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A Curated Resource for Phosphosite-specific Signature Analysis*[S]

Open AccessPublished:September 22, 2020DOI:https://doi.org/10.1074/mcp.TIR118.000943
      Signaling pathways are orchestrated by post-translational modifications (PTMs) such as phosphorylation. However, pathway analysis of PTM data sets generated by mass spectrometry (MS)-based proteomics is typically performed at a gene-centric level because of the lack of appropriately curated PTM signature databases and bioinformatic tools that leverage PTM site-specific information. Here we present the first version of PTMsigDB, a database of modification site-specific signatures of perturbations, kinase activities and signaling pathways curated from more than 2,500 publications. We adapted the widely used single sample Gene Set Enrichment Analysis approach to utilize PTMsigDB, enabling PTMSignature Enrichment Analysis (PTM-SEA) of quantitative MS data. We used a well-characterized data set of epidermal growth factor (EGF)-perturbed cancer cells to evaluate our approach and demonstrated better representation of signaling events compared with gene-centric methods. We then applied PTM-SEA to analyze the phosphoproteomes of cancer cells treated with cell-cycle inhibitors and detected mechanism-of-action specific signatures of cell cycle kinases. We also applied our methods to analyze the phosphoproteomes of PI3K-inhibited human breast cancer cells and detected signatures of compounds inhibiting PI3K as well as targets downstream of PI3K (AKT, MAPK/ERK) covering a substantial fraction of the PI3K pathway. PTMsigDB and PTM-SEA can be freely accessed at https://github.com/broadinstitute/ssGSEA2.0.

