Stan Perchepied

Nicolas Eskenazi

Chiara Giangrande

Julien Camperi

Thierry Fournier

Joëlle Vinh

Nathalie Delaunay

Valérie Pichon
email: valerie.pichon@espci.fr

Development of Immobilized Enzyme Reactors for the characterization of the glycosylation heterogeneity of a protein

Keywords: Immobilized Enzyme Reactor, Glycoproteomics, Glycosylation mapping, Human chorionic gonadotropin, NanoLC-MS/MS

The mapping of post-translational modifications (PTMs) of proteins can be addressed by bottom-up proteomics strategy using proteases to achieve the enzymatic digestion of the biomolecule. Glycosylation is one of the most challenging PTM to characterize due to its large structural heterogeneity. In this work, two Immobilized Enzyme Reactors (IMERs) based on trypsin and pepsin protease were used for the first time to fasten and improve the reliability of the specific mapping of the N-glycosylation heterogeneity of glycoproteins. The performance of the supports was evaluated with the digestion of human Chorionic Gonadotropin hormone (hCG), a glycoprotein characterized by four N-and four Oglycosylation sites, prior to the analysis of the digests by nanoliquid chromatography coupled to tandem mass spectrometry (nanoLC-MS/MS). Firstly, the repeatability of the nanoLC-MS/MS was evaluated and a method to control the identification of the identified glycans was developed to validate them regarding the retention time of glycopeptides in reversed phase nanoLC separation. The repeatability of the digestion with trypsin-based IMER was evaluated on the same hCG batch and on three independent batches with common located glycans up to 75%. Then, the performance of the IMER digestions was compared to in-solution digestions to evaluate the qualitative mapping of the glycosylation.

For the first time, the complementarity of trypsin and pepsin was illustrated for the glycosylation mapping. The potential of IMERs for was illustrated with the comparison of two hCG-based drugs, Ovitrelle® and Pregnyl®.

Introduction

The human proteome is composed of a large and dynamic number of proteins as it has been estimated to more than one million proteoforms [START_REF] Jensen | Modification-specific proteomics: characterization of posttranslational modifications by mass spectrometry[END_REF], while the human genome has only approximately 20,000 protein-coding genes [START_REF] Consortium | Finishing the euchromatic sequence of the human genome[END_REF]. This diversity is partially explained by posttranslational modifications (PTMs) that correspond to covalent modifications of chemical moieties on a protein after its translation. These numerous modifications (phosphorylation, acetylation, amidation, glycosylation …) can alter the activity of the modified proteins and efficient analytical methods are required to elucidate the composition of each proteoform [START_REF] Jmeian | Fast and Efficient Online Release of N-Glycans from Glycoproteins Facilitating Liquid Chromatography-Tandem Mass Spectrometry Glycomic Profiling[END_REF][START_REF] Meller | Preparation of an improved hydrophilic monolith to make trypsin-immobilized microreactors[END_REF][START_REF] Brothier | Miniaturized DNA aptamer-based monolithic sorbent for selective extraction of a target analyte coupled on-line to nanoLC[END_REF]. Glycosylation is one of the most challenging PTM to characterize because of its heterogeneity (N-or O-glycosylation) and its high relative molecular weight (up to several kDa) [START_REF] Zhang | Protein Analysis by Shotgun/Bottom-up Proteomics[END_REF]. Still, there is a need to investigate the glycosylation heterogeneity of a protein because it is tightly related to the protein folding, stability or immune response, and thus abnormal glycosylation is often found on the pathways to genetic disorders. Indeed, it was observed, in 2017, that more than half of genetic disorders may be due to abnormal Nglycosylation [START_REF] Ng | Perspectives on Glycosylation and Its Congenital Disorders[END_REF]. Their structure has the advantage to be conserved and well-known: it consists in a common core structure Manα1-6(Manα1-3)Manβ1-4GlcNAcβ1-4GlcNAc with GlcNAc-initiated antennas or Man residues bound on the terminal Manα residues. A sole β1-4-linked GlcNAc residue attached to the β-mannose of the N-glycan core is defined as bisecting. According to the type of antennae, one can distinguish so-called high mannose, hybrid, and complex N-glycans.

The characterization of glycoproteins can be considered by their analysis at the intact level that has the advantage to preserve the integrity of the analyzed proteoforms and the combination of their potential glycosylation sites [START_REF] Tholey | Top-down proteomics for the analysis of proteolytic events -Methods, applications and perspectives[END_REF]. Due to the potential high number of glycoforms, they first need to be separated, using of liquid chromatography (LC) or capillary electrophoresis to ensure their ionization in mass spectrometry (MS). Yet, this route faces challenges such as the lack of resolution and their low solubility in organic solvents involved for their separation, and their potential bad ionization in MS [START_REF] Angel | Mass spectrometry-based proteomics: existing capabilities and future directions[END_REF]. Moreover, high-resolution high-accuracy MS is required to help the identification of such heterogeneous molecules.

An alternative for glycosylation mapping is to use a glycosidase (e.g. PNGase F) that cleaves the bond between asparagine and the first GlcNAc (i.e. between the peptide and the Nglycan), releasing the N-glycans [START_REF] Jmeian | Fast and Efficient Online Release of N-Glycans from Glycoproteins Facilitating Liquid Chromatography-Tandem Mass Spectrometry Glycomic Profiling[END_REF]. N-glycan mapping, or glycomics, will produce a global view of the N-glycosylation diversity of a single protein, biological fluid, cell type or cell compartment. Note that the N-glycosylation population can be obtain with the use of pronase E that can digest the protein in very small peptides that can be easily analysed for glyco-mapping [START_REF] Temporini | Pronase-Immobilized Enzyme Reactor: an Approach for Automation in Glycoprotein Analysis by LC/LC-ESI/MS n[END_REF][START_REF] Clowers | Site Determination of Protein Glycosylation Based on Digestion with Immobilized Nonspecific Proteases and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry[END_REF]. Yet, one should notice that this approach is very expensive and if more than one glycosylation site is present on the protein, or if the sample contains several proteins, the information about the location is lost.

The third alternative to characterize a glycoprotein is the so-called bottom-up approach and is the most used to date [START_REF] Leymarie | Effective Use of Mass Spectrometry for Glycan and Glycopeptide Structural Analysis[END_REF]. Each protein is treated to generate proteolytic peptides using endoproteases that are next analyzed and N-glycans can be localized on the detected Nglycopeptides by sequencing using MS/MS fragmentation. The digestion with the protease is carried out during 3 to 16 hours (overnight) in solution with sufficiently high enzyme/substrate (E/S) ratio to decrease the duration of digestion. Yet, autoproteolysis of proteases increases with high E/S ratio, thus explaining the ratio of about 1:50 that can be found in the literature [START_REF] Zhu | A LC-MS All-in-One Workflow for Site-Specific Location, Identification and Quantification of N-/O-Glycosylation in Human Chorionic Gonadotropin Drug Products[END_REF][START_REF] Kecskemeti | Preparation and characterization of a packed bead immobilized trypsin reactor integrated into a PDMS microfluidic chip for rapid protein digestion[END_REF]. The development of Immobilized Enzyme Reactors (IMERs) overcomes this limitation by immobilizing proteases. High E/S ratio up to 4,000 have been reported [START_REF] Bonichon | Development of immunosorbents coupled on-line to immobilized pepsin reactor and micro liquid chromatography-tandem mass spectrometry for analysis of butyrylcholinesterase in human plasma[END_REF], leading to a digestion time of less than one hour for greater digestion yields.

These IMERs also have the advantage to be reusable and easily automated. Numerous IMERs based on the immobilization of trypsin [START_REF] Meller | Preparation of an improved hydrophilic monolith to make trypsin-immobilized microreactors[END_REF][START_REF] Hedström | Fast on-column protein digestion with subsequent peptide mapping using tandem mass spectrometry with information dependent acquisition[END_REF][START_REF] Naldi | Towards automation in protein digestion: Development of a monolithic trypsin immobilized reactor for highly efficient on-line digestion and analysis[END_REF][START_REF] Moore | Characterization of an immobilized enzyme reactor for on-line protein digestion[END_REF][START_REF] Sproß | A Capillary Monolithic Trypsin Reactor for Efficient Protein Digestion in Online and Offline Coupling to ESI and MALDI Mass Spectrometry[END_REF][START_REF] Šlechtová | Performance comparison of three trypsin columns used in liquid chromatography[END_REF], and to a lesser extent of pepsin [START_REF] Bonichon | Development of immunosorbents coupled on-line to immobilized pepsin reactor and micro liquid chromatography-tandem mass spectrometry for analysis of butyrylcholinesterase in human plasma[END_REF][START_REF] Geiser | In-line system containing porous polymer monoliths for protein digestion with immobilized pepsin, peptide preconcentration and nano-liquid chromatography separation coupled to electrospray ionization mass spectroscopy[END_REF][START_REF] Carol-Visser | Development of an automated on-line pepsin digestion-liquid chromatographytandem mass spectrometry configuration for the rapid analysis of protein adducts of chemical warfare agents[END_REF],

have been reported in the literature. Yet, the focus was set on the identification of proteins and few information was given on PTMs. Therefore, the performance of the support in terms of digestion yield, repeatability of the digestion, comparison with digestions in solution were evaluated on peptides without consideration of PTMs. The objective of this work was to prepare and use protease-based IMERs to map the glycosylation heterogeneity of a glycoprotein.

We selected a highly glycosylated protein, the human chorionic gonadotropin (hCG), which is the hormone specific to pregnancy. Carbohydrate moieties represent ± 30% of its molecular weight. hCG is a heterodimer of the α subunit (hCGα) and the β subunit (hCGβ) with two Nglycosylation sites on each subunit (respectively noted α N 52 , α N 78 , β N 13 , and β N 30 in order of appearance in the amino acid sequence) and four O-glycosylation sites on hCGβ [START_REF] Fournier | Review: hCGs: Different sources of production, different glycoforms and functions[END_REF][START_REF] Cole | Hyperglycosylated hCG, Pituitary hCG, Cancer hCG and Fetal hCG[END_REF].

Four hCGβ have been reported on Uniprot database (P0DN86, P0DN87, Q6NT52, A6NKQ9).

Several studies used an overnight digestion in solution with trypsin for the characterization of N-glycosylation heterogeneity of a recombinant hCG [START_REF] Zhu | A LC-MS All-in-One Workflow for Site-Specific Location, Identification and Quantification of N-/O-Glycosylation in Human Chorionic Gonadotropin Drug Products[END_REF][START_REF] Toll | Glycosylation patterns of human chorionic gonadotropin revealed by liquid chromatography-mass spectrometry and bioinformatics[END_REF].

