides/proteins.

Until now, the expression of shell matrix proteins under different environmental stress conditions is studied using the mantle gene or mRNA expression.

However, these studies give only partial information about the integration and role of those candidates in the shell matrix. Also, proteins may undergo modification and degradation, which needs to be investigated and requires considerable involvement by the biomineralization community. Present shell proteomic study on mussel population from different locations reported herein is the first study of its type, which yields the protein candidates that are modulated in the shell giving their functional significance in the shell formation and resilience in case of harsh changing conditions. Future studies should consider using shell proteomics as the valuable tool to study the influence of environmental stress on shell formation. In addition, integrated quantitative proteomics, metabolomics and gene expression studies along with the biophysical studies would enhance our understanding of the complex shell forming mechanism.

Annexe

4 .

 4 Arivalagan et al, Biomineralization: Adaptation and shell repair in the context of ocean acidification, Labex short course on biomineralization, poster presentation, 1-5 Dec 2014, Paris, France

Figure 1 . 2

 12 Figure 1.2 SEM micrographs of complex structures of interlocked calcite crystals formed by heterococcoliths.

  Zhang and Gonsalves 1998; Neira-Carrillo et al. 2005). Polycarboxylated polysaccharides made of polyalduronic acid and polymers of uronic, tartaric, gloxylic acids are found in the unicellular algae coccoliths. These type of polysaccharides are involved in nucleation to growth and regulation of calcite crystals in the coccoliths (van der Wal et al. 1983; Marsh and Sass 1984). Sulfated polysaccharides or glycosaminoglycans (GAG) exist in varying lengths and are often found conjugated to proteoglycans. GAG is an alternating copolymer made of hexosamine and galactose or alduronic acid (Casu 1985; Iozzo 1998; Sugahara and Kitagawa 2000). GAGs are attached to proteoglycans through either O-link (serine and threonine) or N-link (Asparagine) (Huckerby 2002). In fact, the proteoglycans play important role in the crystallization processes and are classified into three different groups depending on the GAG structure, amino acid sequence and their potential role. The first group contains lecticans and hyalectans and is characterized by N-terminal globular domain interacting with hyaluronic acid (HA) (a non sulfated form of GAG) and C-terminal selectin domain. The second group consists of leucine rich repeat structures in their core region, which promote protein-protein interactions and play role in the organisation of collagen networks. The third group is usually found in cellular membranes and is involved in functions such as cell adhesion, signaling, proliferation and differentiation (Esko and Lindahl 2001; Bosman and Stamenkovic 2003). The role of proteoglycans in biomineralization has been reported in different organisms. In mollusks, it has been found in the sites of shell regeneration and is found to be one of the essential components in intracellular matrix components (Wada 1964; Saleuddin and Chan 1969). Proteoglycans are suggested to be involved in the inhibition of crystal growth during eggshell formation (Arias et al. 1992; Carrino et al. 1997; Hincke et al. 1999). Mucopolysaccharides are involved in the shell mineralization in corals (Goreau 1959).

  Vitamin A, K, D and E are examples of simple lipids and mono-, di-, tri-, acyl glycerides, phospholipids, sphingolipids and cholesterol are complex form of lipids. Association of lipids with mineralization process has been reported in a wide range of organisms. In case of bacteria, mineralization starts with the formation of vesicle made of lipids and in addition the mineral formed is capsulated inside the lipid layer(Lovley et al. 1987). Similarly, the external skeleton of foraminifera is basically formed inside the lipid vesicle before extruded from the cell(Weiner and Erez 1984). In addition to proteins and glycosaminoglycans, lipids were found in the histological studies investigating shell-repair membrane in snails (Abolinš-Krogis 1973). Phospholipids from coral skeletons show remarkable affinity to Ca2+ in in vitro suggesting their role as seeding site for CaCO3 deposition during shell formation in Scleractinian coral (Isa and Okazaki 1987).

  Normally, bacteria synthesize nano-sized magnetite crystals (Fe3O4), and are eventually aggregated into chains. The crystals are synthesized in a specific vesicle called magnetosome (size 20-100 nm). The formation of magnetosome is associated with the protein belonging to the Mam family such as Mam A, C, D, E, G, K etc. Experimental studies on Magnetospirillum sp. revealed genetically controlled stepwise processes, which leads to the formation of magnetite crystals. Intermediate steps include formation and alignment of vesicle and iron uptake as indicated in figure 1.3 (Suzuki et al. 2006; Tanaka et al. 2006). The protein MamK plays a significant role in the function of the magnetosites (Komeili 2006). The magnetite crystal that are randomly oriented and the net average magnetic dipole is zero. In contrast, the protein MamK arranges and orients the crystals in such a way that the magnetic dipole is at the maximum (Scheffel et al. 2006). MamC and MamF are smaller proteins with molecular weight of approximately 12kDa and possess transmembrane domains that form stable oligomers (Arakaki et al. 2014). MamD and MamG are hydrophobic Leu-Gly rich proteins that acts as a scaffold for mineral nucleation (Amemiya et al. 2007; L. Wang et al. 2012). Apart from the above-mentioned proteins, several other proteins also contribute to the formation and orientation of the magnetite.

Figure 1 . 3 .

 13 Figure 1.3. Schematic of hypothesized mechanism of formation of magnetite in magnetotactic bacteria. The adjacent figure shows the cubo-octahedral magnetite crystal formed by wild type M. magneticum (Arakaki et al. 2015b).

amounts to about 42 . 1 . 5 . 2 . 1

 421521 8% (percentage by weight). Despite its abundance, very meager amount of silica is present in organisms. Biosilicification is a process by which organisms form opal-like amorphous hydrated silica. Precipitation of silica on organic templates could have been abiotic, in which organic molecules merely serves as a template for nucleation or induced by biological activities or combination of both (Mukhopadhyay et al. 2004). Silicon in the form of silicic acid is the basic nutrient for diatoms, silicoflagellates, radiolaria and many sponges, which polymerize silica to build their skeletons (Maldonado et al. 2005). Diatoms, a unicellular algae well known for its spectacular design of cell walls made of amorphous silica. The silica scaffold of the diatom's cell wall is formed intracellularly in a membrane bound compartment and is deposited outside the cell after completion of the mineral forming process. Several organic molecules such as proteins and polysaccharides are involved in the silica biomineralization in diatoms (Kröger and Sumper 1998). Silica in plants A wide range of plants for structural support and host defense uses silica. A recent work demonstrated that transmembrane proteins facilitate the accumulation of silica in the root cells of Oryza sativa (Cooke et al. 2016). Silicon transporter proteins in the plants are modified aquaporins (integral membrane protein found in biological cells for the transportation of water between them), which includes arsenite and glycerol transporters (Ma et al. 2008). In many plants, silica can be a component of the cell wall or it can be stored in a separate organ called phytolith commonly found in grass. For example, in Horsetail grasses millimeter sized nodules of silica are precipitated to from silicic acid and stored.

Figure 1 . 4 .

 14 Figure 1.4. SEM images of silica mineralization in sponges. a. Spicules of Suberites domuncula. b. Silica-chitin framework of glass sponge Farrea occa.

  .6). The embryonic and adult sea urchin endoskeleton is made of magnesian calcite (usually about 5% MgCO3) and occluded matrix proteins. Initially the spicule formed in the larvae is the amorphous form of calcium carbonate and the organic molecules stabilize it. The integral matrix proteins comprises nearly 0.1% of the spicule (Benson et al. 1986). To date, sixteen genes are thought to encode proteins, which form the integral matrix proteins in spicule formation in larvae and adult sea urchin, Strongylocentrotus purpuratus (e.g. SM50, SM30, SM32, SM29, SpC-lectin and PM27). One interesting observation is that nearly all these proteins contain C-type lectin domain (Benson et al. 1987; George et al. 1991; Harkey et al. 1995). Apart chicken (thickness, 0.3 -0.4 mm) (Nys et al. 2004). The calcification time and thickness of the eggshell varies depending on the species, for example the thinnest recorded avain shell is 0.052 mm (Palestine sunbird, Nectarinia osea, extinct family Aeropyornithidae) and the thickest elephant bird egg shell is 4.4 mm (Romanoff and Romanoff 1949; Ar et al. 1979).

Figure 1 . 8 . 1 . 5 . 4

 18154 Figure 1.8. Figure shows the longitudinal section of egg with the contents of the egg with the cross-section of eggshell and its morphological feature (Hincke et al. 2012).

Figure 1 .

 1 Figure 1.9a shows the hierarchical structure of human cortical bone. Bone is composed mainly of osteons (170-250 µm) (Weiner and Wagner 1998) and these osteons have a central canal called Harvesian canal (60-90 µm). Cement line (less than 5 µm) is the highly mineralized outer boundary. Each Harvesian canal is surrounded by lamellae (2-9 µm) (Bernhard et al. 2013) made of bundles of collagen fibers arranged in twisted plywood arrangement. Each fibril contains both extrafibrillar platelets and intracellular matrix stuffed with several organic molecules. Collagen molecule (1.5 nm diameter, 300 nm length) (Bernhard et al. 2013) and hydroxyapatite nanocrystals (50nm width, 25 nm height and 1.5-4 nm thick) along with organic molecules form fine composite structure (Zimmermann et al. 2015).

Figure 1 .

 1 Figure 1.9b Pathway in bone formation and resorption (Imai et al. 2013).

Figure 1 . 11 .

 111 Figure 1.11. Physiology of bivalves (Brusca and Brusca 2003). The first two images depict the interior and exterior regions of shell with different parts. Third image represents the soft internal parts of the bivalves.

Figure 1 .

 1 Figure 1.12. example of bivalve shell microstructures : a. Cross section of Mytilus edulis shell made of combination of different microstructures b. Lamprotula sps. pillar nacre structure made of aragonite c. Aragonite sheet nacre of Nucula sulcata M-1 layer d. Calcite prism from outer layer of M. edulis e. Bocky aragonite prism of Frenamya elongate f. Venus verrucosa shells with composite aragonite prism g. Foliated calcite microstructure of Crassostrea gigas h and i. cross-lamellar and complex cross-lamellar aragonite microstructure of Cerastoderma edule and Barbatia virescens shells respectively j. Glossus humanus shells made of homogenous aragonite microstructure k. Chalky calcite layer of C. gigas l. granular microstructure of Trichomya hirsute.

1 . 9

 19 they are also called IMSP (Insoluble matrix shell protein) (Marie et al. 2011a). Eight different variants have been reported so far. Each variant consists of different conserved domains such as epidermal growth factor, fibronectin-3 and beta lactamase (Marie et al. 2011b). Keith et al reported few unnamed proteins of different lengths from Mytilus edulis shell layers. Some of these proteins have beta sheet structure, which might denote their participation in shell mineralization (Keith et al. 1993). Significance of the bivalve models in this work My thesis work is part of the Marie Curie ITN project, CACHE (CAlcium in a Changing Environment), which aims to provide next generation scientists with skills to address complex environmental issues (e.g. anthropogenic driven climate change) within a integrated and multidisciplinary research framework. The future climate change scenarios have been predicted to have profound effects on marine benthic calcifiers and hence impact ecological balance and biodiversity. The future predicted acidic and warm oceans not only affect the calcified shells but also alter the animal homeostasis and physiology, resulting in reduction of the animal's fitness and increase their susceptibility to microbial infections. So far, only little is known about how these organisms regulate calcium carbonate to produce the shells or how this regulation is affected under unfavorable environmental conditions in individuals and as well as in populations. CACHE network aims to answer the above questions by employing diverse techniques such as crystallography, molecular biology, cell culture, population genetics, whole animal physiology, and biomimicry. Partners in the CACHE network have expertise in diverse research fields and thus their work will contribute to better understand the regulation of calcium in bivalves in a changing environment. The models selected under this program are pacific cupped oyster (Crassotrea gigas), blue mussel (Mytilus edulis), king scallop (Pecten maximus) and soft shell clam (Mya truncata). The first three species are commercially important species in Europe as indicated by the European commission of fisheries data (year 2007).

(

  belonging to Pteriomorphia) about 513 My ago (Plazzi and Passamonti 2010). M. edulis (clade: Mytiloida) diverged from C. gigas (clade: Ostreoida) and P. maximus (clade: Pectinoida) 479 My ago and the divergence time of Pectinoida and Ostreoida was estimated to be around 421 My ago (Ren et al. 2010). From the perspective of shell proteomics, these species are quite valuable models because of their geographical distribution, shell microstructures and phylogeny. Although the literature contains consistent information about the SMPs from C. gigas, very few to no proteins have been reported in the other species.

Figure 1 .

 1 Figure 1.14. left : Map shows the distribution of C. gigas in Europe along with their distribution all over the world (Source: FAO webpage). right :The large shell valve and the animal.

  tidal regions, attached to roughened, scared or pitted surface by means of byssal threads (Seed 1969). The shells of blue mussels have three distinct layers, the myostracum made of aragonite prism separates upper prismatic and lower nacre layer. Myostracum is a thin layer of 4μm and therefore, in most of the studies it not considered as a shell layer. Prismatic structures are fibrillar calcite and the crystals are 1-2 μm thick and 50um long. Aragonite tablets of 0.5 μm thick and 5 μm in length make nacre layer (Bourgoin 1988). Periostracum is a thick leathery layer and gives light brown shade to the mussels (Marie et al. 2011a).

Figure 1 .

 1 Figure 1.15. left : Map shows the distribution of M. edulis in Europe and its distribution worldwide (Source: FAO webpage).

  Norway to the south coast of Portugal. High density of populations is found in the depth of 25-50 m and salinity below 30%. Unlike C. gigas and M. edulis, it cannot withstand high degree of fluctuations and cannot live in extreme environmental conditions (Bøyum 1975). During juvenile stages, it attaches to any substrate with the help of byssus and in adult stage however, it becomes a free swimmer. Adults can reach up to 15cm in length (Strand et al. 1993).

Figure 1 .Figure 1 .

 11 Figure 1.16. left : Distribution of Pecten maximus in Europe and other parts of the globe (Source: FAO webpage).

2 .•

 2 To understand the interactive effects of the environmental factors on the biomineralization process and adaptation by studying the modulation in the relative amounts of SMPs. The first chapter introduced the concepts of biomineralization. Materials and methods are described in the second chapter. The third chapter discuss about the interspecies comparison of the SMPs extracted from four bivalve models and their significance. In fourth chapter, adaptation of M. edulis along a latitudinal gradient is presented and the differential expression of SMPs is correlated to the shell phenotype. Characteristics of the identified SMPs and their association to the shell properties are discussed in the chapter five. Chapter six presents the conclusion about this work and perspectives for the future research. Abolinš-Krogis A. 1973. Fluorescence and histochemical studies of the calcification-initiating lipofuscin type pigment granules in the shell-repair membrane of the snail, Helix pomatia L. Zeitschrift für Zellforsch. und mikroskopische Anat. 142:205-221. • Addadi L, Berman A, Weiner S. 1991. Intracrystalline Proteins from a Sea Urchin and a Mollusk: A Comparison. In: Mechanisms and Phylogeny of Mineralization in Biological Systems. Tokyo: Springer Japan. p. 29-33. • Addadi L, Joester D, Nudelman F, Weiner S. 2006. Mollusk shell formation: A source of new concepts for understanding biomineralization processes. Chem. -A Eur. J. 12:980-987. • Addadi L, Weiner S. 1985. Interactions between acidic proteins and crystals: stereochemical requirements in biomineralization. Proc. Natl. Acad. Sci. 82:4110-4114. • Aitken RNC, Solomon SE. 1976. Observations on the ultrastructure of the oviduct of the Costa Rican green turtle (Chelonia mydas L.). J. Exp. Mar. Bio. Ecol. 21:75-90. • Albeck S, Aizenberg J, Addadi L, Weiner S. 1993. Interactions of Various Skeletal Intracrystalline Components with Calcite Crystals. J. Am. Chem. SOC 115:11691-11697. • Amemiya Y, Arakaki A, Staniland SS, Tanaka T, Matsunaga T. 2007. Controlled formation of magnetite crystal by partial oxidation of ferrous hydroxide in the presence of recombinant magnetotactic bacterial protein Mms6. Biomaterials 28:5381-5389. • Amore G, Davidson EH. 2006. cis-Regulatory control of cyclophilin, a member of the ETS-DRI skeletogenic gene battery in the sea urchin embryo. Dev. Biol. 293:555-564. • Ar A, Rahn H, Paganelli C V. 1979. The Avian Egg: Mass and Strength. The Condor 81:331-337.

