全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
Metabolites  2013 

Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile

DOI: 10.3390/metabo3030741

Keywords: metabolomics, integrative pathway analysis, DEAP, dendrogram sharpening, DELSA, iPOP, longitudinal design, multi-omics data, single linkage.

Full-Text   Cite this paper   Add to My Lib

Abstract:

The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling.

References

[1]  Liu, Y.; Devescovi, V.; Chen, S.; Nardini, C. Multilevel omic data integration in cancer cell lines: Advanced annotation and emergent properties. BMC Syst. Biol. 2013, 7, 14, doi:10.1186/1752-0509-7-14.
[2]  Liu, Q.; Halvey, P.J.; Shyr, Y.; Slebos, R.J.C.; Liebler, D.C.; Zhang, B. Integrative omics analysis reveals the importance and scope of translational repression in microRNA-mediated regulation. Mol. Cell. Proteomics: MCP 2013, 12, 1900–1911, doi:10.1074/mcp.M112.025783.
[3]  Kurland, I.J.; Accili, D.; Burant, C.; Fischer, S.M.; Kahn, B.B.; Newgard, C.B.; Ramagiri, S.; Ronnett, G.V.; Ryals, J.A.; Sanders, M.; et al. Application of combined omics platforms to accelerate biomedical discovery in diabesity. Ann. N.Y. Acad. Sci. 2013, 1287, 1–16.
[4]  Blanchet, L.; Smolinska, A.; Attali, A.; Stoop, M.P.; Ampt, K.A.M.; van Aken, H.; Suidgeest, E.; Tuinstra, T.; Wijmenga, S.S.; et al. Fusion of metabolomics and proteomics data for biomarkers discovery: Case study on the experimental autoimmune encephalomyelitis. BMC Bioinforma. 2011, 12, 254, doi:10.1186/1471-2105-12-254.
[5]  Shen, R.; Olshen, A.B.; Ladanyi, M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 2009, 25, 2906–2912, doi:10.1093/bioinformatics/btp543.
[6]  Vaske, C.J.; Benz, S.C.; Sanborn, J.Z.; Earl, D.; Szeto, C.; Zhu, J.; Haussler, D.; Stuart, J.M. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 2010, 26, i237–i245, doi:10.1093/bioinformatics/btq182.
[7]  Vignot, S.; Soria, J.C. Discrepancies between primary tumor and metastasis: Impact on personalized medicine. Bull. Cancer 2013, 100, 561–568.
[8]  Law, G.L.; Korth, M.J.; Benecke, A.G.; Katze, M.G. Systems virology: Host-directed approaches to viral pathogenesis and drug targeting. Nat. Rev. Microbiol. 2013, 11, 455–466, doi:10.1038/nrmicro3036.
[9]  Tanr?kulu, A.; A??rba?l?, M. Triple therapy (aspirin, clopidogrel and oral anticoagulant) after percutaneous coronary intervention: another call for personalized medicine. Anadolu Kardiyol Derg. 2013, 13, 486–494.
[10]  Blackwell, L.S.; Marciel, K.K.; Quittner, A.L. Utilization of patient-reported outcomes as a step towards collaborative medicine. Paediatr. Respir. Rev. 2013, 14, 146–151, doi:10.1016/j.prrv.2013.04.003.
[11]  Buyse, M.; Michiels, S. Omics-based clinical trial designs. Curr. Opin. Oncol. 2013, 25, 289–295.
[12]  Chen, R.; Mias, G.I.; Li-Pook-Than, J.; Jiang, L.; Lam, H.Y.K.; Chen, R.; Miriami, E.; Karczewski, K.J.; Hariharan, M.; Dewey, F.E.; et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 2012, 148, 1293–1307, doi:10.1016/j.cell.2012.02.009.
[13]  Higdon, R.; Haynes, W.; Stanberry, L.; Stewart, E.; Yandl, G.; Howard, C.; Broomall, W.; Kolker, N.; Kolker, E. Unraveling the complexities of life sciences data. Big Data 2013, 1, 42–50, doi:10.1089/big.2012.1505.