Materials and methods

2.1.

Reagents and analytes

Trypsin from bovine pancreas (≥10,000 BAEE units mg -1 protein), pepsin from porcine gastric mucosa [START_REF] Jmeian | Fast and Efficient Online Release of N-Glycans from Glycoproteins Facilitating Liquid Chromatography-Tandem Mass Spectrometry Glycomic Profiling[END_REF],500 units mg -1 protein), cyanogen bromide-activated Sepharose 4B, dithiothreitol (DTT), iodoacetamide (IAM), sodium chloride (99%), Trizma hydrochloride (TRIS) (99%), sodium acetate (99%), sodium azide (99%), glycine (99%), hydrochloric acid (1 M), 0.5 mL centrifugal filters (10 kDa), and bicinchoninic acid assay (BCA) protein assay reagents were purchased from Sigma Aldrich (Saint-Quentin Fallavier, France). Potassium chloride (99.5%), sodium bicarbonate (99.5%), disodium hydrogen phosphate (99%), formic acid (FA) (99%), and potassium dihydrogen phosphate (99.5%) were ordered from VWR (Fontenay-sous-Bois, France). LC/MS Grade acetonitrile (ACN) and trifluoroacetic acid (TFA)

were purchased from Fischer Chemical (Illkirch, France). Deionized water was obtained from a Milli-Q purification system (Millipore, Molsheim, France).

Two hCG-based drugs were used: Ovitrelle® (Organon, Oss, The Netherlands) and Pregnyl® (Serono Europe Ltd, London, UK). Ovitrelle® (recombinant protein from Chinese Hamster Ovary cells, r-hCG) was conditioned as a 500 µg mL -1 solution in water. Pregnyl® (hCG purified from urine of pregnant women, u-hCG) was obtained as a lyophilized powder (5,000 UI). Considering that 1 UI corresponds to 0.092 µg [START_REF] Berger | The molecular relationship between antigenic domains and epitopes on hCG[END_REF], the drug was solubilized to a final concentration of 500 µg mL -1 in water. The hCG samples were aliquoted and stored at -20°C for later use.

Preparation of IMERs

The procedure was adapted from previous studies from our group [START_REF] Bonichon | Development of immunosorbents coupled on-line to immobilized pepsin reactor and micro liquid chromatography-tandem mass spectrometry for analysis of butyrylcholinesterase in human plasma[END_REF][START_REF] Cingöz | Evaluation of various immobilized enzymatic microreactors coupled on-line with liquid chromatography and mass spectrometry detection for quantitative analysis of cytochrome c[END_REF] Pepsin-based IMER (P-IMER) was washed with 48 mL of glycine buffer (0.2 M, pH 5.0).

Acetate buffer (CH 3 CO 2 Na 0.1 M, NaCl 0.5 M, pH 5.8) and an acidic solution (HCl 0.1 mM, NaCl 0.5 M) were successively percolated through the precolumn thrice to remove unbound pepsin. Finally, the IMER was stored at 4°C in FA solution (pH 3.0, 0.2‰ v/v).

The amount of bound proteases was evaluated with an indirect bicinchoninic acid assay [START_REF] Smith | Measurement of protein using bicinchoninic acid[END_REF].

The calibration curves (5 points) were prepared from 0.1 to 2 mg mL -1 of each protease.

10 µL of supernatant were introduced in a well of a 96-microwell plate. Then, 100 µL of working reagent (bicinchoninic acid and copper (II)) were added. The reaction was performed during 30 min at 37°C under agitation at 300 rpm. After cooling down at room temperature, the absorbance was measured at 562 nm in every well with a UVspectrophotometer (SpectraMax M2, Molecular Devices, St Gregoire, France). The grafting yield was determined by the ratio between the amount of protease recovered in the supernatant with the amount introduced.

Sample preparation

The two hCG-based drug stock solutions (r-hCG and u-hCG) were first filtered on Microcon for pepsin was added. The final volume was adjusted with the same buffer to get a final concentration of hCG of 500 µg mL -1 .

Digestion procedures

Digestion on IMERs

The protocol of digestion was adapted from previous studies from our group [START_REF] Bonichon | Development of immunosorbents coupled on-line to immobilized pepsin reactor and micro liquid chromatography-tandem mass spectrometry for analysis of butyrylcholinesterase in human plasma[END_REF][START_REF] Cingöz | Evaluation of various immobilized enzymatic microreactors coupled on-line with liquid chromatography and mass spectrometry detection for quantitative analysis of cytochrome c[END_REF]. Briefly, 2.5 µg of hCG were introduced in a 5 µL sample loop set on a six-port switching valve connected to an isocratic pump (LC-10 AS, Shimadzu, Champs-sur-Marne, France) and to the IMER set in an independent oven (Croco-cil oven, Interchim, Montluçon, France). Then, the protein was transferred on the IMER with the digestion buffer at 50 µL min -1 . The digestion was carried-out during 30 min at 37°C using a « stop-flow » approach. Finally, 100 µL of the digestion buffer were percolated to collect both peptides and glycopeptides.

Digestion in solution

Samples were digested in solution in the same buffers as IMERs with an enzyme-to-substrate ratio of 1:100. After overnight incubation at 37°C in a Thermomixer (Eppendorf, France), the reaction was quenched with 1 µL of TFA. The digests were subsequently diluted 20-folds in either TRIS buffer for trypsin digests, or FA buffer for pepsin digests, to match the dilution introduced by the IMER protocol. Digests were stored at 4°C before injection until further analysis.

LC-MS analysis

The nano-LC separation of the digests was achieved on a RSLCnano UltiMate 3000 system (ThermoFisher Scientific, Le Pecq, France). Peptides were loaded and desalted onto a C18 trapping column (Acclaim PepMap100, 300 µm i.

Data analysis

The glycopeptide identification and annotation were performed using PMI-Byonic TM (build

Results and discussion

Development of IMERs

Selection of the enzymes

The aim of this work was to study the potential and the complementarity of protease-based IMERs to fasten and improve the reliability of N-glycosylation heterogeneity mapping by testing them with a model glycoprotein, i.e. hCG, a four times N-glycosylated protein.

Trypsin was chosen because it is the most common protease for bottom-up proteomics. It has well-known cleavage sites at the C-terminal side of two amino acids (arginine and lysine) that produces peptides with protonable C-terminus amino-acid, and has a high proteolytic activity while being very stable under various solvent conditions. A computer simulation of expected peptides was carried out with ExPASy server (https://www.expasy.org/) for hCGα and all four human gonadotropin hCGβ (Table S.1). It is worth noting that the algorithm of the software did not take into consideration R.P trypsin cuts (« Keil rules »). Yet, Rodriguez et al. [START_REF] Rodriguez | Does Trypsin Cut Before Proline?[END_REF] have shown that this cleavage site is not less recurrent than R.C that is considered by the server. Therefore, even if not considered by the software, R.P cuts were further investigated.

One tryptic cleavage site was close to α N 52 . To avoid loss of information caused by steric hindrance due to glycosylation, pepsin was also immobilized on a second support because pepsin and trypsin do not have the same specificity. Pepsin cleaves proteins at C-terminal side of hydrophobic amino acids [START_REF] Geiser | In-line system containing porous polymer monoliths for protein digestion with immobilized pepsin, peptide preconcentration and nano-liquid chromatography separation coupled to electrospray ionization mass spectroscopy[END_REF].

Preparation of the IMERs

The selected enzymes were then grafted on Sepharose because this solid support has low non-specific interactions with proteins according to a procedure already described by our group [START_REF] Bonichon | Development of immunosorbents coupled on-line to immobilized pepsin reactor and micro liquid chromatography-tandem mass spectrometry for analysis of butyrylcholinesterase in human plasma[END_REF][START_REF] Cingöz | Evaluation of various immobilized enzymatic microreactors coupled on-line with liquid chromatography and mass spectrometry detection for quantitative analysis of cytochrome c[END_REF]. With evaluation of the amount of enzymes in the supernatant by BCA, the grafting yield of trypsin was estimated at 34%, giving a grafting density of trypsin in the precolumn up to 1.40 nmol µL -1 . Meanwhile, pepsin was grafted on Sepharose beads at 25%. Thus, the grafting density of pepsin was estimated at 0.78 nmol µL -1 .

It is worth noting that these grafting yields are consistent with our previous results: trypsin and pepsin were immobilized on the same support with a grafting yield of 30% [START_REF] Cingöz | Evaluation of various immobilized enzymatic microreactors coupled on-line with liquid chromatography and mass spectrometry detection for quantitative analysis of cytochrome c[END_REF] and 24% [START_REF] Bonichon | Development of immunosorbents coupled on-line to immobilized pepsin reactor and micro liquid chromatography-tandem mass spectrometry for analysis of butyrylcholinesterase in human plasma[END_REF], respectively. In all cases, the grafting yield of trypsin was found higher than the one of pepsin for the same amount of proteases incubated for immobilization. This difference can be explained by the difference of molecular mass between the two (26.6 and 34.6 kDa, respectively). Indeed, as trypsin has a lower molecular mass, the kinetic of diffusion is further increased and the steric hindrance for its immobilization is lower.

Method developed for the identification of N-glycans by LC-MS in the digest

To evaluate the potential of these IMERs for the glycosylation mapping of hCG, it was first necessary to develop a data processing of the chromatograms and the MS spectra. For this, a first digestion was carried out using the T-IMER by transferring a solution of r-hCG (5 µL, 2.5 µg) on the IMER in the conditions previously described by our group for the digestion of Cytochrome C [START_REF] Cingöz | Evaluation of various immobilized enzymatic microreactors coupled on-line with liquid chromatography and mass spectrometry detection for quantitative analysis of cytochrome c[END_REF]. However, unlike cytochrome C, hCG has five disulfide bonds on hCG and 6 on hCG. Therefore, a reduction /alkylation step was carried out to hCG prior to its enzymatic digestion to try to prevent from miss-cleavages. The digestion was further performed in stop flow mode according to previous studies by our group to increase the digestion time and thus the digestion yield. Considering the relative molecular weight of the protein to digest a digestion time of 30 min was chosen.

