Integration of omics sciences to advance biology and medicine

Clinical Proteomics

Pothur Srinivas²  Henry Rodriguez¹  Christopher R Kinsinger¹  Emily S Boja¹ 

[1] Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA;Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD, USA

关键词: Genomics;    Metabolomics;    Proteomics;    Risk prediction;    Clinical application;    Omics science;    Omics integration;   

Others  :  1092787
DOI  :  10.1186/1559-0275-11-45

 received in 2014-09-03, accepted in 2014-12-02,  发布年份 2014

【 摘 要 】

In the past two decades, our ability to study cellular and molecular systems has been transformed through the development of omics sciences. While unlimited potential lies within massive omics datasets, the success of omics sciences to further our understanding of human disease and/or translating these findings to clinical utility remains elusive due to a number of factors. A significant limiting factor is the integration of different omics datasets (i.e., integromics) for extraction of biological and clinical insights. To this end, the National Cancer Institute (NCI) and the National Heart, Lung and Blood Institute (NHLBI) organized a joint workshop in June 2012 with the focus on integration issues related to multi-omics technologies that needed to be resolved in order to realize the full utility of integrating omics datasets by providing a glimpse into the disease as an integrated “system”. The overarching goals were to (1) identify challenges and roadblocks in omics integration, and (2) facilitate the full maturation of ‘integromics’ in biology and medicine. Participants reached a consensus on the most significant barriers for integrating omics sciences and provided recommendations on viable approaches to overcome each of these barriers within the areas of technology, bioinformatics and clinical medicine.

