Skip to main content

Advertisement

SpringerLink
Log in

Phenotype Characterisation Using Integrated Gene Transcript, Protein and Metabolite Profiling

  • Original Research
  • Published:
Applied Bioinformatics

Abstract

Multifactorial diseases present a significant challenge for functional genomics. Owing to their multiple compartmental effects and complex biomolecular activities, such diseases cannot be adequately characterised by changes in single components, nor can pathophysiological changes be understood by observing gene transcripts alone. Instead, a pattern of subtle changes is observed in multifactorial diseases across multiple tissues and organs with complex associations between corresponding gene, protein and metabolite levels.

This article presents methods for exploratory and integrative analysis of pathophysiological changes at the biomolecular level. In particular, novel approaches are introduced for the following challenges: (i) data processing and analysis methods for proteomic and metabolomic data obtained by electrospray ionisation (ESI) liquid chromatography-tandem mass spectrometry (LC/MS); (ii) association analysis of integrated gene, protein and metabolite patterns that are most descriptive of pathophysiological changes; and (iii) interpretation of results obtained from association analyses in the context of known biological processes.

These novel approaches are illustrated with the apolipoprotein E3-Leiden transgenic mouse model, a commonly used model of atherosclerosis. We seek to gain insight into the early responses of disease onset and progression by determining and identifying — well in advance of pathogenic manifestations of disease — the sets of gene transcripts, proteins and metabolites, along with their putative relationships in the transgenic model and associated wild-type cohort. Our results corroborate previous findings and extend predictions for three processes in atherosclerosis: aberrant lipid metabolism, inflammation, and tissue development and maintenance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Table I
Table II
Table III
Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Golub TR, Slonim DK, Tamayo P, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286: 531–7

    Article  PubMed  CAS  Google Scholar 

  2. Chabas D, Baranzini SE, Mitchell D, et al. The influence of the proinflammatory cytokine, osteopontin, on autoimmune demyelinating disease. Science 2001; 294: 1731–5

    Article  PubMed  CAS  Google Scholar 

  3. van’t Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415: 530–6

    Article  Google Scholar 

  4. Nicholson JK, Connelly J, Lindon JC, et al. Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov 2002; 1: 153–61

    Article  PubMed  CAS  Google Scholar 

  5. van der Greef J, Davidov E, Verheij E, et al. The role of metabolomics in drug discovery: a new vision for drug discovery and development. In: Harrigan GG, Goodacre R, editors. Metabolic profiling: its role in biomarker discovery and gene function analysis. London: Kluwer Academic Publishers, 2003: 170–98

    Google Scholar 

  6. Petricoin III EF, Ardekani AM, Hitt BA, et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002; 359: 572–7

    Article  PubMed  CAS  Google Scholar 

  7. Ideker T, Galitski T, Hood L. A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet 2001; 2: 343–72

    Article  PubMed  CAS  Google Scholar 

  8. Kitano H. Systems biology: a brief overview. Science 2002; 295: 1662–4

    Article  PubMed  CAS  Google Scholar 

  9. Ideker T, Thorsson V, Ranish JA, et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 2001; 292: 929–34

    Article  PubMed  CAS  Google Scholar 

  10. Shaffer JP. Multiple hypothesis testing. Annu Rev Psychol 1995; 46: 561–84

    Article  Google Scholar 

  11. Kerr MK, Martin M, Churchill GA. Analysis of variance for gene expression microarray data. J Comput Biol 2000; 7: 819–37

    Article  PubMed  CAS  Google Scholar 

  12. Wolfinger RD, Gibson G, Wolfinger ED, et al. Assessing gene significance from cDNA microarray expression data via mixed models. J Comput Biol 2001; 8: 625–37

    Article  PubMed  CAS  Google Scholar 

  13. Smyth GK, Speed T. Normalization of cDNA microarray data. Methods 2003; 31: 265–73

    Article  PubMed  CAS  Google Scholar 

  14. Patterson SD, Aebersold RH. Proteomics: the first decade and beyond. Nat Genet 2003; 33: 311–23

    Article  PubMed  CAS  Google Scholar 

  15. Ashburner M, Ball CA, Blake JA, et al. Gene Ontology: tool for the unification of biology. Nat Genet 2000; 25: 25–9

    Article  PubMed  CAS  Google Scholar 

  16. Clish C, Davidov E, Oresic M, et al. Integrative biological analysis of the ApoE3-Leiden transgenic mouse. OMICS 2004; 8: 3–14