The digest was analyzed in nanoLC-MS/MS with a routinely used method in proteomics involving a C18-based stationary phase and a gradient from 2% of ACN up to about 40% and next 90%. The nanoLC format lowers the limits of detection of peptides in ESI-MS by reducing the diameter of the separation column. The detection of lower concentrations was also favored by a C18 trapping precolumn that allows the injection of high volumes of sample (up to 2 µL in this work) on the nanoLC-MS device. The repeatability of the LC-MS analysis was studied and the relative standard deviations (RSD) of the retention times of the most intense peaks were below 0.5%, thus exhibiting a high repeatability of the analysis.

Data processing was performed using Byonic TM software. Byonic TM result files display glycan composition as unresolved stereoisomers such as "Hex" or "HexNAc". Knowing that N-glycan biosynthesis is highly conserved in humans, we completed the structural identity of those monosaccharides to be either galactose (Gal), Mannose (Man) or N-acetylglucosamine (GlcNAc). Manual validation of the MS/MS spectra (in regard of the quality of the fragmented spectrum and the peak annotations) allowed us to propose a scoring threshold for each glycosylated peptide. The parameters chosen for the validation of glycopeptide identification are summarized in Table 1. As it is known, monosaccharides like fucose can rearrange between antennas in CID [START_REF] Wuhrer | Mass spectrometry of proton adducts of fucosylated N-glycans: fucose transfer between antennae gives rise to misleading fragments[END_REF]. While we did not perform fucose-specific linkage analysis (such as low collision energy MS/MS), our assumption of core fucosylated glycans was often supported by Y-ion Fuc-GlcNAc-peptide such as in the spectrum provided in supplementary material (Figure S.1), matching annotated human fucosylated N-glycans. To ensure the identification of glycan moieties, we combined the MS and the chromatographic data. As the retention time in reversed-phase LC of a glycopeptide is mostly driven by the peptide hydrophobicity, the combination of one given glycosylation site and one given peptide was considered. All the glycans that have been identified as bound onto these particular peptides by the software formed glycopeptides whose retention times were studied by taking into account their respective Extracted Ions Chromatograms (EIC).

For instance, EICs of glycopeptides from β N 13 (CRPIN 13 LAVEK) are shown on Figure 1. It was observed that the addition of a N-acetylneuraminic acid (NeuAc) on the glycan causes an increase in the retention time of the glycopeptides (k increases by 20% from the monosialylated to the bisialylated glycopeptide). This phenomenon in reversed phase mode was already reported in the literature and the explanation proposed was that: (i) the hydrophobicity of the glycopeptide containing an additional NeuAc increases due to the presence of an acetoamide group [START_REF] Palmisano | Structural analysis of glycoprotein sialylation -part II: LC-MS based detection[END_REF]; (ii) there are some interactions between the acidic moieties of the monosaccharides and active sites on the stationary phase as the same phenomenon was observed both in reversed phase and HILIC mode [START_REF] Kozlik | Nano reversed phase versus nano hydrophilic interaction liquid chromatography on a chip in the analysis of hemopexin glycopeptides[END_REF][START_REF] Ozohanics | High-performance liquid chromatography coupled to mass spectrometry methodology for analyzing site-specific N-glycosylation patterns[END_REF]. This misidentification may come from the software that did not take into account an oxidation either on the peptide or on the NeuAc (accounting for the 16 Da mass shift), thus misidentified the glycopeptide as the molecular weight of a Fuc and an Hex corresponds to the one of a NeuAc and an oxidation with water. Indeed, the isotopic MS profile confirmed this oxidation thanks to similar isotopic signature (see Figure 2B).

On Figure 2A down, no fragmentation spectrum was found for the peak of G3, the observed isomer (t r = 16.40 min). By taking into account its retention time (Figure 2A) and its isotopic signature, this compound could be identified as an isobar of G2 with the same peptidic and glycosidic moiety composition, but having another oxidized function. The difference in retention time could also be explained by a difference on the linkage of the neuraminic acid (e.g. α2,3 or α2,6 linkage) [START_REF] Vreeker | Reversed-phase separation methods for glycan analysis[END_REF]. As the contribution of the location of NeuAc on the retention time is not well understood, the identification has not been confirmed. It is possible to add a derivatization step to differentiate them by MS. Linkage-specific derivatization is mostly achieved on release glycans rather than proteolytic glycopeptides because it also induces peptide modification. This could be very informative when performed between digestion and LC-MS/MS. Furthermore, recent publication [START_REF] Pett | Effective Assignment of α2,3/α2,6-Sialic Acid Isomers by LC-MS/MS-Based Glycoproteomics[END_REF] shows that oxonium ions alone can also be informative on sialic acid linkage, thus eliminating the need of linkage-specific derivatization. However, the full characterization of the glycan composition was not judged necessary to compare sample handling and digestion protocols on IMERs.

Therefore, G3 was not taken into consideration, although Byonic TM identified this glycopeptide with a score high enough to pass our first validation step. Adding the retention time enabled to eliminate false positives from the identified glycopeptides in agreement with previous reports [START_REF] Pabst | Mass + Retention Time = Structure: A Strategy for the Analysis of N -Glycans by Carbon LC-ESI-MS and Its Application to Fibrin N -Glycans[END_REF][START_REF] An | N-Linked Glycopeptide Identification Based on Open Mass Spectral Library Search[END_REF][START_REF] Zeng | pGlyco: a pipeline for the identification of intact N-glycopeptides by using HCD-and CID-MS/MS and MS3[END_REF].

Similarly, another kind of doubtful identification was proposed by Byonic TM . For instance, a glycan composition corresponding to HexNAc 4 Hex 7 on the peptide containing α N 52 was

proposed. Yet, the fragmentation spectrum and the analysis of the retention time led to think that a sialic acid was also present on the glycan. Knowing that the molecular weight of two Hex corresponds to the one of a sialic acid and two oxidations with water, it led to the identification of the HexNAc 4 Gal 2 Man 3 NeuAc 1 glycan with two oxidations located either on the peptide or on the glycan. As the previous misidentification, the glycopeptide was not considered for further data processing because of the ambiguity of the location of the oxidation either on the peptide or of the NeuAc.

Evaluation of the trypsin-based IMER

Repeatability of the digestion for the glycosylation mapping

Once having developed this method to control the acceptability of the identification provided by Byonic TM , the potential of protease-based IMERs for the mapping of glycosylation was studied for the first time. As the digestion efficiency of protease based IMERs for the glycosylation mapping was not known, a digestion time of 30 min was chosen [START_REF] Bonichon | Development of immunosorbents coupled on-line to immobilized pepsin reactor and micro liquid chromatography-tandem mass spectrometry for analysis of butyrylcholinesterase in human plasma[END_REF]. In order to compare digestions on IMERs and in solution, the temperature of the digestion was set at 37°C on IMERs.

T-IMER was first studied because it is based on the most used protease. It was used to digest r-hCG. The analysis of the digest was performed in triplicate. A chromatogram of the resulting digestion is available in Figure S.2A. All the identified glycans in the digest with the previously developed method are reported in Table S.2 in the left column. Some different results were observed between the three analysis runs (R1, R2, and R3) of the same digest of the batch 1 (B1) of r-hCG. Hence, only glycans identified on a specific location in at least two out of three runs were further considered as relevant for a given digestion, and most of misidentifications or unresolved compositions (uncertainty between NeuGc and peptidic oxidation) were discarded while focusing on the most frequently detected glycopeptides. No glycopeptide identified encompasses the α N 78 glycosylation site due to potential steric hindrance of the carbohydrate moiety. Moreover, the identification of glycopeptides from β N 30 was not possible because of the lack of sufficient data from the fragmentation spectra as the resulting peptide on this site is heavy

The performance of the digestion with T-IMER was then evaluated by carrying out the digestion of a batch of r-hCG drug (batch 1) in triplicate (D1, D2, and D3) and each digest was analyzed in triplicate in nanoLC-MS/MS. To study the repeatability of the digestion, the identified glycans were considered (see Table S.2, in the mid column). No glycan identified on α N 52 was fucosylated while glycopeptides from β N 13 have a higher variability in terms of identified glycans (31 different glycans). For the three digestions, the most intense peaks were attributed to biantennary and triantennary complex N-glycans. The three independent digestions of the r-hCG batch 1 gave 81% of common N-glycans out of 42 identified, exhibiting good performances in terms of repeatability of the T-IMER digestions for the glycosylation mapping.

It is noteworthy that all glycans identified on PIN 13 ATLAVEK ( β N 13 ) were also identified on CRPIN 13 ATLAVEK ( β N 13 ). Yet, further analyses were only performed with the latter even though it contains a missed cleavage, because more glycans were identified on this peptide.

It should also be specified that peptides resulting from the trypsin auto-digestion were found in the first digests after the IMER synthesis. The intensity of two of those peptides (SSGTSYPDVLK and LGEDNINVVEGNEQFISASK) was then monitored through the different digests and it was observed that it decreased by a 1.6 factor between the first digestion and the third. This led to think that the post-immobilization washing steps could be further optimized to remove non-grafted trypsin from the IMER before its use.

To further study the repeatability of the digestion on T-IMER, three independent batches of hCG were considered (B1, B2, and B3). The analysis of each digest was only achieved in duplicate to save time. Therefore, in order to stick at the method previously developed, the presence of a glycopeptide was validated if identified in two out of the two runs. The identified glycans can be found in Table S.2 in the right column. Common located N-glycans were calculated at 75% out of 40 identified, exhibiting an inter-batch repeatability of the digestion with T-IMER for the glycosylation mapping. The lower repeatability for inter-batch than a unique batch may be explained by the analysis of the digests only in duplicate.

Indeed, some glycans may be excluded during the validation process, while a third run might have allowed to confirm their presence. Small variations between hCG batches may have also contributed to this lower repeatability.

Comparison of T-IMER with in-solution digestion

After the evaluation of the repeatability of the digestion on T-IMER in terms of number of glycans identified on a specific location, the performance of T-IMER digestion was compared to conventional in-solution digestion using the same sample of hCG to confirm the benefit of IMERs. As the digestions on IMERs and in solution were carried out with 2.5 µg of hCG, by considering an average molecular mass of 35 kDa [START_REF] Cole | Hyperglycosylated hCG, Pituitary hCG, Cancer hCG and Fetal hCG[END_REF] and taking into account the grafting yield, the E/S ratio was evaluated up to 1,430 for T-IMER. This E/S ratio was roughly 150,000 fold higher than the one of digestion in solution (1:100), thus explaining the short digestion time used with IMERs (30 min).