[14]  Kolker, E.; Stewart, E.; zdemir, V. DELSA global for “Big Data” and the Bioeconomy: Catalyzing Collective Innovation. Ind. Biotechnol. 2012, 8, 176–178, doi:10.1089/ind.2012.1528.
[15]  Kolker, E. Editorial: Special issue on data-intensive science. OMICS 2011, 15, 197–198, doi:10.1089/omi.2011.02ed.
[16]  Barga, R.; Howe, B.; Beck, D.; Bowers, S.; Dobyns, W.; Haynes, W.; Higdon, R.; Howard, C.; Roth, C.; Stewart, E.; et al. Bioinformatics and data-intensive scientific discovery in the beginning of the 21st century. Omics: A J. Integr. Biol. 2011, 15, 199–201, doi:10.1089/omi.2011.0024.
[17]  Matthews, L.; Gopinath, G.; Gillespie, M.; Caudy, M.; Croft, D.; de Bono, B.; Garapati, P.; Hemish, J.; Hermjakob, H.; Jassal, B.; et al. Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res. 2009, 37, D619–D622, doi:10.1093/nar/gkn863.
[18]  Ogata, H.; Goto, S.; Sato, K.; Fujibuchi, W.; Bono, H.; Kanehisa, M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999, 27, 29–34, doi:10.1093/nar/27.1.29.
[19]  Fox, G.; Qiu, X.; Beason, S.; Choi, J.Y.; Ekanayake, J.; Gunarathne, T.; Rho, M.; Tang, H.; Devadasan, N.; Liu, G. Biomedical Case Studies in Data Intensive Computing. In Proceedings of the CloudCom ’09 Proceedings of the 1st International Conference on Cloud Computing, Beijing, China, 1–4 December 2009; Springer-Verlag: Berlin/Heidelberg, Germany, 2009; pp. 2–18.
[20]  Gilbert, D.R.; Schroeder, M.; van Helden, J. Interactive visualization and exploration of relationships between biological objects. Trends Biotechnol. 2000, 18, 487–494, doi:10.1016/S0167-7799(00)01510-9.
[21]  Haynes, W.A.; Higdon, R.; Stanberry, L.; Collins, D.; Kolker, E. Differential expression analysis for pathways. PLoS Comput. Biol. 2013, 9, e1002967, doi:10.1371/journal.pcbi.1002967.
[22]  Kolker, E.; Higdon, R.; Welch, D.; Bauman, A.; Stewart, E.; Haynes, W.; Broomall, W.; Kolker, N. Corrigendum to “SPIRE: Systematic protein investigative research environment” [J. Proteomics 75 (1) (2011) 122–126]. J. Proteomics 2012, 75, 3789, doi:10.1016/j.jprot.2012.04.022.
[23]  Ozdemir, V.; Pang, T.; Knoppers, B.M.; Avard, D.; Faraj, S.A.; Zawati, M.H.; Kolker, E. Vaccines of the 21st century and vaccinomics: Data-enabled science meets global health to spark collective action for vaccine innovation. OMICS: A J. Integr. Biol. 2011, 15, 523–527, doi:10.1089/omi.2011.03ed.
[24]  Stewart, E.; Kolker, E. DELSA global workshop: Quantified human initiative. Big Data 2013, 3. in press.
[25]  Ryan, D.; Robards, K. Metabolomics: The greatest omics of them all? Anal. Chem. 2006, 78, 7954–7958, doi:10.1021/ac0614341.
[26]  Stanberry, L.; Haynes, W.; Higdon, R.; Kolker, E. Pathway-centric analysis for multi-omics dataIn preparation.
[27]  Hastings, J.; de Matos, P.; Dekker, A.; Ennis, M.; Harsha, B.; Kale, N.; Muthukrishnan, V.; Owen, G.; Turner, S.; Williams, M.; et al. The ChEBI reference database and ontology for biologically relevant chemistry: Enhancements for 2013. Nucleic Acids Res. 2013, 41, D456–D463, doi:10.1093/nar/gks1146.
[28]  Bairoch, A.; Apweiler, R.; Wu, C.H.; Barker, W.C.; Boeckmann, B.; Ferro, S.; Gasteiger, E.; Huang, H.; Lopez, R.; Magrane, M.; et al. The universal protein resource (uniprot). Nucleic Acids Res. 2005, 33, D154–D159.
[29]  Hartigan, J. Consistency of single linkage for high-density clusters. Am. Stat. 1981, 76, 388–392, doi:10.1080/01621459.1981.10477658.
