Proteome-wide analysis of N-glycosylation stoichiometry using

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Proteome-wide analysis of N-glycosylation stoichiometry using SWATH technology Xiangyun Yang, Wang Zhiyuan, Lin Guo, Zheng-Jiang Zhu, and Yaoyang Zhang J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00480 • Publication Date (Web): 28 Aug 2017 Downloaded from http://pubs.acs.org on September 3, 2017

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Journal of Proteome Research

Proteome-wide analysis of N-glycosylation stoichiometry using SWATH technology Xiangyun Yang123 #, Zhiyuan Wang123 #, Lin Guo123, Zhengjiang Zhu12, Yaoyang Zhang12 * 1. Interdisciplinary Research Center on Biology and Chemistry, 2.Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China; 3. University of Chinese Academy of Sciences #: These authors contributed equally.

* Corresponding author: Dr. Yaoyang Zhang Phone: +86-21-68582550 Fax: +86-21-64166128 E-mail: [email protected]

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Abstract N-glycosylation is a crucial post-translational modification (PTM) and plays essential roles in biological processes. Several methods have been developed for the relative quantification of Nglycosylation at the proteome scale. However, the proportion of N-glycosylated forms in a total protein population, or the “N-glycosylation stoichiometry,” varies greatly among proteins or cellular states and is frequently missing due to the lack of robust technologies. In the present study, we developed a data-independent acquisition (DIA) based strategy that enabled the in-depth measurement of N-glycosylation stoichiometry. A spectral library containing 3,509 N-glycosylated peptides and 17,525 fragment ions from human embryonic kidney cells 293 (HEK-293) cells was established, from which the stoichiometries of 1,186 N-glycosites were calculated. These stoichiometric values differ greatly among different glycosites, and many glycosites tend to occur with low stoichiometry. We then investigated the N-glycosylation changes induced by tunicamycin in HEK-293 cells and by a temperature shift in Chinese hamster ovary (CHO) cells. Quantifying the proteome, N-glycoproteome and N-glycosylation stoichiometry demonstrated that the regulation of N-glycosylation is primarily achieved by adjusting the N-glycosylation stoichiometry. In total, the stoichiometries of 2,274 glycosites were determined in the current study. Notably, our approach can be applied to other biological systems and other types of PTMs.

Key words N-glycosylation; Stoichiometry; Mass spectrometry; Data-independent acquisition (DIA); Sequential window acquisition of all theoretical mass spectra (SWATH); Proteomics; Posttranslational modifications (PTMs); Stable isotope labeling with amino acids in cell culture (SILAC); Quantification; Spectral library

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Introduction N-linked glycosylation, or N-glycosylation, is one of the most common forms of posttranslational modifications (PTMs) of proteins1 and has been widely observed in eukaryotes, prokaryotes2-3 and archaea4. N-glycosylation plays key roles in a variety of biological processes, including the regulation of protein folding5-6, cell signaling7, cell differentiation8 and the immune response9. N-glycosylation has also been associated with a number of diseases10, such as cancer7, 11 and neurodegenerative diseases12. Despite its essential roles in biology, characterizing N-glycosylation adequately and precisely remains challenging because the forms and degrees of sugar side chains vary greatly.13-14 Existing methods involving N-glycopeptide enrichment using lectins15-16, hydrazide chemistry17, hydrophilic interaction liquid chromatography (HILIC)18 and mass spectrometry (MS) have been successfully developed for N-glycoproteomic studies. Because glycosylation is highly heterogeneous and tends to be lost during peptide dissociation in tandem MS (MS/MS), a popular approach is to analyze deglycosylated peptides rather than intact glycopeptides to achieve increased glycoproteome coverage. With or without stable isotope labeling, quantitative N-glycosylation measurements have emerged as robust methods in this field. However, the changes in glycosylation obtained via these quantitative methods represent only the relative differences between two or more biological states. In fact, almost all PTMs are substoichiometric events (Figure S1-A), in which the residue is only partially modified. Therefore, stoichiometry is highly important. Methods have been developed to measure the stoichiometry of phosphorylation19-20, O-glycosylation21 and acetylation22. During the performance of this study, two other investigations of N-glycosylation stoichiometry were reported. The first study used multiple enzymes and obtained a limited number of glycosylation sites23. The second study inferred stoichiometry based on the relative changes in the abundances of both proteins and glycopeptides, eventually obtaining 117 N-glycosite occupancies on human ovarian carcinoma (OVCAR)-3 cells24. The proteome coverages of these studies were low, and in the second case, two biological states were required for stoichiometry calculations. Obviously, a method with enhanced sensitivity and the ability to calculate absolute stoichiometric values for a single sample at the proteome scale would be highly desirable. Quantitative protein analysis using MS is emerging rapidly in biological studies. Shotgun analysis, which is also known as data-dependent acquisition (DDA), and targeted methods are two 3 / 22



