A catalogue of human secreted proteins and its implications

Shivakumar Keerthikumar; Shivakumar Keerthikumar

doi:10.3934/biophy.2016.4.563

AIMS Biophysics

2016, Volume 3, Issue 4: 563-570. doi: 10.3934/biophy.2016.4.563

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A catalogue of human secreted proteins and its implications

Shivakumar Keerthikumar ^,

Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia

Received: 27 September 2016 Accepted: 17 November 2016 Published: 24 November 2016

Under both normal and pathological conditions, cells secrete variety of proteins through classical and non-classical secretory pathways into the extracellular space. Majority of these proteins represent pathophysiology of the cell from which it is secreted. Recently, though more than 92% of the protein coding genes has been mapped by human proteome map project, but number of those proteins that constitutes secretome of the cell still remains elusive. Secreted proteins or the secretome can be accessible in bodily fluids and hence are considered as potential biomarkers to discriminate between healthy and diseased individuals. In order to facilitate the biomarker discovery and to further aid clinicians and scientists working in these arenas, we have compiled and catalogued secreted proteins from the human proteome using integrated bioinformatics approach. In this study, nearly 14% of the human proteome is likely to be secreted through classical and non-classical secretory pathways. Out of which, ~38% of these secreted proteins were found in extracellular vesicles including exosomes and shedding microvesicles. Among these secreted proteins, 94% were detected in human bodily fluids including blood, plasma, serum, saliva, semen, tear and urine. We anticipate that this high confidence list of secreted proteins could serve as a compendium of candidate biomarkers. In addition, the catalogue may provide functional insights in understanding the molecular mechanisms involved in various physiological and pathophysiological conditions of the cell.
- secretory; bioinformatics; integrative; proteomics; extracellular vesicles
Citation: Shivakumar Keerthikumar. A catalogue of human secreted proteins and its implications[J]. AIMS Biophysics, 2016, 3(4): 563-570. doi: 10.3934/biophy.2016.4.563

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Abstract

Under both normal and pathological conditions, cells secrete variety of proteins through classical and non-classical secretory pathways into the extracellular space. Majority of these proteins represent pathophysiology of the cell from which it is secreted. Recently, though more than 92% of the protein coding genes has been mapped by human proteome map project, but number of those proteins that constitutes secretome of the cell still remains elusive. Secreted proteins or the secretome can be accessible in bodily fluids and hence are considered as potential biomarkers to discriminate between healthy and diseased individuals. In order to facilitate the biomarker discovery and to further aid clinicians and scientists working in these arenas, we have compiled and catalogued secreted proteins from the human proteome using integrated bioinformatics approach. In this study, nearly 14% of the human proteome is likely to be secreted through classical and non-classical secretory pathways. Out of which, ~38% of these secreted proteins were found in extracellular vesicles including exosomes and shedding microvesicles. Among these secreted proteins, 94% were detected in human bodily fluids including blood, plasma, serum, saliva, semen, tear and urine. We anticipate that this high confidence list of secreted proteins could serve as a compendium of candidate biomarkers. In addition, the catalogue may provide functional insights in understanding the molecular mechanisms involved in various physiological and pathophysiological conditions of the cell.

References

[1]	Wilhelm M, Schlegl J, Hahne H, et al. (2014) Mass-spectrometry-based draft of the human proteome. Nature 509: 582–587. doi: 10.1038/nature13319
[2]	Mathivanan S (2014) Integrated Bioinformatics Analysis of the Publicly Available Protein Data Shows Evidence for 96% of the Human Proteome. J Proteom Bioinform 07.
[3]	Ranganathan S, Garg G (2009) Secretome: clues into pathogen infection and clinical applications. Genome Med 1: 1. doi: 10.1186/gm1
[4]	Chen Y, Zhang Y, Yin Y, et al. (2005) SPD—a web-based secreted protein database. Nucleic Acids Res 33: D169–D173. doi: 10.1093/nar/gni168
[5]	Ladunga I (2000) Large-scale predictions of secretory proteins from mammalian genomic and EST sequences. Curr Opin Biotechnol 11: 13–18. doi: 10.1016/S0958-1669(99)00048-8
[6]	Walter P, Gilmore R, Blobel G (1984) Protein translocation across the endoplasmic reticulum. Cell 38: 5–8. doi: 10.1016/0092-8674(84)90520-8
[7]	Prudovsky I, Tarantini F, Landriscina M, et al. (2008) Secretion without Golgi. J Cell Biochem 103: 1327–1343. doi: 10.1002/jcb.21513
[8]	Mathivanan S (2012) Quest for Cancer Biomarkers: Assaying Mutant Proteins and RNA that Provides the Much Needed Specificity. J Proteom Bioinform 05.
[9]	Nickel W (2003) The mystery of nonclassical protein secretion—A current view on cargo proteins and potential export routes. Eur J Biochem 270: 2109–2119. doi: 10.1046/j.1432-1033.2003.03577.x
[10]	Petersen TN, Brunak S, von Heijne G, et al. (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8: 785–786.
[11]	Bendtsen JD, Jensen LJ, Blom N, et al. (2004) Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng Des Sel 17: 349–356. doi: 10.1093/protein/gzh037
[12]	Mathivanan S, Fahner CJ, Reid GE, et al. (2012) ExoCarta 2012: database of exosomal proteins, RNA and lipids. Nucleic Acids Res 40: D1241–D1244. doi: 10.1093/nar/gkr828
[13]	Keerthikumar S, Chisanga D, Ariyaratne D, et al. (2016) ExoCarta: A Web-Based Compendium of Exosomal Cargo. J Mol Biol 428: 688–692. doi: 10.1016/j.jmb.2015.09.019
[14]	Kalra H, Simpson RJ, Ji H, et al. (2012) Vesiclepedia: a compendium for extracellular vesicles with continuous community annotation. PLoS Biol 10: e1001450. doi: 10.1371/journal.pbio.1001450
[15]	Kim DK, Lee J, Kim SR, et al. (2015) EVpedia: A Community Web Portal for Extracellular Vesicles Research. Bioinformatics 31: 933–939.
[16]	Nanjappa V, Thomas JK, Marimuthu A, et al. (2014) Plasma Proteome Database as a resource for proteomics research: 2014 update. Nucleic Acids Res 42: D959–D965. doi: 10.1093/nar/gkt1251
[17]	Mathivanan S, Ahmed M, Ahn NG, et al. (2008) Human Proteinpedia enables sharing of human protein data. Nat Biotechnol 26: 164–167. doi: 10.1038/nbt0208-164
[18]	Kandasamy K, Keerthikumar S, Goel R, et al. (2009) Human Proteinpedia: a unified discovery resource for proteomics research. Nucleic Acids Res 37: D773–D781. doi: 10.1093/nar/gkn701
[19]	Keshava Prasad TS, Goel R, Kandasamy K, et al. (2009) Human Protein Reference Database—2009 update. Nucleic Acids Res 37: D767–D772. doi: 10.1093/nar/gkn892
[20]	Pruitt KD, Tatusova T, Brown GR, et al. (2012) NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy. Nucleic Acids Res 40: D130–D135. doi: 10.1093/nar/gkr1079
[21]	Pathan M, Keerthikumar S, Ang CS, et al. (2015) FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics 15: 2597–2601. doi: 10.1002/pmic.201400515
[22]	Yang X, Weng Z, Mendrick DL, et al. (2014) Circulating extracellular vesicles as a potential source of new biomarkers of drug-induced liver injury. Toxicol Lett 225: 401–406. doi: 10.1016/j.toxlet.2014.01.013

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