Mechanistic reconstruction of glycoprotein secretion through ......Quantification of defined IgG...

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1Institute ofMicrobiology, Department of Biology, ETH Zürich, 8093 Zürich, Switzerland.2Institute for Chemical and Bioengineering, Department of Chemistry, ETH Zürich, 8093Zürich, Switzerland. 3Merck Healthcare, Biotech Process Sciences, Route de Fenil 25,1804 Corsier-sur-Vevey, Switzerland.*Present address: Functional Genomic Center Zürich, ETH Zürich,Winterthurerstrasse190, 8057 Zürich, Switzerland.†Corresponding author. Email: [email protected]

Arigoni-Affolter et al., Sci. Adv. 2019;5 : eaax8930 27 November 2019

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Mechanistic reconstruction of glycoprotein secretionthrough monitoring of intracellular N-glycan processingIlaria Arigoni-Affolter1, Ernesto Scibona2, Chia-Wei Lin1*, David Brühlmann3, Jonathan Souquet3,Hervé Broly3, Markus Aebi1†

N-linked glycosylation plays a fundamental role in determining the thermodynamic stability of proteins and isinvolved in multiple key biological processes. The mechanistic understanding of the intracellular machineryresponsible for the stepwise biosynthesis of N-glycans is still incomplete due to limited understanding of invivo kinetics of N-glycan processing along the secretory pathway. We present a glycoproteomics approachto monitor the processing of site-specific N-glycans in CHO cells. On the basis of a model-based analysis ofstructure-specific turnover rates, we provide a kinetic description of intracellular N-glycan processing alongthe entire secretory pathway. This approach refines and further extends the current knowledge on N-glycansbiosynthesis and provides a basis to quantify alterations in the glycoprotein processing machinery.

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INTRODUCTIONProtein secretion in eukaryotic cells is mediated by a complex set ofcompartmentalized reactions. The process initiates in the endoplasmicreticulum (ER) and proceeds toward the Golgi apparatus, the plasmamembrane, or the lysosome by vesicular transport. Posttranslationalmodifications (PTMs) are a hallmark of secretory proteins, and the pro-cessing machinery is specifically localized in the different compart-ments. N-linked protein glycosylation, present in all domains of life(1), is a complex biosynthetic pathway that involves organelle-specificreactions (2). The process begins in the ER lumen, where a definedglycan (Glc3Man9GlcNAc2) is covalently linked to the asparagineresidue in theN-X-T/S consensus sequence of newly synthesized poly-peptides by the oligosaccharyltransferase (OST) enzyme (3). Glycosylhydrolases that are part of the glycan-directed folding and quality con-trol machinery of the ER trim three glucoses and up to four mannoseresidues from the initial Glc3Man9GlcNAc2 glycan (4). The properlyfolded glycoproteins are transferred to the Golgi (5), where multipleenzymes trim and elongate the N-glycan to yield the secreted glyco-protein. It is a characteristic property of the N-glycosylation process tostart with the transfer of a defined oligosaccharide (Glc3Man9GlcNAc2),which is then processed to generate a species-, cell-, and site-specificheterogeneous ensemble of N-linked glycans. The composition of thefinal glycan output is determined by the activities and localization ofprocessing enzymes as well as the structure of the glycoprotein itself(6, 7). N-glycan heterogeneity poses a great analytical challenge andaffects the development and production of therapeutic glycoproteins(8). Mass spectrometry (MS)–based approaches have emerged as validtechnologies to characterize N-glycan profiles (9).

Current knowledge about the N-glycosylation reaction machineryrelies (i) on in vitro experiments that determine the activity and sub-strate specificity of various glycosyl transferase and hydrolases (10–14)and (ii) on microscopy and biochemistry data that provide the local-ization of the different enzymes and substrates within the ER and theGolgi (15–20). In addition, metabolic studies in combination with

mathematical models have contributed to a better understanding ofthe N-glycosylation process (21, 22). Nevertheless, these models haveso far been restricted to mere correlations between inputs and outputs(22–25) and have been developed without the use of empirical infor-mation regarding the dynamics of intracellular N-glycan processing.

We present a novel experimental approach for the characterizationof the secretory pathway inmammalian cells bymonitoring and quan-tifying intracellular, site-specific N-glycan processing. Our methodol-ogy combines parallel reaction monitoring (PRM), a quantitativetargetedMS approach to quantify glycopeptides, with dynamic SILAC(stable isotope labeling of amino acid in cell culture) (26), a protein-labeling technique to follow the kinetics of protein-bound glycans.The experimental data were used to formulate a kinetic model forintracellular glycoprotein processing, the retrieval of activity-basedlocalization profiles, as well as the relative kinetic parameters of glycosyltransferases and hydrolases in the N-glycosylation reaction network.Our results confirm and refine concepts on glycoprotein processingin the ER and theGolgi, suggesting an additional pathway for the exportof folding intermediates from the ER directly to the lysosome.

RESULTSSILAC coupled with nano-UPLC-PRM allows for reliablequantification of glycopeptides from intracellular andsecreted recombinant IgGImmunoglobulin Gs (IgGs) containing a single N-glycosylation siteat each heavy chain (HC) were purified from CHO cell extracts orculture supernatant via protein A capture. Because this purificationmethod requires the initial folding of the CH2 andCH3 domains of theHC (27), the detection of some glycopeptides (i.e., Glc3Man9GlcNAc2)is possibly biased since it can be attributed not only to the action ofthe OST but also to the folding of the aforementioned domains. Forthis reason, the species Glc1–3Man9GlcNAc2 (generated by ER glu-cosidase I and II) were excluded both from the data acquisition andfrom the model (see Material and Methods for more details).

Glycoproteomics analysis was performed after tryptic digestionand reverse-phase chromatography separation. For all detectableIgG tryptic glycopeptides, we performed data-dependent acquisition(DDA) with higher-energy C-trap dissociation (HCD), as describedpreviously (28). DDA data were used to generate the target list forPRM analysis (table S1).