  Arakaki A, Yamagishi A, Fukuyo A, Tanaka M, Matsunaga T. 2014. Co-ordinated functions of Mms proteins define the surface structure of cubo-octahedral magnetite crystals in magnetotactic bacteria. Mol. Microbiol. 93:554-567. • Arias JL, Carrino DA, Fernandez MS, Rodriguez JP, Dennis JE, Caplan AI. 1992. Partial biochemical and immunochemical characterization of avian eggshell extracellular matrix. Arch. Biochem. Biophys. 298:293-302. • Arias JL, Fernández MS. 2008. Polysaccharides and Proteoglycans in Calcium Carbonate-based Biomineralization. Chem. Rev. 108:4475-4482. • Artigaud S, Thorne MAS, Richard J, Lavaud R, Jean F, Flye-Sainte-Marie J, Peck LS, Pichereau V, Clark MS. 2014. Deep sequencing of the mantle transcriptome of the great scallop Pecten maximus. Mar. Genomics 15:3-4. • Bazylinski D, Frankel R. 2004. Magnetosome formation in prokaryotes. Nat. Rev. Microbiol 2:217-230 • Bédouet L, José Schuller M, Marin F, Milet C, Lopez E, Giraud M. 2001. Soluble proteins of the nacre of the giant oyster Pinctada maxima and of the abalone Haliotis tuberculata: Extraction and partial analysis of nacre proteins. Comp. Biochem. Physiol. -B Biochem. Mol. Biol. 128:389-400. • Belcher A, Wu X, Christensen R, Hansma P. 1996. Control of crystal phase switching and orientation by soluble mollusc-shell proteins. Nature 381:56-58. • Benson S, Sucov H, Stephens L, Davidson E, Wilt F. 1987. A lineage-specific gene encoding a major matrix protein of the sea urchin embryo spicule. Dev. Biol. 120:499-506. • Benson SC, Benson NC, Wilt F. 1986. The organic matrix of the skeletal spicule of sea urchin embryos. J. Cell Biol. 102: 1878-86. • Berland S, Ma Y, Marie A, Andrieu J-P, Bedouet L, Feng Q. 2013. Proteomic and Profile Analysis of the Proteins Laced with Aragonite and Vaterite in the Freshwater Mussel Hyriopsis cumingii shell Biominerals. Protein Pept. Lett. 20:1170-1180. • Berland S, Marie A, Duplat D, Milet C, Sire JY, Bédouet L. 2011. Coupling Proteomics and Transcriptomics for the Identification of Novel and Variant Forms of Mollusk Shell Proteins: A Study with P. margaritifera. ChemBioChem 12:950-961. • Bernhard A, Milovanovic P, Zimmermann EA, Hahn M, Djonic D, Krause M, Breer S, Püschel K, Djuric M, Amling M, et al. 2013. Micro-morphological properties of osteons reveal changes in cortical bone stability during aging, osteoporosis, and bisphosphonate treatment in women. Osteoporos. Int. 24:2671-2680. • Bianco P, Hayashi Y, Silvestrini G, Termine JD, Bonucci E. 1985. Osteonectin and GLA-Protein in calf bone: Ultrastructural immunohistochemical localization using the protein A-gold method. Calcif. Tissue Int. 37:684-686.

••

  Samata T, Hayashi N, Kono M, Hasegawa K, Horita C, Akera S. 1999. A new matrix protein family related to the nacreous layer formation of Pinctada fucata. FEBS Lett. 462:225-229. • Sanders D, Brownlee C, Harper JF. 1999. Communicating with calcium. Plant Cell 11:691-706. • Sarashina I, Endo K. 2001. The complete primary structure of molluscan shell protein 1 (MSP-1), an acidic glycoprotein in the shell matrix of the scallop Patinopecten yessoensis. Mar. Biotechnol. 3:362-369. • Sarashina I, Endo K. 2006. Skeletal matrix proteins of invertebrate animals: Comparative analysis of their amino acid sequences. Paleontol. Res. 10:311-336. • Sato-Okoshi W, Okoshi K, Sasaki H, Akiha F. 2010. Shell structure characteristics of pelagic and benthic molluscs from Antarctic waters. Polar Sci. 4:257-261. • Scheffel A, Gruska M, Faivre D, Linaroudis A, Plitzko JM, Schüler D. 2006. An acidic protein aligns magnetosomes along a filamentous structure in magnetotactic bacteria. Nature 440:110-114. Scheltema AH. 1993. Aplacophora as Progenetic Aculiferans and the Coelomate Origin of Mollusks as the Sister Taxon of Sipuncula. Biol. Bull. 184:57-78. • Schröder HC, Boreiko A, Korzhev M, Tahir MN, Tremel W, Eckert C, Ushijima H, Müller IM, Müller WEG. 2006. Co-expression and functional interaction of silicatein with galectin: matrixguided formation of siliceous spicules in the marine demosponge Suberites domuncula. J. Biol. Chem. 281:12001-12009. • Seed R. 1969. The ecology of Mytilus edulis L. (Lamellibranchiata) on exposed rocky shores -I. Breeding and settlement. Oecologia 3:277-316.

  analysis. The peptides eluted from the HPLC are analyzed on line by the mass spectrometer, which consist of three main components: ionization source (e.g. MALDI, ESI) to charge the peptides resulting in the formation of ions, mass analyzer to separate the ions based on their mass/charge ratio (e.g. Quadrupoletime-of-flight, Orbitrap) and detector (e.g. Multichannel plate).InESI (Fenn et al. 1989), small droplets are generated from the solvent containing the peptides which flows through a narrow capillary tube and simultaneously a potential difference is applied between the capillary and the inlet of the mass spectrometer to generate a liquid cone (Taylor cone), which is formed by the columbic force (Taylor 1964). The droplets thus formed, reduces in size by evaporation and when the surface columbic charge exceeds the surface tension it results in the fission of droplets until it reaches the nano-sized droplets see figure2.4. Further evaporation of the solvent results in the production of charged (protonated) peptides in the gas phase. The charged peptides enter the mass spectrometer for analysis.

Figure 2 . 4

 24 Figure 2.4 The illustration of electron spray ionization showing how the ions (charge states) are produced in the ionization chamber. The Taylor cone formed by the columbic force is shown along with the spray needle and the charged molecules enter the mass spectrometer.

Mascot(

  Perkins et al. 1999), SEQUEST (Eng et al. 1994), SpectrumMill (Zhang et al. 2002), X!Tandem (Craig and Beavis 2004), ProbID (Zhang et al. 2002), Phenyx (Mann and Wilm 1994), OMSSA (Geer et al. 2004), etc. are few database search engines. PEAKS is a de novo based search engine that employs and exploits the de novo sequencing results to improve both the speed and accuracy of the database search. The results from these search engines are strictly based on the user specified criteria such as proteolytic enzyme, mass tolerance, fragmentation rules, post-translational modifications etc.

Figure 2 . 2 . 7 . 1

 2271 Figure 2.8 represents the different steps in PSM process (Nesvizhskii et al. 2007). Theoretical peptides are predicted from the database based on the experimental parameters such as the enzyme used for digestion, missed cleavages, modifications etc. For example, if no enzyme is specified in the search parameter it increases the search time and search space and increases the false positive matches

  MS/MS acquisition, quantifier ions from the reagent are observed between m/z =113 and m/z =121 (except m/z= 120) and the signal intensity is used for calculating the relative amounts of the peptides and proteins. Up to eight labeled pools (8-plex, iTRAQ) (Ow et al. 2008) of peptides can be analyzed in a single analysis thereby reducing the analytical time significantly. This method also comes with some limitations such as poor accuracy in the quantification (Louris et al. 1987).

  Vanhaesebroeck 2007). Recently, PAI is replaced by exponentially modified PAI (emPAI), which has the greater proportionality to the protein abundance in a sample (Ishihama et al. 2005). The search engines such as SEQUEST and Mascot uses this method to quantify the proteins. The spectral feature approach integrates all chromatographic areas of a given protein. The area under the curve (AUC) is then correlated with the concentration of peptides (Pavel V. Bondarenko et al. 2002). The ion abundance at specific retention time corresponding to the AUC is measured, which is called ion count. The combined ion and chromatographic features of the peptides are used to quantify the proteins.

Generally, shells are

  exposed to the variety of biotic components such as pathogens, commensals, parasites and other microorganisms in their environment. Therefore shell cleaning is a crucial step to remove most of the aforementioned biotic components as well as cellular contaminants from the tissue of the host organisms. Marie et al suggested the importance of shell cleaning protocols by comparing the proteins identified from the shells of Lymnea stagnalis using two different conditions: simple bleaching using NaOCl (5% v/v, 24 h) and extended bleached (NaOCl, 5% v/v, 24 h followed by NaOCl, 10% v/v, 5h). The simple bleached shell contained cellular contaminants such as actin, myosin, tubulin, etc., and where as the extensive bleaching enhanced the number of shell matrix protein identified without cellular contaminations. Care should be taken while bleaching shell powder with NaOCl wash since there is a potential risk of eliminating the proteins that are less-bound to the CaCO3 crystals (figure2.13) (Marie et al. 2013). Certain species of molluscs possess micro cavities in their external surface and accumulates microorganisms and shell-boring organisms. Hence it is advisable to remove the outer surface by mechanical abrasion, which is defined as the "potentially rich in contaminant zone" (Marie et al. 2013). Metazoans are characterized with diversity of exoskeletons and the same cleaning technique might not work for all. Hence the optimization of shell cleaning procedure should be carried out for each sample.

Figure 2 .

 2 Figure 2.13 Comparison of proteins identified by simple and extensive bleaching of biominerals from L. stagnalis. The extensive bleaching with NaOCl along with ombilic removel yielded quite higher number of identification without cellular contaminants compared to simple bleaching (Marie et al. 2013).

  proteins and novel proteins. Possible role of the newly identified proteins was discussed with the suggestion to the scientific community to look into the role of SMPs in a new perspective other than biomineralization.With the intention to explore if the trend of possessing functionally diverse shell proteins is common in bivalves and to identify the conserved biomineralization tool kit, shell proteomic study was extended to the other three models C. gigas, M. edulis and P. maximus. The results of this work were published in Molecular Biology and Evolution journal. Comparison of the SMPs from the four models led to the identification of basic biomineralization toolkit and SMPs that are polymorph specific. In addition, special emphasize was given to the immunity related proteins and their place in the phenoloxidase cascade, an important immunity pathway.

  organisms. In this study mussels collected from natural low saline Baltic Sea are impacted by salinity, along with several other abiotic factors, that influence the shell formation over generations. Another benefit of this approach is that it enables to gain insight on the global variation in the SMPs right from the initial stages of shell formation. Label free semi-quantitative analysis of the three sites from Baltic (Usedom and Nynashamn) and North Sea (Sylt) shells showed the modulation of 17 SMPs (M. edulis transcriptome) that could be attributed to different functional categories (Fig.1, supplementary table. 1). Further interrogation using M. trossulus transcriptome identified the modulation of seven additional SMPs such as HHIP like protein, shell protein-4, GAL-rich protein, protease inhibitor like protein-B1, matrix protein-1, metalloprotease inhibitor-3 like protein and in addition to a WAP domain containing protein (supplementary fig.1and supplementary table. 1).

Figure 1 .

 1 Figure 1. Heat map of modulated SMPs: List of modulated SMPs identified using M. edulis transcriptome database

Figure 2 :

 2 Figure 2: Modulated SMPs: Schematic representation of modulated biomineralization proteins in Baltic mussels and represented based on their role in shell building

Figure 3 :

 3 Figure 3: Shell microscale structure components: periostracum, mineralized layer thickness and organic content, in Sylt (S), Usedom (U) and Ninashämn (N) populations. a. The graph constructed using periostracum/mineral layer thickness ratio clusters Baltic mussels (Nynashamn and Usedom) and North Sea mussels (Sylt) separately. Similarly, two different boxplots were constructed using mineral layer thickness and periostracum thickness/mineral layer thickness ratio. Boxplots clearly distinguishes the populations. b. Box plot showing the organic content of the shells excluding periostracum c. Principal component analysis performed by combining mineral layer thickness, periostracum thickness and organic content separates the Baltic mussels and North Sea mussels.

  and might include the biotic components that are harmful to mussels. The above scenario might in part influence the Baltic mussels to alter the production and export of the proteins implied in the immune related cascade to the shells and enabling the optimization of the cellular energy for different functions of the organism as a function of both biotic and abiotic factors. Decrypting biomineralization control via SMPs is further complicated due to the modulation of these proteins along with other biomineralization proteins and their possible interaction with the mineral structure during different stages of the shell formation.Quantitative study of SMPs is a valuable approach to study the intricate relation among the phenotype, biological control and the environment of the calcifying organisms. Changes in the salinity along with other factors (biotic or abiotic) in the ocean environment alter the structural and mechanical properties of the shells including their potential biochemical defenses. Although Baltic mussels have successfully responded to low saline environment over several generations, notably by modifying the composition of their shell calcifying matrices, further decline in salinity over a short span in the Baltic Sea might further weaken the using 30 µL of 8 M urea and then with 15 mM dithothreitol (DTT) in 100 mM triethyl ammonium bicarbonate (TEAB), both the steps were carried out at 37°C for an hour each. Alkylation was done using 20µL of 15 mM iodoacetamide in dark and room temperature for 1h. Digestion step was carried out by adding 10 µg of trypsin (Sigma-Aldrich, France) to AIM and ASM samples and incubated at 37°C overnight. The resulting digested peptides were acidified with 10 µL of 10% formic acid and desalted using 4mm Empore TM SPE (Sigma-Aldrich, France) cartridges. Desalted peptides were quantified using Bicinchoninic acid kit (Sigma-Aldrich, France).

FA 90: 10 5 Discussion 5 . 1

 10551 (v/v): 2-40% B in 170 min, then 40-50% B in 10min. The eluted peptides were analyzed by a nano-ESI quadrupole-Orbitrap mass spectrometer (Q Exactive, ThermoFisher Scientific) with the following parameters: one full MS acquisition (Resolution 70,000 at m/z 200, AGC target value 1e6, max. injection time 250 ms), followed by 10 MS/MS (TOP 10) with isolation window 2 m/z, fixed first mass 100 m/z, resolution 17,500 at m/z 200, AGC target value 5e4, max. injection time 120 ms, CE 30 and dynamic exclusion of 60 s. MS/MS spectra obtained from fifteen individual samples were searched against mantle transcriptome database of M. edulis and M. trossulus separately using in-house Mascot server (Matrix Science, London, UK; version 2.4.1). The database search was carried out using following parameters: carbamido-methylation of cysteine as fixed modification and oxidation of methionine, de-amidation of aspartic acid and asparagine as variable modifications, mass tolerance of MS and MS/MS experiments as 20 ppm and 0.5 Da. Scaffold software (Proteome Software Inc, Portland, USA; Chapter-Identification of SMPs in shotgun proteomics Confident identification of the SMPs extracted from the shell is one of the crucial aspects for this work. Several criteria influence the successful identification of the protein, which includes the accuracy and completeness of the database in addition to the capacity to sample and analyze them. Generally, proteomics experiment uses curated and publically available multiple sequence databases such as NCBI (Entrez+RefSeq), EBI, Uniprot (Swiss-prot+TrEMBL), etc. (Nesvizhskii 2014). In case of bivalves, except C. gigas and few other species, all others are non-model organisms and protein identification is inefficient indicating that few sequences are present in the database.