【 授权许可】

   
2014 Boja et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [1]Lane L, Bairoch A, Beavis RC, Deutsch EW, Gaudet P, Lundberg E, Omenn GS: Metrics for the Human Proteome Project 2013-2014 and strategies for finding missing proteins. J Proteome Res 2014, 13(1):15-20.
• [2]Kim MS, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R, Madugundu AK, Kelkar DS, Isserlin R, Jain S, Thomas JK, Muthusamy B, Leal-Rojas P, Kumar P, Sahasrabuddhe NA, Balakrishnan L, Advani J, George B, Renuse S, Selvan LD, Patil AH, Nanjappa V, Radhakrishnan A, Prasad S, Subbannayya T, Raju R, Kumar M, Sreenivasamurthy SK, Marimuthu A, Sathe GJ, et al.: A draft map of the human proteome. Nature 2014, 509(7502):575-581.
• [3]Wilhelm M, Schlegl J, Hahne H, Moghaddas Gholami A, Lieberenz M, Savitski MM, Ziegler E, Butzmann L, Gessulat S, Marx H, Mathieson T, Lemeer S, Schnatbaum K, Reimer U, Wenschuh H, Mollenhauer M, Slotta-Huspenina J, Boese JH, Bantscheff M, Gerstmair A, Faerber F, Kuster B: Mass-spectrometry-based draft of the human proteome. Nature 2014, 509(7502):582-587.
• [4]Low TY, van Heesch S, van den Toorn H, Giansanti P, Cristobal A, Toonen P, Schafer S, Hübner N, van Breukelen B, Mohammed S, Cuppen E, Heck AJ, Guryev V: Quantitative and qualitative proteome characteristics extracted from in-depth integrated genomics and proteomics analysis. Cell Rep 2013, 5(5):1469-1478.
• [5]Aebersold R, Bader GD, Edwards AM, van Eyk JE, Kussmann M, Qin J, Omenn GS: The biology/disease-driven human proteome project (B/D-HPP): enabling protein research for the life sciences community. J Proteome Res 2013, 12(1):23-27.
• [6]Ciriello G, Cerami E, Sander C, Schultz N: Mutual exclusivity analysis identifies oncogenic network modules. Genome Res 2012, 22(2):398-406.
• [7]Zhang B, Wang J, Wang X, Zhu J, Liu Q, Shi Z, Chambers MC, Zimmerman LJ, Shaddox KF, Kim S, Davies SR, Wang S, Wang P, Kinsinger CR, Rivers RC, Rodriguez H, Townsend RR, Ellis MJ, Carr SA, Tabb DL, Coffey RJ, Slebos RJ, Liebler DC, NCI CPTAC: Proteogenomic characterization of human colon and rectal cancer. Nature 2014, 513(7518):382-387.
• [8]Paik YK, Hancock WS: Uniting ENCODE with genome-wide proteomics. Nat Biotechnol 2012, 30(11):1065-1067.
• [9]Vidal M, Chan DW, Gerstein M, Mann M, Omenn GS, Tagle D, Sechi S, Workshop Participants: The human proteome – A scientific opportunity for transforming diagnostics, therapeutics, and healthcare. Clin Proteomics 2012, 9(1):6. BioMed Central Full Text
• [10]Hood LE, Omenn GS, Moritz RL, Aebersold R, Yamamoto KR, Amos M, Hunter-Cevera J, Locascio L, Workshop Participants: New and improved proteomics technologies for understanding complex biological systems: addressing a grand challenge in the life sciences. Proteomics 2012, 12(18):2773-2783.
• [11]Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY, Chen R, Miriami E, Karczewski KJ, Hariharan M, Dewey FE, Cheng Y, Clark MJ, Im H, Habegger L, Balasubramanian S, O'Huallachain M, Dudley JT, Hillenmeyer S, Haraksingh R, Sharon D, Euskirchen G, Lacroute P, Bettinger K, Boyle AP, Kasowski M, Grubert F, Seki S, Garcia M, Whirl-Carrillo M, Gallardo M, et al.: Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 2012, 148(6):1293-1307.
• [12]Hogenesch JB, Ueda HR: Understanding systems-level properties: timely stories from the study of clocks. Nat Rev Genet 2011, 12(6):407-416.
• [13]Ball LE, Hart GW: Post-translational modifications: a major focus for the future of proteomics. Mol Cell Proteomics 2013, 12(12):3443.
• [14]Joehanes R, Johnson AD, Barb JJ, Raghavachari N, Liu P, Woodhouse KA, O'Donnell CJ, Munson PJ, Levy D: Gene expression analysis of whole blood, peripheral blood mononuclear cells, and lymphoblastoid cell lines from the Framingham Heart Study. Physiol Genomics 2012, 44(1):59-75.
