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Bioinformatic Approaches to Metabolic Pathways Analysis

  • Stuart MaudsleyEmail author
  • Wayne Chadwick
  • Liyun Wang
  • Yu Zhou
  • Bronwen Martin
  • Sung-Soo Park
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 756)

Abstract

The growth and development in the last decade of accurate and reliable mass data collection techniques has greatly enhanced our comprehension of cell signaling networks and pathways. At the same time however, these technological advances have also increased the difficulty of satisfactorily analyzing and interpreting these ever-expanding datasets. At the present time, multiple diverse scientific communities including molecular biological, genetic, proteomic, bioinformatic, and cell biological, are converging upon a common endpoint, that is, the measurement, interpretation, and potential prediction of signal transduction cascade activity from mass datasets. Our ever increasing appreciation of the complexity of cellular or receptor signaling output and the structural coordination of intracellular signaling cascades has to some extent necessitated the generation of a new branch of informatics that more closely associates functional signaling effects to biological actions and even whole-animal phenotypes. The ability to untangle and hopefully generate theoretical models of signal transduction information flow from transmembrane receptor systems to physiological and pharmacological actions may be one of the greatest advances in cell signaling science. In this overview, we shall attempt to assist the navigation into this new field of cell signaling and highlight several methodologies and technologies to appreciate this exciting new age of signal transduction.

Key words

Signaling Network Pathway Phenotype Receptor 

Notes

Acknowledgments

This work was supported entirely by the Intramural Research Program of the NIH, National Institute on Aging.