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major approaches. Typically, shotgun analysis enables a deeper proteome coverage25-26 but favors relatively abundant peptides; thus, consistency for low abundant signals is an issue27. In contrast, the conventional targeted method, which is also known as multiple reaction monitoring (MRM), can quantify peptide abundances with enhanced reproducibility and quantitative accuracy but low throughput.28-29 Successful stoichiometric measurements require the simultaneous capture of both modified peptides and unmodified counterparts. Hence, stoichiometry analysis using a shotgun method frequently fails due to missing data from either modified or unmodified peptides. A DIAbased strategy30, sequential window acquisition of all theoretical mass spectra (SWATH), has the advantages of better quantitative reproducibility and improved proteome coverage depth.31-32 The SWATH or DIA strategy has been successfully applied onto many studies in protein and PTM quantifications33-37 since it was developed. The unique features of SWATH technology make it suitable for N-glycosylation stoichiometry analysis because both the modified and corresponding unmodified signals can be extracted in a targeted mode with enhanced sensitivity and reproducibility on a large scale. In the present study, we developed a SWATH-based pipeline to measure N-glycosylation stoichiometry at the proteome scale. The glycosylated peptides were enriched using a lectin mixture and deglycosylated using the PNGase F enzyme. The unbound fraction after lectin enrichment was fractionated using high-pH high-performance liquid chromatography (HPLC), thereby increasing the opportunity to detect the unglycosylated counterparts that co-exist with many other complex peptides. The resulting modified and unmodified peptides were separately analyzed by the SWATH method (Figure 1-A). Using this strategy, we obtained a proteome-scale Nglycosylation stoichiometry map in which the stoichiometries of 2,274 glycosylation sites were calculated in two different cell types. Our data demonstrated that N-glycosylation stoichiometry is broadly distributed, ranging from 0% to 100%. We further applied this pipeline to investigate the influence of tunicamycin38-41 and temperature shifts42-44—two factors previously linked to Nglycosylation. The results revealed that with both tunicamycin treatment and temperature shift, the regulation of N-glycosylation is mainly achieved by adjusting the N-glycosylation stoichiometry rather than the protein abundance. In summary, our study measured N-glycosylation stoichiometry at the proteome scale; the results provide new insights relating to N-glycosylation and have the potential to be extended to other PTMs.

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Experimental procedures Cell culture and stable isotope labeling with amino acids in cell culture (SILAC) Human embryonic kidney 293 (HEK-293) cells were cultured in high-glucose Dulbecco’s modified Eagle’s medium (DMEM) (Thermo Fisher Scientific, USA) containing 10% fetal bovine serum (FBS). HEK-293 cells were treated with 0.5-μg/mL tunicamycin (Sigma-Aldrich, USA) or dimethyl sulfoxide (DMSO) at 37 °C for 24 hours. Then, the tunicamycin concentration was increased to 1 μg/mL for an additional 24 hours. Chinese hamster ovary (CHO) cells were cultured in high-glucose DMEM/F-12 medium containing 10% FBS. CHO cells were cultured at either 33 °C or 37 °C for 48 hours. For SILAC, HEK-293 cells were cultured in high-glucose SILAC-DMEM medium (Silantes, Germany) with either normal amino acids or heavy stable isotope-labeled 13