In order to inject the same quantity of hCG between the two digestion modes, the digest in solution was diluted twenty fold to match the concentration resulting from the digestion on T-IMER. The results in terms of numbers of glycans identified on a specific location are presented in Figure 3. Both digestions shared 93.5% of common N-glycans (42 out of 45)

with only a 30 min digestion on IMER instead of overnight in solution. Even though, the loss of triantennary glycans was noticed on α N 52 . It is worthwhile to notice that both digestion modes involving trypsin led to identified glycans only on α N 52 and β N 13 glycosylation sites and no glycan identified on α N 78 and β N 30 . Once again, the most intense peaks were attributed to glycopeptides containing biantennary and triantennary complex N-glycans for both digestion modes. In the end, the digestions on T-IMER led to similar characterization of the glycosylation heterogeneity of r-hCG and the same peptides, without missed cleavages (R.P cuts were considered insignificant), were identified. Nevertheless, it should also be noted that to avoid autoproteolysis, modified proteases can be used. Yet, such enzymes are usually more expensive, so the immobilization of trypsin could lead to their reusability, which would dramatically decrease the price of a digestion.

Digestions with pepsin-based IMER

The digestions with T-IMER gave partial information on the N-glycosylation heterogeneity of the hCG because of the absence of glycans identified on two N-glycosylation sites ( α N 78 and β N 30 ). To complete this characterization, a digestion with P-IMER was considered. The repeatability of the pepsic digestion was already studied by our group [START_REF] Bonichon | Development of immunosorbents coupled on-line to immobilized pepsin reactor and micro liquid chromatography-tandem mass spectrometry for analysis of butyrylcholinesterase in human plasma[END_REF]. Therefore, P-IMER was used to carry out the digestion of an hCG batch (batch 1). A chromatogram of the resulting digestion is available in Figure S.2B. The comparison of the peptides with a glycosylation site successfully identified after digestion with T-IMER and P-IMER is presented in Table S.3. The digestion with P-IMER led to more heterogeneous glycopeptides for one glycosylation site than with T-IMER, as the proteolysis site-specificity of pepsin is lower than that of trypsin. Besides, the comparison of the glycosylation mapping obtained using T-IMER and P-IMER is depicted in Figure 5. It should be mentioned that the structure propositions are primarily based on the sugar composition inferred from common annotated structures and fragmentation spectra. One should notice that for α N 52 and β N 13 , there are more glycans identified with trypsin than pepsin due to the overall variability in terms of peptide that tends to decrease the signal intensity of each glycopeptide since one given glycan can be present on several peptides varying in length. Therefore, due to the lower signal-to-noise ratio, the probability to have an informative fragmentation spectrum decreases.

Nevertheless, the digestion with P-IMER allowed the identification of glycans on α N 78 and β N 30 , thus highlighting the complementary of trypsin and pepsin. Moreover, common glycans localized on a specific N-glycosylation site were evaluated down to 39% between both enzymes, highlighting the complementarity of the two enzymes to identify a maximum number of N-glycans. Although this was only a qualitative study, the results were also compared to what can be found in literature [START_REF] Valmu | Site-specific glycan analysis of human chorionic gonadotropin -subunit from malignancies and pregnancy by liquid chromatography--electrospray mass spectrometry[END_REF][START_REF] Gervais | Glycosylation of human recombinant gonadotrophins: characterization and batch-to-batch consistency[END_REF]. It was reported that hCG contains mostly complex glycans, especially hCGβ. In this study, the combination of pepsin and trypsin allowed the identification of 48% of complex glycans on hCGβ. The performance of the digestion with P-IMER was also compared with a pepsin digestion in solution. As for T-IMER, the E/S ratio was evaluated and a value up to 800 was calculated for P-IMER. Even if the E/S ratio is lower than for T-IMER, it is still 80,000 fold higher than the ratio used for digestion in solution (1:100). The results in terms of glycans identified on a specific location are presented in Figure 4. Noticeably, according to the glycosylation site, either IMER or in-solution digestion was more efficient. As an example, IMER led to the identification of three additional glycans on α N 52 , whereas the digestion in solution led to seven additional glycans on α N 78 . Unlike tryptic digestions, both digestions shared only 59.5% of common glycans localized on a specific N-glycosylation site. Among the common identified glycans located on a given site, biantennaries and triantennaries were the most intense. Yet, less intense glycans (e.g. hybrids and high mannose) were specifically identified with the pepsin digestion either in-solution or with P-IMER (see Figure 4). This was surprising because pepsin digestion provides more variable peptides that get the glycans, so there is a possibility of decrease of signal for one site of glycosylation. One explanation could be the specificity of each enzyme to cleave the protein with potential steric hindrance of the sugar moieties.

As pepsin and trypsin are complementary, it would have been interesting to immobilize both enzymes on the same support. Yet, the optimum pH for digestion with trypsin is 8.0 while it is 2.0 for pepsin. Therefore, if co-immobilization is considered one of the enzymes would be inactive. Moreover, the activity of pepsin is decreasing rapidly and is practically zero above a pH of 5.5. Yet, if in a pH range from 5.5 to 7.0-7.5 the pepsin is stable and a simple return to a lower pH of 2.0 allows to recover a full activity of the enzyme, a pH greater than 7.5

irreversibly inactivates the pepsin [START_REF] Piper | pH stability and activity curves of pepsin with special reference to their clinical importance[END_REF] which will not allow its use once grafted.

Potential of the IMERs for the glycosylation mapping

Having established the repeatability of the IMER digestion and the complementarity of our two IMERs for N-glycan mapping, the potential of the supports was further evaluated for the discrimination of hCG varying in production way. This is why a second hCG-based drug was considered. The source of production between the two drugs is different as r-hCG is a recombinant one expressed in Chinese Hamster Ovary cells while u-hCG is extracted from urine of pregnant women. Therefore, one can expect that they have two different glycosylation profiles. It is of interest to characterize the glycosylation profile because it is related to the biological activity of the protein [START_REF] Diaz-Cueto | More in-vitro bioactive, shorter-lived human chorionic gonadotrophin charge isoforms increase at the end of the first and during the third trimesters of gestation[END_REF]. The potential of the two IMERs for their digestion combined with LC-MS/MS analysis was evaluated in order to see if the fast Nglycosylation mapping led to significantly different results.

The analysis of the digests was carried out in duplicates and only glycans identified in both runs were further considered. The glycosylation mapping of u-hCG is reported in Figure 6.

Once again, one should note the complementarity of trypsin and pepsin for the glycosylation mapping of u-hCG as only pepsin allowed the identification of glycans on α N 78 and β N 30 .

Surprisingly, it seemed at first that the total number of glycans identified on the glycosylation sites was roughly the same between the two drugs. It is also noteworthy that the most intense peaks obtained with both digestions were attributed to complex N-glycans, thus being in good agreement with previous studies [START_REF] Valmu | Site-specific glycan analysis of human chorionic gonadotropin -subunit from malignancies and pregnancy by liquid chromatography--electrospray mass spectrometry[END_REF][START_REF] Gervais | Glycosylation of human recombinant gonadotrophins: characterization and batch-to-batch consistency[END_REF].

To compare the two drugs in detail, the characteristics of the N-glycans identified thanks to the digestions with both IMERs were compared for each drug and each glycosylation site (Table 2). It appears that both drugs have some similar characteristics. As an example, fucosylated glycans are not found on hCGα while most glycans identified on β N 30 are fucosylated. The amount of glycans lacking sialic acids is also quite similar for r-hCG and u-hCG on each glycosylation site. With the exception on β N 13 , the number of biantennary glycans is about the same for both drugs. But, Table 2 also demonstrates that both drugs have significantly different N-glycosylation mappings. For instance, glycans containing GalNAc were only identified in u-hCG. Moreover, the number of biantennary glycans was higher for u-hCG than for r-hCG.

Conclusions

Two IMERs prepared by immobilizing either trypsin or pepsin were applied to the Nglycosylation mapping of a model highly glycosylated protein, i.e. hCG. After the evaluation of the repeatability of the analysis of a digest using T-IMER by nanoLC-MS/MS, each identified glycopeptide was validated by considering its retention time. This allowed to invalidate in-source fragmentation of two identifications that differ from a single monosaccharide thanks to differences in retention time of the glycopeptides. The identification of 81% of common N-glycans of a given batch of hCG highlighted the repeatability of the IMER digestion. Finding also 93.5% of common N-glycans after an overnight digestion in solution and only 30 min using the IMER highlighted also the great potential of IMERs for glycosylation mapping.

The use of a pepsin-based IMER on the same hCG batch next demonstrated the complementarity of both IMERs. Indeed, despite trypsin allowed the identification of more glycans than pepsin on a specific glycosylation location, two of the four N-glycosylation sites were only characterized with a digestion with pepsin. The potential of these IMERs for glycosylation mapping was also demonstrated with the digestion of two hCG-based drugs leading to significantly different results. At last, IMERs could notably increase the performances of the whole analytical procedure due to their possible re-use, the low digestion time, and their possible integration in an on-line fully automated LC-MS set-up that decrease the analysis cost and improve accuracy.

  Recently, Zhu et al. were able to map the glycosylation heterogeneity of the digested protein on each glycosylation site. They manually implemented thresholds for the automated validation of the glycopeptides identification. In this study, IMERs were used for the first time for glycosylation characterization and localization, hCG being used as a model glycoprotein. Two complementary endoproteases, trypsin and pepsin, were used to obtain more information on the glycosylation mapping. The performances and potential of the IMERs were evaluated with the digestion of two hCG-based drugs. Validation of N-glycans structures based on both chromatographic and mass spectrometry data was performed after nanoLC-MS/MS analysis of the digest.

  d. x 5 mm, 5 µm, ThermoFisher Scientific, Le Pecq, France) at a flow rate of 15 µL min -1 in buffer C: 0.05 % TFA in H 2 O/ACN 98/2 (v/v) for 5 min, before to be transferred to a C18 column (Acclaim PepMap100 C18, 75 µm i.d. x 50 cm, 3 µm, 100 Å, ThermoFisher Scientific) at a flow rate of 220 nL min -1 with a gradient composed of A (0.1 % FA in H 2 O/ACN 98/2 (v/v)) and B (0.1 % FA in H 2 O/ACN 10/90 (v/v)) with 2 to 40 % B in 60 min followed by a rapid increase to 90 % B in 1 min. The detection was carried out with a nano-ESI QqOrbitrap hybrid mass spectrometer (Q Exactive, ThermoFisher Scientific) with the top10 DDA mode: one full MS scan (m/z range 400-2000, resolution 70,000 at 200 m/z, maximum injection time 100 ms, AGC target 3e6) followed by ten MS/MS acquisitions (Top 10, isolation windows 2 m/z, maximum injection time 120 ms, AGC target 1e5, fixed first mass 90 m/z, resolution 17,500 at 200 m/z, normalized collision energy 25, systematic exclusion of z=1 ions and dynamic exclusion of 10 s).