  At present, next generation sequencing technologies come with reasonable cost and enable rapid determination of genome or RNA sequences and provide the tool for protein identification from non-model organisms (Champagne and Boutry 2013). However, the search against EST databases is complicated by their poor quality. Combination of poor quality with database redundancy makes the search not only computer intensive but also results in many incorrect identifications (Nesvizhskii 2007). In addition, during the construction of EST database, care should be taken to minimize the assembly errors, which might result in several sequences encoding for single mRNA and moreover it is hard to differentiate it from homologous proteins. For example, in figure5.1 the contigs 629,670, 894 and 4091 obtained by searching M. truncata peptides against M. truncata EST database looks like an assembly error. However, sequence comparison revealed that contig 629, 270 and 894 are homologous proteins and contig4091 is a separate protein without any overlapping regions between them (figure 5.2). The shell production in the bivalves is spatio-temporally regulated. It has been noted that the rate of calcification changes between individuals, sex and different stages of their life. Moreover, it can modulate depending on the seasonal, daily and diurnal variability (Jolly et al. 2004; Yates and Halley 2006). Hence single time point mRNA expression does not guarantee the presence of all gene products. To overcome this issue, sampling must include many individuals and several time points.

Figure 5 . 1

 51 Figure 5.1 Protein domain architecture of PIF protein is shown at the top and the proteins architecture below represents the transcripts matched by searching M. truncata shell peptides against M. truncata EST database. By comparing these architectures it looks like there exist a transcript assembly error and combining contig4091 with contig629 or contig670 or contig894 would result in the complete mRNA sequence. Since chitin binding domain is always associated with either VWA or glycohydro-18 domain in proteins such as PIF, BMSP and chitinase.

5. 3

 3 and 5.4). Although, the identification of mutation implies biological significance (Yadav et al. 2014), it also can be used to rectify the errors in the database of non-model organisms.

Figure 5 . 2

 52 Figure 5.2 The alignment of the sequences corresponding to the gene products displayed in the figure. 5.1.Although, it seems like all these sequences belong to the single mRNA, the sequence level variation confirms them as homologous proteins.

Figure 5 . 3

 53 Figure 5.3 The sequence identified by searching Sylt mussel shell data against M. edulis EST database. The sequence is homologous to perlwapin and contain one modifeid or mutated peptide.

Figure 5 . 4

 54 Figure 5.4 The above mass spectrum viewcorresponds to the mutated/modified peptide shown in the figure5.3. The substitution of valine with alanine in the peptide LDVSTV(substituted by A)CVVDYK fits the fragmentation pattern precisely, hence the peptide is deemed mutated. The table and the graph below are the fragmentation table and error map of the respective peptide.

  Figure 5.7

Figure 5 . 7

 57 Figure 5.7 The SMP, BMSP identified by searching M. edulis shell data. The sequence contain a phosphopeptide, which is represented as a blue line. The fragmentation table and graph for this peptide is shown in the figure 5.8.

Figure 5 .

 5 Figure 5.8 MS/MS spectrum of phosphopeptide identified in BMSP protein along with their respective peptide fragmentation table.

  Interestingly, two protease inhibitors, metalloase inhibitor and Kunitz domain containing protein exhibit one phosphopeptide each. Caspase is a protease that induces apoptosis and the phosphorylation at serine196 inhibits its protease activity (Cardone et al. 1998). Although kunitz domain containing protein is a protease inhibitor, still it possesses a serine phosphorylation site in its Cterminus. Metallo-ase inhibitor contain phosphorylation site at tyrosine138. Anti-microbial peptide (AMP) mytilin also contains one phosphopeptide. Earlier studies suggested that induction of immune responses in Hyalophora cecropia (Hecht and Anderson 1993; Shelton and Wasserman 1993). Even in human several antimicrobial peptides including defensin family are activated by phosphorylation (De Smet and Contreras 2005) suggesting the possible role of phosphorylation in activation of mytilin shell protein in M. edulis. The shell skeletal and chitin related proteins such as SD-rich, SKP-rich, shell protein-5 and insoluble matrix protein contains phosphopeptides. All the above proteins are highly acidic and rich in aspartic acid residues. In fact, the phosphoprotein phosphodontin from sea urchin S. purpuratus (Mann et al. 2010) and MSP-1 from scallop Patinopecten yessoensis (Sarashina and Endo 2001) are characterized by high acidic residues with pI < 4. In addition, in vitro mineralization assays with dephosphorylated, low-phosphorylated and highly phosphorylated shell matrix proteins revealed that the highly phosphorylated proteins are more efficient in inhibiting crystal growth than low or dephosphorylated proteins (Borbas et al. 1991). The phosphoproteins such as SD-rich, SKP-rich, shell protein-5 and insoluble matrix protein identified in the M. edulis shell might regulate the crystallization in phosphorylation dependent manner.

  contribute to the diversity of shell structures (McDougall et al. 2013). PIF and shematrin have been studied earlier and examplify the evolvability of the SMPs. Pif is a P. margaritifera shell matrix protein that contains Pif 97 and Pif 80 encoded by a single mRNA (UniProtKB/Swiss-Prot: H2A0N4.1). Although Pif is conserved among mollusk species with nacreous layer, their sequence varies remarkably even within Pinctada species (Suzuki et al. 2013). Similarly, shematrins have undergone duplications and extensive diversification which is likely to be facilitated by low-complexity regions and repeated structures, along with the protein primary sequence (McDougall et al. 2013).

Figure 5 . 9

 59 Figure 5.9 The 3D structure of carbonic anhydrase domains modeled using SWISS-MODEL tool (https://swissmodel.expasy.org/interactive) and the molecular phylogenetic tree is constructed using EMBL-MUSCLE tool. The figure shows the different lengths of CA domains in all the four species and M. edulis being the smallest of all.

Figure 5 .

 5 Figure 5.10 The tyrosinase domains of all four species and the molecular phylogeny of tyrosinase is similar to that of phylogeny constructed using mitochondrial genes. M. edulis possess smaller domain in terms of length compared to the other species.

Figure 5 .

 5 Figure 5.11 Variants of tyrosinase proteins identified in the four bivalve models.

Figure 5 .

 5 Figure 5.12 The multiple sequence comparison of VWA domains from the bivalves (C. gigas, M. edulis, M. truncata, P. maximus, P. margaritifera, P. fucata), gastropod (L. gigantean), branchiopod (M. venosa) and coral (A. millepora). The sequences are highly divergent among species except few shared regions between pinctada species and between C.gigas and M. edulis.

Figure 5 .

 5 Figure 5.14 The sequence alignment between the proteins TR71052_c0_g1_i1|m.64539 and TR78416_c1_g1_i4|m.89172 is represented wherein the poly A and poly G rich regions are highly conserved between the sequences.

  9 could be correlated to the mineral deposition in the shell and influence on the shell growth (Joubert et al. 2014). Mantle gene expression studies on M. edulis revealed that not many SMPs are affected by ocean acidification except tyrosinase gene, which increased to 220-fold indicates the organism's excessive attention towards the periostracum (Hüning et al. 2013). Mantle proteomic study of C. gigas subjected to elevated pCO2 revealed that cytoskeletal related proteins such as myosin, calponin-2 and enzymes related to energy metabolism are differentially expressed (Wei et al. 2015).

••- 6 Conclusion and future work 6 . 1

 661 Zhang W, Deng W, Zhou L, Xu Y, Yang W, Liang X, Wang Y, Kulman JD, Zhang XF, Li R. 2015. Identification of a juxtamembrane mechanosensitive domain in the platelet mechanosensor glycoprotein Ib-IX complex. Blood. 125:562-569.ChapterConclusion and future workDespite numerous studies undertaken to understand the biological process and control evolved by mollusk for shell formation, the biomineralization models that have been proposed are far from complete. Understanding biomineralization process needs the integration of many research fields such as biology, crystallography, material science, and chemistry to name a few. My thesis work focused on the biological control in biomineralization using four model bivalves via shell matrix proteins in the context of their life history (environment/evolution).

(

  Bédouet et al. 2006). To date, wide range of toxins and bioactive molecules have been isolated from different molluscs (from all parts excluding shell)(Kamiya et al. 2006), including linear and cyclic bioactive peptides isolated from the mantle of opisthobranch gastropod molluscs. In fact, opisthobranch, a gastropod clade with evolutionary trend towards shell loss, evolved a wide range of biochemical defenses to protect them from predators, that include small molecules and bioactive peptides (DellaMonica et al. 2006). It is worth to search the similar bioactive peptides in the shell matrix. In the next future, shell proteomic study combined with the characterization of secondary metabolites might lead to the identification novel bioactive molecules in the context of immunity and also understand the potential role in biomineralization and signaling events.

  .1) exist and in many instances it is evident that it evolved independently (Knoll 2003). In case of calcium carbonate, it may have evolved twenty eight times in Eucarya, twenty times in Metazoa, four in Plantae

and one time each in cercozoans, haptophytes, heterokonts and alveolates (Knoll 2003). Almost all the living cells are capable of binding and regulating Ca2+ ion and also regulate inorganic carbon chemistry, which might have supported number of organisms to evolve CaCO3 skeletons (MacLennan et al. 1997; Sanders et al. 1999).

1.5.2.2 Silica in sponges

	Sponges are among the lowest tissue organization metazoans to make exoskeleton
	or endoskeleton using minerals. Siliceous sponges, desmospongia and
	hexactinellida use amorphous silica to construct inorganic skeletons i.e. spicules
	see figure 1.4. The silicateins are the member of cathepsin L and papain family of
	protease discovered in the desmosponge Tethya aurantium and plays a significant
	role in silica production (Shimizu et al. 1998a; Cha et al. 1999; Müller 2006). So far,
	three different isoforms of silicateins ( ,	and	) with varying molecular

weights have been reported

(Shimizu et al. 1998b

). These proteins have a signal peptide constituted by

15

amino acids and the matured forms of the proteins are 29, 28 and 27kDa respectively (Müller et al. 2005). The important role of silicateins is the formation of disilicic acid polymer; the serine moiety in the protein uses ortho-silicic acid as the substrate and undergoes condensation reaction and subsequent oligomerization process which results in the growth of disilicic acid chain (Croce et al. 2004). Silintaphins are another group of key proteins that assist in biosilica formation process. Silintaphin-1 is present in silicatein containing organic cylinder (structure that enfolds the growing spicule) and enhances the biosilica-forming activity of silicatein (Wiens et al. 2009). Silintaphin-2 is a Ca2+ binding protein and is activated by bone morphogenic protein. It provides Ca2+ ion for the formation of complex between silicateins and galectins, which is crucial for the organic cylinder of growing spicules (Müller et al. 2011; Wiens et al. 2011). Spicule formation is mediated by the participation of several types of cells such as sclerocyte, archaeocyte, chromocyte and lophocyte. Sclerocytes are the totipotent cells that produce siliceous spicules (see figure 1.5). The differentiation of these cells are initiated by silicate and Fe 3+ ion, which also activates the silicatein genes (Krasko et al. 2000; Krasko et al. 2002). Sclerocytes synthesize extracellular signaling molecules silitaphin-1 and myotrophin, which signals lophocytes to produce collagen and collagen like spongin (Krasko et al. 2000).

1.8.4.1 Aragonite proteins

	proteins in the shell matrix space. Recent approach employs the extraction of SMPs distinct blocks constituted by Ala-and Gly-are present. Gly-block might form anti
	from the shell and matching with the transcriptome, confirms the integrated parallel beta sheets in the nacreous layer, while Asp-block bind to calcium ions
	proteins in the shell (Joubert et al. 2010; Berland et al. 2011). Recently, several (Zhang and Zhang 2006).
	SMPs have been identified using this approach and give meaningful insight about Table 1.1 List of proteins that are found specific to aragonite
	their functions (Berland et al.; Marie, et al. 2011b; Marie et al. 2011c; Gao et al. Protein Species Micro-MW Aminoacid Reference N16, N14, N66 and pearlin belongs to N16 family of proteins, first identified in
	and Traub in 1984 (S Pinctada species (Samata et al. 1999; Kono et al. 2000; Miyashita et al. 2000). 2015; Liao et al. 2015) name structure (kDa) number
	PIF These proteins are characterized with Gly, Tyr and Asn rich regions along with Pinctada fucata Nacre 80-97 -(Suzuki et al. 2009) Weiner et al. 1984) proposed a model for nacre formation (fig 1.13), which MSI60 Pinctada fucata Nacre 60 738/717 (S Sudo et al. 1997) includes chitin, silk fibroin like proteins and acidic matrix components. Chitin The SMPs in the bivalve could be classified into three groups based on their BMSP Pinctada fucata Nacre 100-120 -(Suzuki et al. 2011) Asn-Gly repeats. N16 contains several beta sheets and beta turns with few alpha
	forms the backbone of the mineralization and acts as a scaffold for minerals to isoelectric point (pI) as extremely acidic (pI < 4.5), moderately acidic (4.5 > pI < 7) N16 Pinctada fucata Nacre 16 131/108 (Samata et al. 1999) helices. Pearlin could be extracted with 8M urea and not with EDTA suggesting
	nucleate. Either side of the chitin is surfaced by Gly-and Ala-rich insoluble silk and basic SMPs (pI > 7) (Marin and Luquet 2008). Sarashina and Endo performed Pearlin Pinctada fucata Nacre 14 129 (Miyashita et al. that calcium and magnesium ion is constituted in the protein complex and
	the survey on invertebrate skeleton matrix proteins and reported that among the 2000) potentially imply its role in aragonite crystallization (Matsushiro et al. 2003). fibroin like proteins that adopt β-sheet conformation (Weiner and Traub 1980). Mucoperlin Pinna nobilis Nacre 66.7 636/625 (Marin et al. 2000) 77 proteins considered 43 were acidic, 20 neutral and 14 were basic. In addition, These insoluble protein complexes are overlaid by water-soluble acidic matrix and acquire random coil conformation in solution (Mann et al. 1983). However some skeletal organic matrix are characterized with several repeat sequences and are P20 Pinctada maxima Nacre 20 -(Bédouet et al. 2001) Mucoperlin is one of the heavily glycosylated protein that resembles mucin. It is
	might adopt β-sheet conformation (Worms and Weiner 1986). Chitin and the silk fibroin like proteins are not involved in mineralization and attributes to the mechanical property of the shell and the acidic matrix components assist mineralization (Weiner 1983a). In 2001, the same group proposed a modified version of the earlier model by studying the Atrina shell (Levi-Kalisman et al. 2001), and suggested that β chitin is surfaced by discontinoues or patchy Asp-rich glycoproteins and putative silk gel phase (hydrated gel) occupies the space insoluble proteins carry out specific function to form the nacre structure. List of structure, which is occluded by chitin fibers. and Weiner 1985; Belcher et al. 1996). Later, it was accepted that both soluble and aragonite and helps in nacre formation in mussels (Suzuki et al. 2011). proteins and finally epitaxial nucleation of ACC takes place resulting in mineral evidences show that soluble proteins are involved in polymeric control (Addadi structure (VWA and chitin binding domain) and function. It has high affinity to On these chitin fibers, amorphous calcium carbonates (ACC) stabilizes by Asp-rich form the nacre (Watabe and Wilbur 1960; Wheeler 1992). In contrast, some mussel Mytilus galloprovincialis. This protein is very similar to PIF in terms of both Initially, dense network of chitin fibers assembles on the already formed minerals. work suggested the presence of insoluble matrix proteins along with the Mg 2+ ions BMSP stands for Blue Mussel Shell Protein and was first identified in the blue formation in Atrina shells (Nudelman et al. 2007) and it involves three steps. of Mg 2+ only cannot mediate the formation of such a complex biomaterial. Earlier the hydrated gel. Nudelman et al proposed similar mechanism for prismatic layer content in the extrapallial fluid (Wada and Fujinuki 1976). However, the presence al. 2012). glycoproteins are also found inside minerals (Addadi et al. 1991) and constitute Mg 2+ ions favors aragonite crystal systems, which concords with the high Mg 2+ divergence in primary amino acid sequence with disorganized domains (Suzuki et between chitin sheets (Figure 1.13). Moreover, intra crystalline acidic heavily glycosylated (Sarashina and Endo 2006). Aragonite is thermodynamically less stable than calcite. Yet the mollusks synthesize a ductile and resistant material, nacre using aragonite, which received wide attention from material scientists. Right from the beginning, it is believed that rich in serine and proline and was first discovered in the Mediterranean fan mussel PIF is an acidic matrix protein, first identified in pearl oyster Pinctada fucata Pinna nobilis (Marin et al. 2000). This protein is made of 636 amino acids, of which (Suzuki et al. 2009). PIF consists of two proteins namely pif80 and pif97 and is 11 belong to N-terminal, 222 to C-terminal and the remaining 403 amino acids encoded by a single mRNA. Pif97 contains VWA and chitin binding domains and constitute a tandem repeat region. This protein is able to bind calcium ion and Pif80 contains aragonite or aragonite+calcite binding region. Although these actively participate in nacre formation. P20 is yet another aragonite related proteins show little affinity to calcite crystals, it has high affinity for aragonite protein found in Pinctada maxima, with a molecular mass of 20 KDa and is found in crystals. In vitro studies demonstrated the participation of PIF in nacre formation (Suzuki et al. 2009). Interestingly, PIF from even closely related species show high both acid soluble and in-soluble fractions (Bédouet et al. 2001).
	aragonite specific proteins reported in the literature are shown in the table below. MSI60 is Gly-and Ala-rich protein identified in Pinctada fucata by screening
	mantle cDNA library using the probe designed according to the N-terminal
	sequence of an acid soluble matrix protein (S Sudo et al. 1997). In MSI60 two