13

C615N2-lysine and

C615N4-arginine (Silantes, Germany) with or without tunicamycin treatment. All the experiments

mentioned in the current study were performed in three biological replicates unless there is a specific statement. Protein sample preparation Proteins were extracted and digested with trypsin using a standard protocol.32 Details are provided in the Supporting Information (SI). Notably, the quantity of peptide was not directly determined in the current study, but was inferred from the quantity of the starting protein material. High-pH HPLC fractionation For the proteome profiling experiment, 100 µg of peptides was pre-fractionated with high-pH reverse-HPLC using a 1260 pump (Agilent Technologies, USA). The total gradient time was 80 min (Supplemental Table S1). The eluent was collected every two minutes. The resulting fractions were then combined into six large fractions in a non-contiguous manner. For glycopeptide enrichment and spectral library generation, six fractions were generated from 2.4 mg of tryptic peptides derived from the total protein using a method similar to that described above (Figure 1-A). N-glycopeptide enrichment and deglycosylation with 18O Peptides were incubated with a lectin mixture of concanavalin A (Con A), wheat germ agglutinin (WGA), and Ricinus communis agglutinin (RCA)120 (Vector Labs, USA). The ratio between lectin and peptides was used as recommended by the vendor. The bound glycopeptide was then 5 / 22



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deglycosylated using PNGase F at 37 °C for 3 hours.

18

O water was used during deglycosylation

when necessary. Details can be found in the SI. LC-MS/MS The data for SWATH and the corresponding shotgun analysis for the generation of the Nglycopeptide spectral library were collected on a Triple-TOF 6600 system coupled with an Eksigent 425 nanoHPLC (Sciex, USA). Peptides were separated using a homemade 15-cm C18 reversed-phase column (75-μm inner diameter, Reprospher 100 C18-Aqua, 1.9-μm resin [Dr-Maisch GmbH, Germany]) and a 120-min elution gradient (Supplemental Table S2). For shotgun analysis, the scan range of MS was set as 350 to 1500 m/z, and the accumulation time was 0.25 s. The top-20 most intense peptides with 2-4 charges were selected for tandem MS analysis under sensitive mode. The scan range was 100-1500 m/z, and the accumulation time was 50 ms for tandem MS analysis. The dynamic exclusion time was 20 s, and the mass error range was 50 mDa. For SWATH data acquisition, the m/z range was set to 400 to 1100 for the MS scan, and 60 variable-m/z windows were subjected to tandem MS for glycopeptide analysis. The cycle time was approximately 3.1 s. The quantitative analysis of the proteome or N-glycoproteome was performed on an Orbitrap Fusion mass spectrometer coupled with an Easy1000 nanoHPLC (Thermo Scientific, USA). Peptides were separated by reversed-phase chromatography using a 120-min elution gradient (Supplemental Table S3). The scan range of MS was 350 to 1800 m/z. Peptides with 2-6 charges were selected for tandem MS analysis in the top-speed mode. The maximum injection times for the glycopeptides and total peptides were 120 ms and 50 ms, respectively. High-energy collisional dissociation (HCD) was used to fragment the precursor peptides, and the resulting fragment ions were measured in the ion trap analyzer. The dynamic exclusion time was 60 s, and the mass error range was set to 10 ppm. Data processing for N-glycosylation and protein quantification For proteome and N-glycoproteome analysis, MaxQuant software45-46 was used for both the SILAC and label-free quantifications. “Match between runs” was applied, and the match time window was set within 2 min. The MS data were searched against the respective species-specific (Human and Chinese hamster) Uniprot protein database (download date: March 2016). Trypsin was set as the enzyme, and the maximum missed cleavage was set to 2. Amino acid substitution with Lys8 and Arg10 was used for SILAC data analysis. Carbamidomethyl (C) (+57.02 Da) was set as a fixed modification, and the variable modification was oxidation of methionine (+15.99 Da). Peptide 6 / 22