      Graphical Abstract

      Identifying signaling pathways that are dysregulated in diseases such as cancer is crucial for understanding the molecular mechanisms underlying the disease and ultimately for developing better treatments. Post-translational modifications (PTMs)
      The abbreviations used are:
      PTM
      post translational modification
      PTM-SEA
      PTM signature enrichment analysis
      GSEA
      gene set enrichment analysis
      ssGSEA
      single sample gene set enrichment analysis
      PTMsigDB
      PTM signatures database
      MSigDB
      molecular signatures database
      NP
      NetPath
      WP
      WikiPathways
      FDR
      false discovery rate
      EGF
      epidermal growth factor
      KEGG
      kyoto encyclopedia of genes and genomes
      PSP
      PhosphoSitePlus
      LINCS
      library of integrated network-based cellular signatures
      NaCl
      sodium chloride
      EDTA
      ethylenediaminetetraacetic acid
      Ni
      nickel
      HPLC
      high pressure liquid chromatography
      MS
      mass spectrometry
      LC-MS/MS
      liquid chromatography-MS/MS
      GCT
      gene cluster text
      GMT
      gene matrix transposed.
      1The abbreviations used are:PTM
      post translational modification
      PTM-SEA
      PTM signature enrichment analysis
      GSEA
      gene set enrichment analysis
      ssGSEA
      single sample gene set enrichment analysis
      PTMsigDB
      PTM signatures database
      MSigDB
      molecular signatures database
      NP
      NetPath
      WP
      WikiPathways
      FDR
      false discovery rate
      EGF
      epidermal growth factor
      KEGG
      kyoto encyclopedia of genes and genomes
      PSP
      PhosphoSitePlus
      LINCS
      library of integrated network-based cellular signatures
      NaCl
      sodium chloride
      EDTA
      ethylenediaminetetraacetic acid
      Ni
      nickel
      HPLC
      high pressure liquid chromatography
      MS
      mass spectrometry
      LC-MS/MS
      liquid chromatography-MS/MS
      GCT
      gene cluster text
      GMT
      gene matrix transposed.
      of proteins play a key role in practically every cellular process by regulating activity, localization and interaction of proteins. Mass spectrometry (MS)-based proteomics facilitates profiling of tens of thousands of PTM sites in a single experiment, most importantly phosphorylation, acetylation and ubiquitylation. Because of tremendous improvements in mass spectrometry technologies as well as sample processing workflows, large-scale analyses of PTMs have now become feasible for entire patient cohorts. This is exemplified by recent Clinical Proteomics Tumor Analysis Consortium (CPTAC) landmark studies of breast and ovarian cancer, in which pivotal findings were derived from phosphoproteomic analyses of up to 174 patient tumor samples (
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      ). For example, regulation of specific phosphorylation pathways revealed a novel breast cancer subtype that was unique to the phosphoproteomes of the tumors and could not be observed in RNA, DNA or protein space (
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      • NCI CPTAC
      Proteogenomics connects somatic mutations to signalling in breast cancer.
      ). Such analyses require well-annotated molecular signaling pathways and ideally would incorporate the role of specific modified sites rather than gene products alone. However, databases annotating biological pathways such as KEGG (
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      KEGG: kyoto encyclopedia of genes and genomes.
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      Molecular signatures database (MSigDB) 3.0.
      ) are gene-centric and do not capture information for individual PTM sites. Therefore, pathway analysis of acquired phosphoproteomes typically involves collapsing discrete measurements of PTM sites on proteins into a single measurement represented by e.g. the mean or median of corresponding sites, which are mapped to their respective gene symbols. This procedure allows the resulting data set to be used for gene-centric pathway analysis, sacrificing the additional information provided by individual PTM sites (Fig. 1) and likely diluting the signal represented in the phosphoproteome data. The information loss is especially profound if multiple phosphosites on the same protein are differentially abundant. The lack of appropriately curated databases has impeded development of bioinformatics analysis tools that leverage site-centric PTM signatures. Further, existing tools that can be directly applied to site-centric data sets, such as PHOXTRACK (
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      PHOXTRACK-a tool for interpreting comprehensive data sets of post-translational modifications of proteins.
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      ,
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      The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics.
      ), KEA (
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      KEA: kinase enrichment analysis.
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      ) or PSEA (
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      PSEA: Kinase-specific prediction and analysis of human phosphorylation substrates.
      ), are solely intended to detect signatures of kinase activity in phosphorylation data by screening for known kinase substrate sites, and do not encompass analysis of molecular signaling pathways or signatures of kinase inhibition.
      Figure thumbnail gr1
      Fig. 1Pathway analysis of phosphoproteome data sets. A, The majority of proteins have multiple phospho-acceptor sites that can be differentially occupied and vary in abundance levels measured by MS. The four examples illustrate three proteoforms with varying number of phosphorylation sites (represented by amino acid residue and position in protein sequence) which are quantified with different fold changes (represented as numbers in parenthesis). Protein A exemplifies the presence of two protein isoforms carrying an isoform-specific phosphorylation site. B, Gene-centric pathway analysis typically involves combining fold changes of multiple sites mapping to the same gene symbol by calculating a center value of abundance (mean or median) or by choosing a single site characterized by high degree of variance (thus information) across a sample cohort. Additional information provided by multiple phosphorylation sites on a single protein as well as different sites on protein isoforms are not considered. Resulting gene-centric expression matrix can then be queried against gene-centric pathway databases such as Reactome, KEGG or MSigDB, in which each pathway is represented as a collection of gene symbols. C, Site-centric pathway analysis takes all quantified phosphorylation sites into account and requires a database of pathways annotated using individual phosphorylation sites. For that purpose, we developed PTMsigDB, annotating signatures of pathways, kinases and perturbations at the level of sites. Each site is annotated with the direction of regulation in a signature, i.e. whether its abundance is decreased or elevated, exemplified by blue and red arrows, respectively. Quantitative, site-centric phosphoproteomic data can be directly queried against PTMsigDB to identify signatures of phosphosites that correlate with annotated signature sets in PTMsigDB.
      Here we present the first iteration of PTMsigDB, a collection of PTM site-specific signatures that have been assembled and curated from published data sets (Fig. 1). The current version is comprised exclusively of phosphorylation signatures but serves as a foundation for signatures of other PTMs like ubiquitination or acetylation to be added in the future. Although PTMsigDB enables kinase signature analysis similar to published tools described above, it additionally includes many curated signature sets of PTM sites mined from PhosphoSitePlus (
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      PhosphoSitePlus, 2014: mutations, PTMs and recalibrations.
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      ) that have been i) quantitatively characterized in specified perturbation studies; ii) are known substrates of kinases; or iii) are known to be activated/deactivated in signaling pathways. A unique feature of PTMsigDB is the annotation of each PTM site with the direction of regulation, i.e. increased or decreased abundance upon a specific perturbation or in the context of signaling through a canonical pathway. We further present an extension of the widely used single sample Gene Set Enrichment Analysis (ssGSEA) (
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      ) that enables PTM Signature Enrichment Analysis (PTM-SEA) illustrated here for MS-based phosphoproteomics data sets.
      We first assessed the utility of our approach using a well-characterized, literature-derived phosphoproteome data set of epidermal growth factor (EGF)-perturbed HeLa cells (
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      ) and compared PTM-SEA to regular gene-centric ssGSEA. We next applied PTM-SEA to phosphoproteomes of human cell lines treated with different cell cycle inhibitors that have been profiled in the Library of Integrated Network-Based Cellular Signatures (LINCS) (
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      ). Finally, to demonstrate the value of PTM-SEA in perturbation studies targeting clinically relevant cancer pathways, we applied our tools to analyze a deep phosphoproteome of a PI3Ka-inhibited breast cancer cell line.