• [15]Sen SK, Boelte KC, Barb JJ, Joehanes R, Zhao X, Cheng Q, Adams L, Teer JK, Accame DS, Chowdhury S, Singh LN, Kavousi M, Peyser PA, Quigley L, Priel DL, Lau K, Kuhns DB, Yoshimura T, Johnson AD, Hwang SJ, Chen MY, Arai AE, Green ED, Mullikin JC, Kolodgie FD, O'Donnell CJ, Virmani R, Munson PJ, NISC Comparative Sequencing Program; CHARGE Consortium, et al.: Integrative DNA, RNA, and Protein Evidence Connects TREML4 to Coronary Artery Calcification. Am J Hum Genet 2014, 95(1):66-76.
• [16]Zhang EE, Liu AC, Hirota T, Miraglia LJ, Welch G, Pongsawakul PY, Liu X, Atwood A, Huss JW 3rd, Janes J, Su AI, Hogenesch JB, Kay SA: A genome-wide RNAi screen for modifiers of the circadian clock in human cells. Cell 2009, 139:199-210.
• [17]Shankar A, Sun L, Klein BE, Lee KE, Muntner P, Nieto FJ, Tsai MY, Cruickshanks KJ, Schubert CR, Brazy PC, Coresh J, Klein R: Markers of inflammation predict the long-term risk of developing chronic kidney disease: a population-based cohort study. Kidney Int 2011, 80(11):1231-1238.
• [18]Ng PC, Murray SS, Levy S, Venter JC: An agenda for personalized medicine. Nature 2009, 461(7265):724-726.
• [19]Micheel CM, Nass SJ, Omenn GS, Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials, Board on Health Care Services, Board on Health Sciences Policy, Institute of Medicine: Evolution of Translational Omics: Lessons Learned and the Path Forward. Washington (DC): National Academies Press (US); 2012.
• [20]Ng SB, Buckingham KJ, Lee C, Bigham AW, Tabor HK, Dent KM, Huff CD, Shannon PT, Jabs EW, Nickerson DA, Shendure J, Bamshad MJ: Exome sequencing identifies the cause of a mendelian disorder. Nat Genet 2010, 42(1):30-35.
• [21]Harismendy O, Ng PC, Strausberg RL, Wang X, Stockwell TB, Beeson KY, Schork NJ, Murray SS, Topol EJ, Levy S, Frazer KA: Evaluation of next generation sequencing platforms for population targeted sequencing studies. Genome Biol 2009, 10(3):R32. BioMed Central Full Text
• [22]Imes CC, Austin MA: Low-density lipoprotein cholesterol, apolipoprotein B, and risk of coronary heart disease: from familial hyperlipidemia to genomics. Biol Res Nurs 2013, 15(3):292-308.
• [23]Shankavaram UT, Varma S, Kane D, Sunshine M, Chary KK, Reinhold WC, Pommier Y, Weinstein JN: Cell Miner: a relational database and query tool for the NCI-60 cancer cell lines. BMC Genomics 2009, 10:277. BioMed Central Full Text
• [24]Maier CW, Long JG, Hemminger BM, Giddings MC: Ultra-Structure database design methodology for managing systems biology data and analyses. BMC Bioinformatics 2009, 10:254. BioMed Central Full Text
• [25]Taniguchi Y, Choi PJ, Li GW, Chen H, Babu M, Hearn J, Emili A, Xie XS: Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 2010, 329(5991):533-538.
• [26]Vreede FT, Chan AY, Sharps J, Fodor E: Mechanisms and functional implications of the degradation of host RNA polymerase II in influenza virus infected cells. Virology 2010, 396(1):125-134.
• [27]Brar GA, Yassour M, Friedman N, Regev A, Ingolia NT, Weissman JS: High-resolution view of the yeast meiotic program revealed by ribosome profiling. Science 2012, 335(6068):552-557.
• [28]Vinayagam A, Stelzl U, Foulle R, Plassmann S, Zenkner M, Timm J, Assmus HE, Andrade-Navarro MA, Wanker EE: A directed protein interaction network for investigating intracellular signal transduction. Sci Signal 2011, 4(189):rs8.
• [29]Homer N, Szelinger S, Redman M, Duggan D, Tembe W, Muehling J, Pearson JV, Stephan DA, Nelson SF, Craig DW: Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genet 2008, 4(8):e1000167.
• [30]Tanner S, Shen Z, Ng J, Florea L, Guigó R, Briggs SP, Bafna V: Improving gene annotation using peptide mass spectrometry. Genome Res 2007, 17(2):231-239.
• [31]Perez-Riverol Y, Alpi E, Wang R, Hermjakob H, Vizcaíno JA: Making proteomics data accessible and reusable: current state of proteomics databases and repositories. Proteomics 2014. doi:10.1002/pmic.201400302 [Epub ahead of print]
• [32]Tarvin KA, Sandusky GE: Using molecular profiled human tissue to accelerate drug discovery. Expert Opin Drug Discov 2014, 12:1-5. [Epub ahead of print]
• [33]Rogersa S, Cambrosiob A: Making a new technology work: the standardization and regulation of microarrays. Yale J Biol Med 2007, 80(4):165-178.