1.8.4.2 Calcitic proteins

	Shematrin is a glycine rich protein from the prismatic shell of Pinctada fucata with MSI-31 is a Gly-rich calcite related protein in P. fucata and however, it was
	XGnX (X is hydrophobic amino acid) repeats and RKKKY, RRKKY or RRRKY in their identified by screening mantle cDNA library of P. fucata using the same probe used
	C-terminal sequence. There are seven known variants of shematrin numbering to identifying MSI-60, which is an aragonite specific protein. It has ten poly-Gly
	from one to seven each differing in glycine content ranging from 27% to 43.5% blocks (each made of 3-5 residues) in N-terminal and a C-terminal ESEEDX motif
	(Yano et al. 2006). that binds Ca 2+ ion (Sudo et al. 1997). Aspein from P. fucata and asprich protein
	from Atrina rigida possess about 60.4% and 39%-50% Asp respectively
	Table 1.2 List of calcite specific proteins reported in the literature (Tsukamoto et al. 2004; Gotliv et al. 2005). So far, ten variants of Asprich proteins
	Protein have been identified and out of which seven are fully sequenced. These seven Species Micro-MW Aminoacid Reference
	name sequences are highly homologous and contain six domains: N-terminal structure (kDa) number
	Shematrin hydrophobic domain, a basic domain, two acidic domains seperated by a variable Pinctada fucata Prisms --(Yano et al. 2006)
	P12 acidic domain and a domain with DEAD repeats that might have Mg 2+ binding Mytilus californianus Prisms 5.5 -(Weiner 1983b)
	P16		Mytilus californianus	Prisms		14.5		-	(Weiner 1983b)
	Fraction capacity. Interestingly, the sequence of acidic domain is homologous to that of Crassostrea virginica Foliated 54 -(Wheeler et al. 1987)
	RP-1 calsequestrin, which has high affinity to Ca 2+ ion (Shin et al. 2000). Fraction RP-1 is
	MSI31		Pinctada fucata	Prisms		31		334	(S Sudo et al. 1997)
	MSP-1		Patinopecten	Foliated	74.5		840	(Sarashina and Endo
			yessoensis					2001)
	Aspein Protein name Pinctada fucata Species	Prisms	MW	41	413/394 Aminoacid	(Tsukamoto et al. Reference
						(kDa)			number	2004)
	Prismalin-Nacrein	Pinctada fucata Pinctada fucata	Prisms	50	11.89		447	105	(Suzuki et al. 2004) (Miyamoto et al. 1996)
	14	N66		Pinctada maxima		66			568/546	(Kono et al. 2000)
	Asprich MSI7	Atrina rigida Pinctada fucata	Prisms	7	20-30	209-244 95/76	(Gotliv et al. 2005) (Zhang et al. 2003)
	family Caspartin		Pinna nobilis		38			-	(Marin et al. 2005)
	Calprismin Gigasin	Pinna nobilis Crassostrea gigas	Prisms	38 6.7-37.6		-58-340	(Marin et al. 2005) (Marie et al. 2011c)
	45-kDa protein	Mytilus edulis		45			-	(Keith et al. 1993)
	21-kDa protein	Mytilus edulis		21			-	(Keith et al. 1993)
	Proteins such as MSP-1, Aspein and Asprich are rich in aspartic acid, which is 5-kDa protein Mytilus edulis 5 -(Keith et al. 1993)
	important in shell matrix proteins by virtue of its abundance, since most of the
	Similar to aragonite-specific proteins, the calcite structures too have their unique nacreous and prismatic layer proteins are composed of about 30% and 50% of
	set of proteins. Calcite is thermodynamically stable and however the presence of aspartic acid respectively (Nakahara et al. 1980; Albeck et al. 1993). MSP-1 found Mg 2+ in the extrapallial fluid inhibits calcite formation and this raised a question in Patinopecten yessoensis is composed of D-rich domains, repeating SG domains
	regarding how molluscs control calcite precipitation. Recently, it has been and K domain. The Asp-Gly-Ser-Asp motif found in D domains is suggested to have
	suggested that acidic matrix proteins in particular aspartic acid rich proteins such calcium biding sites and could control the crystal growth (Sarashina and Endo
	as aspein, asprich etc. favor calcite precipitation (Table 1.2). 2001).

yet another protein that is similar to asp rich protein first noticed in Crassostrea virginica

(Wheeler et al. 1987

). An interesting aspect of this protein is that it has only 30% Asp but presence of phosphoserines permits the protein to perform the functions that could be attributed to the high Asp containing proteins

(Wheeler et al. 1991)

.

Prismalin-14 extracted from Pinctada fucata is rich in Gly, Tyr and Asp. Asp regions were predicted to interact with Ca 2+ ions and in-vitro studies demonstrated the ability of this protein to inhibit calcium carbonate crystals

(Suzuki et al. 2004

). Similarly, P12, P16 from Mytilus californianus and calprismin from Pinna nobilis were found specific to prismatic layer and efficiently bind Ca 2+ ions

(Weiner 1983b; Marin et al. 2005

).

1.8.3.1 Proteins common to both aragonite and calcite

Table

1

.3 lists the matrix proteins common to both aragonite and calcite polymorphs. Nacrein is one of the most frequently identified proteins in the shells of bivalves; although it was first identified in the nacre of Pinctada fucata, it is also found in prismatic layers. It has a carbonic anhydrase domain with Gly-Xaa-Asn repeats and it mainly catalyzes the reverse hydration reaction of carbon dioxide resulting in the formation of carbonate ions

(Miyamoto et al. 1996

). N66 of Pinctada maxima is very similar to nacrein with carbonic anhydrase domains, only difference being that Gly-Xaa-Asn repeats in nacrein is replaced with Gly-Asn repeats in N66

(Kono et al. 2000)

.

Unlike MSI-60 (aragonite specific) and MSI-31 (calcite specific), MSI-7 is found in both the shell layers and is rich in glycine with many GGG and GG repeats and shares high similarity with MSI-31

(Zhang et al. 2003)

. Caspartin of Pinna nobilis is also a Ca 2+ binding protein and has the ability to inhibit the calcium carbonate crystals in vitro. It is one of the highly acidic protein constituted by 79% acidic amino acids (Marin et al. 2005). Table 1.3 SMPs reported in both calcitic and aragonitic layers Gigasin family proteins are present in the calcified shell layers of Crassostrea gigas;

Table 1 .

 1

	Species name	Periostracum	Outer shell layer	Middle shell	Inner shell layer
				layer
	Crassostrea gigas	<1 µm	Calcite prism	Foliated calcite +	Foliated calcite
				chalky calcite
	Mytilus edulis	>10 µm	Calcite prism	Aragonite prism	Aragonite nacre
	Pecten maximus	<1 µm	Foliated calcite	Cross-lamellar	Foliated calcite
				aragonite
	Mya truncata	≈3 µm	Granular	-	Cross-lamellar
			aragonite

4

The four bivalve models and their shell characteristics are listed below aragonite

1.9.1 Pacific oyster -Crassostrea gigas

Crassostrea gigas is euryhaline bivalve with asymmetric valves, belonging to the taxon Pteriomorphia and clade Ostreoida. Although native to pacific coast of Asia, it was introduced into America, Europe and Australian continents in late 19th century. It is an estuarine species and sedentary attached to hard substrates such as rock, debris and other oysters forming an oyster reef.

•

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Table 1

 1

illustrates this difficulty when shell protein digest from M. truncata shell is searched against different databases.

Table 5 .

 5 1 shows the number of proteins identified by searching M. truncata shell peptides against different databases

Database No of protein families identified with one peptide No of protein families identified with at least 2 peptides Unassigned peptides

	Swissprot	(other	1	0	14178
	metazoa)
	NCBInr (bivalvia)	9	0	14169
	P. maximus EST	31	0	14135
	M. edulis EST		30	0	14130
	C. gigas EST		25	2	14144
	M. truncata EST	96	67	13781

Table 5 .

 5 2 shows the list of possible phosphoSMPs identified in M. edulis

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•

-1. BLAST results and domain architecture of SMPs identified in four bivalve models C. gigas SMPs identified using NCBI database C. gigas SMPs identified using EST database M. edulis SMPs identified using EST database P. maximus SMPs identified using EST database Annexe-3. Shell peptides identified in the different species Shell peptides from C. gigas

