Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-20T13:22:50.544Z Has data issue: false hasContentIssue false
  • English
  • Français

A classification of tasks for the systematic study of immune response using functional genomics data

Published online by Cambridge University Press:  21 September 2005

C. HEDELER ,
N. W. PATON ,
J. M. BEHNKE ,
J. E. BRADLEY ,
M. G. HAMSHERE  and
K. J. ELSE
C. HEDELER
Affiliation:
School of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK Faculty of Life Sciences, Michael Smith Building, The University of Manchester, Oxford Road, Manchester M13 9PT, UK
N. W. PATON
Affiliation:
School of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
J. M. BEHNKE
Affiliation:
School of Biology, Nottingham University, Nottingham NG7 2RD, UK
J. E. BRADLEY
Affiliation:
School of Biology, Nottingham University, Nottingham NG7 2RD, UK
M. G. HAMSHERE
Affiliation:
School of Biology, Nottingham University, Nottingham NG7 2RD, UK
K. J. ELSE
Affiliation:
Faculty of Life Sciences, Michael Smith Building, The University of Manchester, Oxford Road, Manchester M13 9PT, UK
Get access Rights & Permissions [Opens in a new window]

Abstract

A full understanding of the immune system and its responses to infection by different pathogens is important for the development of anti-parasitic vaccines. A growing number of large-scale experimental techniques, such as microarrays, are being used to gain a better understanding of the immune system. To analyse the data generated by these experiments, methods such as clustering are widely used. However, individual applications of these methods tend to analyse the experimental data without taking publicly available biological and immunological knowledge into account systematically and in an unbiased manner. To make best use of the experimental investment, to benefit from existing evidence, and to support the findings in the experimental data, available biological information should be included in the analysis in a systematic manner. In this review we present a classification of tasks that shows how experimental data produced by studies of the immune system can be placed in a broader biological context. Taking into account available evidence, the classification can be used to identify different ways of analysing the experimental data systematically. We have used the classification to identify alternative ways of analysing microarray data, and illustrate its application using studies of immune responses in mice to infection with the intestinal nematode parasites Trichuris muris and Heligmosomoides polygyrus.