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Solutions containing different concentrations of (glyco)peptidesfrom secreted IgG samples were tested (10 mM to 200 pM, equivalentto 100 pmole to 1 fmole of glycopeptides per analysis). Whereas themost abundant glycopeptides were still detectable at 200 pM, lessabundant ones, such as the sialylated glycopeptides, had a signal-to-noise ratio that prevented quantification at concentrations below 100 nMtotal IgG (fig. S1).Our limit of detection (200pMIgG)was comparable tovalues obtained in previous studies (29). Nevertheless, for a completeglycopeptide quantification, our limit of quantification was at 100 nMIgG. To obtain the best fragmentation intoY ions (intact peptides withfragmentation at the glycan level), we tested different normalized col-lision energies (NCEs). NCE = 16%, as opposed to NCE = 22%, gavethe best fragmentation pattern: It led to decreased abundance ofthe dominant Y1 ion (peptide with one N-acetylglucosamineresidue) and the oxonium ions (N-acetylglucosamine ions and di-saccharide ions) and increased the abundance of characteristic Y ionsand unfragmented precursor ion (fig. S2) (30).

As opposed to canonical tandemMSmethods, where the precur-sor isolation window (Q1) is maintained as low as possible (31), weadjusted the analytical procedure and increased the Q1 isolationwindow to 6 Th, so that both heavy and light glycopeptide precursorswere simultaneously isolated and fragmented (Fig. 1A). To evaluatethe consistency of MS2-level quantification among all the differentglycoforms, we analyzed different mixing ratios of heavy and lightIgGs and compared peak area– or peak height–based quantification.The ratio of all glycoforms was consistent with the original IgG mix-ture (Fig. 1B and data not shown). The peak height–based quantifica-tion resolved into amore robust analysis for low-abundant glycoformsso that we kept the peak height–based quantification throughout thewhole study.

We compared the N-glycan distribution of secreted and intra-cellular IgG gained with MS1 quantification (28) with the MS2 quan-tification method described above (Fig. 1C). For both MS1- andMS2-based quantification, the N-glycan profile of secreted IgGsconsisted of 80% complex N-glycan, whereas the intracellular IgGspresented 80% of high-mannose N-glycan structures. The consistencybetween MS1- andMS2-based quantification and the agreement withprecedent studies on secreted or intracellular IgG glycan profilingusing different MS approaches (23, 32) proved the suitability of ourPRMmethodology for N-glycan profiling (representativeMS2 spectrafor each glycan structure are shown in fig. S3).

To monitor intracellular IgG glycan processing, we coupled thePRM-based analytical setup to a dynamic SILAC regime. We purifiedIgGs from cell extracts that were lysed at different time points afterswitching to heavy SILACmedium. The fractional labeling of the dif-ferent tryptic glycopeptides is reported in Fig. 2A. Labeled IgGglycopep-tides bearing intermediates synthesized early in the N-glycosylation/secretory trajectory were detected shortly after the pulse (i.e., high-mannose structures detected already after 10 min). In contrast, morecomplex Golgi-derived fucosylated, galactosylated, or sialylatedN-glycopeptides appeared only after 20 to 60min (Fig. 2A), indicatingdelayed accumulation/synthesis of the corresponding intracellularglycoform (17–19).

Development of a mathematical model enabled thederivation of quantitative kinetic information andrefinements of the canonical N-glycosylation networkOur fractional labeling data provided information about the turnoverrates of the intracellular pools of defined IgG-bound glycans but can-


not directly reveal the kinetic information and enzymatic activitywindows along the secretory pathway. Therefore, we developed amathematical model (detailed in the Supplementary Materials). Thebest-fitting turnover reactions (Fig. 2A), the intracellular steady-stateN-glycan distribution (fig. S4A), and the final secreted N-glycan pro-files (fig. S5A) were produced with the ER and Golgi networkspresented in Fig. 2 (B and C). A simple N-glycosylation model as-suming a bare sequential order of glycosylation reactions did not fitthe data successfully. To correctly reproduce the experimental data,it was necessary to include spatially separated pools of intracellularIgGs that carry the same high-mannose (Man9–5) glycans. The differ-ent pools are related because a high mannose–bearing IgG can befound in both the ER and the cis-Golgi, and within the ER, high-mannose isoforms can account for different folding states of the protein.In the ER, high-mannose structures are generated by the collaborativeaction of ER-localized alpha-mannosidases (ER-mannosidase I and/orEDEMs) implicated in the buildup of the degradation signal presenton the not properly folded glycoproteins (33–35). In contrast, a dis-tinct Golgi-localized pool of Man8–5 structures is generated by theGolgi-mannosidase I that trims Man9 to Man5 with slightly differentspecificities (36).

Our data implied a faster turnover of the Golgi-generated struc-tures as compared with the oligomannose structures in the ER (Fig. 2A).Mechanistically, the fast turnover of Golgi species can be explained byshort residence time of glycoproteins in the Golgi cisternae. Converse-ly, slow ER turnover can be attributed to a slow export to the Golgi orthe cytosol. This was also confirmed by the model, which, on the oneside, predicted slower kinetic parameters for the formation and deg-radation of the Man7–5 structures (table S2), and on the other side,predicted a bigger IgG pool for folding intermediates in the ER ascompared with the pool of folded IgG in the Golgi (fig. S5B). Unex-pectedly, we observed a Man4GlcNAc2 glycan with an early onset andfast turnover. This structure has not been characterized in detail, andthere are controversial opinions regarding its generation either byGolgi alpha-mannosidases (37, 38) or by lysosomal mannosidases(39). Neither ER-mannosidases nor Golgi-mannosidase I can trimfurther than Man5 due to their a-1,2 specificity (10). It has been re-ported that protein aggregates, generated in the ER, are cleared fromthe ER and transported in single-membrane vesicles directly to thelysosome (40, 41). Thus, we hypothesized the Man4GlcNAc2 struc-ture is generated by the action of a lysosomal mannosidase on IgGHCs that are cleared from the ER in a pathway that differs from thewell-characterized ER-associated degradation (ERAD) pathway (repre-sented by the collaborative action of ER-mannosidase I and EDEMs).The integration of this pathway into the model allowed for accuratefitting of the Man4GlcNAc2 kinetics (Fig. 2A). Moreover, the additionofMG132, a potent inhibitor of the proteasome 26S complex, showed amarked accumulation of ERAD-relevant Man7-Man5 species withoutaffecting the Man4GlcNAc2 turnover (fig. S6A), thereby excluding itas an ERAD intermediate.

The mathematical model was used to calculate the trajectory ofIgGs through the entire N-glycosylation network (Fig. 2, B and C).The model predicted the distribution of the activity profiles of themain Golgi enzymes (Fig. 3A) and the distribution of the respectiveN-glycan substrate abundance (Fig. 3B) along the Golgi compart-ment. On the one side, the agreement with enzyme localizations re-solved bymicroscopy (15–18) was appreciated, and on the other side,our data provided experimental support for inferred mathematicalmodel outputs (22, 24, 25).