    Article  PubMed  CAS  Google Scholar 

  17. van den Maagdenberg AMJM, Hofker MH, Krimpenfort PJA, et al. Transgenic mice carrying the apolipoprotein E3-leiden gene exhibit hyperlipoproteinemia. J Biol Chem 1993; 268: 10540–5

    PubMed  Google Scholar 

  18. van Vlijmen BJ, van’t Hof HB, Mol MJ, et al. Modulation of very low density lipoprotein production and clearance contributes to age- and gender-dependent hyperlipoproteinemia in apolipoprotein E3-Leiden transgenic mice. J Clin Invest 1996; 97: 1184–92

    Article  PubMed  Google Scholar 

  19. Leppanen P, Luoma JS, Hofker MH, et al. Characterization of atherosclerotic lesions in apo E3-leiden transgenic mice. Atherosclerosis 1998; 136: 147–52

    Article  PubMed  CAS  Google Scholar 

  20. Mensenkamp AR, van Luyn MJ, van Goor H, et al. Hepatic lipid accumulation, altered very low density lipoprotein formation and apolipoprotein E deposition in apolipoprotein E3-Leiden transgenic mice. J Hepatol 2000; 33: 189–98

    Article  PubMed  CAS  Google Scholar 

  21. van Vlijmen BJ, van den Maagdenberg AM, Gijbels MJ, et al. Diet-induced hyperlipoproteinemia and atherosclerosis in apolipoprotein E3-Leiden transgenic mice. J Clin Invest 1994; 93: 1403–10

    Article  PubMed  Google Scholar 

  22. Jong MC, Dahlmans VE, van Gorp PJ, et al. In the absence of the low density lipoprotein receptor, human apolipoprotein C1 overexpression in transgenic mice inhibits the hepatic uptake of very low density lipoproteins via a receptor-associated protein-sensitive pathway. J Clin Invest 1996; 98: 2259–67

    Article  PubMed  CAS  Google Scholar 

  23. Hartemink AJ, Gifford DK, Jaakkola TS, et al. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pac Symp Biocomput 2002, 437–49

    Google Scholar 

  24. Muller C, Schafer P, Stortzel M, et al. Ion suppression effects in liquid chromatography-electrospray-ionisation transport-region collision induced dissociation mass spectrometry with different serum extraction methods for systematic toxicological analysis with mass spectra libraries. J Chromatogr B Analyt Technol Biomed Life Sci 2002; 773: 47–52

    Article  PubMed  CAS  Google Scholar 

  25. Wang W, Zhou H, Lin H, et al. Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Anal Chem 2003; 75: 4818–26

    Article  PubMed  CAS  Google Scholar 

  26. Stevens JP. Applied multivariate statistics for the social sciences. Mahwah (NJ): Lawrence Erlbaum Associates, 2002

    Google Scholar 

  27. Sheskin DJ. Handbook of parametric and nonparametric statistical procedures. Boca Raton (FL): Chapman & Hall/CRC, 2000

    Google Scholar 

  28. Schölkopf B, Smola A, Müller KR. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 1998; 10: 1299–319

    Article  Google Scholar 

  29. Karsch-Mizrachi I, Ouellette BF. The GenBank sequence database. Methods Biochem Anal 2001; 43: 45–63

    Article  PubMed  CAS  Google Scholar 

  30. Altschul SF, Gish W, Miller W, et al. Basic local alignment search tool. J Mol Biol 1990; 215: 403–10

    PubMed  CAS  Google Scholar 

  31. Pruitt KD, Maglott DR. RefSeq and LocusLink: NCBI gene-centered resources. Nucleic Acids Res 2001; 29: 137–40

    Article  PubMed  CAS  Google Scholar 

  32. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000; 28: 29–34

    Article  Google Scholar 

  33. Bader GD, Hogue CWV. BIND: a data specification for storing and describing biomolecular interactions, molecular complexes and pathways. Bioinformatics 2000; 16: 465–77