2. 15 . 7 )

 157 , on a restricted database consisted of 5 annotated sequences from Swiss-Prot database associated to hCG sub-units (UniProt accession numbers: P0DN86; P01215; P0DN87; A6NKQ9; Q6NT52). The glycan database was the Byonic TM "N-glycan 309 mammalian no sodium" database. Cleavage residue was set to: i) C-terminal cutter of KR aminoacids allowing up to 2 missed cleavages for trypsin digestions; ii) non-specific for pepsin digestion. Error tolerance was set to 10 ppm in MS mode and 20 mDa in MS/Ms mode. Modifications were set as follows: Carbamidomethylation (Nterm, C, K), Oxidation (M), and Deamidation (N, Q) as common1; N-glycan (N) as rare1. Maximum number of modifications set to 2 common modifications, 1 rare modification.

Figure 1 .

 1 Figure 1. Extracted Ion Chromatograms of glycoforms of CRPIN 13 ATLAVEK sequence ( β N 13 ) produced from r-hCG digestion with T-IMER and analysis by LC-MS/MS exhibiting the influence of sialic acids and fucose on the retention time of the glycopeptide (only its glycan moiety was represented).

Figure 1

 1 Figure1also shows the influence of the addition of a fucose (Fuc) that decreases, to a lesser extent (k decreases by 1.5% for the biantennary bisialylated glycopeptide), the retention time of the glycopeptides. The presence of three hydroxyl groups with fucose should contribute to increase the hydrophilicity of the fucosylated glycopeptides[START_REF] Ozohanics | High-performance liquid chromatography coupled to mass spectrometry methodology for analyzing site-specific N-glycosylation patterns[END_REF]. Similarly, the influence of the addition of hexose (Hex) and N-acetylglucosamine (GlcNAc) on the retention time of the resulting glycopeptides was studied. It was shown that the retention time decreases with the addition of one of these neutral monosaccharides because of their hydrophilicity.

Figure 2 .

 2 Figure 2. (A) Extracted Ions Chromatograms of two glycopeptides identified on αN52 by ByonicTM software obtained by the nanoLC-MS/MS analysis of the T-IMER digest of r-hCG. (B) Isotopic MS profile combined from 16.08 to 16.6 min.

Figure 3 .

 3 Figure 3. Comparison of the numbers of N-glycans identified on a specific glycosylation site after the digestion of r-hCG with trypsin-based IMER and in solution.

Figure 4 .

 4 Figure 4. Comparison of the numbers of N-glycans identified on a specific glycosylation site after the digestion of r-hCG with pepsin-based IMER and in solution.

Figure 5 .

 5 Figure 5. Glycans identified on glycopeptides after digestions on trypsin-and pepsin-based IMERs of r-hCG and analysis by LC-MS/MS. (Common identified Nglycans between both enzymes: 39%).

Figure 6 .

 6 Figure 6. Glycans identified on glycopeptides after digestions on trypsin-and pepsin-based IMERs of u-hCG and analysis by LC-MS/MS. (Common identified Nglycans between both enzymes: 41%).

Figure S. 1 .Figure S. 2 .

 12 Figure S.1. MS/MS spectrum of a glycopeptide identified on βN13 after a tryptic digestion

Table 1 .

 1 Byonic TM score threshold used for the validation of the glycopeptide identification.

	Enzyme Glycosylation site Byonic TM Score
	Pepsin	α N 52	> 150
		β N 13	> 80
	Trypsin α N 52	> 30
		α N 78	> 15
		β N 13	> 10
		β N 30	> 150

Table 2 .

 2 Comparison of the N-glycosylation mapping of r-hCG (batch 1) and u-hCG based on a relative qualitative characterization of the N-glycans identified on each N-glycosylation site after digestions on T-IMER and P-IMER and analysis by LC-MS/MS.

	αN52	r-hCG	u-hCG	βN13	r-hCG	u-hCG
	High mannose	9% (1) 8% (1)	High mannose	19% (6) 10% (3)
	Monoantennary	64% (7) 62% (8)	Monoantennary	41% (13) 26% (8)
	Biantennary	27% (3) 31% (4)	Biantennary	19% (6) 52% (16)
	Triantennary	-	-	Triantennary	22% (7) 13% (4)
	Not sialylated	46% (5) 54% (7)	Not sialylated	50% (16) 42% (13)
	Fucosylated	-	-	Fucosylated	25% (8) 39% (12)
	GalNAc containing -	8% (1)	GalNAc containing -	16% (5)
	Total number	11	13	Total number	32	31
	αN78	r-hCG	u-hCG	βN30	r-hCG	u-hCG
	High mannose	14% (1) -	High mannose	-	-
	Monoantennary	-	40% (2)	Monoantennary	-	20% (1)
	Biantennary	43% (3) 40% (2)	Biantennary	75% (3) 80% (4)
	Triantennary	43% (3) 20% (1)	Triantennary	25% (1) -
	Not sialylated	14% (1) -	Not sialylated	25% (1) -
	Fucosylated	-	-	Fucosylated	100% (4) 80% (4)
	GalNAc containing -	-	GalNAc containing -	-
	Total number	7	5	Total number	4	5

* Number of corresponding identified N-glycans

Table S . 3 .

 S3 Peptides (> 5 amino acids) with glycosylation site identified by Byonic TM after a digestion on T-IMER and on P-IMER of r-hCG (batch 1) and analysis by LC-MS/MS.

			GGFKVEN 78 HTACHCSTCY
			GGFKVEN 78 HTACHCSTCYYHKS
	α N 52 α N 78 (continued)	Peptide sequence after digestion on T-IMER Peptide sequence after digestion on P-IMER N 52 VTSESTCCVAK SRAYPTPLRSKKTMLVQKN 52 VTSE Peptide sequence after digestion on T-IMER Peptide sequence after digestion on P-IMER
			RAYPTPLRSKKTMLVQKN 52 VTSES GGFKVEN 78 HTACHCSTC
			PTPLRSKKTMLVQKN 52 VTSESTCC GFKVEN 78 HTACHCSTCY
		-	VQKN 52 VTSESTCCVAKSYNRVTVMGGF GFKVEN 78 HTACHCSTC
			VQKN 52 VTSESTCCVAKSYNRVTVMGG KVEN 78 HTACHCSTCY
			VQKN 52 VTSESTCCVAKSYNRVTVM KVEN 78 HTACHCSTC
		CRPIN 13 ATLAVEK	VQKN 52 VTSESTCCVAKSYNRV VQKN 52 VTSESTCCVAKSYNRVTV TWASKEPLRPRCRPIN 13 ATL
		PIN 13 ATLAVEK	VQKN 52 VTSESTCCVAKSYNRVTVMG ASKEPLRPRCRPIN 13 ATL
			VQKN 52 VTSESTCCVAKSYN SKEPLRPRCRPIN 13 ATL
			VQKN 52 VTSESTCCVAKS SKEPLRPRCRPIN 13 ATLA
			VQKN 52 VTSESTCC EPLRPRCRPIN 13 ATL
			QKN 52 VTSESTCCV PLRPRCRPIN 13 AT
			KN 52 VTSESTCCVAKSYNRVTVM PLRPRCRPIN 13 ATLA
	α N 78	PRCRPIN 13 ATLAVEKEG Peptide sequence after digestion on T-IMER Peptide sequence after digestion on P-IMER -NRVTVMGGFKVEN 78 HTACHCSTCY N 13 ATLAV
			MGGFKVEN 78 HTACHCSTC

β N 13

Peptide sequence after digestion on T-IMER Peptide sequence after digestion on P-IMER

β N

30 Peptide sequence after digestion on T-IMER Peptide sequence after digestion on P-IMER

	β N 30	-	ITVN 30 TTIC

X HexNAc(3)Hex(4) X X X X X X X X HexNAc(3)Hex(4)NeuAc(1) X X X X X X X X X HexNAc(3)Hex(5) X X X X X X X X X HexNAc(3)Hex(5)NeuAc(1) X X X X X X X X HexNAc(3)Hex(6) X HexNAc(3)Hex(6)NeuAc(1) X X X X X X X X HexNAc(4)Hex(4) X X X X X X X X HexNAc(4)Hex(4)NeuAc(1) X X X X X X X HexNAc(4)Hex(5) X X X X X X X X X HexNAc(4)Hex(5)NeuAc(1) X X X X X X X X XHexNAc(4)Hex(5)NeuAc(2) X X X X X X

Acknowledgement

This work has received the support of "Institut Pierre-Gilles de Gennes" (Laboratoire d'excellence, "Investissements d'avenir" program ANR-10-IDEX-0001-02 PSL and ANR-10-LABX-31), of PSL University, and the Conseil Régional d'Île-de-France.

Supplementary Material

D

HexNAc( 4)Hex( 4)Fuc( 1)

HexNAc(4)Hex( 4)Fuc( 1)NeuAc( 1)

Glycosylation site β N 13 (continued)

Proposed structure

Repeatability of the analysis Repeatability of the digestion Repeatability batch-to-batch

HexNAc(4)Hex( 5)Fuc( 1)NeuAc( 1)

HexNAc(4)Hex( 5)Fuc( 1)NeuAc( 2)

HexNAc(4)Hex( 5)NeuAc( 1)

HexNAc(4)Hex( 6) X HexNAc(4)Hex( 6)NeuAc(1) X X X X HexNAc(5)Hex(6) X X X X HexNAc(5)Hex( 6)Fuc(1)NeuAc(1)


Marco A Deriu

Nicolas Tsapis
email: nicolastsapis@u-psud.fr

Magali Noiray

Gianvito Grasso

Nabil El Brahmi

Serge Mignani

Jean-Pierre Pierre Majoral

Elias Fattal

Andrea Danani
email: andrea.danani@idsia.ch

Nabil El Brahmi

Elucidating the role of surface chemistry on cationic phosphorus dendrimer-siRNA complexation

Keywords: cationic phosphorus dendrimers, pyrrolidinium, isothermal titration calorimetry, molecular dynamics, simulations, binding free energy, affinity, stoichiometry, siRNA, surface chemistry, dendriplex, delivery. Graphical

come

Introduction

Since small interfering RNA (siRNA) administration has been proposed as a therapeutic strategy many years ago 1 , research has been focusing on improving siRNA penetration into cells for efficient interference 2 . Indeed siRNA therapy is limited by the poor penetration of naked siRNA across cell membranes due to its negative charge and large molecular weight. To efficiently cross cell membrane, siRNA are usually complexed with cationic molecules to yield positively charged complexes with a size ranging from tens to a few hundreds of nanometers. Complexation may also protect siRNA from degradation by enzymes.