	No 10 No No No No 38 39 40 41 No 42 43 44 45 46	No 10 11 12 13 14 15 No 16 17 18 19 20 21 No 22 23 24 25 26 No 27 28 29 30 No 31 32 33 34 35 36 No 37 38 39 40 41 42 NTFLEAIMK No Protein accession numbers Protein molecular weight (Da) Protein identificati on % Num ber of uni que pep tid es % sequen ce covera ge Peptide sequence TR100036c 0_g2_i4_f r1 27,098.9 99,80 2 14,00% EIPNGEAVPNPCTEDPEDK TNGVELGDPECVWER TR100170c 1_g1_i1_f r1 19,522.1 99,80 2 12,80% ALCFTFTDR CFEYFDDEIPCLK TR100170c 1_g4_i1_f r2 11,284.6 99,90 2 28,10% YTDGDFEECIECFEYFDDK YTDGDFEECIECFEYFDDKTQC IQANK TR100922c 2_g2_i10_ fr2 52,642.0 100,00 3 15,90% LPDVNPDGTPR LPIVNPDGTPR LPSVNPDGTPR TR102431c 1_g2_i3_f r1 9,036.2 99,80 2 26,50% DGEVYSGASCGCQIDWEAK SCRDGEVYSGASCGCQIDWEAK TR102983c 1_g3_i2_f r1 12,371.7 99,80 2 29,00% YGNVHDDFEHDQTEVDIQEVVR YGNVQDLLK TR103522c 1_g6_i1_f r3 134,382.5 99,90 2 2,24% GYCEIFSDICK TDSAQDVLSTIGSSK TR107902c 0_g1_i1_f r1 100,244.6 100,00 3 3,27% NFLTESER TEALTLDDIIFGK VPFDFGSR TR108655c 0_g3_i1_f r1 65,412.3 100,00 9 17,40% GDPYFFNVESSGK GKIESFNILDQEPIR IESFNILDQEPIR LFELEIPHR LIEDECPVR LSYLEAYSVDQQTGNVK NNGLCDYFGR STITNYAVSISNEENSR VQGYQPLTFR TR108889c 0_g1_i3_f r2 108,880.3 100,00 2 3,22% EWLPSNFADNIPSFDSNR LDETTYEEYLNGK TR108978c 0_g1_i2_f r1 59,278.0 100,00 2 4,72% DAIVNDVVCPEGK GCGLGTIFSQDK TR109265c 0_g1_i1_f r1 118,877.1 100,00 2 2,40% NDIGYEQQQVASIR VDEEGDLYLHK TR109485c 0_g1_i1_f r3 123,095.4 100,00 11 13,50% DCGTPYLTCANIDGQWK FGCGFLGFNR GLCSAYTDIGR LQVFDCLK MPISEYVGYVAIK NGLMSTYDIYDPR NLNHIGGVLTESDIER SDGLSYEGSYLDYVLVDNR SIPDAVLDMWTIK SQSFISVFDSLGELCKPR TQQEVQSLALR TR110022c 0_g2_i2_f r2 25,189.0 100,00 5 30,50% DGQNVLTEALGVK DKNPGVIVSVVK DSFYYNELMYSILTTIGER NPGVIVSVVK NTPYLFLDDTVTR YATSLDVMAHR TR110022c 0_g3_i1_f r2 52,518.1 100,00 13 36,40% AYKEDDGSLFPVPFEFLK DLFDAYNR DSFYYNELMYSILTTIGER EDDGSLFPVPFEFLK FFTTLDPSTVDIAR HMFSVDDLR ILETADDMHGYNTLMTLFPDR LPTDFTGTYTNDIYGK LQDPSIEDYFLTIR NEIYTPLGMAK NLGIFIAMTGEDK TALSSYISDLYLDK YGYATFDLQR Protein accession numbers Protein molecular weight (Da) Protein identificati on % Num ber of uni que pep tid es % sequen ce covera ge Peptide sequence TR111401c 1_g2_i3_f r1 116,663.5 100,00 8 12,50% DASIATAVIDNLEPATMYYFQA SSR ICDSTQTSASITDLSPR LQVLEASTAALK LYAVNDVGFGEPAIQTFK NVQLELSR TFGGGGAVVMCQTK TTVILTWEPPVLLAGDGIIR WISDFLDDDTVHEFK TR111401c 1_g3_i2_f r2 53,958.4 100,00 6 22,30% ICDATQTSASITDLSPR IEGTTPNTVTLR IMIQTQVLR LQILEDTVSGLNSVMNR TFGGGGAVVMCQTK TSVLLTWNKPLVLPGDGIIR VYFAAVDESGYQPQGEVVFR TR111969c 0_g5_i1_f r1 48,866.2 100,00 3 7,44% CEIECSATEISCK FGFLDCSK TEVVIQTSNGQVK TR112400c 0_g1_i3_f r3 97,196.8 100,00 3 4,18% DVVCDLDAAR NFDGLDLDWEYPANR TDLSQDFCTGR TR112560c 0_g3_i12_ fr2 52,399.5 100,00 7 20,40% AFILLPGEPLLFK EITETAFPFR MIIEAYRPLGFVGEVYMFK TAQQHLEIPIR TELISTAANCTAECK TIIENGCVLR TTVVSTHNYNQFGPR TR112576c 1_g2_i1_f r1 37,975.6 100,00 3 10,20% CLNFGLVSTR GTEDGEDFIYCR LECPEDEQITR Protein accession numbers Protein molecular weight (Da) Protein identificati on % Num ber of uni que pep tid es % sequen ce covera ge Peptide sequence TR113224c 0_g11_i3_ fr1 12,215.2 100,00 3 37,20% APVVGPAVGPYPK VASNPQPVVIQGPSQR VQNIFSGSPNQISYPR TR113582c 0_g2_i1_f r3 43,551.3 100,00 3 9,72% DLQVCLMEVEHDR NVCMVMNIDPICK QVTSMQLPGQPR TR113680c 0_g3_i1_f r3 89,396.7 100,00 8 13,40% ANSPATASGFVAMGAAR FQDISEPLPR GNIYSLER GVSLDMQSQALDR QLQIQSGINPETNYPSTSQR SPGAYQPYVNCVQR TILGTPLIR YFGNGDGEVMNGPFR TR113891c 0_g4_i1_f r2 74,371.2 100,00 4 6,93% MGSVNDGSTVVGPDLK SGIGPADQLQR TGLLAELR VLLVEAGESDR TR114607c 0_g1_i1_f r3 96,951.2 100,00 4 5,37% ALFNDILSPK EISFYEFLEK IVQAIFQNIDIDTLMENK LANIPIPPK Protein accession numbers Protein molecular weight (Da) Protein identificati on % Num ber of uni que pep tid es % sequen ce covera ge Peptide sequence TR114620c 0_g2_i2_f r2 67,726.1 100,00 14 37,90% ENGNTLAQDLEPEHITFSADEK EYIVTADEGDSK FPSSYISDTTYSYK GGNLIDPEGAIGLLK GPECESLAVAYSGTR ILYVTGEER IPSIQHDALLGK ISTLYVPSR LNVVDLSNPSDPIMLYR MDPTDVEYCGNHVFVSVANDEK PEDGK TVLGTWGEAFDGGLLTDMDTDD IK VLVALEGEAFAR YKLDPSDEDGGINIYPYPIYGM YQPDAIR YNSHDYPEFK TR114620c 0_g3_i1_f r1 17,829.0 99,80 2 17,00% GLDNPLPNPR VPVKPLQSSPLPESTPK TR114644c 0_g2_i6_f r3 84,491.2 100,00 11 23,00% DNAALYPVSYEFLK DSYYYNDLMYGIIAK FCGDSCVLSSANDMAR FYMIPTGAAEHLIDIDTLK ITNLIDGVNIDK LGMTSTSFFTSPAFNPFLQDVA QGYVK LTEDISGSQWETLVTAEILNR SMVNPGLAVSVVK TTINNAFMNCR TTLSSYIADIYHK VVFTHALGVK TR114991c 0_g2_i1_f r1 74,766.5 100,00 7 16,80% GFFLVDGEPLLIDYR GLFSMSDTVHNVQCVYEAR MGENPVDVTAAMNPLTVK NDAFYILAESNGK NDAFYILAESNGKFPDTTGAR SCNSPPPLTIPK VVDSNERPLQQVQR YLEAYSLDR Protein accession numbers Protein molecular weight (Da) Protein identificati on % Num ber of uni que pep tid es % sequen ce covera ge Peptide sequence TR114991c 0_g5_i1_f r1 29,565.0 100,00 8 51,90% CEVPVLSAECGMSSMK CPPGYTEVDCGLDTASITAAGA CR GICFNGISCYCPEDYIGTK GLFSMSDTVHNVQCVYEAR LTEMFTNVPGYK MYELDIPLEAIAPCSLK NGGACDYFGR TENPETGDTTYEVEVATAHTK TR115222c 0_g3_i5_f r3 68,300.3 99,90 2 4,79% CFQDCVYMGATYK GDNCNTCVCANTGDVR TR115602c 1_g2_i3_f r1 155,732.6 99,80 2 1,72% GIIVEQLPAESTEMK STLDVLER TR115654c 0_g1_i1_f r2 127,527.9 100,00 2 2,92% LDLGLGAVEVAGK NTLNPVGVSLFDETEVENLR TR116732c 0_g4_i2_f r1 84,535.0 100,00 12 20,80% DSSEGMAVVIFR DVQNAIESDIFK FDEELFDLK GMTTVISEMQGDLSLVQK LGVDLTINGETR LLMQQVAALSK NSLFVYDDR QNNDIEVLQVDLK SSADLGEIVLR TANGMNSLVIEELQQEVNK VAFTSQISLEGAMSDVK YSLLEEELDYVR TR117215c 2_g3_i1_f r2 68,994.0 100,00 2 5,73% GLLAEGPLGDNELAQVEFLSK YLDGALYDSTVTDR Protein accession numbers Protein molecular weight (Da) Protein identificati on % Num ber of uni que pep tid es % sequen ce covera ge Peptide sequence TR117339c 1_g9_i2_f r3 70,691.1 100,00 4 9,02% GLCDEDPCLQK IEQDCADSTFYNPEK LLTFIAQTDTSPK NSFIEIPYLSGIALDK TR118445c 0_g1_i1_f r3 169,706.4 100,00 7 6,52% GINSPLFVLQSK IIYQEALQNQAIADK LSDLGNVFGR MLNIGEYNTGFK NPLYVVGSPLMVSSK SSNPQPAPIPIPVNR YPSNDGGFSVLSGGLSPSK TR118896c 0_g2_i2_f r1 154,198.6 100,00 9 8,98% DYGTEATQMEANEGSR EYVEQPIAR FEGYLNGVQATK GAIVQLLDR LTSNTPNTPSSPGGGDVSLR SGISVFTVGVGYNTDEEELK VAIMIVNGK VELEDQVAR VGVVLLGNTDGLQIPISGNR TR119072c 0_g2_i1_f r2 72,456.1 100,00 3 6,00% FQQNSPVQQQANFR MQEMGNPFQQPAER WYSAGQNLISR TR119072c 1_g2_i3_f r3 91,438.5 100,00 3 4,30% APETIDTSDFVK FGDPSANMLNLIK IGSGNIQNLSK TR119141c 2_g1_i1_f r2 99,740.4 100,00 4 4,88% AAPSFFEMVQQIVAR AAPSNAFLAAIR MPLLPPLR Protein accession numbers Protein molecular weight (Da) Protein identificati on % Num ber of uni que pep tid es % sequen ce covera ge Peptide sequence TR119712c 0_g3_i1_f r2 90,382.5 100,00 4 7,28% ASVIGDVMGPSISGLDSLLK DLEGGLPVLK NLVTYIQDNQVK SFSQASDFITIDENVVQK TR120030c 0_g1_i1_f r1 165,339.8 100,00 4 3,86% FNIDNTNGPAEGPSK LTSDLLLSESTGGEINR MNINIPDGSIDIGR SILQDGVVTSER TR156518c 0_g1_i1_f r6 9,412.1 99,80 2 21,30% FLNSLNLNEK LIADLWR TR50602c0 _g1_i1_fr 2 10,539.7 99,80 2 21,90% DYCDYLGQIYK GQAFVDIVGR TR87059c0 _g1_i1_fr 3 32,861.2 99,80 2 8,41% TNVGTSGSTSNSLMEAK YSVEAGITR TR89651c0 _g1_i1_fr 3 21,101.2 100,00 2 16,90% ECYAECSYETEQCR SETCDTTCICLTDCASR TR89714c0 _g2_i1_fr 3 27,014.0 100,00 4 26,10% DFIIQGGDFEHGDGTGGR IVIGLFGDTVPITTR NFAALATGEYGFGYK SIYVGDSFLDENFDIK TR92140c0 _g1_i1_fr 2 22,159.6 99,80 2 15,70% STGGNYQGNVINPIK TEVGAQQGMLGQLQK TR95528c0 _g1_i1_fr 1 40,303.6 100,00 3 6,55% DFLDGFLDDK DFLDGFLDDKIEDR LWCEAVIR No Protein accession numbers Protein molecular weight (Da) Protein identificati on % Num ber of uni que pep tid es % sequen ce covera ge Peptide sequence 52 TR97545c0 _g1_i1_fr 2 34,633.7 100,00 4 22,20% ELNDPNALATEIVVDEPNYSHI VVVR LFAVNAEGFGEPAIVQFTTPK NVQIELSR TFGGGGAVVMCQTK 53 TR97983c0 _g1_i4_fr 1 10,849.1 99,80 2 47,10% GPGCAVKPLSLPK GPGCNSISSAGTLSSYGQVVSG SNVYSANGCGSDGK 54 TR98022c0 _g1_i1_fr 1 11,384.3 99,70 2 15,20% DDAVTIQEYK LDVNKDDAVTIQEYK 55 TR99682c0 _g1_i1_fr 2 88,346.7 100,00 2 3,06% TTDPLSPTTLSIPLK VEFLQSLPK Shell peptides from M. edulis Protein accession numbers Protein molecular weight (Da) Protein identification % Nu mb er of un iq ue pe pt id es % sequen ce covera ge Peptide sequence Ion score Best Mascot Ion score 53,5 57,4 60,4 91,8 53,9 39 53,7 62,5 42,7 108 39,9 60,5 54 35,8 48,4 37,7 68,4 40,6 66,6 44,1 77,8 45,4 49,8 62,7 34,5 51 45,3 56,7 57,2 46,1 50,8 65,3 41,9 62,4 79,7 77,7 39 39,2 54,7 47,2 88,4 59,1 59,1 81,3 81,8 37,8 50,1 57,2 92,2 42 60,9 42,2 50,1 52,5 93,2 36,8 35,3 88,3 70,2 39,9 61,5 60,8 58,8 Best Mascot Ion score 49 91,4 97,8 97,1 45,3 67,5 46,5 65,3 90,2 50,7 49 122 67,5 31,4 47 38 37,5 95,1 46,9 49 58 68,9 44,4 49,5 41,9 65,5 52,7 68,8 52,1 35,5 50,8 Best Mascot Ion score 46 72,2 97,8 57,1 64,8 68,5 59,6 57,9 43,1 81,6 53,8 43,8 38,2 68,5 49,9 42,3 63,8 39 45,1 41,2 59,6 29,9 Best Mascot 35,4 62,3 89,1 81,5 62,4 52,9 36,1 74,1 87,3 39,1 70,1 88,4 53,7 50,5 45,5 37,9 45,9 79,9 79,4 57 55,3 38,3 76,8 70,2 56,8 56,1 42,2 98,8 37,3 101 72,5 91,7 39,1 39,4 55,6 Best Mascot Ion score 44,4 97,3 64,8 37,3 45,8 80 47,2 67,3 43,7 67,3 55,3 37,7 54,1 71,2 54,9 54,9 53,5 91,4 57,8 60,5 41 49,4 87,4 37,6 75,7 84,4 49 43,2 Best Mascot Ion score 42,2 38,5 52,4 39,2 54,8 69,3 83,8 58 58 48,5 60 67,9 45,9 57,3 61,5 53,5 44,9 43,3 62 75,6 75,7 56,5 40,2 41,9 64,6 42,8 60,5 43 32,4 Best Mascot Ion score 48,8 38,9 44,7 58,3 56,7 61,5 57,2 56,3 55,3 39,4 39,3 68 41,2 54 34 98 71,6 90,2 67,2 102 45,1 43,5 37,2 52,1 42,4 Best Mascot Ion score 74,9 89,3 45,3 67,5 60,5 34,5 48,8 41,4 43,2 49,2 Best Mascot Ion score TR101774c1_g1_i2_fr 2 19,179.0 100,00 4 27,30% LSESQIPDVSK 58,6 SDGNEENDFGIDLTDQLK 53,6 TDQMENRVEFLGESANER 42,6 VEFLGESANER 60,2 TR107330c0_g2_i1_fr 3 11,918.3 100,00 3 23,50% DMWCAIGTGFSDQDCDCTIR 101 KYEQFVNGVGWK 73,8 YEQFVNGVGWK 61,5 TR108072c0_g1_i1_fr 3 35,485.0 100,00 5 23,10% ELLDTVNDLGDITTLLSK 123 NQMLSNPLGVLTK 95,8 QLLAEYPNIEHVLNAAK 67,9 RNQMLSNPLGVLTK 49,4 VIDFYDHHTECVGVLMCIFAAVP HPR 50,7 TR108072c0_g2_i1_fr 3 34,309.2 100,00 6 28,20% DLLDTVNDLGDITTLLSK 142 IATDNGHDHDSLLYAEAR 77,4 KLLADYPNIGHVLNGAR 79,4 LIDSHDHTTECVGEMMCLLAALP ESK 70,5 LLADYPNIGHVLNGAR 82,2 LNNPLALLTK 66,1 TR109871c0_g1_i2_fr 1 25,921.7 100,00 3 17,60% ILHSLLSQR 47,3 IVQTINNGPGSQIDGR 91,1 NALNILQGLLENVGK 105 TR111695c0_g1_i1_fr 3 31,003.2 99,80 2 11,70% ETTVQQNEPTEFK 52 VGPNGFPDR 42,4 TR114498c0_g1_i2_fr 2 20,392.3 99,80 2 10,70% FNCLFQK 48,4 LDVSTACVVDYK 77,4 TR115714c0_g1_i1_fr 2 13,538.1 100,00 2 20,50% GEGSLSVLDVIDANR 68,5 RGEGSLSVLDVIDANR 48,8 TR115714c0_g11_i1_f r3 12,600.6 100,00 2 9,82% GGESLSVLDVIDANR 68,5 RGGESLSVLDVIDANR 46,9 TR115954c0_g1_i3_fr 1 22,028.2 99,80 2 22,30% KGECDQPIAYGNTDCTAHTVR 50,8 YNPLSQNEVQYYLQELQELK 90,3 Protein accession numbers Protein molecular weight (Da) Protein identification % Nu mb er of un iq ue pe pt id es % sequen ce covera ge Peptide sequence Best Mascot Ion score TR118200c0_g3_i1_fr 2 13,985.3 99,80 2 11,20% ADFSDIFESNQAR 76,2 KADFSDIFESNQAR 88,1 TR119212c0_g1_i2_fr 2 31,499.7 99,90 2 11,90% AMDLSDVGFVNYLK 80,4 TGSEICEVVLSDVAQIVFK 85,6 TR119493c0_g1_i1_fr 2 24,394.4 99,80 2 18,80% EDPFDLSGIMSMLDGPNK 93,5 MGPMEIMSLMTQVQTLQESLDK 88,1 TR121983c0_g1_i1_fr 1 39,136.7 100,00 6 29,30% ANVQATAGSNDGPIDR 65,8 ETGWFQQVSENPLIMQQILR 46 FLFNASTPGQTAAIR 79,8 GNAVNFANTNSQR 52 NLAENGNLQQFIDR 45,5 VQMIVTISETGNMENLISNIGVL NR 132 TR122091c0_g5_i3_fr 1 44,279.7 100,00 4 17,40% ANQQLNDDQTVVSSGSCPELPTK 103 QADPINQQQPFIPLETSK 60 SSASKPADPVNQSSTQVDTNK 41,8 YEDNILIIESK 68,2 TR124546c0_g2_i1_fr 2 52,206.7 100,00 3 