Peptide-Centric Approaches Provide an Alternative Perspective To Re

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Peptide-centric approaches provide an alternative perspective to re-examine quantitative proteomic data Zhibin Ning, Xu Zhang, Janice Mayne, and Daniel Figeys Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.5b04148 • Publication Date (Web): 20 Jan 2016 Downloaded from http://pubs.acs.org on January 21, 2016

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Analytical Chemistry

Peptide-centric approaches provide an alternative perspective to re-examine quantitative proteomic data Zhibin Ning†, Xu Zhang†, Janice Mayne, Daniel Figeys* Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Rd., Ottawa, ON, Canada, K1H8M5 †

Contributed equally To whom correspondence should be addressed: Daniel Figeys Phone: 613-562-5800 ext 8674. Fax: 613-562-5655. E-mail: dﬁ[email protected] *

Abstract Quantitative proteomics can provide rich information on changes in biological functions and processes. However, its accuracy is affected by the inherent information degeneration found in bottom-up proteomics. Therefore, the precise protein inference from identified peptides can be mistaken since an ad hoc rule is used for generating a list of protein groups that depends on both the sample type and the sampling depth. Herein, we propose an alternative approach for examining quantitative proteomic data which is peptide-centric instead of protein-centric. We discuss the feasibility of the peptide-centric approach which was tested on several quantitative proteomic datasets. We show that peptide-centric quantification has several advantages over protein level analysis: (1) it is more sensitive for sample segregation, (2) it avoids the issues associated with protein inference, (3) and it can retrieve significant peptides lost in protein-centric quantification for further downstream analysis.

Main Text Bottom-up proteomics has undergone extensive development in the last decade. It is well known that the modern bottom-up proteomics is peptide-based.1 In a typical shotgun proteomics workflow, peptides are resolved by chromatography after proteolysis, then ionized and analyzed by mass spectrometry (MS). Search engines identify the best peptide-spectra match (PSM) from databases to generate a protein list which is usually the end result of proteomics. In addition, the peptide ion intensity can be extracted for quantification. Quantitative techniques have evolved significantly so that they can be carried out using either finely designed metabolic or chemical labeling, or simply a label-free strategy following proteomic profiling. However, a well-recognized shortcoming remains; the protein digestion step leads to information degeneration causing a loss of association between the peptide and the parent protein (especially for in-solution digestion).2,3 This association cannot be easily retrieved because many peptides are not unique to a given protein. In fact, different inference algorithms used to associate peptides to proteins can lead to different protein lists for the same dataset, making it difficult to compare and integrate proteomic data.

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Protein inference is imperfect in bottom-up proteomics In current proteomic workflows, the information degeneration that results from enzymatic digestion of the proteome can lead to an ambiguity of the protein source for shared peptides.2 Attempts to address this ambiguity include exporting the shortest protein list covering all peptides identified2, Occam’s razor algorithm 4, or IsoformResolver for peptide-centric protein inference 5. In addition, efforts to reconstruct the connectivity between the peptide and protein include peptide detectability6, or using PAnalyzer to address the ambiguities that arise during the protein inference process for data independent MSE analysis by protein classification7. To date, no perfect solution exists for the inaccuracy in linking peptides back to proteins. Protein paralogs and isoforms, however, can be grouped together since they share many identified peptides. Moreover, proteins with quite different sequences and biological functions can be grouped together if they contain even a low number of identified shared peptides. Therefore, determining which protein to consider further within the same protein group poses problems for most users, especially when the number of peptides identified is limited.

Protein quantification is affected by erroneous protein inference Beyond protein identification, protein quantification can be significantly compromised because of the ambiguity caused by shared peptides. Recently, a new nomenclature “proteoform” was proposed to help the organization of the proteomic data into gene-centric.8 However, being gene-centric does not help clarify the quantification of different proteoforms. It is very common to find inconsistent profiles between peptides within the same protein group. In addition to incorrect assignment of shared peptides, inconsistent profiles can be attributed to false peptide identification, peptide abundance, peptide instability, and/or differences in electrospray ionization (ESI) efficiency. Inconsistency in protein quantitation can also take place when a peptide has partially occupied post-translationally modified (PTM) sites. As a result, an arbitrary integration of peptide to protein quantification (by means of average, sum or median) is not correct without explaining the source of inconsistency. In fact, most of the time, significantly different peptide ratios and abundances suggest the presence of other protein interference, and therefore, the peptide profiling could be used for protein isoform inference.2 A couple of efforts have been made to fine-tune protein group quantification from its constituent peptides. For example, the PQPQ strategy tried to increase protein quantification accuracy by peptide correlation analysis, assuming that the distinct peptides should have the same quantification profile among samples.9 BP-Quant used Bayesian statistics to identify peptide sets exhibiting similar statistical behavior relating to a protein to improve quantification estimates.10 Considering the presence of shared peptides that do not come from the same predecessors, it would be ideal to only use protein-distinct peptides for quantification. However, the number of distinct peptides per protein is limited and restricting the quantitation to the distinct peptides would reduce the quantification efficiency. Shared peptides are still widely included for better quantification coverage, with careful allocation to balance unequivocal peptide assignment and accurate quantification, although biased for proteins with less distinct peptides.4 On the other hand, even peptides from the same protein do not always share the same quantitation profile.9,11 Feature-based and averaging strategies (which are more popular) can both be used to elucidate protein quantification.11,12 However, from an analytical point of view, this protein-centric goal is not currently achievable in the framework of bottom-up proteomics. Instead, only top-down proteomics offer the ultimate solution to achieve definitive protein identification and quantification, in theory, for each proteoform.13