Keywords

classificationsystematic immunological bioinformaticsintestinal nematode
Type
Review Article
Information
Parasitology , Volume 132 , Issue 2 , February 2006 , pp. 157 - 167
Copyright
2005 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bader, G. D., Betel, D. and Hogue, C. W. V. ( 2003). BIND: the Biomolecular Interaction Network Database. Nucleic Acids Research 31, 248–250.CrossRefGoogle Scholar
Bancroft, A. J., Else, K. J. and Grencis, R. K. ( 1994). Low-level infection with Trichuris muris significantly affects the polarization of the CD4 response. European Journal of Immunology 24, 3113–3118.CrossRefGoogle Scholar
Bellaby, T., Robinson, K. and Wakelin, D. ( 1996). Induction of differential T-Helper-Cell responses in mice infected with variants of the parasitic nematode Trichuris muris. Infection and Immunity 64, 791–795.Google Scholar
Blader, I. J., Manger, I. and Boothroyd, J. C. ( 2001). Microarray Analysis Reveals Previously Unknown Changes in Toxoplasma gondii-infected Human Cells. The Journal of Biological Chemistry 276, 24223–24231.CrossRefGoogle Scholar
Blake, J. A., Richardson, J. E., Bult, C. J., Kadin, J. A., Eppig, J. T. and THE MEMBERS OF THE MOUSE GENOME DATABASE GROUP ( 2003). MGD: The Mouse Genome Database. Nucleic Acids Research 31, 193–195.CrossRefGoogle Scholar
Bretscher, P. A., Wei, G., Menon, J. N. and Bielefeldt-Ohmann, H. ( 1992). Establishment of stable, cell-mediated immunity that makes “susceptible” mice resistant to Leishmania major. Science 5069, 539–542.CrossRefGoogle Scholar
Byon, J. Y., Ohira, T., Hirono, I. and Aoki, T. ( 2005). Use of a cDNA microarray to study immunity against viral hemorrhagic septicaemia (VHS) in Japanese flounder (Paralichthys olivaceus) following DNA vaccination. Fish and Shellfish Immunology 18, 135–147.CrossRefGoogle Scholar
Byström, J., Wynn, T. A., Domachowske, J. B. and Rosenberg, H. F. ( 2004). Gene microarray analysis reveals interleukin-5-dependent transcriptional targets in mouse bone marrow. Blood 103, 868–877.Google Scholar
Cornell, M., Paton, N. W., Hedeler, C., Kirby, P., Delneri, D., Hayes, A. and Oliver, S. G. ( 2003). GIMS: an integrated data storage and analysis environment for genomic and functional data. Yeast 20, 1291–1306.CrossRefGoogle Scholar
Cook, D. N., Wang, S., Wang, Y., Howles, G. P., Whitehead, G. S., Berman, K. G., Church, T. D., Frank, B. C., Gaspard, R. M., Yu, Y., Quackenbush, J. and Schwarz, D. A. ( 2004). Genetic regulation of endotoxin-induced airway disease. Genomics 83, 961–969.CrossRefGoogle Scholar
Croker, B. A., Krebs, D. l., Zhang, J. G., Wormald, S., Willson, T. A., Stanley, E. G., Robb, l., Greenhalgh, C. J., Förster, I., Clausen, B. E., Nicola, N. A., Metcalf, D., Hilton, D. J., Roberts, A. W. and Alexander, W. S. ( 2003). SOCS3 negatively regulates IL-6 signaling in vivo. Nature Immunology 4, 540–545.CrossRefGoogle Scholar
Dahl, J. L., Kraus, C. N., Boshoff, H. I. M., Doan, B., Foley, K., Avarbock, D., Kaplan, G., Mizrahi, V., Rubin, H. and Barry III, C. E. ( 2003). The role of RelMtb-mediated adaptation to stationary phase in long-term persistence of Mycobacterium tuberculosis in mice. Proceedings of the National Academy of Sciences, USA 100, 10026–10031.CrossRefGoogle Scholar
Deschoolmeester, M. L. and Else, K. J. ( 2002). Cytokine and chemokine responses underlying acute and chronic Trichuris muris infection. International Reviews of Immunology 21, 439–467.CrossRefGoogle Scholar
Domachowske, J. B., Bonville, C. A., Easton, A. J. and Rosenberg, H. F. ( 2002). Differential expression of proinflammatory cytokine genes in vivo in response to pathogenic and nonpathogenic pneumovirus infections. The Journal of Infectious Diseases 186, 8–14.CrossRefGoogle Scholar
Edwards, A. D., Chaussabel, D., Tomlinson, S., Schulz, O., Sher, A. and Reis E. Sousa C. ( 2003). Relationships among murine CD11c(high) dendritic cell subsets as revealed by baseline gene expression patterns. Journal of Immunology 171, 47–60.CrossRefGoogle Scholar
Else, K. J. and Wakelin, D. ( 1988). The effects of H-2 and non-H-2 genes on the expulsion of the nematode Trichuris muris from inbred and congenic mice. Parasitology 96, 543–550.CrossRefGoogle Scholar
Eppig, J., Blake, J. A., Burkart, D., Goldsmith, C., Lutz, C. and Smith, C. ( 2002). Corralling conditional mutations: a unified resource for mouse phenotypes. Genesis 32, 63–65.CrossRefGoogle Scholar
Gollub, J., Ball, C. A., Binkley, G., Demeter, J., Finkelstein, D. B., Hebert, J. M., Hernandez-boussard, T., Jin, H., Kaloper, M., Matese, J. C., Schroeder, M., Brown, P. O., Botstein, D. and Sherlock, G. ( 2003). The Stanford Microarray Database: data access and quality assessment tools. Nucleic Acids Research 31, 94–96.CrossRefGoogle Scholar
Grencis, R. K. ( 2001). Cytokine regulation of resistance and susceptibility to intestinal nematode infection – from host to parasite. Veterinary Parasitology 100, 45–49.CrossRefGoogle Scholar
Hoffmann, K. F., McCarty, T. C., Segal, D. H., Chiaramonte, M., Hesse, M., Davis, E. M., Cheever, A. W., Meltzer, P. S., Morse, H. C. 3rd and Wynn, T. A. ( 2001). Disease fingerprinting with cDNA microarrays reveals distinct gene expression profiles in lethal type-1 and type-2 cytokine-mediated inflammatory reactions. The FASEB Journal 15, 2545–2547.CrossRefGoogle Scholar