References

  1. 1.
    Luttrell, L. M. (2003) “Location, location, location”: activation and targeting of MAP kinases by G protein-coupled receptors. J Mol Endocrinol 30, 117–26.PubMedCrossRefGoogle Scholar
  2. 2.
    Maudsley, S., Martin, B. and Luttrell, L. M. (2005) The origins of diversity and specificity in G protein-coupled receptor signaling. J Pharmacol Exp Ther 314, 485–494.PubMedCrossRefGoogle Scholar
  3. 3.
    Schadt, E. E., Lamb, J., Yang, X., Zhu, J., Edwards, S., Guhathakurta, D., Sieberts, S. K., Monks, S., Reitman, M., Zhang, C., Lum, P. Y., Leonardson, A., Thieringer, R., Metzger, J. M., Yang, L., Castle, J., Zhu, H., Kash, S. F., Drake, T. A., Sachs, A. and Lusis, A. J. (2005) An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet 37, 710–7.PubMedCrossRefGoogle Scholar
  4. 4.
    Chen, Y., Zhu, J., Lum, P. Y., Yang, X., Pinto, S., MacNeil, D. J., Zhang, C., Lamb, J., Edwards, S., Sieberts, S. K., Leonardson, A., Castellini, L. W., Wang, S., Champy, M. F., Zhang, B., Emilsson, V., Doss, S., Ghazalpour, A., Horvath, S., Drake, T. A., Lusis, A. J. and Schadt, E. E. (2008) Variations in DNA elucidate molecular networks that cause disease. Nature 45, 429–35.CrossRefGoogle Scholar
  5. 5.
    Hilsenbeck, S. G., Friedrichs, W. E., Schiff, R., O’Connell, P., Hansen, R. K., Osborne, C. K. and Fuqua, S. A. (1999) Statistical analysis of array expression data as applied to the problem of tamoxifen resistance. J Natl Cancer Inst 91, 453–9.PubMedCrossRefGoogle Scholar
  6. 6.
    Martin, B., Pearson, M., Brenneman, R., Golden, E., Wood, W., Prabhu, V., Becker, K. G., Mattson, M. P. and Maudsley, S. (2009) Gonadal transcriptome alterations in response to dietary energy intake: sensing the reproductive environment. PLoS One 4, e4146.PubMedCrossRefGoogle Scholar
  7. 7.
    Martin, B., Brenneman, R., Golden, E., Walent, T., Becker, K. G., Prabhu, V. V., Wood, W. 3 rd, Ladenheim, B., Cadet, J. L. and Maudsley, S. (2009) Growth factor signals in neural cells: coherent patterns of interaction control multiple levels of molecular and phenotypic responses. J Biol Chem 284, 2493–511.PubMedCrossRefGoogle Scholar
  8. 8.
    Quackenbush, J. (2002) Microarray data normalization and transformation. Nat Genet 32, 496–501.PubMedCrossRefGoogle Scholar
  9. 9.
    Zhao, Y., Li, M. C. and Simon, R. (2005) An adaptive method for cDNA microarray normalization. BMC Bioinformatics 6, 28.PubMedCrossRefGoogle Scholar
  10. 10.
    Kepler, T. B., Crosby, L. and Morgan, K. T. (2002) Normalization and analysis of DNA microarray data by self-consistency and local regression. Genome Biol 3, RESEARCH0037.Google Scholar
  11. 11.
    Yang, Y. H., Dudoit, S., Luu, P., Lin, D. M., Peng, V., Ngai, J. and Speed, T. P. (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucl Acids Res 30, e15.PubMedCrossRefGoogle Scholar
  12. 12.
    Zien, A., Aigner, T., Zimmer, R. and Lengauer, T. (2001) Centralization: a new method for the normalization of gene expression data. Bioinformatics 17, S323–31.PubMedCrossRefGoogle Scholar
  13. 13.
    Sasik, R., Calvo, E. and Corbeil, J. (2002) Statistical analysis of high-density oligonucleotide arrays: a multiplicative noise model. Bioinformatics 18, 1633–40.PubMedCrossRefGoogle Scholar
  14. 14.
    Troyanskaya, O. G., Garber, M. E., Brown, P. O., Botstein, D. and Altman, R. B. (2002) Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics 18, 1454–61.PubMedCrossRefGoogle Scholar
  15. 15.
    Lee, M. L., Whitmore, G. A., Björkbacka, H. and Freeman, M. W. (2005) Nonparametric methods for microarray data based on exchangeability and borrowed power. J Biopharm Stat 15, 783–97.PubMedCrossRefGoogle Scholar
  16. 16.
    Li, H., Wood, C. L., Getchell, T. V., Getchell, M. L. and Stromberg, A. J. (2004) Analysis of oligonucleotide array experiments with repeated measures using mixed models. BMC Bioinformatics 5, 209.PubMedCrossRefGoogle Scholar
  17. 17.
    Meuwissen, T. H. and Goddard, M. E. (2004). Bootstrapping of gene-expression data improves and controls the false discovery rate of differentially expressed genes. Genet Sel Evol 36, 191–205.PubMedCrossRefGoogle Scholar
  18. 18.
    Reiner, A., Yekutieli, D. and Benjamini, Y. (2003) Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19, 368–75.PubMedCrossRefGoogle Scholar
  19. 19.
    Lange, V., Picotti, P., Domon, B., Aebersold, R. (2008) Selected reaction monitoring for quantitative proteomics. Mol Systems Biol 4, 222.Google Scholar
  20. 20.
    Griffin, T. J., Xie, H., Bandhakavi, S., Popko, J., Mohan, A., Carlis, J. V. and Higgins, L. (2007) iTRAQ reagent-based quantitative proteomic analysis on a linear ion trap mass spectrometer. J Proteome Res 6, 4200–4209.PubMedCrossRefGoogle Scholar
  21. 21.