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Fig. 1. Validation of the dynamic SILAC-PRM methodology. (A) Typical MS2 spectrum of an IgG glycopeptide obtained during SILAC-PRM acquisition using thebroad isolation window (6 Th) and normalized collision energy (NCE) of 16% to achieve glycan-level fragmentation of light and heavy glycopeptides simultaneously(ratio, 1:5). Schematic representation of the glycopeptides of interest is shown above the corresponding peak. Gray long squares indicate the peptide backbone (with N,asparagine, indicating the N-glycosylation site; and R, arginine), blue squares indicate N-acetylglucosamine residues, green circles indicate mannose residues, and redtriangles indicate fucose residues. Masses and charge states of the peak signals are reported. Mass differences between peaks that correspond to sugar moieties areindicated with the corresponding geometric representation of the sugar. (B) SILAC-PRM data acquisition of 100% heavy and 100% light IgG glycopeptides mixed in a5:1 ratio prior to MS injection. Quantification of defined IgG glycoforms (individual bars) was conducted by averaging the peak area (black bars) or peak height (graybars) of defined glycotransitions (fragment ions at the glycan level) (n = 3). Details about the glycoforms and the glycotransitions used for the quantification are listed intable S1. (C) N-glycan profiling analysis of purified intracellular and secreted IgGs. After PRM data acquisition, quantification was performed either on the MS1 level(light gray), by averaging the intensity of the extracted ion chromatograms, or on the MS2 level, by averaging the intensity of defined glycotransitions (dark gray)(n = 3). The relative abundance of each N-glycoform (x axis) compared with the sum of all the glycoforms is reported (y axis) for secreted (top graph) and intracellular(bottom graph) IgGs.

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Fig. 2. Intracellular N-glycan processing. (A) Intracellular IgG molecules were harvested at the time indicated after the switch of the cells from light to heavy SILACmedium (x axis) and analyzed by SILAC-PRM. The fractional labeling (y axis) of intracellular pools of IgG peptides bearing different N-glycan intermediates (shown assymbols) is given over time (n = 3; except for complex sialylated structures, n = 2). The modeled turnover kinetics are shown as curves. (B) IgG fluxes through the ERprocessing pathway calculated by the model. The size of the arrows is proportional to the flux through each reaction indicated (numerical values predicted by themodel are indicated in the figure as percentage). Upper rows reflect folded IgGs transported to the Golgi, middle rows reflect folding intermediates in the folding/ERADpathway, and the lower rows refer to the lysosome degradation of aggregates (left) and cytoplasmic degradation by proteasome (right). Blue proteins refer to folded,and purple proteins indicate partially folded IgGs. Different N-glycan structures are shown as symbols. (C) IgG flux through the Golgi N-glycan processing pathway. Thesize of the arrows is proportional to the flux through each reaction indicated. The colors of the arrows indicate the different enzymes catalyzing the reaction (for thecolor code, see Fig. 3A). Circles highlight the major glycoforms found on secreted IgGs. Gray glycoproteins refer to IgG glycostructures that were included in the datameasurements but did not provide reliable signals due to low abundance (below limit of quantification), preventing a flux calculation (no arrows).

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The refined N-glycosylation network correctly predicts theeffect of N-glycan processing inhibitorsTo evaluate the robustness of our model, we perturbed the system bythe addition of well-defined processing inhibitors andmonitored theeffect on the intracellular N-glycan processing and the IgG N-glycanprofiling. Upon swainsonine (SWA) addition, a potent inhibitor of theGolgi-localized alpha-mannosidase II, N-glycan processing in the ERremained almost unchanged, whereas the N-glycan synthesis in theGolgi shifted completely toward hybrid N-glycan species (Fig. 4, Aand B, and figs. S4B, S5C, and S6B). The relative distribution of theactivity profiles of the main Golgi enzymes and the distribution ofthe respective N-glycan substrate abundance showed only small shiftsalong the Golgi length x axis (Fig. 4, C and D) as compared with theuntreated cells (Fig. 3, A and B). The enzymatic activity (y axis), espe-cially for GalT (beta1,4-galactosyl-transferase) and SiaT (alpha2,3 and2,6-sialyl transferase), showed an up to fivefold increase. This is pos-sibly due to the easier accessibility of the hybrid substrate structure(cf. Discussion). Conversely, kifunensine (KIF), an inhibitor ofa-1,2-mannosidases, substantially affected both the flux and the


N-glycan processing in both the ER and the Golgi (Fig. 4, E andF, and figs. S4C, S5D, and S6C). Notably, neither the SWA nor KIFaddition affected the turnover kinetics of the Man4GlcNAc2 glyco-protein, confirming a processing pathway independent of ER- orGolgi-localized mannosidases (fig. S7A). To test this hypothesis,we treated cells with bafalomycin (BAF), a substance known to affectpH homeostasis in the lysosome and to reduce lysosomal hydrolasesactivity. BAF treatment had a single effect on the turnover of theMan4GlcNAc2, strongly reducing its turnover (fig. S7B), whereasthe turnover of Man9GlcNAc2 (fig. S7C), an ER-generated structure,or FA2, a Golgi-generated N-glycan (fig. S7D), did not show devia-tions from the control turnover data (Fig. 2A), except for a shift onthe time axis (a delay), probably due to an overall effect of reducedbiosynthesis and secretion speed under BAF treatment. Under SWAand KIF treatment, the production of Golgi-generated glycan is pre-vented due to the absence of the correct substrate. The turnover ofMan9GlcNAc2, an ER-generated structure, did not show significant de-viation from the control conditions under SWA treatment, but a delaywas observed under KIF treatment, as expected (fig. S7C). These datasupported the hypothesis of a direct export from theER to the lysosome.

DISCUSSIONWe monitored the intracellular N-glycan processing of IgGs. Theexperimental data were used to develop a mathematical model forN-glycoprotein processing in CHO cells. The model was based on thecurrent knowledge of the N-glycosylation pathway in mammaliancells (42), and upon a refinement of the canonical network, we wereable to deduce a robustmathematical description of the process. In theER, N-glycan processing is primarily involved in the folding and qual-ity control process that ensures the exit of correctly folded proteinfrom the ER. In contrast, Golgi processing is characterized by spatialseparation of enzymes and a continuous flow of the substrate throughthe organelle.