Complexing agents for nucleic acid delivery are numerous and include different polymeric or lipid nanocarriers such as chitosan, polyethyleneimine, cationic lipids and dendrimers. Cationic lipids or polymers complexing nucleic acids have been so far the most adapted self-assembling molecules allowing subcellular delivery of nucleic acids [3][4][5][6][7][8][9][10][11][12][13] . A promising strategy is represented by polycationic dendrimers 14,15 . Dendrimers are unique tree-like branched polymers with biomolecules-like properties, low polydispersity and high degree of versatility; outstanding features for nanomedicine [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] . Dendrimer customization consists of tuning either the structure flexibility [31][32][33][34] or surface properties 35 . Polymer flexibility has been demonstrated to play a crucial role for complexation with siRNA 32,36,37 . In literature, flexibility/rigidity ratio was considered a critical parameter in nanoparticle design 38 . For example, rigid polycationic dendrimers were shown to reorganize their peripheral groups to enhance contacts while interacting with nucleic acids 31,32 .

In connection with siRNA delivery, cationic phosphorus dendrimers have been demonstrated to be good candidates for gene delivery carriers after in vivo experiments 35,39 . In particular, in a recent study 35 , two different cationic, phosphorus dendrimers of generation 3 were synthesized and functionalized with either cationic pyrrolidinium or morpholinium surface groups and called DP and DM, respectively. It is worth mentioning that DP and DM differ only for their terminal groups. Both dendrimers were efficient to complex an anti TNF-α siRNA but complexes were shown more stable in vitro for pyrrolidinium, leading to more effective anti-inflammatory effect by inhibition of TNF-α both in vitro and in vivo 35 . These findings show that a small chemical modification may result in a significant change of the biological activity of the compounds. A complex relationship between chemical formula, structural reorganization and dendrimer-siRNA binding mode might be the cause of such a significant difference. Noteworthy, this relationship can hardly be inferred only by biological in vitro and in vivo experiments due to the high number of variables and complexity of the investigated biological system. In this context, a coupled approach based on experimental and computational modelling may provide fruitful information on molecular mechanisms driving interactions of ligand-target systems [40][41][42] , such as a polymer-siRNA complexes 15,33,34,[41][42][43] .

Here, following this strategy, we used single-molecule resolution techniques such as Isothermal Titration Calorimetry (ITC) [44][45][46][47] together with Molecular Dynamics simulations [48][49][50][51] , to shed light on the molecular reasons behind the DP and DM exhibited activity 35 . In more details, single-molecule level investigation of dendrimer-siRNA interactions was used to better understand how changes in dendrimer structure and biophysical properties affect the interaction with siRNA and lately emerges in a different biological activity, as recently shown 35 . Outcomes of the present research may also be used for further design of siRNA delivery agents characterized by the best compromise between complexation stability and release ability.

Material and methods

Molecular Systems

Phosphorus dendrimers of generation 3 were synthesized and characterized as described earlier 35 , leading to dendrimers bearing 48 terminal cationic pyrrolidinium groups (DP) or 48 morpholinium groups (DM) as shown in Figure 1. Dicer substrate asymmetric duplex siRNA directed against TNF-α as provided by Eurogentec (Liège, Belgium) as dried, purified and desalted duplexes. The TNF-α siRNA consisted of the following sequences:

Experimental Protocol

A 250 mL of HEPES buffer was prepared at 0.01M and pH 7.4 starting from a solid powder of pure HEPES (Sigma-Aldrich, France, MW ~ 238 g/mol) and RNase-free water (Sigma-Aldrich, France). The same HEPES buffer was used to dilute powders of DP, DM and siRNA.

Isothermal titration calorimetry (ITC) was performed on a VP-ITC from Microcal (GE Healthcare Bio-Sciences, Milwaukee, WI, USA) with an active cell volume of 1.4 ml. For all experiments the dendrimer solution was injected into the siRNA solution at 10 min intervals, to allow equilibration between injections.

The siRNA solution at 0.04 mM was placed in the calorimetric cell and the dendrimer solution (concentration of 0.35 mM) was injected via the titration syringe with volume increments of 10µL. Each injection produced a heat of reaction, which was determined by integration of the heat flow tracings. This mode is capable of providing both the binding isotherm and the total binding enthalpy. In Supporting Information S.1.1, Table S1 ITC setup data are reported to set up all dendrimer-siRNA binding experiments.

The heat of dilution, was determined in control experiments by injecting the corresponding dendrimer dispersion into the buffer solution (Supporting Information S.1.2). The heats of dilution were subtracted from the heats determined in the corresponding dendrimer-siRNA binding experiments. For each tested dendrimer (DM, DP), one dilution and two binding experiment repetitions were performed (Table S1). The injection heats were calculated using the Microcal, LLC ITC package for Origin® version 7.0.

Simulation Protocol

System Set Up

The 3D models of two different dendrimers were developed by using Avogadro chemical editor 52 and Linux bash scripts made in house (Supporting Information S.1.3). The General Amber Force Field (GAFF) 53 was considered to describe the DM and DP dendrimer topologies. Partial charges were calculated considering separate residues by the RESP fitting method at the HF/6-31G level of theory using AM1-BCC charges as in a number of previous works 34,37,47,54,55 . A proper distribution of the partial charges was provided, which correctly takes into account the conformation of dendrimer and related bonds. Then, the topology and the parametrization were developed using antechamber and GROMACS associated tools [56][57][58][59][60] and a in house code. AMBER99SB-ILDN force-field [61][62][63] were chosen to describe the topology of the siRNA double strand.

Molecular Dynamics

For both DM and DP systems the following procedure was followed. siRNA and dendrimer 3D models were positioned at a starting distance of about 2 nm, in a dodecahedron box filled with explicit water molecules (TIP3P) and ions (NA+ and CL-) at a physiological concentration (0.15 M). The resulting molecular systems (DP-siRNA and DM-siRNA) were composed by roughly 81000 interacting particles. Each system was energy minimized by 1000 steps of steepest descent energy minimization algorithm. A 100 ps position restrained MD was performed at 300 K 64 and 1 atm 65 . Finally, 5 repetitions of 100ns long MD were performed in the NVT ensemble. Atom velocities were randomly initialized following a Maxwell Boltzmann distribution. In total, 500 ns of MD was performed to sample the conformational space of each considered system (DM-sirNA and DP-siRNA). GROMACS 5 package was used for all MD simulations and data analysis 60 . Longranged electrostatic interactions were calculated at every step with the Particle-Mesh Ewald method with a cut-off of 1 nm. A cut-off of 1 nm was also applied to Lennard-Jones interactions. The LINCS algorithm 66 approach allowed an integration time step of 2 fs. The Visual Molecular Dynamics (VMD) 67 package was used for the visual inspection of the simulated systems. To calculate the binding enthalpy the well-known "Molecular Mechanics / Generalized Born model augmented with the solvent accessible surface area term (GBSA)" approach has been considered, as employed in earlier literature to estimate and compare binding energies calculated from MD simulations 43,[68][69][70][71][72][73] .

As a comparison term, a 100 ns MD was also performed for a siRNA double strand alone in water and ions.

System preparation followed the same procedure described above for the dendrimer-siRNA systems.

Results & Discussion

Dendrimer customization consists of tuning either the structure flexibility [31][32][33][34] or surface properties 35 .

Polymer flexibility has been demonstrated to play a crucial role for complexation with siRNA 32,36,37 . In literature, flexibility/rigidity ratio was considered a critical parameter in nanoparticle design 38 . For example, rigid polycationic dendrimers were shown to reorganize their peripheral groups to enhance contacts while interacting with nucleic acids 31,32 . Instead, this work focuses mostly on the latter strategy, where slight chemical modifications on dendrimer terminals may result in significant differences in dendrimer efficiency in binding siRNA and drive the biological activity of the dendriplexes 35 . In fact, DM and DP are both generation 3 dendrimers, and differ for 1 atom in the terminal groups. Hence, in this case, the relationship between dendrimer flexibility and siRNA complexation, well explained in earlier literature 31-34 , may not be a sufficient argumentation to shed light on reasons behind the DP improved efficacy and biological activity 35 . Here instead, the surface chemistry plays a pivotal role in driving phenomena at the nanoscale level, which results at higher scales in thermodynamic properties such as binding free energy, enthalpy/entropy balance and stoichiometry as shown by experimental and computational results in the following.

Experimental Results

ICT experiments allowed to characterize the interaction properties 44,[74][75][76] of the investigated systems DM/siRNA and DP/siRNA following set-up reported in (Supporting Information S.1.1, Table S1). In a greater detail, the binding curve obtained from a plot of the heats from each injection against the dendrimer-siRNA ratio is shown in Figure 2. The binding curve is analyzed with the one site binding model to determine dissociation constant (k d ), stoichiometry (N), reaction enthalpy (ΔH), entropy (ΔS) and free energy (ΔG), quantified in Table 1. Interestingly, differently from DM-siRNA, the DP-siRNA system showed a deviation from the one site model beyond a molar ratio of 1, in both experimental repetitions. This deviation might be related to supramolecular features of DP-siRNA complexes. A slight DP tendency to self-assembly was also inferred by dilution experiments (Supporting Information S.1.2). Interestingly, the enthalpic contribution is quite similar, slightly lower for DP (ΔH DP /ΔH DM equal to 0.88±0.06), meaning that the conformational entropy plays a role in determining the higher DP affinity. This property is exponentially related to binding free energy ΔG=ΔH-TΔS, ΔH being the binding enthalpy, T the temperature and ΔS the contribution related to conformational entropy change from the unbound to the bound state. Interestingly, the binding free energy is higher in case of DP-siRNA complex, if compared with DM (ΔG DP /ΔG DM equal to 1.17±0.02), despite DM showed the highest enthalpic contribution (ΔH DP /ΔH DM equal to 0.88±0.06). This means that the higher affinity of DP is likely more associated with a lower entropy reduction associated with the system complexation (ΔS DP /ΔS DM equal to 0.84±0.07). In other words, after complexation, the conformational space accessible to DP-siRNA, with respect to the unbound condition, is less reduced than in the case of DM-siRNA complex. The above-mentioned differences are statistically significant considered standard deviations obtained by averaging independent experiment repetitions for both DM-and DP-siRNA complexation. Summarizing, whereas DP and DM showed a comparable binding enthalpy, and binding free energy, stoichiometric data let suppose multivalence properties for DP with roughly 2 siRNA molecules bound to a single dendrimer. By contrast, only 0.5 to one siRNA binds to DM. In agreement with recent biological experiments 35 , data coming from ITC (Table 1) suggested DP as more efficient toward a stable dendrimer-siRNA complex. In particular DP was characterized by higher affinity with respect to DM and higher stoichiometry indicating that DP is able to bind 2 siRNA molecules, whereas DM binds less than 1 on average.