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Peptide-centric analysis works for bottom-up proteomics LC-MS analysis in bottom-up proteomics provides information on the m/z of the precursor, fragmentation linked to the amino acid sequence, and signal intensity for the peptides, but not directly for the protein. As well, information is obtained on the retention time for each precursor. Herein, we propose a conceptual alternative of handling quantitative proteomics data. Instead of focusing exclusively on protein-centric quantitation, where protein quantification is deduced from peptide quantification, we propose to focus on peptide-centric quantitation analysis (as shown in Figure 1). In the peptide-centric workflow the peptide, instead of protein, is subjected to statistical analysis and filtering, and if necessary assembled into protein afterwards. The idea originates from metabolomics where features (m/z) are used for analysis, with or without identification. In proteomics, the features can be transformed into a peptide sequence before subsequent functional analysis. This conceptual change will avoid the primary issues brought by protein inference in protein-centric quantification. Peptide origin and quantification discrepancy are no longer intractable, because all modified and un-modified counterparts are analyzed as individual features. The false identification of one peptide in a given sample would no longer affect a specific protein’s quantification. The peptide-centric strategy conveys an obvious benefit for easy comparison of datasets. It would be easier and more practical to compare or integrate results from multiple search engines at the peptide level.

The importance of proteotypic peptides for identification and quantification was realized in 2007.14 Then other protein inference strategies were developed such as peptide-centric database search strategy15, spectra library16 and IsoformResolver5. In 2011, the Q-FISH algorithm was developed that focused on MS/MS spectra clustering for quantitative analysis17, and the idea of peptide-centric quantitation began to emerge. Peptide based PTM analysis software packages already exist18, but recently, a peptide-centric approach was proposed for SWATH data interpretation.19 In addition to the above mentioned PQPQ9 and BP-Quant10, a recently published study developed an approach for averaging peptide-level expression changes to yield more accurate protein expression differences than the conventional method20. Herein, we re-visit the concept of peptide-centric analysis and propose an alternative strategy focusing on peptides rather than proteins for current quantitative proteomics that. Briefly, instead of bundling peptide quantification into a protein group analysis, the peptide quantification is directly used for statistical and functional analyses. We proved that peptide level quantification outperforms protein level quantification for all datasets we have tested. We also identified important peptides that were eliminated by conventional protein-centric quantification. These peptides are potential candidates for subsequent multiple reaction monitoring (MRM) validation. In addition, this peptide-centric strategy is especially applicable in metaproteomics (where shared peptides also occur across multiple species)21, peptidomics22, and for secretome analysis (where multiple peptides are generated from parent proteins and have distinct biological functions)23. In these analyses, any information degeneration that occurs in protein-centric proteomics would significantly affect quantification, protein level identification, and subsequent functional analysis.

Peptide-centric quantification is statistically favored One immediate benefit of using peptides for quantification is obvious: the number of data entries is significantly greater at the peptide level than at the protein level; wherein, the peptides from the same protein origin would be deemed as technical replicates. The average number of identified unique peptides for each protein is generally around 5-1024,25 (Fig S1), which means that there are on average 5-10 times more features available for statistical analysis at the peptide level, surely favored by statistics.19 It is also important to consider that missing values often 3 / 10



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occur in protein-centric analysis because of the stochastic nature of data dependent MS acquisition and is amplified for large populations of samples. The information obtained from the quantified proteins could be from different subsets of peptides in each sample as illustrated in Fig S2 and is discussed extensively in reference26. This discrepancy affects two aspects of real-world experimentation: one is between a protein and its peptides quantification within one sample (Fig S3a, S3b); the other is between a protein and its peptides’ quantification profile across samples (Fig S3c). The dynamic range of peptide quantification is larger than the quantification range at the protein level. Peptides with large fold changes are more likely to pass statistic tests compared to damped protein profiles. The larger amplitude of quantified changes also means higher sensitivity at the peptide level. In contrast, relative protein quantitation is usually diminished since (1) it uses the mean or median of all the peptides’ ratios or intensities (including the miss-assigned ones), and (2) the fact that not all peptides are present across samples. This ‘damping’ effect occurs even when employing the various algorithms developed to modulate the peptide quantification to the protein level.20,27