The correct reproduction of the Man4GlcNAc2 kinetic was onlypossible with the incorporation of an additional degradation pathwaythat directly acts early in the ER (as Man4GlcNAc2 has an earlyturnover rate). On the basis of previous ER-phagy studies (43) andrecent descriptions of a vesicular transport from the ER to the lysosome(41), we propose thatmannose trimming toMan4GlcNAc2 (and pos-sibly further, to not proteinA capture purifiable glycopeptides) reflectsthe quality control process involved in protein aggregate clearancefrom the ER, referred to as ERLAD (ER-to-lysosome–associated deg-radation) (41). Our glycoproteomics approach differentiates thispathway from ERAD, which can be accurately followed by N-glycanprocessing down to Man5 (cf. Fig. 2A and fig. S7A). From our data,ERLAD acts much more rapidly than ERAD, indicating a much lon-ger half-life for ERAD substrates as compared with their ERLADcounterparts. This observation is in line with previously describedmodels, where a stochastic association of unfolded protein andchaperoneswith the signal-generating EDEMs determines the half-lifeof ERAD substrates (44, 45). In contrast, glycoprotein aggregates in-teracting with calnexin (through Glc1Man9GlcNAc2 N-glycans) areselected for export to the lysosome (41). On the basis of the kineticdata and the model predictions, we propose that aggregation of poly-peptides occurs after translocation into the ER lumen, where nascentpolypeptides fail to associate with folding chaperones and thereforeaggregate. We hypothesize that the N-glycan profile on these aggre-gates is a mixture containing at least some Glc1Man9GlcNAc2, as

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Fig. 3. Empirical enzyme activities and substrates distribution along the Golgi.(A) The apparent activities of the different glycosyl hydrolases and transferases ascalculated by the model along the normalized Golgi, from cis to trans, are shown.Inset: Enlarged y axis to reveal galactosyl- and sialyl-transferase activities. ManI,Golgi alpha-mannosidase I (light green); GnTI, N-acetylglucosamine transferase I(light blue); ManII, alpha-mannosidase II (dark green); FucT, a-1,6-fucosyl-transferase(red); GnTII, N-acetylglucosamine transferase II (dark blue); GalT, beta1,4-galactosyl-transferase (yellow); SiaT, alpha2,3 and 2,6-Sialyl transferase (purple). (B) Intracellulardistribution of the different N-glycoforms of IgG calculated by the model along thenormalized Golgi is shown. Abbreviations and the corresponding structures are ex-emplified in table S1.

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inhibition of downstream glycan processing (with KIF and SWA)does not influence theMan4GlcNAc2 kinetics (assumed to be indic-ative of ERLAD).

Our SILAC-PRMmethodology allowed us to follow the N-glycanmaturation along the entire secretory pathway with a spatial-temporalresolution. For each of the routes taken by a newly synthesized IgG, wecan now assign an N-glycan “marker”: The secretion trajectory is char-acterized by hybrid or complex N glycan structures (46), ERAD byMan7-Man5 species, and, as suggested by our data, ERLAD is charac-terized by paucimannose structures, like Man4GlcNAc2 (reflecting deg-radation intermediates).

The mathematical model retrieved in vivo kinetic informationfor many enzymes along the N-glycosylation network. On the basisof the product formation, we obtained a functional map of enzymeactivity distribution along the Golgi and noted a full agreement of thespatial localization of the enzymes determined by microscopy experi-


ments (17–19). The measured Golgi enzyme activity in our experi-mental setup reflected a combination of multiple factors (catalyticactivity of the enzyme, enzyme concentration exposure time, and gly-can substrate accessibility) rendering ourmodel cell, protein, and evenglycosylation site specific. An example of this specificity was evident inthe SWA inhibition experiment (Fig. 4, C and D), where the activity-based model output shifted the localization and activity of GalT andSiaT.We speculate that this shift did not represent an altered localiza-tion of the enzymes but rather increased accessibility of the glycan sub-strate to the enzymes. Crispin and colleagues (47) showed that theprotein-glycan interaction in the Fc region of IgG is reduced for hybridas compared with complex N-glycans, allowing better galactosylationof hybrid glycan structures.

Our methodology and the new insights presented here allow forthe calculation of the effects of altered processing enzyme level orchanged enzyme localization on site-specific N-glycan composition.

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(ERAD: ~0%)

100%

Fig. 4. Effects of defined N-glycan processing inhibitors. Data acquisition and analysis under SWA (A to D) and KIF (E and F) was performed as for the controlconditions (cf. Figs. 2 and 3). Fluxes through the ER processing pathway (A and E) and Golgi glycosylation networks (B and F) as calculated by the model are shown. Thesize of the arrows is proportional to the flux through each reaction indicated (numerical values predicted by the model are indicated in the figure as percentage). Themajor products in the presence of the inhibitors are circled. (C) The calculated activity of the different glycosyl hydrolases and transferases along the normalized Golgi isshown. (D) Intracellular dynamic distribution of N-glycan intermediates along the normalized Golgi is shown. See Fig. 3 for abbreviations of processing enzymes andprocessing intermediates.

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This is relevant to assess the quality of recombinant glycoprotein pro-duction in cell culture. On the other side, it offers a more reliable basisfor the implementation and optimization of mathematical modelsused in the product design of glycoproteins as biopharmaceuticals.

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MATERIALS AND METHODSPreparation and purification of heavy- and light-labeledhuman IgGCHO-S cells stably expressing IgG (provided by Biopharma MerckAG, Switzerland) were cultivated in suspension in expansion me-dium (customized medium by Biopharma Merck AG) at 320 rpm,37°C, 5% CO2 in a shaking incubator (Adolf Kuhner AG, Birsfelden,Switzerland). For the SILAC labeling, cells were centrifuged for3 min at 300 relative centrifugal force (rcf) and resuspended at 5 ×105 cells/ml either in light or in heavy SILAC medium (containinglight L-arginine and L-lysine or 13C-arginine and 13C,15N-lysine, re-spectively). Cells were subcultured in SILAC medium every 2 daysfor 6 days to reach complete heavy isotope labeling. Cells (5 × 107)from light or heavy culture were collected after centrifugation andlysed with 10 ml of lysis buffer [3% Triton X-100, 110 mM KAc,20 mM Hepes, 2 mM MgCl2 (pH 7.2), and 1X complete EDTA-freeprotease inhibitor cocktail; Roche]. Protease inhibitor cocktail tablets(complete EDTA-free protease inhibitor cocktail tablets; Roche) wereadded to the cleared supernatants. The IgGs were purified via pro-tein A capture by adding 200 ml of protein A–Sepharose 4 Fast Flowbeads (GE Healthcare Life Science) to the cell lysates and culture su-pernatant solutions. Batch binding was performed under continuousrotation for 3 hours. Beads were then washed by centrifugation with10 ml of 0.02 M sodium phosphate buffer. IgGs were eluted by shak-ing the beads for 10 min in 800 ml of 0.1 M citric acid in a 1.5-mlEppendorf tube. Eluates were placed onto 30K cutoff Micro filter(Millipore) and washed three times with water.