Computational

Although ITC technique was able to quantify the above mentioned properties with high precision, it is still limited in elucidating molecular phenomena and mechanisms of action, which can be inferred as hypotheses based on the experimental evidence. Computational modelling may help toward a rational description of the molecular phenomena characterizing dendrimer-siRNA interaction to shed light on cause-effect relationships ruling binding properties of dendrimer-siRNA systems. In this context, the dendrimer-siRNA interaction mechanism has been investigated by means of MD simulations as described in Material and Method section. For each dendrimer-siRNA system, data analysis has been performed taking the last 20 ns of each MD replica (five replicas for each system) as a statistical ensemble.

A first observation concerns the complexation kinetics, which in both systems is very fast. A qualitative view of the complexation kinetics is given by analyzing the buried surface vs time curves for all simulations (Figure 3a, and Figure 3b). In all cases, three main phases can be identified: during the first 20 ns the dendrimer approaches the siRNA molecule and start binding to it as clearly showed by buried surface curves (Figure 3a, and Figure 3b). Then, the structures rearrange to optimize the interaction surface until a structural stability is reached, at most, in the last 20 ns.

In average, the total interaction surface characterizing the DM-siRNA complex is significantly larger than DP-siRNA complex, as it is for both the hydrophilic and hydrophobic components (Figure 3c). This result reflects also on the binding enthalpy (Figure 3d) which is significantly higher in case of DM-siRNA complex.

To calculate the binding enthalpy the well-known "Molecular Mechanics / Generalized Born model augmented with the solvent accessible surface area term (GBSA)" approach has been considered. It is worth mentioning that the ratio ΔH DP /ΔH DM = 0.74±0.13 extracted by MM/GBSA calculations applied to DMand DP-siRNA simulations is in excellent agreement with experimental ITC data (ΔH DP /ΔH DM equal to 0.88±0.06 reported in Table 1). With the aim of highlighting the influence of the dendrimer macromolecule on the siRNA structural arrangement, representative snapshots of dendrimer-siRNA complexes are also shown in Figure 4.

MD simulations revealed that the dendrimer-siRNA complexation may drive the siRNA toward a significant conformational change as already detected in earlier literature 77 . In detail, the visual inspection of the MD snapshots reported in Figure 4, let suppose a strong effect of the DM in driving a siRNA conformational change. To provide a quantitative measure of the previously mentioned siRNA conformational modification, we have considered the angle parameter, θ, as reported in the following. The angle θ was calculated as the angle between two vectors, each connecting the center of the siRNA filament to an extremity (Figure 5, right pictures). siRNA center and the extremity positions were calculated as a point identified by the center of mass (COM) of 3 selected basis pairs (Figure 5 bottom, right). Noteworthy, the DP-siRNA interaction did not induce any significant conformational changes of the siRNA macromolecule during complexation, as shown in Figure 5. One can infer that the explored angle θ (Figure 5, black bar) does not significantly differ from θ explored by a siRNA alone in water (Figure 5, green bar). A significant difference was observed in case of DM, where a larger conformational change was detected due to the siRNA molecule wrapping around the dendrimer. This molecular event was quantified by a high reduction of the θ parameter characterizing siRNA curvature, i.e., θ=120±45° (Figure 5, red bar). Noteworthy, θ average and standard deviation are calculated by all the conformations sampled in the last 20 ns of the 5 MD replicas, to increase the statistical significance of the analysis. Moreover, this result is in line with the calculated buried surface in case of DM-siRNA, much higher than the interaction surface characterizing the DP-siRNA complex. A precise information on molecular phenomena driving the dendrimer-siRNA complexation is given by the Radial Distribution Function (RDF) as a function of dendrimer-siRNA distance (Figure 6). RDFs have been calculated for two well defined atom groups in the dendrimer terminals (namely T1 and T2) and siRNA (namely, groove and backbone). T1 group is composed by protonated nitrogen atoms, present in both morpholinium (DM) and pyrrolidinium (DP) terminals. T2 group is composed by the oxygen atoms in morpholinium terminal and, for the sake of comparison, one carbon atom in the pyrrolidinium terminal ring (T1 and T2 are clearly shown in Figure 6). Figure 6 shows three main curves for DM-siRNA and DP-siRNA complexes. The blue curves in Figure 6 show the RDF of T1 group with respect to siRNA groove, as constituted by electronegative N atoms on A, U, C, and G bases. The red curves in Figure 6 show RDF of T2 group with respect to siRNA groove as constituted by positively charged NH on A, U, C, G bases. Finally, the black curves in Figure 6 shows the RDF of T1 and T2 taken together with respect to siRNA backbone identified by ribose P atoms.

Interestingly, the main difference between DM and DP lies on T2 group (Figure 6, red curve) which, in case of DM-siRNA complex, are characterized by a peak at a distance 0.2 nm (primary interactions with siRNA groove), indicating that DM terminals are completely embedded in the siRNA double strand. As expected, other primary interaction peaks (distance peak 0.35 nm) were found in both complexes (DM-siRNA and DP-siRNA) in T1-siRNA groove RDF (Figure 6, blue curve) and T1T2-siRNA backbone (Figure 6, black curve) RDF. Figure 4 and Figure 6 suggest the key role played by dendrimer terminals in the dendrimer-siRNA complexation. In greater detail, although DP and DM differ only for one atom, this change influences the balance between hydrophilic and hydrophobic character of the dendrimer. The morpholinium oxygen bears a partial negative charge which makes the morpholinium terminal slightly more polar than the pyrrolidinium terminal. Interestingly, the above-mentioned difference on terminals does not result in any significant change in terms of dendrimer structural properties when the dendrimer is considered alone in water. Instead, the higher DM terminals' polarity plays a crucial role when the dendrimer complexes with siRNA as clearly highlighted by RDF curves shown in Figure 6 and by the higher DM-siRNA interaction surface (Figure 3c).

Taken together MD results shown in Figure 3, Figure 4, Figure 5, and Figure 6 are in excellent agreement with experimental data. More in detail, MD simulations showed a DM higher binding enthalpy and buried surface (Figure 3c, andd). The molecular explanation of this DM-siRNA stronger interaction may be ascribed to the presence of morpholinium oxygens which showed a higher tendency to non-covalently bind to siRNA groove (RDF red curve in Figure 6). On the other hand, this DM molecular characteristic is paid on the entropic side with a reduced multiplicity of the accessible conformational microstates of the dendriplex. In a greater detail, the siRNA molecule is constrained to assume a curved and wrapped arrangement around DM, being anchored by morpholinium oxygens (Figure 4, and Figure 5). Differently, DP employs only protonated N atoms on pyrrolidinium terminals to interact with the siRNA groove and does not affect the siRNA conformation when complexed with it. In other words, DP showed a higher plasticity in complexing siRNA.

In summa, experimental and computational evidences highlighted the higher complexation stability of the DP-siRNA dendriplex. More in detail, the complexation stability is here quantified by the complexation free energy, sum of two contributions: i) the enthalpic contribution, which depends on the interaction strength (the deeper is the energy minimum, the more stable is the complex); ii) the entropic contribution, which depends on the ability of the bound system (e.g., dendriplex) to explore the conformational space with respect to the unbound condition (non-interacting dendrimer|siRNA systems). The more constrained is the dendriplex, the lower is the complexation stability.

DP and DM behave similarly when alone in water. Hence, the conformational entropy of non-interacting DM|siRNA and DP|siRNA unbound systems can be reasonably supposed comparable. The DM-siRNA bound state is characterized by a stronger interaction strength (ΔH DP /ΔH DM equal to 0.88±0.06 reported in Table 1). However, the dendriplex is limited in exploring the conformational space (high DM-siRNA interaction surface and siRNA wrapped around the dendrimer). Instead, the DP-siRNA complex is characterized by a lower interaction strength, but the DP plasticity allows for a larger exploration of the dendriplex conformational space. In practice, DP can bind the siRNA in many more different ways with respect to DM, thus making the DP-siRNA bound state more likely.

Hence, the higher stability of the DP-siRNA complex results by a clever combination of enthalpic and entropic contribution to the complexation free energy.

In addition to previous evidences, the terminal-siRNA distance analysis, in Table 2, provides fruitful information on dendrimer terminals' contribution to dendrimer-siRNA interaction. In a greater detail, the percentage of dendrimer terminals found at selected distance range from the siRNA is reported (Table 2).

In Table 2 only average values are reported, calculated by considering as an ensemble the trajectory snapshots in last 20 ns of all five replicas per each investigated complex. Standard deviations are not reported since always one order of magnitude lower than the related average value.

Four main distance ranges were defined in Table 2: "Primary Interactions (d ≤ 0.35 nm)" range indicates terminals mostly involved in stabilizing the dendrimer-siRNA complexation; "Secondary Interactions (0.35 > d ≤ 0.45 nm)" range indicates terminals which still contribute to dendrimer-siRNA complexation; "Close to siRNA (d > 0.45nm ≤1nm)" range indicates terminals which can be recruited to enhance or stabilize the complex; "Free terminals (d > 1nm)" range indicates terminals considered free to move under thermal fluctuations and not involved into interactions. In fact, the short-ranged cut-off was set-up to 1 nm, meaning that the van der Waals interactions are not even calculated beyond this distance.