    Article  PubMed  CAS  Google Scholar 

  34. Leonard JE, Colombe JB, Levy JL. Finding relevant references to genes and proteins using a Bayesian approach. Bioinformatics 2002; 18: 1515–22

    Article  PubMed  CAS  Google Scholar 

  35. Kerr MK, Churchill GA. Experimental design for gene expression microarrays. Biostatistics 2001; 2: 183–201

    Article  PubMed  Google Scholar 

  36. Hartemink A, Gifford D, Jaakkola T, et al. Maximum likelihood estimation of optimal scaling factors for expression array normalization. SPIE International Symposium on Biomedical Optics 2001 (BiOS0l). In: Bittner M, Chen Y, Dorsel A, et al., editors. Microarrays: optical technologies and informatics. Proceedings of SPIE. Vol. 4266: 132–40

  37. Butte AJ, Tamayo P, Slonim D, et al. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc Natl Acad Sci U S A 2000; 97: 12182–6

    Article  PubMed  CAS  Google Scholar 

  38. Kose F, Weckwerth W, Linke T, et al. Visualizing plant metabolomic correlation network using clique-metabolite matrices. Bioinformatics 2001; 17: 1198–208

    Article  PubMed  CAS  Google Scholar 

  39. Schölkopf B, Smola A. Learning with kernels: support vector machines, regularization, optimization and beyond. Cambridge (MA): MIT Press, 2002

    Google Scholar 

  40. Goto S, Okuno Y, Hattori M, et al. LIGAND: database of chemical compounds and reactions in biological pathways. Nucleic Acids Res 2002; 30: 402–4

    Article  PubMed  CAS  Google Scholar 

  41. Bairoch A. The ENZYME database in 2000. Nucleic Acids Res 2000; 28: 304–5

    Article  PubMed  CAS  Google Scholar 

  42. Mensenkamp AR, Teusink B, Baller JF, et al. Mice expressing only the mutant APOE3Leiden gene show impaired VLDL secretion. Arterioscler Thromb Vasc Biol 2001;21: 1366–72

    Article  PubMed  CAS  Google Scholar 

  43. Huang C, Wahlund LO, Svensson L, et al. Cingulate cortex hypoperfusion predicts Alzheimer’s disease in mild congnitive impairment. BMC Neurol 2002 Sep 12; 2(1): 9

    Article  PubMed  Google Scholar 

  44. Chawla A, Repa JJ, Evans RM, et al. Nuclear receptors and lipid physiology: opening the X-files. Science 2001; 294: 1866–70

    Article  PubMed  CAS  Google Scholar 

  45. Wolfrum C, Borrmann CM, Borchers T, et al. Fatty acids and hypolipidemic drugs regulate peroxisome proliferator-activated receptors alpha- and gamma-mediated gene expression via liver fatty acid binding protein: a signaling path to the nucleus. Proc Natl Acad Sci U S A 2001; 98: 2323–8

    Article  PubMed  CAS  Google Scholar 

  46. Ferrari DM, Nguyen Van P, Kratzin HD, et al. ERp28, a human endoplasmic-reticulum-lumenal protein, is a member of the protein disulfide isomerase family but lacks a CXXC thioredoxin-box motif Eur J Biochem 1998; 255: 570–9

    Article  PubMed  CAS  Google Scholar 

  47. Narayanaswami V, Szeto SS, Ryan RO. Lipid association-induced N- and C-terminal domain reorganization in human apolipoprotein E3. J Biol Chem 2001; 276: 37853–60

    PubMed  CAS  Google Scholar 

  48. Kreeft AJ, Moen CJ, Hofker MH, et al. Identification of differentially regulated genes in mildly hyperlipidemic ApoE3-Leiden mice by use of serial analysis of gene expression. Arterioscler Thromb Vasc Biol 2001; 21: 1984–90

    Article  PubMed  CAS  Google Scholar 

  49. Schild H, Rammensee HG. gp96: the immune system’s Swiss army knife. Nat Immunol 2000; 1: 100–1

    Article  PubMed  CAS  Google Scholar 

  50. Basu S, Binder RJ, Ramalingam T, et al. CD91 is a common receptor for heat shock proteins gp96, hsp90, hsp70, and calreticulin. Immunity 2001; 14: 303–13