Peptide-centric quantification performs better in segregating experimental groups Fortunately, the technique used for protein level statistical analysis can be readily applied to the peptide level without any issues. We tested whether using the same statistical methods for the peptide-centric approach outperformed the protein centric approach for sample differentiation. We first used the data reported by Deeb et al. for protein expression profiling for the classification of diffused large B-cell lymphoma subtypes (DLBCL and GC-DLBCL).28 Principal component analysis (PCA) was performed at the peptide and protein levels (Fig 2A). We included peptides with valid quantification values in all samples (the same criteria as published for the protein level). The peptide level PCA analysis showed superior performance in separating HBL1 and Ocily3 cell lines as shown in Figure 2A. Furthermore, we noted that compared to the protein level PCA analysis, PCA performed with their peptide data gave shorter distances within technical replicates. As well, larger distances between different cell subtypes were also observed at the peptide level, and in particular between ABC-DLBCL and GC-DLBCL. Our second example consists of a peptide-centric versus protein-centric analysis of in-house data that compares the gut metaproteome from control individuals and those diagnosed with two prevalent forms of inflammatory bowel disease (IBD), Crohn’s disease (CD) and ulcerative colitis (UC) is shown in Figure 2B. A protein level PCA analysis was unable to distinguish controls from diseased sample, however peptide level quantification successfully segregated controls from IBD. The higher quality of separation is due to better sensitivity and specificity of quantification at the peptide level rather than at the protein level.

Rescuing peptides by peptide-centric examination Another benefit worth highlighting is that peptide-centric quantification can identify and utilize the significantly changed peptide features, which may have been lost via a protein-centric quantification strategy. To examine this idea, we re-analyzed the circadian data from two similar reports29,30 using the same threshold for both protein and peptide levels with the JTK algorithm31, an algorithm routinely used to identify circadian proteins. Using Robles’ dataset and an adjusted p value (ADJ.P) cutoff of 0.05 and circadian period of 12-36 hours, we identified 480 circadian protein groups (here defined as “pass the test” of the JKT algorithm) and 1237 circadian peptides, of which 405 circadian peptides do not belong to any of the JTK approved circadian proteins. There are 413 circadian proteins and 3264 circadian peptides (with circadian period of 12-24 hours) from Chiang’s dataset, where 2246 circadian peptides do not belong to any JTK approved circadian proteins. An example from the above dataset is shown in 4 / 10


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Analytical Chemistry Figure S4 to illustrate this discrepancy. Protein anti-colorectal carcinoma light chain (IPI00462809, Uniprot Q7TS98) was determined not to significantly change during the circadian cycle. However, two of its unique peptides (IDGSERQNGVLNSWTDQDSK and MDMRTPAQFLGILLLWFPGMK) are of high quality in terms of identification (present in all 48 samples) and quantification, they pass the JTK test, and they appear to be circadian. The two peptides were discarded by the protein-centric analysis possibly because of the discrepancy with the majority of other peptides. Another example comes from Chiang’s dataset.29 Pyruvate kinase isozyme complex (Pkm2) is reportedly related to the circadian control of sugar utilization.32 Isoform M1 has 45 “unique +razor” peptides for protein quantification in MaxQuant output, while isoform M2 only has 3 for quantification (41 shared peptides with the isoformM2 were excluded). Both isoforms M1 and M2 of Pkm2 did not pass the JTK test, as shown in Figure 2C; however, we found 15 peptides belonging to isoform M1 passing the JTK test(Figure 2D), with 14 peptides shared with isoform M2. Technically we cannot tell whether the 14 peptides originated from isoform M1 or M2. In the above cases, the circadian peptides remain valuable candidates for downstream MRM analysis or ELISA validation. “Uniqueness” is no longer a concern within the scope of the peptide-centric strategy. As long as a peptide shows significant changes among samples, it can be selected as a candidate for developing a MRM workflow or peptide antibody based assays regardless if it is unique to a single protein entry or a protein group.