SILAC labelingCHO-S cells growing in expansion medium were transferred tolight SILACmedium as described above and subcultured every 2 daysfor 6 days. For the last subculturing prior to the experiment, the cellswere seeded to a cell density of 1 × 106 cells/ml. On the day of theheavy chase experiment, cells (at concentration 5 × 106 cells/ml) wereresuspended in heavy SILAC medium (hereby referred to as time 0,t0). Then, cells were incubated at 320 rpm, 37°C, 5% CO2 for 4 hours.At different time points, 8 ml of culture (corresponding to roughly5 × 107 cells) was sampled and subjected to centrifugation for 3 minat 300 rcf. Cell pellets and supernatants were flash frozen with liquidnitrogen and kept at−20°Cprior to protein purification. For proteinAcapture, the cells were lysed with 10 ml of lysis buffer, and proteaseinhibitor cocktail tablets were also added to the cleared supernatants.The IgGs were purified as described above.

Cell treatment with glycosylation enzyme inhibitorsCells were pretreated in SILAC light medium with SWA at 20 mMfinal concentration (48) for 2 hours. At time point t0, the cells wereswitched to the SILAC heavy medium containing 13C-arginine and13C,15N-lysine, and 20 mM swainsonine was added to maintain theSWA concentration. Cells (5 × 107) were collected at different points(0, 10, 20, 30, 60, 90, 120, 180, and 240 min). IgGs were purified viaprotein A capture and subjected to SILAC-PRM analysis. The sameprocedure was followed for KIF treatment using 10 mM final concen-


tration (48). For the BAF andMG132 treatments, 100 nM and 80 mMfinal concentrations were used, respectively (49, 50).

Sample preparation for MS analysisIgGs were further processed according to the FASP (filter-aidedsample preparation) procedure modified by Wisniewski et al. (51).Shortly, 25 to 100 mg of IgGs was reduced with 100mMdithiothreitolin 50 mM ammonium bicarbonate (ABC) buffer (pH 8.5) for 30 minat 37°C and alkylated with 130 mM iodacetamide in 50 mM ABCbuffer for 25 min at 37°C. Sequencing-grade trypsin (Promega)was used to digest proteins at the ratio of 1:80 overnight at 37°C. Pep-tides were collected by centrifugation, and the filters were washedonce with water and once with 10% acetonitrile in ddH2O. All flow-through fractions were pooled and dried via SpeedVac centrifugation(Thermo Fisher Scientific). All samples were desalted by C18 ZipTip(Millipore) prior to MS analysis.

Nano-UPLC-DDA and nano-UPLC-PRM for glycopeptideidentification and quantificationThe discovery of all glycoforms was first performed on the LTQ-Orbitrap Velos (Thermo Fisher Scientific) as previously described (28).All quantitative experiments were performed with the nanoACQUITYUPLC (ultra performance liquid chromatography) system (Waters),coupled online to a calibrated Q Exactive HF mass spectrometer(Thermo Fisher Scientific) with a PicoView nanospray source 500model (New Objective). The tryptic samples were dissolved in 2%acetonitrile/0.1% formic acid, loaded onto a nanoACQUITY UPLC2G C18 trap column (180 mm by 20 mm, 100-Å, 5-mm particle size),and separated on a nanoACQUITY UPLC BEH130 C18 column(75 mmby 250mm, 130-Å, 1.7-mmparticle size) at a constant flow rateof 300 nl/min, with a column temperature of 50°C and a linear gradi-ent of 1 to 35% acetonitrile/0.1% formic acid in 42 min, followed by asharp increase to 98% acetonitrile in 2 min and then held isocraticallyfor another 10min. For the DDA analysis, one scan cycle comprised afull scan MS survey spectrum, followed by up to 10 sequential HCDscans based on the intensity. Full-scan MS spectra [800 to 2000 mass/charge ratio (m/z); for inhibitory experiments, 500 to 2000m/z] wereacquired in the FT-Orbitrap at a resolution of 60,000 at 400m/z, whileHCDMS/MS spectra were recorded in the FT-Orbitrap at a resolutionof 30,000 at 400 m/z. HCD MS/MS spectra were performed with atarget value of 5 × 105 by the collision energy setup at an NCE of 22%.

For PRM analysis, one scan cycle comprised a full-scan MS surveyspectrum, followed by 10 sequential PRM scans based on the inclusionlist. Full-scan MS spectra (800 to 2000m/z) were acquired in the FT-Orbitrap at a resolution of 60,000 at 400 m/z, while PRM MS/MSspectra were recorded in the FT-Orbitrap at a resolution of 60,000at 400 m/z. PRM MS/MS spectra were performed with a target valueof 5 × 105 by the collision energy setup at an NCE of either 22 or 16%.Automatic gain control target values were 3 × 106 for full FT-MS. ForSILAC-PRM experiments, the isolation lists can be found in table S1.Briefly, the setup of each precursor ion was the average of observedlight-labeled glycopeptides from Velos measurement and their theo-retical corresponding heavy-labeled m/z. The quadrupole isolationwindow for each precursor was 6 m/z.

Data analysis and SILAC-nano-HPLC-PRMmethodology evaluationTo set up and evaluate the SILAC-PRMmethodology for glycopeptidequantification, heavy-labeled IgG glycopeptides were mixed with

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light-labeled ones at the ratios of 5:1, 4:2, 2:4, and 1:5 before MS anal-ysis. Data were first analyzed with the XCalibur 3.0 (Thermo FisherScientific) software manually. For quantification, one or three frag-ment (transition) ions of each precursor were used for manual extrac-tion and inspection based on the abundance, interference-free signal,and representative potential to each structure. Mass tolerance (10 ppm)was used for peak extraction in each PRM spectrum. For somestructures, only two transitions were used for quantification. Both peakarea and height of each transition were evaluated as quantificationmethods.

Semiautomated glycopeptide analysis using skylineMS2-level quantification was performed with the software Skyline(52), similarly to Pan and colleagues (32), by manually inserting theunique glycotransitions into the software (fig. S2B). All fragment ionswere defined on the basis of their chemical formula and added intoSkyline manually. In addition, the presence of glycan oxonium ionswas also used to confirm all types of glycopeptides with the same prin-ciple as described for XCalibur analysis. The peak height of each tran-sition was used for quantification.