DM employs the 57% of terminals (52.11% of primary interactions + 4.59% of secondary interactions) to optimize the complexation with siRNA, leaving the 25% of its terminals free and eventually available to catch another siRNA molecule. This is in line with a higher buried surface and also a higher binding enthalpy with respect to DP. It is also interesting to notice (Figure 4) how DM attaches both the siRNA center and the extremities exerting a bending action on the siRNA as quantified by the angle parameter θ described above (Figure 5). On the other hand, DP behaves in an opposite way. DP employs the 35% terminals (31.97% of primary interactions + 3.26% of secondary interactions) to optimize the complexation with siRNA, leaving the 49% of its terminals free to move. This implies a higher ability of DP to bind other siRNA molecules. Summarizing, DP employs only a lower percentage of its terminals to complex siRNA with respect to DM, an interesting feature which was also observed in experimental ICT stoichiometry data. In greater detail, data in Table 1 suggest DP able to bind around 2 siRNA molecules and DM less than one. In close agreement, computational data quantifying the percentage of dendrimer terminals effectively involved in the dendriplex formation (Table 2), indicated that DP employs only 35% of its terminals to provide the complexation, whereas the 49% of terminals are free to fluctuate under thermal motions and eventually to catch another siRNA. Instead, DM employs the most part of its terminals (57%) to complex the siRNA and leaves free only 25%.

But there is more, DM-siRNA complexes are also characterized by DM terminals anchoring and pulling the siRNA to be wrapped around the dendrimer. Hence, the complexation mechanisms involved also a significant conformational change of the siRNA. This may result in a competitive mechanism among DM particles in solution, which, on one side repel each other due to the negative charged surface (because of morpholinium oxygens not present in DP), and on the other side sequestrates siRNA and forces it to wrap around the dendrimer.

Taken together, experimental and computational results of the present research suggest how little modifications of the dendrimer surface chemistry (e.g., DP and DM differing for a single atom in the terminal group) may significantly affect the mechanism driving dendriplex complexation.

In greater detail, DP exhibits a collaborative strategy where a single DP is able to link more than one siRNA without requiring it to wrap around the dendrimer. Therefore, more than one siRNA may interact with one dendrimer, but also more than one dendrimer may attach the same siRNA in different areas. This might explain the 2.25 ± 0.03 stoichiometry and the self-assembly behavior that ITC binding curves with molar ration higher than 1 suggest (Figure 2). On the other hand DM-siRNA interaction consists of dendrimer and siRNA in a one-to-one exclusive interaction. A competition between DM dendrimers to bind a single siRNA might explain the 0.60 ± 0.03 stoichiometric value.

In conclusion, DP is more efficient than DM in binding siRNA due to the higher plasticity in the complexation dynamics. Due to the lower dendrimer-siRNA interaction strength, DP may also be more efficient than DM in releasing siRNA under specific conditions.

Depending on the specific application, one could even think to play more with surface chemistry and, for example, to design hybrids with a different percentage of pyrrolidinium and morpholinium terminals to tailor the balance between enthalpic and entropic contributions resulting in desired affinity and stoichiometry.

Conclusions

The study here proposed investigated the effect of surface chemistry on dendrimer-siRNA complexation dynamics and mechanism. More in detail, this research considered two different cationic, phosphorus dendrimers of generation 3, namely DP and DM, differing only for one atom in the terminal group. Earlier, experiments indicated that DP and DM dendrimers were efficiently complexing an anti TNF-α siRNA but complexes were shown more stable in vitro for DP, leading to more efficient anti-inflammatory effect by inhibition of TNF-α both in vitro and in vivo. Here, a coupled experimental/computational (ITC/Molecular Dynamics) approach was employed to shed light on mechanisms driving the dendrimer-siRNA complexation. Outcome of our research indicated DP to be more efficient in binding siRNA. In greater detail, DP higher efficiency with respect to DM lies in two main features: 1) DP-siRNA complexation maximizes the entropic contribution to the binding free energy and 2) DP requires a lower number of terminals to directly bind the siRNA while leaving a large part of them free to fluctuate under thermal motion to eventually catch another siRNA molecule.

Our work provides a further proof on the suitability of computational molecular modeling, such as Molecular Dynamics, coupled with high resolution experimental techniques, such as ITC. The suggested platform can be used i) to predict the efficiency of nanoparticle-siRNA hybrids to screen and select the best hits before further in vitro and in vivo tests, or ii) to deeply understand experimental results by providing fruitful insight in mechanism and molecular phenomena driving nanoparticle-siRNA complexation, as the case of DP and DM considered in this work.

Sense 5 '

 5 -pGUCUCAGCCUCUUCUCAUUCCUGct-3', Antisense 5'-AGCAGGAAUGAGAAGAGGCUGAGACAU-3', where the lower case letters indicate 2'-deoxyribonucleotides, underlined capital letters represent 2'-Omethylribonucleotides 105 and P is a phosphate residue (Mw 17,047 Da).

Figure 1 .

 1 Figure 1. DP and DM Formula, Structure, Generation, molecular weight and solubility information. From a structural point of view, DP and DM differ only for the terminal group.

Figure 2 .

 2 Figure 2. Two experiment repetitions (black and white squares) are reported for both DM-siRNA (top panel) and DP-siRNA (bottom panel) investigated systems. The solid red lines are the calculated curves using the best-fit parameters. The one site model was used to fit the data. Dilution signal was subtracted from titration signals before fitting.

Figure 3 .

 3 Figure3. Dendrimer-siRNA buried total surface vs. time curves, calculated for 5 repetitions of 100 ns long MD simulations for DM-siRNA (a) and DP-siRNA (b). Decomposition of interaction surface in its hydrophobic and hydrophilic components (c) calculated as average ± standard deviation over the last 20 ns of all replicas taken as ensemble. Binding Enthalpy was also calculated as average ± standard deviation over the last 20 ns of all replicas taken as ensemble.

Figure 4 .

 4 Figure 4. Representative snapshots of DM-siRNA complex (left) and DP-siRNA complex taken from the last 20 ns of a MD trajectory among the 5 replicas.

Figure 5 .

 5 Figure 5. Effect on siRNA structural conformation due to interaction with the dendrimer (dendrimer is not shown in the picture). The angle θ is calculated between two vectors connecting the center of the filament to extremity bases along the filament axis. Center of mass of siRNA center and extremity segment are calculated by selecting 3 basis pairs (selected bases are shown in the figure on the siRNA sequence). The angle value is calculated step by step throughout the overall equilibrium MD. In case of siRNA alone in water θ is quantified as 163±9° (green bar). Average and standard deviation are obtained by fitting with a gaussian distribution the simulation data (top-left panel) in the last 50 ns. Interestingly the DM-siRNA complex is characterized by a nucleic acid structure significantly bent (red bar, θ=120±25°), whereas the DP does not seems to affect the average conformation of siRNA (bottom left panel, black bar). For dendrimer-siRNA complexes, angle average and standard deviation are calculated by all the conformations sampled in the last 20 ns of the 5 MD replicas.

Figure 6 .

 6 Figure6. Radial Distribution Function (RDF). The distribution of atom-atom distances have been calculated between: atom T1 and siRNA groove, electronegative N on A,U,C,G bases (blue curve); atom T2 and siRNA groove, i.e., positive NH on A,U,C,G bases (red curve); atoms T1 and T2 and siRNA backbone, constituted by atoms P and bound oxygens (black curve). T1 is the charged Nitrogen, present in both pyrrolidinium and morpholinium dendrimer terminal type. T2 is instead the oxygen atom in the morpholinium terminal of DM. For comparison reasons a carbon atom has been chosen to represent T2 in the pyrrolidinium terminal of DP. RDFs have been calculated on the statistical ensemble constituted by frames in the last 20 ns of all 5 MD replicas for each dendrimer-siRNA complex.

Table 1 .

 1 Main results for DM-siRNA and DP-siRNA investigated systems in terms of stoichiometry (N), dissociation constant (k d ) and binding enthalpy (ΔH), entropy (ΔS) and free energy (ΔG).

	Property	Unit	DP	DM	DP/DM
	N siRNA/dendrimer	#	2.25 ± 0.03	0.60 ± 0.03	3.75 ± 0.53
	k d	µM	5.60 ± 0.85 21.49 ± 2.11	4.02 ± 1.06
	ΔH	kcal/mol	-47.00 ± 2.83 -53.25 ± 0.21	0.88 ± 0.06
	TΔS	kcal/mol	-39.65 ± 2.95 -46.96 ± 0.21	0.84 ± 0.07
	ΔG	kcal/mol	-7.35 ± 0.12	-6.29 ± 0.00	1.17 ± 0.02

Another interesting feature emerging for DP can be deduced by stoichiometry data, which indicated the multivalent character of siRNA binding to DP. In detail, the estimated number of siRNA molecules bound to each DP dendrimer is roughly four times higher than DM.

Table 2 .

 2 Terminal-siRNA distance. The percentage of dendrimer terminals found at a selected distance range from the siRNA is reported. Four main distance ranges are defined: "Primary Interactions (d ≤ 0.35 nm)" range indicates terminals mostly involved in stabilizing the dendrimer-siRNA complexation; "Secondary Interactions (0.35 > d ≤ 0.45 nm)" range indicates terminals which still contribute to dendrimer-siRNA complexation; "Close to siRNA (d > 0.45nm ≤1nm)" range indicates terminals which can be recruited to enhance or stabilize the complex; "Free terminals (d > 1nm)" range indicates terminals considered free to move under thermal fluctuations and not involved into interactions. Percentage values have been calculated on statistical ensemble constituted by frames in the last 20 ns of all 5 MD replicas for each dendrimer-siRNA complex. Standard deviations are not reported since always one order of magnitude lower than the related average value.

		Primary Interactions	Secondary Interactions	Close to siRNA	Free Terminals
		0.35nm ≤ d	0.35nm < d ≤ 0.45nm	0.45nm < d ≤1nm	d > 1nm
	DM	52.11%	4.59%	18.70%	24.60%
	DP	31.97%	3.26%	15.84%	48.93%

Acknowledgements

AD and MAD gratefully acknowledge the financial support from the Swiss National Foundation through the grant IZCNZ0-174842 ("Computational modeling to characterize and optimize dendrimer based nanocarriers for siRNA delivery in Chronicle Obstructive Pulmonary Disease"). The authors would also like to acknowledge the financial support provided by COST-European Cooperation in Science and Technology, to the COST Action MP1404: Simulation and pharmaceutical technologies for advanced patient-tailored inhaled medicines (Siminhale). This work was also supported by the Swiss National Supercomputing Centre (CSCS). E. Blattes, A. Vercellone, H. Eutamène, C.-O. Turrin, V. Théodorou, J.-P. Majoral, A.-M. Caminade, J. Prandi, J. Nigou and G. Puzo, Proc. Natl. Acad. Sci. U. S. A., 2013, 110, 8795-800.

Conflicts of Interest

There are no conflicts to declare