    Dayon, L., Pasquarello, C., Hoogland, C., Sanchez, J. C. and Scherl, A. (2010) Combining low- and high-energy tandem mass spectra for optimized peptide quantification with isobaric tags. J Proteomics 73, 769–77.PubMedCrossRefGoogle Scholar
  22. 22.
    Elias, J. E. and Gygi, S. P. (2007) Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods 4, 207–14.PubMedCrossRefGoogle Scholar
  23. 23.
    Eng, J. K., McCormack, A. L. and Yates, J. R. (1994) An approach to correlate tandem massspectral data of peptides with amino-acid-sequences in a protein database. J Am Soc Mass Spectrom 5, 976–89.CrossRefGoogle Scholar
  24. 24.
    Perkins, D. N., Pappin, D. J. C., Creasy, D. M. and Cottrell, J. S. (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–67.PubMedCrossRefGoogle Scholar
  25. 25.
    Clauser, K. R., Baker, P. and Burlingame, A. L. (1999) Role of accurate mass measurement (+/− 10 ppm) in protein identification strategies employing MS or MS/MS and database searching. Anal Chem 71, 2871–2882.PubMedCrossRefGoogle Scholar
  26. 26.
    Zhang, N., Aebersold, R. and Schwilkowski, B. (2002) ProbID: a probabilistic algorithm to identify peptides through sequence database searching using tandem mass spectral data. Proteomics 2, 1406–12.PubMedCrossRefGoogle Scholar
  27. 27.
    Creasy, D. M. and Cottrell, J. S. (2002) Error tolerant searching of uninterpreted tandem mass spectrometry data. Proteomics 2, 1426–34.PubMedCrossRefGoogle Scholar
  28. 28.
    Nesvizhskii, A. I., Vitek, O. and Aebersold, R. (2007) Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat Methods 4, 787–97.PubMedCrossRefGoogle Scholar
  29. 29.
    Gstaiger, M. and Aebersold, R. (2009). Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nat Rev Genet 10, 617–27.PubMedCrossRefGoogle Scholar
  30. 30.
    Mueller, L. N., Brusniak, M-Y., Mani, D. R. and Aebersol, R. (2008). An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomic data.J Proteome Res 7, 51–61.PubMedCrossRefGoogle Scholar
  31. 31.
    Binns, D., Dimmer, E., Huntley, R., Barrell, D., O’Donovan, C. and Apweiler, R. (2009) QuickGO: a web-based tool for Gene Ontology searching. Bioinformatics 25, 3045–6.PubMedCrossRefGoogle Scholar
  32. 32.
    Adams, M. D., Celniker, S. E., Holt, R. A., Evans, C. A., Gocayne, J. D., Amanatides, P. G., Scherer, S. E., Li, P. W., Hoskins, R. A., Galle, R. F., et al. (2000) The genome sequence of Drosophila melanogaster. Science 287, 2185–95.PubMedCrossRefGoogle Scholar
  33. 33.
    Liu, M., Liberzon, A., Kong, S. W., Lai, W. R., Park, P. J., Kohane, I. S. and Kasif, S. (2007) Network-based analysis of affected biological processes in type 2 diabetes models. PLoS Genetics 3, e96.PubMedCrossRefGoogle Scholar
  34. 34.
    Hirschman, L., Yeh, A., Blaschke, C. and Valencia, A. (2005) Overview of BioCreAtIvE: critical assessment of information extraction for biology. BMC Bioinformatics 6, S1.PubMedCrossRefGoogle Scholar
  35. 35.
    Camon, E. B., Barrell, D. G., Dimmer, E. C., Lee, V., Magrane, M., Maslen, J., Binns, D. and Apweiler, R. (2005) An evaluation of GO annotation retrieval for BioCreAtIvE and GOA. BMC Bioinformatics 6, S1-S17.CrossRefGoogle Scholar
  36. 36.
    Dressman, H. K., Muramoto, G. G., Chao, N. J., Meadows, S., Marshall, D., Ginsburg, G. S., Nevins, J. R. and Chute, J. P. (2007) Gene expression signatures that predict radiation exposure in mice and humans. PLoS Medicine 4, e106.PubMedCrossRefGoogle Scholar
  37. 37.
    Eisen, M., Spellman, P. T., Brown, P. O. and Botstein, D. (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95, 14863–8.PubMedCrossRefGoogle Scholar
  38. 38.
    Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D. and Futcher, B. (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 9, 3273–3297.PubMedGoogle Scholar
  39. 39.
    Faustino, R. S., Behfar, A., Perez-Terzic, C. and Terzic, A. (2008) Genomic chart guiding embryonic stem cell cardiopoiesis. Genome Biol 9, R6.PubMedCrossRefGoogle Scholar
  40. 40.
    Martin, B., Pearson, M., Brenneman, R., Golden, E., Keselman, A., Iyun, T., Carlson, O. D., Egan, J. M., Becker, K. G., Wood, W. 3 rd, Prabhu, V., de Cabo, R., Maudsley, S., Mattson, M. P. (2008) Conserved and differential effects of dietary energy intake on the hippocampal transcriptomes of females and males. PLoS One 3, e2398.PubMedCrossRefGoogle Scholar
  41. 41.
    Stranahan, A. M., Lee, K., Becker, K. G., Zhang, Y., Maudsley, S., Martin, B., Cutler, R. G. and Mattson, M. P. (2008) Hippocampal gene expression patterns underlying the enhancement of memory by running in aged mice. Neurobiol Aging [Epub ahead of print]Google Scholar
  42. 42.