[30]  Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning, 2nd ed. ed.; Springer: Berlin/Heidelberg, Germany, 2009.
[31]  Hartigan, J. Distribution Problems in Clustering. In Classification and Clustering; Van Ryzin, J., Ed.; Mathematics Research Center Publication, No. 37; 1977.
[32]  Barnett, V. Interpreting Multivariate Data; Wiley Series in Probability and Mathematical Statistics; John Wiley & Sons Ltd.: Chichester, UK, 1981.
[33]  Stanberry, L.; Nandy, R.; Cordes, D. Cluster analysis of fMRI data using dendrogram sharpening. Hum. Brain Mapp. 2003, 20, 201–219, doi:10.1002/hbm.10143.
[34]  Murua, A.; Stanberry, L.; Stuetzle, W. On Potts model clustering, kernel K-means, and density estimation. J. Comput. Graph. Stat. 2008, 17, 629–658, doi:10.1198/106186008X318855.
[35]  Stanford Center for Biomedical Informatics Research (BMIR) at the Stanford University School of Medicine. Protege Project.
[36]  Kamburov, A.; Pentchev, K.; Galicka, H.; Wierling, C.; Lehrach, H.; Herwig, R. ConsensusPathDB: Toward a more complete picture of cell biology. Nucleic Acids Res. 2011, 39, D712–D717, doi:10.1093/nar/gkq1156.
[37]  Bukreyev, A.; Whitehead, S.S.; Prussin, C.; Murphy, B.R.; Collins, P.L. Effect of coexpression of interleukin-2 by recombinant respiratory syncytial virus on virus replication, immunogenicity, and production of other cytokines. J. Virol. 2000, 74, 7151–7157, doi:10.1128/JVI.74.15.7151-7157.2000.
[38]  Haynes, L.M.; Moore, D.D.; Kurt-Jones, E.A.; Finberg, R.W.; Anderson, L.J.; Tripp, R.A. Involvement of toll-like receptor 4 in innate immunity to respiratory syncytial virus. J. Virol. 2001, 75, 10730–10737, doi:10.1128/JVI.75.22.10730-10737.2001.
[39]  Rallabhandi, P.; Phillips, R.L.; Boukhvalova, M.S.; Pletneva, L.M.; Shirey, K.A.; Gioannini, T.L.; Weiss, J.P.; Chow, J.C.; Hawkins, L.D.; Vogel, S.N.; et al. Respiratory syncytial virus fusion protein-induced toll-like receptor 4 (TLR4) signaling is inhibited by the TLR4 antagonists Rhodobacter sphaeroides lipopolysaccharide and eritoran (E5564) and requires direct interaction with MD-2. mBio 2012, 3, doi:10.1128/mBio.00218-12.
[40]  Burgel, P.; Nadel, J. Roles of epidermal growth factor receptor activation in epithelial cell repair and mucin production in airway epithelium. Thorax 2004, 59, 992–996, doi:10.1136/thx.2003.018879.
[41]  Ornitz, D.M.; Itoh, N. Fibroblast growth factors. Genome biology 2001, 2, REVIEWS3005. 11276432
[42]  Monick, M.M.; Cameron, K.; Staber, J.; Powers, L.S.; Yarovinsky, T.O.; Koland, J.G.; Hunninghake, G.W. Activation of the epidermal growth factor receptor by respiratory syncytial virus results in increased inflammation and delayed apoptosis. J. Biol. Chem. 2005, 280, 2147–2158.
[43]  Laplante, M.; Sabatini, D.M. mTOR signaling at a glance. J. Cell Sci. 2009, 122, 3589–3594, doi:10.1242/jcs.051011.
[44]  DeFrancesco, L. Omics gets personal. Nat. Biotechnol. 2012, 30, 332–332, doi:10.1038/nbt.2184.
[45]  Li-Pook-Than, J.; Snyder, M. iPOP goes the world: Integrated personalized omics profiling and the road toward improved health care. Chem. Biol. 2013, 20, 660–666, doi:10.1016/j.chembiol.2013.05.001.
[46]  Blumenberg, M. SKINOMICS: Transcriptional profiling in dermatology and skin biology. Curr. Genomics 2012, 13, 363–368.