7,91% FIETLAQTGQLDR 84,8 MITDTVLTEFQK 94,9 VDLIQDLLLVPK 67,2 TR125042c0_g1_i1_fr 3 73,988.0 99,80 2 0,00% INAIIINLFSR 54,2 INGIIINLFSR 42 TR126544c1_g2_i1_fr 4 43,006.0 99,80 2 0,00% NHIIKVIFSR 42,1 NQIIKVIFSR 45,1 TR128837c0_g1_i2_fr 1 60,483.1 99,80 2 5,51% LIWTLGGGLTDR 45,3 NVGNEWINWFMVISGDSR 47,6 TR129143c0_g7_i1_fr 1 26,981.9 100,00 2 15,00% IYSPELPVEYEQCPIDGGELGK 50,7 YPYNYQHIEDALK 48,1 Protein accession numbers Protein molecular weight (Da) Protein identification % Nu mb er of un iq ue pe pt id es % sequen ce covera ge Peptide sequence Best Mascot Ion score TR130526c0_g2_i2_fr 2 41,420.2 100,00 6 23,70% DEAYNPEYETCCDEK 48,8 IYNPLYESCCSGNLLSQR 66,8 LSYSLCSTGMQPR 63,8 SCGGESYSPLHETCCSGK 35,3 VESFYQCCSDK 35,7 VYNPENAVCCGGK 40 TR131851c0_g4_i1_fr 2 19,370.0 99,80 2 28,10% IPVENIPQPTK 46,2 SPVYSDYIISDSPLR 56,3 SVLQAMYTDSDIPYWDELFTR 45 TR132367c0_g1_i2_fr 1 46143,50 100,00 4 13,20% FMTDFMTTFNQIMSLK 88,5 NNGEFVMSQSGPGIK 70,7 TIDFSDLAPGSK 80,8 VDNGQGIAFR 58,7 TR133415c0_g1_i5_fr 2 29,798.9 99,90 2 7,69% QTFTSQFGSFK 72,5 SGPFTVLSR 83,3 TR134349c0_g3_i2_fr 1 24,125.5 100,00 6 33,30% AHFSYDNNLCEGPDNWCK 71,2 EYNFAQIHVQLGQDEK 43,9 KSQSPININTGLTR 61,3 NNGHAPYFEVHDR 49,1 SQSPININTGLTR 73,6 YDDEITVR 52,7 TR134487c0_g2_i1_fr 2 22,099.1 99,20 1 5,05% MSIINANYGR 55,5 TR134692c0_g1_i1_fr 1 48,688.3 100,00 5 8,30% IIIQLLTR 67,2 LLNLLFSR 64,7 LYAYDYYK 45,8 LYAYDYYKDDNSK 76 SSASAAAIAAASAR 125 TR134692c0_g2_i2_fr 1 47,546.7 99,80 2 9,19% GGSSAAAAAAAAASSGGGGGYGG K 56,9 NWLGGSGGSSAAAAAAAAAGAGA GGGR 75,8 TR135765c0_g1_i1_fr 1 55037,00 100,00 2 4,30% NAIAAVLSR 40,8 VTEGFFANLPGR 48,6 Protein accession numbers Protein molecular weight (Da) Protein identification % Nu mb er of un iq ue pe pt id es % sequen ce covera ge Peptide sequence Best Mascot Ion score TR138092c0_g3_i2_fr 1 55,383.0 100,00 2 5,38% DFLVTGNIENGR 61,3 GNINCCGVSQAPCR 51,5 TR140757c0_g1_i1_fr 1 46,379.3 100,00 3 9,26% DGSDSVTISGNLPSASK 51,8 ERDGSDSVTISGNLPSASK 78,7 LKENAETSISDNVNSLVNSLVK 68,6 TR140757c1_g1_i6_fr 2 70,518.0 100,00 9 23,20% ALSMSGITSAAGR 85,1 APSVSGITPAAGR 68,9 ASSLSGMTSPVGGASSR 71 IASPSELFGMNMNTGTGSTDDSD ILVVALLPK 68,8 IVDADGMGDFHLEFPNLIIQPTL TEK 53,6 NAAVNLQLK 48,6 NTAINLQLR 48,2 QPSVGINIDAGMK 55,4 SISIPVQGTASSSAHTTAGSTNR 93,6 TR140838c0_g2_i4_fr 1 30,489.7 100,00 6 29,90% ASAYYGSDVELLDFVTEAEK 49,8 KGLNLLNTFSTK 77,1 LLNDELLTR 59,4 LTINNIDSLVK 54,1 LVNMMYGEFDAK 55 SVIFEYEEPDDIDQGYK 51,5 TR142598c0_g3_i1_fr 2 47,969.5 100,00 4 13,60% MFTVQFPNTPDINVELGAMR 51,9 SPVYSDYIISDSPLR 56,3 TVSQLNWELFR 62 YLSAEAQNYIR 51,8 TR144609c0_g6_i3_fr 1 23,029.6 99,80 2 13,30% DLFEIPVTGLGVTR 56,7 LLYTSFTDQLDFK 60,6 TR145042c0_g5_i2_fr 2 56,079.8 100,00 2 6,81% CVNDCGGDIPCGSGYK 38,8 TSLVLTDTGSHLGASGIGSSR 44,1 TR146218c1_g1_i1_fr 2 101,908.9 100,00 3 4,33% FLTGSINGMTSR 56,6 LVEHFITQTDIGSDR 56 MTQIGVIITDGR 78,3 Protein accession numbers Protein molecular weight (Da) Protein identification % Nu mb er of un iq ue pe pt id es % sequen ce covera ge Peptide sequence Best Mascot Ion score TR147503c1_g1_i1_fr 1 57,897.2 100,00 12 31,50% ACPSGTIYSENQCR 96,4 CVEGISVPSCCPK 58,4 DLCDDECETPSILTR 60,7 DNPMTIGLCTGQNGFHGEIDELE IYTACIPK 50,7 GGLAFDYSHISLR 72,1 INFDDGFK 51 INFDDGFKDISK 56,8 IPVNGGSLNTLSEK 86,2 LKTDDVDVLSR 71,6 QLTQTSCPSLPDPMNR 73,2 TDDVDVLSR 60 YRPEPIISNCGPNGDPSVEIVVH R 66,7 TR147503c1_g3_i2_fr 1 23,981.5 100,00 3 32,90% GTTLSTGTASVNIGSDSAK 47,6 IPVNGGSLNTLSEK 86,2 LKTDDVDVLSR 71,6 REEDGDNTYTIIQGK 53,8 TDDVDVLSR 60 VLPAKPVTQINIEK 37 TR147503c1_g4_i1_fr 2 32,291.7 99,80 2 14,40% QVSGLTVNKPSYTNLVK 49,5 VLPAKPVTQINIEK 37 YSNSVASNFLTK 54,3 TR149180c2_g4_i1_fr 1 99,300.7 100,00 12 21,70% GGPVIALETLADVPSPPR 69,6 ICDSTQTIASITDLTPR 51,6 LADMEQATQELVR 80,6 LEWSSDFLDDDTTHVFR 50,6 NVEIPLVEPDYPYIVVVR 77,1 QDMFTTAENQLR 52,7 QLQIMTIR 51,3 SIVQNGQTLLDFIYQER 73,3 VGADVFETVITNLTPAQVYYFR 58,6 VGLGEAAVTTYMTPK 69 VYFVPVDMNGLQTAGEVVFR 53,5 WELVEIFDPEAR 66,7 Protein accession numbers Protein molecular weight (Da) Protein identification % Nu mb er of un iq ue pe pt id es % sequen ce covera ge Peptide sequence Best Mascot Ion score TR151061c0_g2_i2_fr 3 145,382.7 100,00 5 4,92% IDANSLTVQPK 69,8 ILSDTVITGNIDVR 87,3 KSESVLLPSLR 44,8 LTSLIMQPLPPER 45,1 VLSENLSGNPSR 66 TR152155c0_g2_i1_fr 2 11,618.6 100,00 3 63,50% FPNSMNPMNSQGFQR 49,7 NFGMGMNNQQMSQNTFQQR 43,8 TQQQQQQQQQQMQNSPSQELIQQ NQQK 58,5 TR17938c0_g1_i1_fr1 22,820.5 100,00 6 37,40% FIQEAQLPESMK 46,8 GMAPIPGIDR 42,7 GMSVGIVADLWR 75,2 IGTGSMSSIAIIADLWR 66 ITLGTDIIGEGGR 82,8 VDVEFINEMK 68,6 TR49703c0_g2_i1_fr3 13,963.5 99,90 2 29,90% DGSGTGPGTDGSQTGTGQTSVDI K 61,7 DLLIFTNTDTPGSK 56 TR88901c0_g2_i1_fr2 23,011.8 100,00 5 46,20% ECMIQPQYGDCSGGR 77,7 FATEDECEIACEGEEEEEPDNDI PDIDGPDFDVPEYNR 36,6 GFYFDSILDDCVYGCCSNGGNR 74,3 LDVYAQLYR 77,7 NNVNNVNTGINR 64,8 Shell peptides from P. maximus N Protein accession numbers Protein molecula r weight (Da) Protein identific ation % Nb unique peptides % sequen ce covera ge Peptide sequence Best Mascot Ion score TR22041c0_g1 _i1_fr1 27,512.6 100,0 0 3 19,00% EYELNVMTGTVGR 61,7 IGVIVTYGLSPSPVNVAQQAFLAR 53,5 TDLGLLEMNR 70,5 TR36614c0_g1 _i1_fr1 64,217.5 100,0 0 5 12,80% GNNGDLSCLIHK 55,8 HNGHCPVATSNAAVCGATCDSDGNCPL GEK 66,6 LSSSGKNNAGDPTGDIVGGAK 59 NNAGDPTGDIVGGAK 104 SSVVTGGQWWEEDPNMVAK 97 TR36767c0_g1 _i1_fr2 41,573.3 100,0 0 2 7,28% VASQQIGNPAISFSASK 71,3 YSAGGCPIIR 57,9 TR40712c0_g1 _i1_fr2 61,706.4 99,90 2 4,88% NAQVNSGNVVYDQGGNR 47,8 TIANVDHAPGR 43,4 TR41871c0_g1 _i1_fr2 35,046.7 100,0 0 2 8,75% FFACPEFSVCGNGEVPEPTR 89 GFIPQIVCQK 44,3 TR44286c0_g2 _i2_fr1 36,531.1 99,80 2 8,90% SSPLTGNIAVSPGSLR 39,9 VGVSQNVGTFDGR 85,5 TR44968c0_g1 _i2_fr1 46,233.8 100,0 0 4 12,30% AVPLDPCDYIANQCR 77 QEDWWGTNLR 47,7 QITNLCEEVVGQK 62 WGPPQIYASPPR 62,7 TR48941c0_g1 _i1_fr1 57,834.4 99,80 2 3,14% VEECGPTGSVLTPDR 98,2 VEECGPTGSVLTPDRR 60 TR48941c0_g1 _i2_fr1 14,349.6 99,80 2 20,70% VCPGEVCAVDEK 64 VEECGPTGSVLTPDGR 85,1 TR49150c0_g2 _i1_fr3 8,925.7 99,80 2 35,40% DCICPAVMDPWCGEEGDYNNLCELMCK 21,4 KDCICPAVMDPWCGEEGDYNNLCELMC K 48,5 N Protein accession numbers Protein molecula r weight (Da) Protei n identi ficati on % Nb unique peptid es % sequen ce covera ge Peptide sequence Best Mascot Ion score TR50357c0_g2 _i5_fr3 63,455.4 100,0 0 8 27,50% CICDKDWAGYDCR 50,3 CSNHGTCVLGTYCYCDFGYYGDNCEK 32,1 EQLGPGVFSYTMAMSVQR 97 FEVDIVDYLQYPLLNGAGTTQSK 88,1 FISLHGYDDCTLK 65,5 QTFAPLDWMTEFTCTYER 91,9 TFSGENLIMNDQFVADHLR 39,6 VTGTWVTINPLNEAMEIYFDYK 83,3 TR52216c0_g2 _i1_fr2 33,562.8 100,0 0 2 12,20% FFNVIEECCSYADK 57,4 NILILGDDEDNDNGDDTGYVFK 66,4 TR52321c0_g4 _i1_fr1 43,405.7 99,90 2 7,71% DPPEVSGAINVCVK 44,2 FYTNNCPDIQECQNK 66,5 TR52879c1_g3 _i1_fr2 108,048. 4 100,0 0 8 12,80% HIETFLTETLPK 41,1 LPTISESNQPTISDNYIHNIGGNFLPL K 40,8 NLGSQIFLNK 60,4 SLVDAQPLPIK 64 TDNNLPTFDDK 47,3 TDNNLPTFDDKLPTESNFLPIIGGNNL PPVIGNQLPDK 33,6 TVCGANFDER 41 TVETFVVSTLPK 79,9 TR53650c4_g1 _i1_fr3 139,124. 3 100,0 0 2 2,21% TFSVGGDDTDIPAWIDK 49,7 VYDSLQTEFR 43,4 TR53739c0_g1 _i2_fr2 47,357.4 99,80 2 6,55% FIGELDIEVGK 45,8 LSEENLEDFLMALDEK 103 TR54101c0_g2 _i2_fr2 58,203.6 100,0 0 4 9,64% LIIPPVPTLDR 45 NCMSCPGTCVYGGK 34,9 YCVTENECTYCK 43,1 YYSCPELQQCK 44,2 N Protein accession numbers Protein molecula r weight (Da) Protei n identi ficati on % Nb unique peptid es % sequen ce covera ge Peptide sequence Best Mascot Ion score TR55122c0_g 1_i1_fr2 89,549.0 100,0 0 1 3 22,40% DYYFAITAFTR 52,2 GIGNEVEVQLR 89,5 GTPLGPNSIR 44,6 ICDASQTSASITDLTPK 109 LFDIIQK 53,1 SSSFPTSYVATK 48 STTIPITDPTLSYIVVVR 80,5 VAAVDGQVVESNTIPEVIHTPDVIPSP PVNLK 74,3 VETFLISTQANIK 85,1 VFQWIYQGR 57,1 VTFAPPDTVEGK 53 VYFQPLEIDGSITQPEVVFR 99,7 WTITEVPNPNAR 53,1 TR55299c0_g 2_i1_fr1 66,926.7 100,0 0 6 15,80% AASQCPSCDLINTESK 47,8 ETTASLNIADSSNK 81,7 EYANTIAIFNWLNEGNNIVTK 56 FNNDFPDLEAR 45,9 LILGLVPAPLGLVFR 23,5 VTELNEYILENYIAR 92,8 TR55869c0_g 4_i2_fr1 17,749.9 100,0 0 3 21,00% FMAADDLNTCR 56,7 VVPNITPDGR 55,3 YYGCPVVECFSK 57 TR56488c0_g 2_i1_fr1 95,202.2 100,0 0 8 12,50% ACAIQIGHGWGLENFVGDIDQLEIYK 63,5 ANELVFSR 61,9 DGSIVIATDPVSIR 76,9 ETGSTPYSNSALISNGDCGR 76,8 FTNDDFGFTFIVR 65,1 FVQCFFR 44,6 GDDVIAFYK 55,6 LTCDYAFR 45,9 TR56804c0_g 1_i1_fr3 67,437.5 100,0 0 4 7,28% DALPAPLNTESGPK 43,5 IPVANIQGPK 38,1 IRIPVANIQGPK 31,8 QPLIGPPFGPVDPSVQR 52,9 TR57234c0_g 1_i3_fr3 89,687.1 100,0 0 2 4,10% LPGGNSFIPYDVISDIHR 36,8 NAGNINPSSDYPNK 44,3 N Protein accession numbers Protein molecula r weight (Da) Protei n identi ficati on % Nb unique peptid es % sequen ce covera ge Peptide sequence Best Mascot Ion score TR58328c1_g 1_i1_fr1 126,273. 8 100,0 0 4 4,14% EEYTEVTSAVQR 68,2 IADYFVMDR 74,4 IVNYVYDNAMR 42,2 TDDTGELISVYNSK 102 TR58506c0_g 1_i9_fr1 151,236. 0 100,0 0 2 3 24,20% EAGFMVVLR 68,3 EIYGYQPTSITDFFTYK 78 EVIESVLDAVPDSVQVQLR 60,8 FEPWNPPQFAR 52,2 GGELSYYMLLR 61,8 GLMGEGTYGDATLEYIK 73,6 GTYSNYNVEFNYLR 94,8 GWTEEYSISATNFDPVQLIQLDSLR 99,8 GYNSGSAFFDK 64,5 HVCPNAEIELEALHYTFLSQSYNK 64,1 HYTLEDFK 43,6 IGHHNDCFLADSIDR 53,7 KVEQDFITIK 63,2 LGYTMALVESVLPNTVIR 74,8 LWMPNEDIFLR 52,8 NVIMGGETCSLDTTDER 135 REVIESVLDAVPDSVQVQLR 50 TAWSGLWNGFHYR 48,4 TTIISQDFLK 45,8 VEQDFITIK 64,1 VIAGLEGIPTGYSHLSYQPLTTQLQNP GR 87 VLNDDFEDK 54,7 VPTSVTPASYQMWLAIGDK 111 TR58981c0_g 2_i5_fr2 50,171.0 100,0 0 2 5,39% MMDISCSASGCVDR 52,9 VNILFASYGR 77,2 TR58981c0_g 2_i7_fr2 27,572.3 99,80 2 8,61% GYTLSIITAR 56,2 SLGQFANQAVR 56,3 N Protein accession numbers Protein molecula r weight (Da) Protei n identi ficati on % Nb unique peptid es % sequen ce covera ge Peptide sequence Best Mascot Ion score TR59099c0_ g2_i1_fr1 58,600.8 100,0 0 1 5 42,20% ALGWQMQDQQESIHTQQR 37,4 ATISYDLPR 66,5 GSCGVPYPLMNAALDGLHAR 44,4 HDFDGLDIDWQFPATR 44 IVFNNFLGGVMIWTLDMDDYR 92,3 IVLGLPMYGR 38,3 KHDFDGLDIDWQFPATR 45,4 LLQWEFEHEEEPDDK 77,4 MGLAPEDIDPYLCTHVVFAYATLDDTG TQIR 40,9 RVPYVVINNEWIGYDNPESIK 44,2 TVSSEENMQHFAEEAINYLR 71,6 VCFYSGWSLYR 68,6 VPDGYETEELNLFR 113 VPYVVINNEWIGYDNPESIK 116 WVNWINMK 44 TR59191c0_ g1_i1_fr1 161,119. 1 100,0 0 7 5,03% LQQEQQMQEQLR 57,4 QAQLLIQQEQQR 73,7 QEQLLIEQEQQR 88,2 QEQLLLEQER 70,5 QEQLMIEQER 44,5 QRQEQLLIEQEQQR 53,9 TGTWQDPAASLK 59,9 TR59191c0_ g1_i1_fr3 152,547. 8 100,0 0 5 5,07% QLWDLLDSAITQYK 96,7 SNWAWPEAK 55,8 SSNHIIQPANGAGGPGWK 38 SVSSMNTQVQPIANINQK 86,7 TTTTDNFILQFK 50 TR59324c0_ g1_i1_fr3 96,250.2 99,80 2 3,33% TNDLVIFLETDK 78,2 VFTATVNGQSDSTPTTGK 57,1 TR59365c0_ g1_i1_fr1 140,941. 4 99,80 2 1,87% FNNFETFEVK 41 LSSESFNIFNAYR 72,3 N Protein accession numbers Protein molecula r weight (Da) Protei n identi ficati on % Nb unique peptid es % sequen ce covera ge Peptide sequence Best Mascot Ion score TR59927c0_ g1_i1_fr2 103,304. 1 100,0 0 1 6 27,00% GSLISACMVEK 68,4 GWTTADNLSLR 81 IYIQELLK 59,4 KGSLISACMVEK 46 KVTPAPSQPQPAKPIIPK 35,4 LTQYEDSPTCSACSSSEYLECDHQINR 83,8 MDYDMDEPTHSILWTDEFFGPGFGIVD SGPFK 57,8 NIIGYGDFIER 72,9 RPIVIPGEGSFIPYDLISNVHSHGPNL DAAYGGPNFLGWNR 44,4 SAGDVNPSLDYPDK 94,2 SAGDVNPSLDYPDKGPPSHR 52 SFVDYIWYQFR 85,7 SISLEEYQNGPHR 80,5 TVDMAAGQMGFQQPR 90,3 VTPAPSQPQPAKPIIPK 37,4 WVGEQMSILQSASQDPIFFLHR 59,5 TR60488c0_ g1_i8_fr2 130,932. 