• [34]Field D, Garrity G, Gray T, Morrison N, Selengut J, Sterk P, Tatusova T, Thomson N, Allen MJ, Angiuoli SV, Ashburner M, Axelrod N, Baldauf S, Ballard S, Boore J, Cochrane G, Cole J, Dawyndt P, De Vos P, DePamphilis C, Edwards R, Faruque N, Feldman R, Gilbert J, Gilna P, Glöckner FO, Goldstein P, Guralnick R, Haft D, Hancock D, et al.: The minimum information about a genome sequence (MIGS) specification. Nature Biotech 2008, 26:541-547.
• [35]Martínez-Bartolomé S: Guidelines for reporting quantitative mass spectrometry based experiments in proteomics. J Proteomics 2013, 95:84-88.
• [36]Ivanov AR, Colangelo CM, Dufresne CP, Friedman DB, Lilley KS, Mechtler K, Phinney BS, Rose KL, Rudnick PA, Searle BC, Shaffer SA, Weintraub ST: Interlaboratory studies and initiatives developing standards for proteomics. Proteomics 2013, 13(6):904-909.
• [37]Rudnick PA, Clauser KR, Kilpatrick LE, Tchekhovskoi DV, Neta P, Blonder N, Billheimer DD, Blackman RK, Bunk DM, Cardasis HL, Ham AJ, Jaffe JD, Kinsinger CR, Mesri M, Neubert TA, Schilling B, Tabb DL, Tegeler TJ, Vega-Montoto L, Variyath AM, Wang M, Wang P, Whiteaker JR, Zimmerman LJ, Carr SA, Fisher SJ, Gibson BW, Paulovich AG, Regnier FE, Rodriguez H, et al.: Performance metrics for liquid chromatography-tandem mass spectrometry systems in proteomics analyses. Mol Cell Proteomics 2010, 9(2):225-241.
• [38]Addona TA, Abbatiello SE, Schilling B, Skates SJ, Mani DR, Bunk DM, Spiegelman CH, Zimmerman LJ, Ham AJ, Keshishian H, Hall SC, Allen S, Blackman RK, Borchers CH, Buck C, Cardasis HL, Cusack MP, Dodder NG, Gibson BW, Held JM, Hiltke T, Jackson A, Johansen EB, Kinsinger CR, Li J, Mesri M, Neubert TA, Niles RK, Pulsipher TC, Ransohoff D, et al.: Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat Biotechnol 2009, 27(7):633-641.
• [39]Sansone SA, Fan T, Goodacre R, Griffin JL, Hardy NW, Kaddurah-Daouk R, Kristal BS, Lindon J, Mendes P, Morrison N, Nikolau B, Robertson D, Sumner LW, Taylor C, van der Werf M, van Ommen B, Fiehn O, MSI Board Members: The metabolomics standards initiative. Nat Biotechnol 2007, 25(8):846-848.
• [40]Mertens P, Yang F, Liu T, Mani DR, Petyuk VA, Gillette MA, Clauser KR, Qiao JW, Gritsenko MA, Moore RJ, Levine DA, Townsend R, Erdmann-Gilmore P, Snider JE, Davies SR, Ruggles KV, Fenyo D, Kitchens RT, Li S, Olvera N, Dao F, Rodriguez H, Chan DW, Liebler D, White F, Rodland KD, Mills GB, Smith RD, Paulovich AG, Ellis M, et al.: Ischemia in tumors induces early and sustained phosphorylation changes in stress kinase pathways but does not affect global protein levels. Mol Cell Proteomics 2014, 13(7):1690-1704.
• [41]Kuhn E, Whiteaker JR, Mani DR, Jackson AM, Zhao L, Pope ME, Smith D, Rivera KD, Anderson NL, Skates SJ, Pearson TW, Paulovich AG, Carr SA: Interlaboratory evaluation of automated, multiplexed peptide immunoaffinity enrichment coupled to multiple reaction monitoring mass spectrometry for quantifying proteins in plasma. Mol Cell Proteomics 2012, 11(6):M111.013854.
• [42]Bjornson ZB, Nolan GP, Fantl WJ: Single-cell mass cytometry for analysis of immune system functional states. Curr Opin Immunol 2013, 25(4):484-494.
• [43]Qiu J, LaBaer J: Nucleic acid programmable protein array a just-in-time multiplexed protein expression and purification platform. Methods Enzymol 2011, 500:151-163.
• [44]Newman RH, Hu J, Rho HS, Xie Z, Woodard C, Neiswinger J, Cooper C, Shirley M, Clark HM, Hu S, Hwang W, Jeong JS, Wu G, Lin J, Gao X, Ni Q, Goel R, Xia S, Ji H, Dalby KN, Birnbaum MJ, Cole PA, Knapp S, Ryazanov AG, Zack DJ, Blackshaw S, Pawson T, Gingras AC, Desiderio S, Pandey A, et al.: Construction of human activity-based phosphorylation networks. Mol Syst Biol 2013, 9:655.
• [45]Boja E, Jortani SA, Ritchie J, Hoofnagle AN, Težak Ž, Mansfield E, Keller P, Rivers RC, Rahbar A, Anderson NL, Srinivas P, Rodriguez H: The journey to regulation of protein-based multiplex quantitative assays. Clin Chem 2011, 57(4):560-567.