    Ginos, M. A., Page, G. P., Michalowicz, B. S., Patel, K. J., Volker, S. E., Pambuccian, S. E., Ondrey, F. G., Adams, G. L. and Gaffney, P. M. (2004) Identification of a gene expression signature associated with recurrent disease in squamous cell carcinoma of the head and neck. Cancer Res 64, 55–63.PubMedCrossRefGoogle Scholar
  43. 43.
    Draghici, S., Kulaeva, O., Hoff, B., Petrov, A., Shams, S. and Tainsky, M. A. (2003) Noise sampling method: an ANOVA approach allowing robust selection of differentially regulated genes measured by DNA microarrays. Bioinformatics 19, 1348–59.PubMedCrossRefGoogle Scholar
  44. 44.
    Man, M. Z., Wang, X. and Wang, Y. (2000) POWER_SAGE: comparing statistical tests for SAGE experiments. Bioinformatics 16, 953–9.PubMedCrossRefGoogle Scholar
  45. 45.
    Alexa, A., Rahnenfuhrer, J. and Lengauer, T. (2006) Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 22,1600–7.PubMedCrossRefGoogle Scholar
  46. 46.
    Grossmann, S., Bauer, S., Robinson, P. N. and Vingron, M. (2007) Improved detection of overrepresentation of Gene Ontology annotations with parent child analysis. Bioinformatics 23, 3024–31.PubMedCrossRefGoogle Scholar
  47. 47.
    Schlicker, A., Rahnenfuhrer, J., Albrecht, M., Lengauer, T. and Domingues, F. S. (2007) GOTax: investigating biological processes and biochemical activities along the taxonomic tree. Genome Biol 8, R33.PubMedCrossRefGoogle Scholar
  48. 48.
    Martin, B., Brenneman, R., Becker, K. G., Gucek, M., Cole, R. N. and Maudsley, S. (2008) iTRAQ analysis of complex proteome alterations in 3xTgAD Alzheimer’s mice: understanding the interface between physiology and disease. PLoS One 3, e2750.PubMedCrossRefGoogle Scholar
  49. 49.
    Qin, X., Ahn, S., Speed, T. P. and Rubin, G. M. (2007) Global analyses of mRNA translational control during early Drosophila embryogenesis. Genome Biol 8, R63.PubMedCrossRefGoogle Scholar
  50. 50.
    Thomas, P. D., Mi, H. and Lewis, S. (2007) Ontology annotation: mapping genomic regions to biological function. Curr Opin Chem Biol 11, 4–11.PubMedCrossRefGoogle Scholar
  51. 51.
    Karp, P. D., Riley, M., Paley, S. M. and Pelligrini-Toole, A. (1996) EcoCyc: an encyclopedia of Escherichia coli genes and metabolism. Nucl Acids Res 24, 32–9.PubMedCrossRefGoogle Scholar
  52. 52.
    Keseler, I. M., Collado-Vides, J., Gama-Castro, S., Ingraham, J., Paley, S., Paulsen, I. T., Peralta-Gil, M. and Karp, P. D. (2005) EcoCyc: a comprehensive database resource for Escherichia coli. Nucl Acids Res 33, D334–7.PubMedCrossRefGoogle Scholar
  53. 53.
    Kim, S-Y. and Volsky, D. J. (2005) PAGE: Parametric analysis of geneset enrichment. BMC Bioinformatics 6, 144–156.PubMedCrossRefGoogle Scholar
  54. 54.
    Mootha, V. K., Lindgren, C. M., Eriksson, K. F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., Houstis, N., Daly, M. J., Patterson, N., Mesirov, J. P., Golub, T. R., Tamayo, P., Spiegelman, B., Lander, E. S., Hirschhorn, J. N., Altshuler, D. and Groop, L. C. (2003). PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34, 267–73.PubMedCrossRefGoogle Scholar
  55. 55.
    Dahlquist, K. D., Salomonis, N., Vranizan, K., Lawlor, S. C. and Conklin, B. R. (2002) GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways, Nat Genet 31, 19–20.PubMedCrossRefGoogle Scholar
  56. 56.
    Pandey, R., Guru, R. K. and Mount, D. W. (2004) Pathway Miner: extracting gene association networks from molecular pathways for predicting the biological significance of gene expression microarray data. Bioinformatics 20, 2156–8.PubMedCrossRefGoogle Scholar
  57. 57.
    Zhang, B., Kirrov, S. and Snoddy, J. (2005) WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucl Acids Res 33, W741–8.PubMedCrossRefGoogle Scholar
  58. 58.
    Kanehisa, M., Goto, S., Kawashima, S. and Nakaya, A. (2002) The KEGG databases at GenomeNet. Nucl Acids Res 30, 42–6.PubMedCrossRefGoogle Scholar
  59. 59.
    Bouchahda, M., Adam, R., Giacchetti, S., Castaing, D., Brezault-Bonnet, C., Hauteville, D., Innominato, P. F., Focan, C., Machover, D. and Lévi, F. (2009) Rescue chemotherapy using multidrug chronomodulated hepatic arterial infusion for patients with heavily pretreated metastatic colorectal cancer. Cancer 115, 4990–9.PubMedCrossRefGoogle Scholar
  60. 60.
    McClatchy, D. B., Liao, L., Park, S. K., Venable, J. D. and Yates, J. R. (2007) Quantification of the synaptosomal proteome of the rat cerebellum during post-natal development. Genome Res 17, 1378–8.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Stuart Maudsley
    • 1
    Email author
  • Wayne Chadwick
    • 1
  • Liyun Wang
    • 1
  • Yu Zhou
    • 1
  • Bronwen Martin
    • 2
  • Sung-Soo Park
    • 1
  1. 1.Receptor Pharmacology UnitNational Institute on Aging, National Institutes of HealthBaltimoreUSA
  2. 2.Metabolism UnitNational Institute on Aging, National Institutes of HealthBaltimoreUSA

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