0 100,0 0 5 7,37% ALQDLGIANASGLYQK 87,2 EAPLADVNTGLNTFAGEQQPLDGK 80,6 IDTFQQGQPMQPLNK 49,9 MDALMINPSQAPTAATQQK 63,1 SVMADYSIIK 46,1 TR60920c1_ g2_i1_fr3 63,068.5 100,0 0 7 19,60% AASFGYSDAMFDYNNR 78,2 DGLWFLSR 52,7 EVSPTAALTSANIVVHYQGLLK 84,3 GIQFVMEDVVK 59,4 LDYETNEIER 66,3 SSDAITNEQFAFNNYEDFNVHQCLDSL PALGQK 90,3 TFLVFLK 43 TR61168c2_ g1_i4_fr3 98,138.6 100,0 0 6 10,90% DDIGWMIVVESK 72,3 EVAFSYVHPFGTNEMNALPGCLSTSGS AK 65,7 GDYFSFFGR 73,3 HVGDWTLVYR 58,9 QAEVLAIYVDAEELKPPPQYR 60,3 VLSSSWTDIMSR 83,7 TR61626c0_ g4_i1_fr2 90,517.2 100,0 0 4 5,62% AQLLYYPK 42,5 GELYELWNSK 56,9 GLLADEEICR 66,1 VTTAPCQELITTYNPK N Protein accession numbers Protein molecula r weight (Da) Protei n identi ficati on % Nb unique peptid es % sequen ce covera ge Peptide sequence Best Mascot Ion score 38 TR62133c0_g 2_i1_fr1 88,918.6 100,0 0 4 7,39% FLSPAYDDGFNSPR 66,4 SVGISAEDILK 41,8 VAALGLDAIQQLSK 83,5 VTSYLDGSAIYGSTQAEESR 63,7 39 TR62203c0_g 3_i1_fr2 119,005. 9 100,0 0 3 3,72% GGEIELTVELPK 64,1 IDINGLTLTGQLQDLLSR 106 YGLGIIGAR 61,6 40 TR62355c0_g 2_i10_fr1 63,799.1 100,0 0 4 7,17% DGCLTIPGEER 43,5 DTFLFYDYK 46 GIVLTFAPVNK 61,2 LSCSGEPIPR 47 41 TR62876c0_g 3_i1_fr1 164,720. 2 99,80 2 1,44% ADGLFGALVIR 56 YDVIIDASNR 59,8 42 TR62884c1_g 2_i1_fr3 59,812.3 100,0 0 3 10,70% HGDMTTYTIDAHVR 38 NCECPSGFHGYDCGLK 46,2 YRDEKPDIFAIPNTACK 42,5 43 TR62884c1_g 3_i1_fr1 47,869.8 99,80 2 4,44% LVVYNQLSNTK 57,7 MFTLIER 43,4 44 TR64395c2_g 3_i2_fr3 137,624. 4 100,0 0 2 2,41% SIISINGVAQDIGNK 57,7 VGDIINIAR 78 45 TR66414c0_g 1_i1_fr2 8,270.7 99,80 2 40,50% IHSPLVALAGFQHPTVTK 35,3 KEEVSPILSYAK 47 46 TR7898c0_g1 _i1_fr3 20,658.0 99,80 2 15,50% EQPVYTDPVLPSR 43 VDTQNEPIPSIEPNR 46,8 Shell peptides from M. truncata N Protein accession numbers Prote in molec ular weigh t (Da) Protein identifica tion % Nb unique peptides % seque nce cover age Peptide sequence Best Masco t Ion score Mya_contig0 0108 257 4 39,00 100, 00 9 3,84% ANTDPWGQVR 35,3 DSYDNGLMASVR 65,5 EFAVSSAVFLR 65 LVIGMGLYGR 56,8 NFDGLDFDWEYPANR 34,6 RVVDVPASFR 39,3 SQDVRPVHVPQAPPAQPR 22,9 TSRPALLLTSAVAAGK 46,1 YADLISEIR 65,9 Mya_contig0 0162 229 8 68,50 100, 00 4 2,17% GVPISAIIFGGR 38 IDWDQLFSLPK 44,4 NSIFTNVGETADGEFFWEGLEDEISNK 67,3 VLAEGDGEFVR 33,1 Mya_contig0 0178 228 6 98,90 100, 00 9 6,79% ALGINMFAVGIGHSIDR 25,5 ALHDLVSSYNLGPK 62,8 EPYSVNNAVLEPDNNLVGTIR 40,1 KEQVLMLNTFDQLLVELHGLMK 32,5 NELLGIASR 74,9 NPPSPANAFLLEPGNTLVGTIR 78,5 SMMDLVFVLDGSDSIHPK 43,3 VGLVVYSTNIAQTVPLTYDK 50,8 VTFLCLPGFVK 46,1 Mya_contig0 0348 195 0 64,10 100, 00 14 7,32% ATLPPPINMYDEVIR 31,8 AVGTGVTGDYAEVR 48,1 FAPSVFAAR 40 FIVNQQTPDGDFTETGTVIHK 47,6 GETVVIQANIFDYR 47 GSPSLSLETTAYALLTYSAK 41,9 INGFNILNWLAK 42 LLESPQYEILSVDGSGVSTVISGSEQK 110 NQPVLISQVDPGTVTK 35 SEETEAQYVLSVTTSDETK 82,5 SFAQASQLGLITIDSNLMDR 68 TGGIDGPTFTR 50,3 TGLGVAPLR 47,9 VALTVTVGTK 31,9 N Protein accessio n numbers Prote in molec ular weigh t (Da) Protein identifica tion % Nb unique peptides % seque nce cover age Peptide sequence Best Masco t Ion score Mya_cont ig00475 180 1 08,40 100, 00 4 2,15% DGSFSAFGESDK 41,9 IVLYFDEIGSR 70,2 SFALTVEIPR 31,9 VMAVADLMGPSIK 34 Mya_cont ig00608 163 3 38,90 99,8 0 2 1,87% LGANAILGVSLAVCK 37,6 SGETEDTFIADLVVGLSTGQIK 75,6 Mya_cont ig00629 165 0 03,90 100, 00 9 6,24% FNGNLGFR 70,7 FNIITQLCEPR 65,6 IPVAAEGLDFGK 82,9 LGEPTFDLFLLK 72,6 LPYAQQSYDLVYVR 68,2 QGLIETGEMLDK 44,7 TELCYLADTDCR 84,4 TIAGSQFFLQEGLDGTTSVMK 111 YLQLALEER 54,7 Mya_cont ig00670 164 3 39,20 100, 00 12 9,53% CFFPFDK 35 EELMLDVMAR 41,3 EIVQTVPYNPNGWNILR 31,2 HQVMVTFDDIHDK 39,5 IGQALDSLSGK 75,2 NYGDGYIAIIGK 90,3 SEITFQPAISVGPK 85,2 TALGSYVAWK 38,1 VLDKPIDGMTVIDPGWDR 38,1 WNSEFCTCVGSPK 54,2 YGPCMDPTGAAMQNGFVGSVAEVIFSSC LDTYGR 19 YNHVAFPELR 35,5 Mya_cont ig00696 160 1 68,90 100, 00 2 1,11% LQITDSTFTR 39,6 NFEFFDVDFSK 42,8 Mya_cont ig00714 160 1 54,70 99,8 0 2 1,28% CEQSILDLER 57,9 DFSGLCVPVGQTVK 38,6 Mya_cont ig00764 152 8 91,90 100, 00 3 2,61% ATELSQFLETNNPVITNK 41,8 RPSPVFYEGYETGEGGPGFR 45,5 VIEANFATQR 51,4 N Protein accessio n numbers Prote in molec ular weigh t (Da) Protein identifica tion % Nb unique peptides % seque nce cover age Peptide sequence Best Masco t Ion score 12 Mya_cont ig00894 143 0 21,30 100, 00 4 6,39% ATLDLSLTGGMVK 89,9 DGMLHLGVQGPGGAEAVSR 36 ITVYGTLTTSTGPGEAAVFDGK 99,1 LESAFTQQSFDAFYLLMK 77 13 Mya_cont ig00902 144 2 15,10 100, 00 7 6,97% AAGVATATDYPWDPNDFR 60,2 AVGGGGLYTYDGIQR 78,6 DPLFFSHHSFVDEIWER 26,6 GGELLNVDYR 71,6 ITSELMVAAFDECGR 39,7 IYLFIVESFLR 82,2 LGISESSGVIQVK 86,8 14 Mya_cont ig01082 136 4 07,60 100, 00 6 6,61% GDDETTEAGFIGVIYR 66,3 GLGDPTLDLFIEDGMVK 80,9 IACDLQANVNCNIDYCSGK 33,6 KGDDETTEAGFIGVIYR 73,4 VVTNQVSTSGQGATPHNLEVR 32,1 YLTGGVTVAGNGLEIGGCGK 71,4 15 Mya_cont ig01412 127 7 95,50 100, 00 4 2,77% FEAITGDPYR 51,3 QVEIELLR 41,2 SDSTVGIFVR 69,2 VEPNNGQLFTATTR 34,6 16 Mya_cont ig01578 124 2 36,00 100, 00 3 3,83% FGLNTLEAEVNGQIIR 85,7 HGAVNIGGNLCEIGNESFGGFIGIVDEI R 31,3 LDIPGASAIAR 88,1 17 Mya_cont ig01866 113 8 97,80 99,8 0 2 2,38% TCKPELLLTFNNGLLDESR 64,7 YTSDHTEIPVGEQR 24,4 18 Mya_cont ig02027 111 3 85,20 99,7 0 2 2,47% GLISLFDVSSTNTR 36,2 VGLVTFSDTVRPIFDFNTK 23,2 19 Mya_cont ig02057 104 5 77,50 100, 00 3 3,26% MLAEIQAAMVAEEK 67,1 QIFIDNMKPGVDPLSILIGLTPEK 34,7 SGVNPVIANSADLAK 61,6 20 Mya_cont ig02175 107 5 26,50 99,9 0 2 1,40% DIEQVFLEK 47,9 KDIEQVFLEK 42,1 N Protein accessio n numbers Prote in molec ular weigh t (Da) Protein identifica tion % Nb unique peptides % seque nce cover age Peptide sequence Best Masco t Ion score 21 Mya_cont ig02267 108 3 19,30 99,8 0 2 1,65% IEIGLFGDVAPK 52,2 NNPISILYR 36,3 22 Mya_cont ig02331 104 4 88,10 99,8 0 2 3,21% DPTQIPWGTAGAEYVVESTGVFTTTEK 44,5 VPVQDVSVVDLTVR 98,3 23 Mya_cont ig02445 100 7 92,00 100, 00 8 17,67 % GGHINAEILSLVSK 32,6 HDAEGLLEDGSLELLR 67,2 MDNLEVSFDR 73,2 SIVAGQSLWHVSER 61,4 TFTDDVFWDWR 54,9 VLIDGAITVEPDQINVR 39,2 VLTTTGTETSEYFNMGEYFNR 63,4 VLVNDKDGVILAGTK 29,8 24 Mya_cont ig02494 103 3 20,80 100, 00 2 2,99% GQFLCTLETVR 66,9 TDIIITDPLLLFK 73,7 25 Mya_cont ig02502 101 0 38,60 100, 00 5 16,75 % APAAVPPSKPAAGGAPVPPPVVPPKPVSR 50,8 DLVCVDGEEVFFSECHAICAGK 93,8 GLPSGADVPDNR 63,2 QYNNDCLLQCAGEMR 71,9 YRAPAAVPPSKPAAGGAPVPPPVVPPKPV SR 39,3 26 Mya_cont ig02532 103 9 65,70 100, 00 3 3,91% DATAMLEYVEHFQR 37,1 LIQLVAQK 35,7 LTSELNNLVFQK 55,5 27 Mya_cont ig02653 97 89 3,10 100, 00 3 4,73% EVTIELENTQIIVQELTK 53,6 QNLQVQLAALQADYDNLNAR 67,4 SQILIEVDNLQGLNEQLSK 172 28 Mya_cont ig03151 93 26 8,90 100, 00 5 6,44% HDYPSSPNNNYPPPQTNDYPPPPK 24,7 HRVEDLLEYETPEINK 73,4 LYQPYESIVAK 67,2 NQDGPLPGYTEK 35,8 TASLSLYER 56,3 29 Mya_cont ig03197 91 47 4,00 99,8 0 2 2,80% AMMNHCPLEGGYCIHTIPPPPPR 20 TEFDFSCR 36,8 30 Mya_cont ig03389 91 89 5,80 99,8 0 2 4,16% HQVEDLLEYETPEINNDGNNVDTPNGPDN NGNK 24 IDLYGTNDGHES 37,8 N Protein accessio n numbers Prote in molec ular weigh t (Da) Protein identifica tion % Nb unique peptides % seque nce cover age Peptide sequence Best Mascot Ion score 31 Mya_cont ig03434 88 66 8,30 100, 00 9 12,17 % AGALNIEIR 58,8 ANYKPGDEVQFR 46,8 ENFYFEVK 32,8 FEVTVTLPATAK 50,8 IEFFPNMPYNFKPGLGAYPVK 33 LYNSGFFDVR 73,7 MPLSDMPPLGR 72,8 YSISAEMVGIESSK 87,3 YTFGKPVLGPVVLTLSK 37,4 32 Mya_cont ig03642 84 64 5,80 100, 00 3 4,67% AAMAAITGGAAGYPGMGGPAPR 67,8 FSNPLAAAAER 88,4 TMGWSADQIADTLGDK 64,2 33 Mya_cont ig04051 81 58 8,90 99,8 0 2 2,80% ILTGNGAYELR 48,4 LPNGVFEIQPLGTTPFK 102 34 Mya_cont ig04091 82 90 2,70 100, 00 4 5,91% DLFQALAFK 44,6 ENIQQVVDQIVQSSR 31,5 FALVEYR 42,2 LIEAIIGSAQEEMSLQGFSADVQHEIEK 48,5 35 Mya_cont ig04135 80 79 1,50 99,8 0 2 3,12% IVADPDQYGVELDKLEIR 47,3 YSNDGPSDTITVK 45,7 36 Mya_cont ig04331 79 97 2,90 100, 00 2 2,99% NEAQLFGTVLPK 35,8 TEIPGTPIEVLQSQFVR 31,3 37 Mya_cont ig04627 79 63 7,40 100, 00 6 11,70 % DLMAQATCTALLDTGSGNVYAVR 80,7 LSLTCFNALHVYTPEK 69,2 QEIVINDLTK 47,5 QLIEDNVVTQNESER 121 RPCNAVQYAGCEASTGPDDQYGVAYDYR 35,6 YDSCTGYFCGPNFCCCR 56,8 38 Mya_cont ig05301 73 23 6,20 100, 00 2 2,90% AALFDFNGINNPSLK 67,5 YQEFVELMNK 37,9 39 Mya_cont ig05387 72 38 7,50 100, 00 3 4,18% GFVGYIDDLR 42,1 QLGSVEIALNQQMK 66,8 YPEGVEISVPYK N Protein accessi on numbers Prote in molec ular weigh t (Da) Protein identifica tion % Nb unique peptides % seque nce cover age Peptide sequence Best Mascot Ion score 40 Mya_con tig0599 7 67 71 7,70 99,9 0 2 3,02% GNVGDTIEIYLK 45,2 NMANFPLNIVPK 38,7 41 Mya_con tig0685 8 63 51 8,60 99,8 0 2 7,64% ALLEAAGGPSQAQGLAQLFSQLSQTGGHT PQTTGSK 69,6 QQKPTGFELGASVPLGDAAQR 31,8 42 Mya_con tig0702 7 63 02 9,70 99,8 0 2 4,63% SGNNYNPPTEAIKPTK 32,6 TINNGVYSGLFSMVAALK 107 43 Mya_con tig0714 3 60 18 4,10 100, 00 3 7,02% EYYLVCADGFGDR 70,3 LDYNIGYSNIR 80,7 VTIGHFDNAGFICAEGFDDAEAMVICR 18,4 44 Mya_con tig0751 2 57 41 0,10 100, 00 9 18,45 % ADDASDIFDNSQLR 80,2 AEFEGVTVYK 59,4 ILVYFVRPDGEIVADGLNFVVDDAYENK 39,5 QFGDVPWVVR 43,9 SVLLLGDGNDITPGR 52,4 SVLLLGDGNDITPGRVEDK 27,6 VTADANSMVHVVAVDK 44,4 VTMGNTVDLSIGSPVTVSFTSSASPPPTA VR 47,2 VTVTFDQESSNTK 68,6 45 Mya_con tig0754 9 57 27 9,80 99,9 0 2 4,17% ALVDTDQSSLSYFIR 49,9 LQVDPESGDEPNVR 52,8 46 Mya_con tig0776 4 58 37 8,30 99,8 0 2 4,24% VDLQANYYISSVVIHNR 24,7 YFASTGVDGEVK 39,1 47 Mya_con tig0783 3 56 61 0,20 100, 00 5 8,38% DTIVKPYQPYESIDAK 47,8 HHIPDGNLPGYTEK 37,3 VQDGPLPGYTEK 41,2 YDYPPPPK 41,5 YNNIFPK 40,5 48 Mya_con tig0800 9 55 35 4,00 100, 00 3 7,37% LPPPVPVETSPIFILPPIGPLPPDTSR 24,6 MALQFLSPVGR 84,9 TLSTTEYTIMPR 46,9 49 Mya_con 54 93 100, 4 11,76 HKVEDLLEYETPEINHNINNVYTPSK 38,1 tig0839 3 0,60 00 % TAPLSLFER 60,4 VEDLLEYETPEINHNINNVYTPSK 39,8 YSPDNNGNTYNALR 45,8 50 Mya_con tig0853 4 52 81 2,20 99,8 0 2 4,25% MDVATWENEVVIYDNK 61,1 RPTVTFQAPYW 36,1 N Protein accessi on numbers Prote in molec ular weigh t (Da) Protein identifica tion % Nb unique peptides % sequen ce covera ge Peptide sequence Best Mascot Ion score 51 Mya_con tig0909 8 51 22 4,60 99,9 0 2 5,58% LLPVCGTDSMTYLSTCFFTR 112 YGRPPPPPQIANTK 68,2 52 Mya_con tig0919 1 52 29 6,10 99,8 0 2 5,50% FIDQSIYNEYTSQPGLIK 51,8 RPPVMSNFNEQLWMGR 24,8 53 Mya_con tig0924 0 50 71 5,30 99,9 0 2 3,43% LPTFGIGHLVK 24,6 VLEIYLDTK 32,2 54 Mya_con tig0925 1 50 94 7,90 99,8 0 2 4,83% ELVPDSQCTNGAAFIYIPDR 44,1 SATFWSFNR 48,7 YEVLISSDPANYVVR 87,9 55 Mya_con tig0939 9 47 71 2,50 100, 00 4 8,63% FNGNLGFR 70,7 IPFAQQPLDNIYVR 74,1 QDWLESYYK 41,5 VIVVYDGNWLQLALVK 70,5 VPIAAEGLDFGK 89,8 56 Mya_con tig0965 2 47 26 6,30 100, 00 6 18,96% EDNDGDGLFDENPDYSGTDWCK 72,5 ELVSDSQCTNGAAFIYIR 43,2 LVDWQWHPLR 43,5 QACFTVEEFWPHTAGDGASFVPLGSSVSL TVPVPR 35,9 SATFWSLNR 63 YEVLISSDPANYVVR 87,9 57 Mya_con tig0984 5 45 62 1,90 100, 00 2 4,32% DCDGPCPCNGGQQPPSPYAPAAPCGCDNE YKPVCTSTGNTYR 79,9 ETVAYDNYCVAN 78,1 IRDLEAEFEVEQR 40,3 58 Mya_con tig0996 0 46 30 4,00 100, 00 2 4,91% VDLGQSYNIDYVTLFNR 36,8 Mya_con tig1009 0 45 62 2,50 100, 00 3 6,51% AAIVSPDPVTPGR 31,3 VTDPLISASPAVK 49,1 WNGGFEGEFR 59 N Protein accession numbers Prote in molec ular weigh t (Da) Protein identifica tion % Nb unique peptides % sequen ce covera ge Peptide sequence Best Mascot Ion score 60 Mya_conti g10609 42 86 9,80 100, 00 5 14,67% CQAECNNEIVK 38 CQAECNNEIVKDNSYCTATAN 34,3 VDAPYNPPSGYGDSYTPCGCDNVPNPVCT ESGNTYQNR 93,4 VSYVCTYDGK 68,4 VSYVCTYDGKTEEECDLK 24,1 61 Mya_conti g11212 41 89 9,70 100, 00 3 11,52% ANNNGVYSSLLSMVAALK 92,8 LQYPYEIELVYNNNTSK 33,3 TDKANNNGVYSSLLSMVAALK 24,8 62 Mya_conti g11333 39 72 3,30 99,8 0 2 3,11% LSEVTIQITALTNDK 72,7 LSEVTIQITALTNDKR 41 63 Mya_conti g11498 38 90 7,50 100, 00 3 7,84% CPSGTIGVYYAK 53,4 NDVILCENSK 52,4 YGDDCSGVGNYLYLK 57 64 Mya_conti g12770 32 23 1,50 99,8 0 2 6,55% QLIKEDIDTSR 28,3 SISEELQEAQLQFFR 59,6 65 Mya_conti g12788 33 54 7,50 99,8 0 2 6,06% CVIPFDDFK 33,7 VGGGGVPRPQDIGQR 38,9 66 Mya_conti g13474 30 45 9,30 100, 00 2 8,91% FSDQSIYNEYTDTPGLIK 81,1 VVDPIGDVLTGEGR 109 67 Mya_conti g16947 16 76 9,60 99,8 0 2 12,81% DRPVGVPEAFILPK 24,6 YVQLQLMNVR 59 NTPDSYCLTDVR 34,4