      DISCUSSION

      We describe an approach to perform phosphosite-specific signature analysis (PTM-SEA) based on a curated database of phosphosite-specific signatures (PTMsigDB). Curation and maintenance of such a database requires knowledge from domain experts, which in this study are represented by curators from PhosphoSitePlus, NetPath and WikiPathways. We consider PTMsigDB to be the foundation of a more comprehensive resource for PTM site-specific signatures of pathways and perturbations, rather than a mature and completely developed database. We aim to further extend PTMsigDB when more curated, site-specific molecular pathways are made available by repositories such as PSP. Moreover, we envision that this curation process will become a community effort in which researchers studying PTMs in particular pathways will contribute to curating these pathways at the level of PTM sites. Resources like WikiPathways that are maintained by the scientific community recently started to include PTM site-specific pathway annotations and provide a promising source for future expansion of PTMsigDB.
      We have demonstrated the potential of PTMsigDB in analyzing MS-based phosphoproteomic data sets derived from perturbation studies involving EGF, cell cycle inhibitors and a specific PI3K inhibitor. The relatively small total number of signature sets (∼490) and unequal representation of perturbations, kinases and signaling pathways still limits the true potential of a PTM site-centric database. Our approach performs best when applied to perturbation data sets in which we expect a clear molecular phenotype. Subtle abundance differences in unperturbed baseline phosphoproteomes are much more challenging to detect with the current selection of available signature sets in PTMsigDB, especially because of the underrepresentation of molecular signaling pathways. Curation of the latter requires domain experts as well as the availability of detailed metadata accurately describing the biochemical experiments that were conducted to study signaling events in a pathway. However, these metadata (such as dosage of stimulant, cell system and time points of treatment) are often buried in respective subsections of manuscripts rather than stored together with the data itself, making curation of pathways prohibitively and unnecessarily difficult.
      About 40% of signatures in PTMsigDB were derived from perturbation studies in which pathways were specifically activated (e.g. via growth factors) or inhibited (e.g. via kinase inhibitors). The cellular responses upon perturbation treatment are represented in perturbation signatures in PTMsigDB. Many of the respective phosphosites are of low stoichiometry and even after enrichment for modified peptides are of too low abundance to be sequenced by single-shot LC-MS/MS analysis. Although kinase-substrate signatures in PTMsigDB consist of 46 sites on average, this number is substantially lower for perturbation signatures (16 sites on average). Therefore, deeper phosphoproteome coverage is required to detect signatures beyond kinase activity, which typically involves extensive sample fractionation.
      PTMsigDB contains signature sets for 45 kinase inhibitors (KI) and, to our knowledge, presents the first set of KI signatures curated at the level of phosphorylation sites. Although it is well known that KIs often target more than one protein, a systematic characterization of clinically relevant KIs and their targets was released only recently (
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      ). The availability of molecular signatures of KIs curated at the PTM-site level enables further systematic screening for potential off-target effects of certain KIs. Though the number of KI signatures is still relatively small in the current version of PTMsigDB, this category of signatures is expected to be especially valuable for the scientific community.
      Phosphosite-specific analysis of kinase substrate relationships is not an entirely new concept and it has been successfully employed by several groups (
      • Weidnery C.
      • Fischery C.
      • Sauer S.
      PHOXTRACK-a tool for interpreting comprehensive data sets of post-translational modifications of proteins.
      ,
      • Hernandez-Armenta C.
      • Ochoa D.
      • Goncalves E.
      • Saez-Rodriguez J.
      • Beltrao P.
      Benchmarking substrate-based kinase activity inference using phosphoproteomic data.
      ,
      • Lachmann A.
      • Ma'ayan A.
      KEA: kinase enrichment analysis.
      ,
      • Mischnik M.
      • Sacco F.
      • Cox J.