    Article  PubMed  CAS  Google Scholar 

  51. Fournier T, Medjoubi NN, Porquet D. Alpha-1-acid glycoprotein. Biochim Biophys Acta 2000; 1482: 157–71

    Article  PubMed  CAS  Google Scholar 

  52. Edlund H. Transcribing pancreas. Diabetes 1998; 47: 1817–23

    Article  PubMed  CAS  Google Scholar 

  53. Bahary N, Zon LI. Development. Endothelium — chicken soup for the endoderm. Science 2001; 294: 530–1

    Article  PubMed  CAS  Google Scholar 

  54. Gene Expression Database (GXD). Mouse Genome Informatics website, The Jackson Laboratory, Bar Harbor, ME, USA [online]. Available from URL: http://www.informatics.jax.org [Accessed 2003 Jan]

  55. Taniguchi Y, Komatsu N, Moriuchi T. Overexpression of the HOX4A (HOXD3) homeobox gene in human erythroleukemia HEL cells results in altered adhesive properties. Blood 1995; 85: 2786–94

    PubMed  CAS  Google Scholar 

  56. Hamada J, Omatsu T, Okada F, et al. Overexpression of homeobox gene HOXD3 induces coordinate expression of metastasis-related genes in human lung cancer cells. Int J Cancer 2001; 93: 516–25

    Article  CAS  Google Scholar 

  57. Glinka A, Wu W, Delius H, et al. Dickkopf-1 is a member of a new family of secreted proteins and functions in head induction. Nature 1998; 391: 357–62

    Article  PubMed  CAS  Google Scholar 

  58. Schier AF, Shen MM. Nodal signalling in vertebrate development. Nature 2000; 403: 385–9

    Article  PubMed  CAS  Google Scholar 

  59. Zorn AM. Wnt signalling: antagonistic Dickkopfs. Curr Biol 2001; 11: R592–5

    Article  PubMed  CAS  Google Scholar 

  60. Polakis P. Wnt signaling and cancer. Genes Dev 2000; 14: 1837–51

    PubMed  CAS  Google Scholar 

  61. Nusse R. The Wnt gene website, Stanford University [online]. Available from URL: http://www.stanford.edu/~rnusse/pathways/targets.html [Accessed 2003 Jan 7]

Download references

Acknowledgements

We thank Temple Smith, Scott Mohr, David Shalloway, Dan Kilburn and Robert McBurney for critical evaluation of the approach and manuscript, and Ted Marple and Stacey Horrigan for technical assistance. This project was supported by Beyond Genomics Inc., Waltham, MA, USA.

Author information

Authors and Affiliations

  1. Beyond Genomics Inc., 40 Bear Hill Road, Waltham, Massachusetts, 02451, USA

    Matej Orešič, Clary B. Clish, Eugene J. Davidov, Eric Neumann, Aram Adourian, Stephen Naylor, Jan van der Greef &  Thomas Plasterer

  2. VTT Biotechnology, Espoo, Finland

    Matej Orešič

  3. Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA

    Eugene J. Davidov

  4. TNO Pharma, Zeist, The Netherlands

    Elwin Verheij, Jack Vogels & Jan van der Greef

  5. Gaubius Laboratorium, TNO Prevention and Health, Leiden, The Netherlands

    Louis M. Havekes

  6. Leiden/Amsterdam Center for Drug Research and the Departments of Cardiology and Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands

    Louis M. Havekes & Jan van der Greef

  7. Aventis, Cambridge, Massachusetts, USA

    Eric Neumann

  8. Boston University School of Medicine, Boston, Massachusetts, USA

    Stephen Naylor

Authors
  1. Matej Orešič
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Clary B. Clish
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eugene J. Davidov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elwin Verheij
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jack Vogels
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Louis M. Havekes
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Eric Neumann
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Aram Adourian
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Stephen Naylor
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jan van der Greef
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Thomas Plasterer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thomas Plasterer.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Orešič, M., Clish, C.B., Davidov, E.J. et al. Phenotype Characterisation Using Integrated Gene Transcript, Protein and Metabolite Profiling. Appl-Bioinformatics 3, 205–217 (2004). https://doi.org/10.2165/00822942-200403040-00002

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2165/00822942-200403040-00002

Keywords

Search

Navigation