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tolerance was set to 20 ppm. The false discovery rate (FDR) was controlled with a decoy database and set to no more than 1%. Only unique peptides and razor peptides were used for quantification. N-glycopeptide spectral library and N-glycosylation stoichiometry The spectral library of N-glycopeptides was established from four replicates of a twodimensional (2D)-LC shotgun analysis of a lectin-enriched fraction using ProteinPilot. Only the identified peptide sequences with the signature motif "N-!P-S/T/C" (“!P” means any amino acids other than proline) and a confidence score over 0.95 were included in the library. For each peptide, up to six fragment ions, which must contain the N-glycosite, were chosen from high to low intensity. The ion masses in the corresponding unglycosylated peptide library were calculated based on the mass difference between Asn and 18O-Asp (-2.98 Da). For example, a hypothetical deglycopeptide with m/z series of 202.98, 302.98, 402.98, 502.98, 602.98 and 702.98 will have a counterpart unglycopeptide whose m/z values are 200.00, 300.00, 400.00, 500.00, 600.00 and 700.00 in the library. Notably, in the current study, unglycopeptides are only peptides with glycosylated counterparts and not other unrelated peptides. Deglycopeptides are frequently called glycopeptides for convenience purpose. The SWATH data were matched to the above spectral libraries using PeakView 2.1 software. The retention time was recalibrated with 10 spiked-in synthetic standard peptides (Supplemental Table S4). The extracted ion chromatograms (XICs) of the fragment ions were extracted, and the fragment ion XICs were scored by considering the consistency of the peak apex RT, the consistency of peak width at half max and consistency of the XIC peak area with the library fragment ion pattern. After all the peptides were scored, a false discovery rate (FDR) was computed using a similar target – decoy strategy that was done in shotgun analysis.32 For every target sequence, a pseudo-reverse (decoy) sequence (reversed the sequence but kept the C-term the same) was generated. Both the target and the decoy sequences were scored as above. They were then ranked based on this score. Then the FDR was computed by 2*# of decoys in the list divided by total. The FDR was set to no more than 1%. The reverse hits were only used to calculate and control the FDR, but not taken into the quantification. Eventually, the intensities of the deglycosylated and unglycosylated peptides were separately deduced from the summed peak areas of their fragment ions and were used in the subsequent stoichiometry calculations according to the equation presented in Figure 1-B. If the same N-glycosite was detected by different peptide forms (in most cases are different charge

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states), the summed intensities of glycopeptides containing the same site were used to calculate the stoichiometry. Statistical analysis The statistical analysis using unpaired t-test were performed. The P-value was calculated without further adjustment.

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Results N-glycosylation spectral library Spectral libraries of N-glycosylated peptides and corresponding unglycosylated peptides were constructed for SWATH quantification. The N-glycopeptides were identified through database searching using ProteinPilotTM software47-48 with optimized parameters, in which the “probability of deamidation” was changed from default value of 0.16 to 0.6 (Figure S3-C). The default probabilities were only optimized for normal sample such as whole cell lysate, but not suitable for detection of samples with frequent deamidation. To increase the coverage of the N-glycopeptide library, four parallel biological samples were used; each sample was fractionated to six fractions, and all fractions were subjected to glycopeptide enrichment and analysis. To enhance the discrimination between deglycosylated and unglycosylated counterparts, which have precursor ion mass difference of 2.98 Da, we exclusively selected the fragment ions containing N-glycosylation sites to construct our N-glycosylation-specific library (Figure S1-BC) using an R-language script written inhouse. For HEK-293 cells, the database search resulted in 5,385 glycopeptides. 3,509 of the identified glycopeptides corresponding to 893 proteins were scored with confidence greater than 0.95 and contained the signature motif of "N-!P-S/T/C" and were thus included in the Nglycopeptide spectral library (Figure 2-AB). The spectral library for unglycosylated counterpart peptides was inferred from the deglycosylated ones in which all unglycosylated ions were 2.98 Da lighter than the corresponding deaminated