[47]  Gonzalez de Castro, D.; Clarke, P.A.; Al-Lazikani, B.; Workman, P. Personalized cancer medicine: Molecular diagnostics, predictive biomarkers, and drug resistance. Clin. Pharmacol. Therapeutics 2013, 93, 252–259, doi:10.1038/clpt.2012.237.
[48]  Pesce, F.; Pathan, S.; Schena, F.P. From -omics to personalized medicine in nephrology: Integration is the key. Nephrol. Dial. Transpl. Off. Publ. Eur. Dial. Transpl. Assoc.-Eur. Renal Assoc. 2013, 28, 24–28.
[49]  Rojo Venegas, K.; Aguilera Gmez, M.; Caada Garre, M.; Snchez, A.G.; Contreras-Ortega, C.; Calleja Hernndez, M.A. Pharmacogenetics of osteoporosis: Towards novel theranostics for personalized medicine? Omics J. Integr. Biol. 2012, 16, 638–651, doi:10.1089/omi.2011.0150.
[50]  Chen, R.; Snyder, M. Promise of personalized omics to precision medicine. Wiley Interdiscip. Rev. Syst. Biol. Med. 2013, 5, 73–82, doi:10.1002/wsbm.1198.
[51]  Mias, G.I.; Snyder, M. Personal genomes, quantitative dynamic omics and personalized medicine. Quant. Biol. 2013, 1, 71–90, doi:10.1007/s40484-013-0005-3.
[52]  Zaas, A.K.; Chen, M.; Varkey, J.; Veldman, T.; Hero, A.O.; Lucas, J.; Huang, Y.; Turner, R.; Gilbert, A.; Lambkin-Williams, R.; et al. Gene expression signatures diagnose influenza and other symptomatic respiratory viral infection in humans. Cell Host Microbe 2009, 6, 207–217, doi:10.1016/j.chom.2009.07.006.
[53]  Rosenberger, C.M.; Podyminogin, R.L.; Navarro, G.; Zhao, G.W.; Askovich, P.S.; Weiss, M.J.; Aderem, A. miR-451 regulates dendritic cell cytokine responses to influenza infection. J. Immunol. (Baltimore, Md.: 1950) 2012, 189, 5965–5975, doi:10.4049/jimmunol.1201437.
[54]  Swan, M. The quantified self: Fundamental disruption in big data science and biological discovery. Big Data 2013, 1, 85–99, doi:10.1089/big.2012.0002.
[55]  Lanza, G.; Ferracin, M.; Gaf, R.; Veronese, A.; Spizzo, R.; Pichiorri, F.; Liu, C.g.; Calin, G.A.; Croce, C.M.; Negrini, M. mRNA/microRNA gene expression profile in microsatellite unstable colorectal cancer. Mol. Cancer 2007, 6, 54, doi:10.1186/1476-4598-6-54.
[56]  Panguluri, S.K.; Bhatnagar, S.; Kumar, A.; McCarthy, J.J.; Srivastava, A.K.; Cooper, N.G.; Lundy, R.F.; Kumar, A. Genomic profiling of messenger RNAs and microRNAs reveals potential mechanisms of TWEAK-induced skeletal muscle wasting in mice. PLoS One 2010, 5, e8760, doi:10.1371/journal.pone.0008760.
[57]  Hrydziuszko, O.; Viant, M.R. Missing values in mass spectrometry based metabolomics: An undervalued step in the data processing pipeline. Metabolomics 2011, 8, 161–174, doi:10.1007/s11306-011-0366-4.
[58]  Webb-Robertson, B.J.M.; Matzke, M.M.; Metz, T.O.; McDermott, J.E.; Walker, H.; Rodland, K.D.; Pounds, J.G.; Waters, K.M. Sequential projection pursuit principal component analysis-dealing with missing data associated with new -omics technologies. BioTechniques 2013, 54, 165–168.
[59]  Weckwerth, W. Metabolomics: Methods and Protocols. In Methods in Molecular Biology; Humana Press: Totowa, NJ, USA, 2007. No. 358.
[60]  Kolker, E.; Higdon, R.; Haynes, W.; Welch, D.; Broomall, W.; Lancet, D.; Stanberry, L.; Kolker, N. MOPED: Model organism protein expression database. Nucleic Acids Res. 2012, 40, D1093–D1099, doi:10.1093/nar/gkr1177.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133