      • Schneider H.-C.
      • Schäfer M.
      • Hendlich M.
      • Crowther D.
      • Mann M.
      • Klabunde T.
      IKAP: A heuristic framework for inference of kinase activities from Phosphoproteomics data.
      ,
      • Suo S.-B.
      • Qiu J.-D.
      • Shi S.-P.
      • Chen X.
      • Liang R.-P.
      PSEA: Kinase-specific prediction and analysis of human phosphorylation substrates.
      ). Instead of using the kinase expression as proxy for its activity, the abundance profiles of phosphorylation sites that are known substrates of the respective kinase are monitored. In this study we detected differential signatures of cyclin-dependent kinase (CDK) 1 and 2 in three human cancer cell lines upon treatment with different cell cycle inhibitors. Most importantly, treatment with paclitaxel resulted in increased activity of CDK1/2 whereas other inhibitors showed decreased kinase activity. Paclitaxel is an antineoplastic chemotherapy medication that targets tubulin by stabilizing the microtubule polymer preventing it from disassembly. The resulting inhibition of the mitotic spindle function blocks the progression of mitosis in which the concentration of cyclin A is high. Cyclin A specifically binds to CDK1, and the resulting complex is in an active state phosphorylating a multitude of different substrates that were identified by PTM-SEA.
      Application of PTM-SEA to the deeply fractionated phosphoproteome of PI3K inhibited human breast cancer cells detected signatures that covered a substantial part of the canonical PI3K-AKT-mTOR and Ras-Raf-MEK-ERK pathways inhibitory signatures of kinases downstream (MEK, AKT, ERK) of PI3K. We also detected increased activity of the catalytic subunit α of casein kinase 2 (CK2a), a ubiquitous protein kinase known to be dysregulated in multiple cancers (
      • Unger G.M.
      • Davis A.T.
      • Slaton J.W.
      • Ahmed K.
      Protein kinase CK2 as regulator of cell survival: implications for cancer therapy.
      ). Attenuation of PI3K/Akt signaling upon selective inhibition of CK has been reported previously (
      • Siddiqui-Jain A.
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      • Streiner N.
      • Chua P.
      • Pierre F.
      • O'Brien S.E.
      • Bliesath J.
      • Omori M.
      • Huser N.
      • Ho C.
      • Proffitt C.
      • Schwaebe M.K.
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      • Anderes K.
      CX-4945, an Orally Bioavailable Selective Inhibitor of Protein Kinase CK2, Inhibits Prosurvival and Angiogenic Signaling and Exhibits Antitumor Efficacy.
      ). Our detection of higher activity levels of this kinase after inhibition of the PI3K pathway is an interesting observation for which the rationale remain unclear. Importantly, signatures of several PI3K inhibitors were enriched upon treatment and demonstrated decreased phosphorylation levels of sites that are part of these signatures, thereby supporting the selection of phosphosites for these inhibitors in PTMsigDB. Not surprisingly, the strongest effects of PI3K inhibition were observed in the cell proliferation machinery, exemplified by significantly reduced activity of mitotic kinases (CDKs and Aurora kinases).
      With ongoing curation efforts to create PTM site-specific signatures spanning more molecular signaling pathways in PTMsigDB, we expect to achieve a more fine-grained view on signaling events disrupted by targeted inhibition of cancer-relevant pathways. We present here a uniform method and guidelines to the community to create these PTM signatures. In cases where no site-specific information is available yet, our gene-centric-redundant ssGSEA approach provides an alternative to standard gene centric approaches.

      DATA AVAILABILITY

      The original mass spectra for the PI3K perturbation experiment may be downloaded from MassIVE (http://massive.ucsd.edu) using the identifier: MSV000083128. The data is directly accessible via ftp://massive.ucsd.edu/MSV000083128.
      Mass spectra of identified phosphopeptides were uploaded to MS-Viewer (http://msviewer.ucsf.edu/prospector/cgi-bin/msform.cgi?form=msviewer) and can be accessed with search key gxo6ksgdby or using the link below: http://msviewer.ucsf.edu/prospector/cgi-bin/mssearch.cgi?report_title=MS-Viewer&search_key=gxo6ksgdby&search_name=msviewer, PTMsigDB can be downloaded from https://github.com/broadinstitute/ssGSEA2.0.

      Acknowledgments

      We thank Alexander R. Pico from the Gladstone Institutes and cofounder of WikiPathways for constructive discussions around PTMsigDB.

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