Hubbard, T., Barker, D., Birney, E., Cameron, G., Chen, Y., Clark, L., Cox, T., Cuff, J., Curwen, V., Down, T., Durbin, R., Eyras, E., Gilbert, J., Hammond, M., Huminiecki, L., Kasprzyk, A., Lehvaslaiho, H., Lijnzaad, P., Melsopp, C., Mongin, E., Pettett, R., Pocock, M., Potter, S., Rust, A., Schmidt, E., Searle, S., Slater, G., Smith, J., Spooner, W., Stabenau, A., Stalker, J., Stupka, E., Ureta-Vidal, A., Vastrik, I. and Clamp, M. ( 2002). The Ensembl genome database project. Nucleic Acids Research 30, 38–41.CrossRefGoogle Scholar
Ji, M. J., Su, C., Wu, H. W., Zhu, X., Cai, X. P., Li, C. L., Li, G. F., Wang, Y., Zhang, Z. S. and Wu, G. L. ( 2003). Gene expression profile of CD4+ T cells reveals an interferon signaling suppression associated with progression of experimental Schistosoma japonicum infection. Cellular Immunology 224, 55–61.CrossRefGoogle Scholar
Kanehisa, M., Goto, S., Kawashima, S. and Nakaya, A. ( 2002). The KEGG databases at GenomeNet. Nucleic Acids Research 30, 42–46.CrossRefGoogle Scholar
Kell, D. B. and Oliver, S. G. ( 2003). Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. BioEssays 26, 99–105.Google Scholar
Lang, R., Pauleau, A. L., Parganas, E., Takahashi, Y., Mages, J., Ihle, J. N., Rutschman, R. and Murray, P. J. ( 2003). SOCS3 regulates the plasticity of gp130 signaling. Nature Immunology 4, 546–550.CrossRefGoogle Scholar
Mak, T. W., Penninger, J. M. and Ohashi, P. S. ( 2001). Knockout Mice: A Paradigm Shift in Modern Immunology. Nature Reviews Immunology 1, 11–19.CrossRefGoogle Scholar
Menge, D. M., Behnke, J. M., Lowe, A., Gibson, J. P., Iraqi, F. A., Baker, R. L. and Wakelin, D. ( 2003). Mapping of chromosomal regions influencing immunological responses to gastrointestinal nematode infections in mice. Parasite Immunology 25, 314–349.CrossRefGoogle Scholar
Mueller, A., O'Rourke, J., Grimm, J., Guillemin, K., Dixon, M. F., Lee, A. and Falkow, S. ( 2003). Distinct gene expression profiles characterize the histopathological stages of disease in Helicobacter-induced mucosa-associated lymphoid tissue lymphoma. Proceedings of the National Academy of Sciences, USA 100, 1292–1297.CrossRefGoogle Scholar
Mulder, N., Apweiler, R., Attwood, T., Bairoch, A., Barrell, D., Bateman, A., Binns, D., Biswas, M., Bradley, P., Bork, P., Bucher, P., Copley, R., Courcelle, E., Das, U., Durbin, R., Falquet, L., Fleischmann, W., Griffiths-Jones, S., Haft, D., Harte, N., Hulo, N., Kahn, D., Kanapin, A., Krestyaninova, M., Lopez, R., Letunic, I., Lonsdale, D., Silventoinen, V., Orchard, S., Pagni, M., Peyruc, D., Ponting, C., Selengut, J., Servant, F., Sigrist, C., Vaughan, R. and Zdobnov, E. ( 2003). The InterPro Database, 2003 brings increased coverage and new features. Nucleic Acids Research 31, 315–318.CrossRefGoogle Scholar
Noordewier, M. and Warren, P. V. ( 2001). Gene expression microarrays and the integration of biological knowledge. TRENDS in Biotechnology 19, 412–415.CrossRefGoogle Scholar
Pan, W. ( 2002). A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 18, 546–554.CrossRefGoogle Scholar
Quackenbush, J. ( 2002). Microarray data normalization and transformation. Nature Genetics 32 (Suppl.), 496–501.CrossRefGoogle Scholar
Rahn, W., Redline, R. W. and Blanchard, T. G. ( 2004). Molecular analysis of Helicobacter pylori-associated gastric inflammation in naïve versus immunized mice. Vaccine 23, 807–818.CrossRefGoogle Scholar
Ricciardi-Castagnoli, P. and Granucci, F. ( 2002). Interpretation of the complexity of innate immune responses by functional genomics. Nature Reviews Immunology 2, 1–8.CrossRefGoogle Scholar
Rogner, U. C. and Avner, P. ( 2003). Congenic mice: cutting tools for complex immune disorders. Nature Reviews Immunology 3, 243–251.CrossRefGoogle Scholar
Sherlock, G. ( 2000). Analysis of large-scale gene expression data. Current Opinion in Immunology 12, 201–205.CrossRefGoogle Scholar
THE GENE ONTOLOGY CONSORTIUM ( 2000). Gene Ontology: tool for the unification of biology. Nature Genetics 25, 25–29.
Tong, H. H., Long, J. P., Li, D. and DeMaria, T. F. ( 2004). Alteration of gene expression in human middle ear epithelial cells induced by influenza A virus and its implication for the pathogenesis of otitis media. Microbial Pathogenesis 37, 193–204.CrossRefGoogle Scholar
Wheeler, D. L., Church, D. M., Federhen, S., Lash, A. E., Madden, T. L., Pontius, J. U., Schuler, G. D., Schriml, L. M., Sequeira, E., Tatusova, T. A. and Wagner, L. ( 2003). Database resources of the National Center for Biotechnology. Nucleic Acids Research 31, 28–33.CrossRefGoogle Scholar
Xenarios, I., Salwinski, L., Duan, X. J., Higney, P., Kim, S. M. and Eisenberg, D. ( 2002). DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Research 30, 303–305.CrossRefGoogle Scholar
Supplementary material: PDF

Hedeler Supplementary data file 1

Classification of relevant information

Download Hedeler Supplementary data file 1(PDF)
PDF 131.6 KB
Supplementary material: PDF

Hedeler Supplementary data file 2

Classification of questions/tasks

Download Hedeler Supplementary data file 2(PDF)
PDF 150.2 KB