Acknowledgements

Acknowledgements

This study was supported by the funding from the CACHE (Calcium in a Changing Environment) initial training network (ITN) under the European Union Seventh Framework Programme, reference grant agreement number 605051.

lunches with Dr. Sophie. I could not have imagined having better supervisors for my doctorate study. I am grateful to Marie Curie (ITN) scholarship from European Union for fully funding this doctoral research for three years. Many thanks to Prof. Melody Clark (CACHE coordinator) for the moral support and useful discussions.

List of Publications

protein inference problem

Although, PSM is relatively an easy process, attributing the validated peptide sequences to a protein is a difficult task and involves complex statistical computations. Figure 2.10 represents the possibilities of grouping peptides to different proteins in a database search when bottom-up strategy is used.

Non-random grouping of peptides Generally, correctly identified peptides tend to match a small number of proteins in the database. Contrarily, incorrect peptides match to random protein sequences and increases the number of identified proteins. Every incorrect peptide with high score retrieves a false identification. As a result, small error in the peptide assignment significantly increases the error in protein identification.

Consequently, protein identification using single peptide called "one-hit wonder" is not reliable (Nesvizhskii 2005).

Shared peptides

Often the problem of shared peptides is encountered in eukaryote protein identification. Single peptide matches two to many protein sequences in the database (figure 2.10 and 2.11) and this is due to the presence of homologous proteins, splicing variants and mostly due to redundant sequences in the database. This scenario, increases the number of proteins identified and might lead to incorrect biological interpretation. The grouping of the proteins based on their peptide profile might reduce the error during the result interpretation. calcification processes and this organism may become more vulnerable to predators and environmental stressors. Rows a, b and c represents the microstructures of Sylt, Usedom and Nynashamn populations respectively. In each row the interface of nacreous layer, myostracum and prismatic layer is followed by magnified nacreous and prismatic structure images. In Baltic mussels nacreous layers is formed by relatively thin aragonite tablets (ladder) and prismatic layer composed of irregular calcite prism (arrow).

Author contributions

Supplementary table 1: List of modulated proteins identified using M. edulis (Figure 1) and M. trossulus (Supplementary figure 1) transcriptome.( see Annexe 2) Supplementary figure 1.. List of modulated SMPs identified using M. trossulus transcriptome database Supplementary figure 2. Scanning electron microscopy showing the impaired microstructure of the Baltic mussel shell. The sketch on the top shows the area of the shell were the study was performed. Rows a, b and c represents the microstructures of Sylt, Usedom and Nynashamn populations respectively. In each row the interface of nacreous layer, myostracum and prismatic layer is followed by magnified nacreous and prismatic structure images. In Baltic mussels nacreous layers is formed by relatively thin aragonite tablets (ladder) and prismatic layer composed of irregular calcite prism (arrow).

Materials and methods

Sample collection

Mytilus mussels approx. aging 2-4 yrs. were collected from North Sea coast, Sylt (54°54′ N 8°20′ E) and Baltic Sea coasts, Usedom (53°56′ N 14°05′ E) and Nynashamn (53°56′ N 14°05′ E) respectively. The shells were separated from the animals and air-dried.

Microscopy

Mussel shells were mechanically fragmented. Prior analysis, the fresh fractured transections were gold-coated. Scanning electron microscopy was carried out using Hitachi SU3500 (Plateau technique de microscopie électronique, MNHN), microstructure analysis was performed in the area where the thickness of nacre and prism were 1:1 ratio.

Sample preparation for proteomics

Five individual's shells collected from each location were washed separately with 5-10% sodium hypochlorite (NaOCl) to remove organic impurities and periostracum.

Latter shells were washed with milli-Q® water and air-dried. The shells were then cut in to small pieces and polished with dremel tool. Prior to powdering step the shell pieces were washed briefly with NaOCl and milli-Q® water and air-dried. Powdered shell pieces were graded using 250 µm mesh. 400 mg of shell powder was decalcified using 5% cold acetic acid for an hour and 10% cold acetic acid over night. The process was ensured for complete dissolution of CaCO 3 . The homogenate was centrifuged (14,000 rpm, 20 mins, 4°C) to separate acid soluble (ASM) supernatant and acid insoluble matrix (AIM) pellet. AIM was washed with milli-Q® water several times to remove acid completely and freeze-dried. ASM was washed with milli-Q® water on a 10 kDa filter (Sartorius, VIVASPIN 20) to remove acid and to enrich the proteins. The resulting ASM was freeze-dried. Both AIM and ASM were denatured version 3.6.5) was used to validate the identification results. The following criteria were set in scaffold for the validating the proteins: peptide identification probability greater than 95% as specified by Peptide Prophet algorithm and protein identification probability greater than 95% as specified by Protein Prophet algorithm with minimum two unique peptides (containing at least eight amino acids) matched. BLAST2GO tool Label free semi-quantitative approach was performed in PEAKS® studio software (Bioinformatics Solutions Inc, Waterloo, Canada; version 7) to identify differentially expressed proteins. The differential expression patterns are based on log2 ratio of average signal intensity area across samples. The results were validated using the following parameters: [1] The peptides significance threshold: ≥ 10×10log P and protein significant threshold ≥ 20×10logP [2] The filter for MS signal: ≥ 10 6 average intensity area and quality of the signal ≥ 0.5 [3] Mass error tolerance is 20 ppm [4] Confident sample number ≥ 3. [5] Protein fold change ≥ 2. Normalization was done using TIC. FDR threshold is 1%. Reference sample for relative quantification was auto-detected by the software based on the sample profile.

Principle component analysis

Shell thickness: shells of same size range (n=3) were taken for the shell/periostracum thickness study. Shell thickness measurements were performed on electron microscopy scans of fresh fractured shell transections (supplementary figure 2), in the area where the thickness of nacre and prism were 1:1 ratio. The mineralized layer and the periostracum thickness were measured each individual repeatedly (n=3) over the region of interest. Samples were considered independent. The null hypothesis -shell and periostracum thickness respectively -are identical (equal medians) was considered by using the non-parametric Kruskal-Wallis Test, without assuming the data to follow normal distribution. The low p-value allows us to reject the null hypothesis (i.e. equal medians) at high significance.

Statistics

The above described shell thickness and shell organic content data were combined. A principal components analysis was performed using the 'princomp' tool within R and fitted to vector ordination. that SMPs can contribute to the study of calcium management in molluscs.

Supplementary table-2: Principal component analysis -distribution of variance

Future work should include shell proteomic data in addition to the existing tools.

Annexes