18

O-labeled ions. The final N-glycopeptide spectral

library consisted of 3,509 peptides and 17,525 fragment ions (Figure 2-A). We investigated whether the N-glycosylation site-specific library is sufficient for ion quantification. Although the number of quantifiable transition ions can be reduced when considering only those containing glycosites, over 82% of glycopeptides still have more than 4 fragment ions that can be used for quantification (Figure 2-C). As expected, compared to all quantifiable ions, the overall intensities of glycosite-containing transition ions were reduced but not dramatically. The majority of ions quantified using these two different ion populations have comparable intensity levels—102 to 103—in the library (Figure 2-D). Therefore, the spectral library built in this study is N-glycosylation specific and sufficient for SWATH quantification.

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SWATH quantification of the flow-through fraction using high-pH HPLC fractionation As discussed in the SI, 160 μg of starting material was sufficient to achieve both good coverage of N-glycopeptide and quantitative accuracy (see the supplementary results). However, in addition to the glycopeptide in the enriched fraction, the stoichiometry calculation requires the measurement of the paired unglycopeptide, which co-exists with many other irrelevant peptides. In the conventional single-shot SWATH analysis of the flow-through fraction, two problems can arise. First, although the targeted ion extraction of SWATH technology enhances the sensitivity, extracting the relatively low signals from the complicated peaks remains challenging. The average MS intensities of unglycosylated peptides are lower than those of the total peptides by an order of magnitude (Figure 3-A). On the other hand, the loading capacity of a nano-reverse-phase HPLC column is typically as low as approximately 2 μg, which is far below the starting amount of the enriched fraction derived from 160 μg of peptides. As glycopeptides constitute an extremely small proportion of the total peptides, the loading amount relative to the starting material of the flowthrough fraction is much smaller than the enriched fraction. To calculate the stoichiometry, the loading difference between binding and flow-through fractions must be corrected. For instance, loading enriched and flow-through samples, derived from 160 μg and 2 μg of starting material, respectively, will require a subsequent 80-fold correction that may magnify the technical variations and affect the calculation accuracy. Compared to single-shot analysis, 2D HPLC (six-shot) analysis of flow-through fractions using 12 μg of starting material increased the number of quantified unglycopeptides approximately fourfold (Figure 3-B), which could significantly increase the success rate of the stoichiometry calculation. Furthermore, the MS intensities of unglycopeptides with fractionation are comparable to those of the corresponding glycopeptide, thereby increasing the quantitative accuracy (Figure 3-A). Notably, the loading difference between bound and flow-through fractions decreased from 80-fold (160/2) to 13.3-fold (160/12), which could decrease the undesirable enlarged variation. We next investigated whether fractionation of the flow-through affects the quantification because the standard SWATH method uses a single-shot strategy. First, the high-pH HPLC separation and subsequent combination of fractions resulted in good separation, with more than 88% of the peptides distributed in just one or two fractions (Figure 3-CD). We compared the MS intensities generated by single-shot analysis of a 2-μg sample and six-shot analysis of a fractionated 12-μg sample using whole cell lysate. The summed MS intensities from 2D-LC analysis were 10 / 22