Peptide-centric quantification is broadly applicable In addition to the previously mentioned generic benefits of this peptide-centric strategy, there are other fields where this strategy can find extensive application. Protein PTMs play important roles in biological function regulation. It would be more practical and logical to scrutinize proteomics PTM data on peptide level, to compare profiles of different modification status of the same peptide to pinpoint possible PTM cross-talks. Take the phosphorylation as a simple example, changes of a specific phosphorylation site due to the regulation of the site phosphorylation, instead of protein metabolism, will be more likely to be signaling pathway related. This can usually be discovered by examining the profiles of phosphorylated peptides and non-phosphorylated counter parts. In meta-proteomics, the protein inference issue is exaggerated because of the high protein sequence homology between species, and lower sequence coverage due to the greatly increased complexity of the sample. The peptide-centric approach has already been explored for meta-proteomic profiling to avoid the protein inference issue.33,34 This benefit also applies to analyses of multiple organisms.35 The peptide-centric approach clearly applies to peptidomic analysis. As well, there are many truncated proteins and shed peptides from the cell surface or isoforms that are present in the secretome and plasma.36,37 These truncated and shed peptides often occur through regulated processes (and dysregulated processes during disease). They are biologically relevant, and therefore, it would be wrong to assign these truncated peptides back to intact proteins instead of as truncated forms. Furthermore, the quantification of the truncated peptide should be considered separately and not as part of the intact protein. An extreme case is peptidomics, a relatively new field compared to proteomics, where only endogenous peptides (mostly neuropeptides) without digestion are analyzed.22 The peptidome database is far from complete; therefore, a classical proteomic database is still preferred for higher identification rates. Again, there is no need for protein inference for peptidomic analysis.

Conclusion and Perspective Herein, we propose that proteomics analyses would benefit from peptide-centric strategies, an alternative to the present and widely-used protein-centric strategies. Using only distinct peptides for protein quantification would avoid all the issues brought by the protein inference, but all shared peptides accounting a large proportion of identifications would be lost. This study is not proposing that we should wholly replace the protein-centric workflow. We are suggesting employing a new angle to view the quantitative information. It has to be noted that protein-based quantification and normalization methods like iBAQ38, emPAI39, and MaxLFQ26 do not work at the 5 / 10



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peptide level, where full protein sequence is considered for protein level normalization. While the connectivity of peptides to protein is better for bridging the quantification profiles between samples 9,10,26, they still suffer from the protein inference issue. We emphasize that peptide-centric quantification makes more sense and performs better for analyses exemplified in this manuscript and probably for many other applications in theory, and it is also complimentary to the current proteomics workflow. LC-MS features without any peptide sequence identification can be further explored and recovered if significantly changes occur among samples.40,41 Generally, only a small fraction of features have peptide identification mainly due to instrument limitations.42 More software tools including MaxQuant and OpenMS can now do feature detection without prior identification information. We propose that performing statistical analyses which include normalization, filtering, missing value imputation, t-test, ANOVA and PCA analyses at the peptide level can reveal changes that are missed when the focus is only on proteins. Presently the annotation databases are mainly gene-centric or protein centric, not peptide-centric. Therefore functional analysis of significantly changed peptides still has to go back to protein or gene level. Peptides should be listed as separate entities for network/functional analysis, even though from the same parent protein. This will make the peptide-centric based functional analysis more accurate and informative with more signature nodes than the protein-centric approach in the network output from proteomics experiment. Our group is actually working on the software for functional analysis on peptide level. In addition, these peptides can directly be used as features for

describing sample differences or for diagnostic analysis. We would also like to encourage peptide inspection for quality control (QC) on selected protein targets. The discrepancy in peptide quantifications from the same protein does not necessarily equate systematic error. Instead, the discrepancy might point to presence of PTMs, alternative cleavage forms and protein isoforms which can all introduce quantification discrepancy. We also argue that the consistency between distinct peptides of the same protein could be used as a gold QC rule for quantification and optimization based on the assumption that the unique peptide should follow the same quantification profiles.43 For example, decreased consistency between two distinct peptides of the same protein after normalization indicates that the improper normalization method was selected. This rule does not apply when the unique peptide has an internal proteolysis site in specific samples or if the peptide is not proteotypic. This dimension of information could only be achieved by peptide-centric analysis.

Supporting Information The Supporting Information is available free of charge on the ACS Publications website, including all supplemental figures and, more detailed description of the data analysis procedure.

Author Information The authors declare no competing financial interest.

Acknowledgement We would like to acknowledge a Canada Research Chair in Proteomics and Systems Biology. Funding for this project is from NSERC-Canada, CIHR and Genome Canada.

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Figures Figures Figure 1 Proposed peptide-centric quantitative proteomic workflow. After LC-MS analysis, database searching and quantification analysis, peptides instead of assembled proteins are directly subjected to statistical and functional analysis, which avoids issues brought by protein inference.

Figure 2 Benefit of peptide-centric quantification. (A) PCA score plots at protein or peptide level for diffuse large B-cell lymphoma (DLBCL) subtype dataset 28. (B) PCA score plots at protein or peptide level for metaproteomic dataset of inflammatory bowel diseases (IBD). (C) Profiles of pyruvate kinase isozymes M1/M2 (Pkm2), isoform M1 and M2 in Chiang’s dataset 29, which didn’t pass JTK test as circadian proteins. (D) Circadian peptides shared by isoforms M1 and M2 of Pkm2.

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