Fractional labeling quantification and analysisFor control experiments and SWA-treated cells, the fractional labelingfor a given glycopeptide at each time point was defined as the ratiobetween the measured intensity of the heavy-labeled species H andthe sum of the heavy- and light-labeled species (H + L). We noticedthat this type of normalization produced biased values for the heavy-to-light ratio in KIF-treated cells since a large intracellular pool ofMan8–5 glycans was still present 6 hours after the addition of theER-mannosidase I inhibitor. This resulted in a significant underesti-mation of the fraction labeling for most of the high-mannose species,which was not due to reduced enzyme kinetics (for example, Golgi-mannosidase I is not affected by KIF) but due to the residual amountof light species in the system. To account for this, the values of heavy- andlight-labeled species at intermediate time points were normalized withrespect to the steady-state value of heavy- and light-labeled fractions.

Mathematical model of intracellular N-glycan processingTheory and assumptionsThe IgG used in the study contains a single N-glycosylation site lo-cated at the Asn297 consensus sequence of each HC, leading to a gly-coprotein with two separate glycan moieties. Because of samplepreparation for MS analysis, the information about the combinationof glycans present on a single IgG is lost. Therefore, the mathematicalmodel only deals with single glycosylated HCs rather than describingthe behavior of single glycans on dimerized monoclonal antibodies(mAbs). Themodel further assumes the intracellularmachinery to op-erate at constant concentrations of enzymes and substrates, with thelatter only varying with respect to its isotopic composition (i.e., heavy-or light-labeled peptides). The network included N-glycan processingreactions, catalyzed by hydrolases and glycosyl transferases, proteinfolding reactions of IgG molecules with a given glycan structure inthe ER, transport between organelles, and degradation reactions. Incontrast to the ER, we assumed a nonhomogenous distribution of bothsubstrates and enzymes in the Golgi, mathematically represented by aspatial distribution reflecting a cisternal maturation model (53). Froman engineering perspective, the ER behaves similarly to a well-mixedcontinuous reactor, which is continuously fed unglycosylated peptidesand produces correctly folded, glycan-bearing proteins with the max-


imum possible efficiency (i.e., it is characterized by a high enzyme-to-substrate ratio). On the other hand, the Golgi acts as a low-conversionplug flow reactor with limited residence time, where the output islargely dominated by N-glycan accessibility and yields site-, protein-,and cell-specific processing.Modeling of the N-glycan pathway in the ERAfter the addition of the isotopic tracers in the medium, the incorpo-ration of heavy-labeled amino acids (H-AAs) on peptides occurs inthe cytoplasm during RNA translation. Although the extracellularAA composition switch is very rapid, assuming an instantaneousswitch from 100% light- to 100% heavy-labeled peptide productionleads to inaccurate predictions for the kinetics of the ER species inthe model (data not shown). This is probably due to the presence ofan intracellular pool of light-labeled amino acids (L-AAs) with a de-layed consumption kinetic. To account for this, we assumed that theproduction of H-labeled HCs (qHp ) follows a first-order kinetic behav-ior (Eq. 1)

qHp ¼ qmaxp � qmax

p e�t=t ð1Þ

whereqmaxp indicates themaximum cell productivity at steady state and

t is the time constant representing the delay in the turnover of intra-cellular H-AAs. This time constant was arbitrarily set to 10 min asmeasured by 13C flux analysis on CHO cells (54).

The N-glycosylation pathway starts in the ER with the attachmentof the Glc3Man9GlcNAc2 glycan on the HC N-S-T motif catalyzed bythe OST enzyme. For the sake of simplicity, this step was assumed tooccur cotranslationally with the peptide synthesis so that the produc-tion of the H-Glc3Man9GlcNAc2 glycan is equal to qHp .

The Glc3Man9GlcNAc2-bearing HC is a transient species wherethe peptide is in a linear chain conformation deriving from the trans-location into the ER. Since the purification method used (protein Aaffinity capture) requires the initial folding of the CH2 and CH3 do-mains of the HC (27), the detection of Glc3Man9GlcNAc2 glycopep-tides is possibly biased, since it can be attributed not only to theaction of the OST but also to the folding of the aforementioned do-mains. For this reason, the species Glc1–3Man9GlcNAc2 (generated byER glucosidase I and II) were excluded from themodel, and the kineticconstants relative to ER glucosidase I and II were set to be 100 timesfaster than the cell-specific productivity qmax

p , due to the impossibilityof correctly identifying their values.

After glucose trimming, we assume the HCs to reach a partiallyfolded, proteinA–binding state represented by theMan9GlcNAc2 struc-ture. This structure can either enter the quality controlmachinery of theER (and lose mannose residues due to the action of EDEMs) or foldcorrectly and become available for translocation to the Golgi. It is im-portant to mention that the folding step included in the model and inthe network presented in Fig. 2B lumps together a series of transitions,which include HC folding, light chain (LC) folding, HC-LC dimeriza-tion, and disulfide bond formation, which are indistinguishable froman N-glycosylation point of view. For the sake of simplicity, the reac-tion rate representing the single-step transition from partially to com-pletely folded is assumed to follow a first-order kinetic (Eq. 2) (55) andto share the same kinetic constant kfolding for all the partially foldedglycoprotein isoforms in the ER (denoted by the term SNF)

rfolding ¼ kfoldingSNF ð2Þ

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The transport to the Golgi apparatus was assumed to be selectivefor all correctly folded species SF (this is mathematically equivalent toassuming a perfect recycling of nonfolded species) and to follow first-order kinetics (with constant kGolgiTransport), similarly to folding (Eq. 3).To prevent parameter unidentifiability, the transport from the ERto the Golgi was assumed to be much faster than the folding step, andkGolgiTransport was constrained to be 104 times kfolding

rGolgiTransport ¼ kGolgiTransportSF ð3Þ

Noncorrectly folded species in the ER can enter two different deg-radation pathways: the ERAD pathway, leading to cytosolic degrada-tion, and a secondary pathway, leading to lysosomal transport anddegradation (see Results and Discussion).