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theoretically six-fold higher than those from single-shot analysis. The overall quantitative result was highly consistent with the theoretical value (Figure 3-C). However, detailed analysis revealed that the peptides in multiple fractions tended to have slightly larger summed intensities than those present in only one or two fractions. This could be because a more concentrated peptide that exists in fewer fractions is more likely to be influenced by ion suppression (Figure 3-D). In summary, prefractionation of the flow-through could increase the detectability of the unglycopeptides and reduce the variation introduced by the loading difference between bound and flow-through fractions without affecting quantification. N-glycosylation stoichiometry of HEK-293 cells treated with tunicamycin We applied this strategy to HEK-293 cells to map their N-glycoproteome stoichiometry. In total, 1,028 N-glycosites were quantified (Figure S4-A), approximately 70% of which were identified from at least two of three replicates. The R2 was above 0.9 for the glycopeptide measurements, suggesting good reproducibility (Figure S4-B). In addition, 1,413 unglycosites were quantified in the flow-through. However, the quantitative reproducibility of unglycopeptides analysis was poor: 58.1% of the peptides could be detected in at least two replicates, and the R2 was approximately 0.5 (Figure S4-B). This suboptimal reproducibility could result from biological variation and the influence of the high abundant of co-existing signals present in the extremely complex flow-through fraction. N-glycosylation stoichiometry was calculated by equation in Figure 1-B. Only glycopeptides and unglycopeptides detected at least twice were used to calculate the stoichiometry. The stoichiometry was defined as 100% or 0% when a counterpart was missing for the glycosite or unglycosite, respectively. Finally, the stoichiometries of 1,186 N-glycosites from HEK-293 cell proteins were calculated. The stoichiometric values are distributed across the whole range from 0% to 100%. However, the majority of glycosylation (46% in native HEK-293 cells, 67% in tunicamycintreated HEK-293 cells) sites are likely to have relatively low N-glycosylation occupancies (2 and Pvalue < 0.05. However, tunicamycin does not affect protein abundances significantly, unlike its effects on N-glycosylation; only 36 of 5,098 proteins were up-regulated and 6 proteins downregulated according to the same thresholds (Figure 5-A). When comparing significantly altered Nglycosylation and its corresponding protein expression, the extent of the change caused by tunicamycin is much larger for N-glycosylation than for the protein level (Figure 5-AB and S5-B). Hence, we conclude that the N-glycosylation regulation induced by tunicamycin is not mainly dependent on changing its protein expression level. N-glycosylation is regulated by changing its stoichiometry To further investigate the underlying regulatory insights of N-glycosylation in HEK-293 cells, we explored the relationship among N-glycosylation stoichiometry, N-glycosylation expression and protein expression (Figure 5-B, S5-B). First, the proteins with down-regulated glycosylation or down-regulated glycosylation stoichiometry were not distributed differently than the total proteins, suggesting that the down-regulation of glycosylation or stoichiometry does not rely on a global change in protein abundances (Figure 5-B). The fold-changes of glycosylation and corresponding glycosylation stoichiometry were positively correlated (Figure S5-B). This result confirmed that the substantial changes in glycosylation and stoichiometry are not the result of changes at the protein level.

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To evaluate the reliability of our experiment, we used the fold-change values of Nglycosylation stoichiometry and protein expression to infer the fold-change value of glycosylation using the following equation: Fold-change of glycosylation = Fold-change of protein × Fold-change of stoichiometry The calculated glycosylation expression fold-change was then compared to data obtained by direct SILAC quantification (Figure S5-C). We found that the calculated values were in good agreement with those obtained by direct quantification, with both suggesting global downregulation of N-glycosylation after tunicamycin treatment. Notably, although the relative changes in stoichiometry can be inferred from relative changes in protein and glycosylation, our strategy can calculate the absolute degree of stoichiometry for a single sample, thereby providing additional valuable insight into N-glycosylation. Proteome and N-glycoproteome changes caused by temperature shift We next applied a similar strategy to CHO cells to study the N-glycosylation changes induced by a shift in temperature, which was previously linked to N-glycosylation 49-50. The N-glycosylation stoichiometry and relative changes in protein and N-glycosylation were quantified using CHO cells cultured at 33 °C and 37 °C, respectively. In three replicates, 1,064 N-glycosites and 1,302 unglycosites were identified in CHO cell cultures using SWATH technology, approximately 80% of which could be identified in at least two out of three replicates (Figure S6-A). Thereafter, 956 and 980 N-glycosite (in total, 1,088 Nglycosites) stoichiometries were calculated using the peptides captured at least twice from the 33 °C and 37 °C samples, respectively, and overall, 52% and 44% of these sites, respectively, are likely to have relatively low occupancy (

Proteome-wide analysis of N-glycosylation stoichiometry using

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