The lysosomal degradation pathway leads to the formation of theMan4 glycan. From a kinetic standpoint, the appearance of this spe-cies is best explained by assuming its generation deriving solely fromGlc1Man9. This species undergoes a transition to a terminally mis-folded state, possibly linked to aggregation, with kinetic constant kagg.Because of the lack of information concerning lysosomal transport,this step was assumed to be instantaneous and to follow a first-ordermechanism (Eq. 4). After lysosomal translocation, the Man9Glc1 gly-can was assumed to be trimmed down to a terminal Man4GlcNAc2species (kLysMan, Eq. 5), which is then degraded together with its proteinbackbone (kLysDeg , Eq. 6). Since the parameter kLysMan could not becorrectly identified by the model, and given that the Man4GlcNAc2appearance is very fast compared with most of the species detected,we assumed aggregation and lysosomal transport to be instantaneoussteps in this process and therefore set kLysMan to be 100 times kagg

rLysT ¼ kaggMan9Glc1 ð4Þ

rLysMan ¼ kLysManMan9Glc1Lys ð5Þ

rLysDeg ¼ kLysDegManLys4 ð6Þ

The ERAD degradation pathway is glycan specific, since it istriggered by the exposure of terminal a-1,6-mannose (56). This wasimplemented in the model by including first a transport term to thecytosol for Man7–5GlcNAc2–bearing nonfolded glycoproteins withfirst-order kinetics with respect to substrate concentration (kCytT ,Eq. 7) and subsequent degradation (kCytDeg, Eq. 8)

rCytT ¼ kCytT for Man7�5GlcNAcNF2 ð7Þ

rCytDeg ¼ kCytDegMan7�5GlcNAcCyt2 ð8Þ

Modeling of the N-glycan pathway in the Golgi apparatusAfter translocation from the ER, folded glycoproteins (i.e., correctlyfolded and dimerized mAbs) travel through the various stacks ofthe Golgi, where the N-glycans are modified by the action of severalcompartmentalized enzymes, before being secreted to the extracellularenvironment. The transport through the Golgi apparatus is assumed


to occur at a constant linear velocity (the residence time in the Golgiwas fixed at 20 min according to the secretion data), and glycoproteinsare secreted only after they reach the end of the entire system. Like allother reaction rates considered, the action of glycosyl transferases isassumed to follow first-order kinetics with respect to the substrateconcentration. To correctly represent the spatial compartmentalizationof the different enzymes, the kinetic parameters for Golgi-residentenzyme-catalyzed reactions were assumed to vary with respect to theposition along the Golgi stacks (represented by the coordinate z).Although this behavior can, in principle, be modeled using any con-tinuous function, we decided to define the window of activity of allGolgi-resident enzymes using normal Gaussian distributions (Eq. 9)

kiðzÞ ¼ Ei;maxe�z�zi;max

2wi

� �2ð9Þ

where ki(z) refers to the value of the kinetic constant for the reactioncatalyzed by Golgi enzyme i at position z, and Ei,max, zi,max, and wi

represent the peak height, the peak position, and the width of theenzymatic window of activity, respectively. The peak height and thepeak width of the distribution are highly correlated in defining the totalwindow of the activity (i.e., the area under the curve for a given en-zymes). For example, the area under the curve remains the same ifthe peak height decreases and the peak width increases by a certainamount. For this reason, the parameter wi was constrained to takethe same value for all Golgi-resident enzymes to prevent a priori param-eter unidentifiability.Material balancesThe mass balance for the species S in the ER (either folded or un-folded) assumes homogeneous distribution of both enzymes andsubstrates (perfect mixing) and perfect selectivity for transport(i.e., only folded proteins move to the Golgi, and only nonfoldedproteins are degraded). Mathematically, this translates as follows(Eq. 10)

∂Si∂t

¼ qin � ∑N:R:

j¼1virj � qout ð10Þ

The terms qin and qout represent transport in and out of the ERcompartment, respectively. Inward transport is zero for all speciesexcept for Glc3Man9GlcNAc2-bearing peptides, where it equals qHp(Eq. 1), while outward transport depends on the folding and glyco-

sylation processing as previously discussed. The term ∑N:R:

j¼1virj ac-

counts for all the reactions described in the ER network. In thisterm, the indexes i and j are used for counting substrates and reac-tions, respectively, such that vi is the stoichiometric coefficient forsubstrate i in reaction j (with sign −1 for reactants and +1 forproducts) and r is the corresponding reaction rate.

Contrary to the ER, glycoproteins in the Golgi are not homoge-neously distributed but vary in concentration along the entire ap-paratus. To account for this, the material balances include a term torepresent the special distribution of species Si as presented in Eq. 11

∂Si∂t

¼ � zGolgitGolgi

∂Si∂z

� ∑N:R:

j¼1virj ð11Þ

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The terms zGolgi and tGolgi represent the length and residencetime in the Golgi, respectively, and their ratio defines the velocityof glycoprotein transport in the Golgi. The Golgi length was normal-ized to 1, and tGolgi was assumed to be 20 min, independently of theglycan or the position of the mAb. Note that Eq. 11 corresponds to amodel of cisternal maturation for the Golgi apparatus (53).

The mass balances for both ER and Golgi were solved for heavy-labeled species, starting from the initial condition of 100% light-labeled species in the ER. For the Golgi, the initial condition Si,Golgiwas set as nonzero only for the Man9–5GlcNAc2 glycoproteins (ERtransport) and equal to (Eq. 12) at each time point and at z = 0

Si;Golgi ¼ kGolgiTransportSFi;ERðtiÞ � kGolgiTransportS

Fi;ERðti�1Þ

� �⋅ ðti � ti�1Þ ð12Þ

Model construction and parameter estimationThe system of ordinary differential equations (ODEs) of the ER wassolved in MATLAB R2017a (Mathworks Inc., Natick, MA) using thebuilt-in ODE15s solver. The system of partial differential equationsof the Golgi was computed in FORTRAN using the solver DLSODESfrom ODEPACK. The solution was discretized along the Golgi axis zusing first-order central finite differences (50 grid points). The entiresystem was numerically solved from 0 to 500 min after the L-Hswitch, with a resolution of 5 min. Parameter optimization was per-formed using the “genetic algorithm” function of MATLAB initia-lized with a Latin hypercube sampling technique. The objectivefunction was defined as the sum of least square errors between exper-imental data and the model output for ratios, intracellular fractions,and secreted fractions of every species, except for Glc3Man9GlcNAc2and Glc1Man9GlcNAc2. The error in the calculation of intracellularand secreted fractions was normalized with respect to the abundancyof each fraction. Evaluation of confidence intervals was performedwith the “nlparci” function of MATLAB. The complete list of opti-mized parameters, together with their confidence intervals, ispresented in table S2. Note that all the kinetic parameters were nor-malized with respect to qmax

p , which was arbitrarily set to 1. Everyparameter was identifiable in the control condition (i.e., the confi-dence intervals for the parameter do not include 0). Parameters thatinclude 0 in their confidence interval were set to 0. Parameters that arerelated to reactions that do not occur due to inhibitions in the forma-tion of one ormore precursors were excluded from the calculation dueto a priori unidentifiability.Justification for the use of first-order rate equationsFor an enzyme-catalyzed reaction following the Michaelis-Mentenkinetics, the reaction rate r for the conversion of a generic substrateS can be written as (Eq. 13)

r ¼ Vmax½S�KM þ ½S� ð13Þ

During pulse-chase experiments, the reaction rate for the con-sumption of a heavy-labeled substrate assumes the form in Eq. 14

r ¼ Vmax½S�HKM þ ½S�L þ ½S�H ð14Þ

The model assumes intracellular steady state with respect to en-zyme and substrate concentration as well as enzyme activity. Under


these assumptions, the amount of substrate S is constant and can bewritten as (Eq. 15)

S ¼ SL þ SH ¼ const: ð15Þ

Since the enzyme activity is also assumed constant and the KM

value is constant by definition, the equation for the rate equationcan be simplified as shown in Eq. 16

r ¼ k½S�H ð16Þ

where the constant k accounts for the enzymatic activity and thecompetitive inhibition present in the system investigated. This sim-plification is also valid for more complex kinetic mechanisms, sincethe cell machinery is supposed to operate at a constant enzyme,total H + L substrate, and inhibitor concentration.Kinetic theories for in vivo Man4GlcNAc2 generationConsidering the light- to heavy-labeled turnover rates for the variousER species, Man4GlcNAc2 is slightly slower than Man9GlcNAc2 butsignificantly faster than all other high-mannose species. If all thenonfolded glycoproteins in the ER could aggregate with the same ki-netic constant (fig. S8A), the model would predict a slower turnoverrate of Man4GlcNAc2 compared with the one experimentally de-tected, since the total pool of Man9GlcNAc2 is lower in size thanthe sum of all other (slower) high-mannose species. Another possi-bility is that aggregation occurs at the Glc1Man9GlcNAc2 level. Thisspecies is known to interact with calnexin and calreticulin, chaperonesresponsible for disulfide bond formation. Errors in this process mightlead to the formation of very unstable intermediates that could possi-bly be extremely prone to aggregate (fig. S8B). The model confirmsthat this is a possible behavior, since it can fit the Man4GlcNAc2 datacorrectly for control, SWA-, and KIF-treated experiments. Alterna-tively, it is still possible to consider every unfolded glycoprotein inthe ER to be eligible for aggregation, but the propensity for this to oc-cur decreases the more units of mannose are trimmed, due to the re-peated interactions with chaperones that can stabilize the structure ofthe proteins (fig. S8C). To test this hypothesis, we assumed the aggre-gation rate kagg to scale for high-mannose species using the followingrelation (Eq. 17)

kagg ¼kagg;Man9Glc1

xn�ið17Þ

where kagg,Man9Glc1 is the aggregation constant for the monoglucosi-lated glycoproteins, x and n are scaling factors, and i is the number ofmannose units trimmed. For the model to correctly predict theMan4GlcNAc2 kinetics, it was found that the value of x has to be lowerthan 10−4, indicating that aggregation is prevalently happening at theGlc1Man9GlcNAc2 level even if this mechanism is assumed.

SUPPLEMENTARY MATERIALSSupplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/11/eaax8930/DC1Fig. S1. PRM detection and quantification limit.Fig. S2. Collision energy optimization and Skyline implementation into the pipeline.Fig. S3. Representative MS2 spectra of IgG N-glycopeptides from SILAC-PRM analysis.Fig. S4. Data prediction accuracy by the model: intracellular IgG N-glycan distribution.

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Fig. S5. Data prediction accuracy by the model: secreted IgG N-glycan distribution.Fig. S6. Effects of different perturbations on N-glycan processing.Fig. S7. Man4GlcNAc2 generation.Fig. S8. Kinetic mechanisms considered for Man4GlcNAc2 formation.Table S1. List of glycans selected for PRM analysis of glycopeptides.Table S2. ER-related parameters.

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Acknowledgments: We thank M.-E. Losfeld for the continuous contribution with fruitfuldiscussions. We also thank G. Storti, D. Baur, and T. Villiger for the inputs in the mathematicalmodeling. We thank M. Morbidelli and M. Wolf for the support. All the mass spectrometricacquisitions were performed at the Functional Genomic Center Zürich. Funding: This workwas supported by Merck, ETH Zurich, and the Swiss National Science Foundation grant310030_162636 for M.A. Author contributions: M.A. and C.-W.L. designed the project. C.-W.L.developed the SILAC-PRM methodology. I.A.-A. contributed to the project design and therefinement of the methodology and performed all the SILAC experiments and the dataanalysis. C.-W.L. helped and supervised during mass spectrometric data analysis. E.S.developed the mathematical model. I.A.-A., C.-W.L., E.S., and M.A. wrote the manuscript. D.B.,J.S., and H.B. contributed to the design of the study. Competing interests: The authorsdeclare that they have no competing interests. Data and materials availability: All dataneeded to evaluate the conclusions in the paper are present in the paper and/or theSupplementary Materials. Additional data related to this paper may be requested from theauthors. Upon request, mass spectrometric acquisitions and the mathematical model codescan be accessed. The IgG cell line has been acquired by the company Fresenius Kabi, and it isno longer in use at Merck. Request for the IgG cell line should be submitted directly to theFresenius Kabi company after contacting H.B. for detailed information.

Submitted 2 May 2019Accepted 18 September 2019Published 27 November 201910.1126/sciadv.aax8930

Citation: I. Arigoni-Affolter, E. Scibona, C.-W. Lin, D. Brühlmann, J. Souquet, H. Broly, M. Aebi,Mechanistic reconstruction of glycoprotein secretion through monitoring of intracellularN-glycan processing. Sci. Adv. 5, eaax8930 (2019).

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N-glycan processingMechanistic reconstruction of glycoprotein secretion through monitoring of intracellular

Ilaria Arigoni-Affolter, Ernesto Scibona, Chia-Wei Lin, David Brühlmann, Jonathan Souquet, Hervé Broly and Markus Aebi

DOI: 10.1126/sciadv.aax8930 (11), eaax8930.5Sci Adv

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