Informatics-Driven Infectious Disease Research

  • Bruno Sobral
  • Chunhong Mao
  • Maulik Shukla
  • Dan Sullivan
  • Chengdong Zhang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 273)

Abstract

Informatics-driven approaches change how research and development are conducted, who participates, and enables systems-oriented views of science and research. Most life sciences researchers have a very strong desire for the full integration of data and analysis tools delivered through a single interface. Infectious disease (ID) research and development provides a uniquely challenging and high impact opportunity. The biological complexity of infectious disease systems, which are composed of multiple scales of interactions between potential pathogens, hosts, vectors, and the environment, challenges information resources because of the breadth of organism-organism and organism-environment interactions. Applications of integrated data for ID serves a variety of constituencies, such as clinicians, diagnostician, drug and vaccine developers, and epidemiologists. Thus there is a complexity that makes ID an opportune area in which to develop, deploy and use CyberInfrastructure.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Williams, K.P., Sobral, B.W., Dickerman, A.W.: A Robust Species Tree for the Alphaproteobacteria. J. Bacteriol. 189(13), 4578–4586 (2007)CrossRefGoogle Scholar
  2. 2.
    Williams, K.P., et al.: Phylogeny of Gammaproteobacteria. The Journal of Bacteriology 192(9), 2305–2314 (2010)CrossRefGoogle Scholar
  3. 3.
    Wattam, A.R., et al.: Analysis of ten Brucella genomes reveals evidence for horizontal gene transfer despite a preferred intracellular lifestyle. J. Bacteriol. 191(11), 3569–3579 (2009)CrossRefGoogle Scholar
  4. 4.
    Gillespie, J.J., et al.: An Anomalous Type IV Secretion System in Rickettsia is Evolutionarily Conserved. BMC Microbiology (2009)Google Scholar
  5. 5.
    Xu, Q., Dziejman, M., Mekalanos, J.J.: Determination of the transcriptome of Vibrio cholerae during intraintestinal growth and midexponential phase in vitro. Proc. Natl. Acad. Sci. U.S.A 100(3), 1286–1291 (2003)CrossRefGoogle Scholar
  6. 6.
    Gilchrist, C.A., et al.: Impact of intestinal colonization and invasion on the Entamoeba histolytica transcriptome. Molecular and Biochemical Parasitology 147(2), 163–176 (2006)CrossRefGoogle Scholar
  7. 7.
    van Erp, K., et al.: Role of strain differences on host resistance and the transcriptional response of macrophages to infection with Yersinia enterocolitica. Physiol Genomics 25(1), 75–84 (2006)CrossRefGoogle Scholar
  8. 8.
    Mazandu, G.K., Opap, K., Mulder, N.J.: Contribution of microarray data to the advancement of knowledge on the Mycobacterium tuberculosis interactome: Use of the random partial least squares approach. Infection, Genetics and Evolution 11(1), 181–189 (2011)CrossRefGoogle Scholar
  9. 9.
    Camarena, L., et al.: Molecular Mechanisms of Ethanol-Induced Pathogenesis Revealed by RNA-Sequencing. PLoS Pathog 6(4), e1000834 (2010)CrossRefGoogle Scholar
  10. 10.
    Sharma, C.M., et al.: The primary transcriptome of the major human pathogen Helicobacter pylori. Nature 464(7286), 250–255 (2010)CrossRefGoogle Scholar
  11. 11.
    Sharma, C.M., Vogel, J.: Experimental approaches for the discovery and characterization of regulatory small RNA. Current Opinion in Microbiology 12(5), 536–546 (2009)CrossRefGoogle Scholar
  12. 12.
    Ventura, C.L., et al.: Identification of a Novel <italic>Staphylococcus aureus</italic> Two-Component Leukotoxin Using Cell Surface Proteomics. PLoS ONE 5(7), e11634 (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Dowling, P., et al.: Recent advances in clinical proteomics using mass spectrometry. Bioanalysis 2(9), 1609–1615 (2010)CrossRefGoogle Scholar
  14. 14.
    Fuhrmann, S., Streitz, M., Kern, F.: How flow cytometry is changing the study of TB immunology and clinical diagnosis. Cytometry Part A 73A(11), 1100–1106 (2008)CrossRefGoogle Scholar
  15. 15.
    Bernstein, J.M., et al.: Further observations on the role of Staphylococcus aureus exotoxins and IgE in the pathogenesis of nasal polyposis. The Laryngoscope, n/a–n/a (2010)Google Scholar
  16. 16.
    Kim, H., et al.: Inflammation and Apoptosis in Clostridium difficile Enteritis Is Mediated by PGE2 Up-Regulation of Fas Ligand. Gastroenterology 133(3), 875–886 (2007)CrossRefGoogle Scholar
  17. 17.
    Snyder, E.E., et al.: PATRIC: the VBI PathoSystems Resource Integration Center. Nucleic Acids Res. 35(Database issue), D401–D406 (2007)Google Scholar
  18. 18.
    de la Torre, J.C.: Reverse genetics approaches to combat pathogenic arenaviruses. Antiviral Research 80(3), 239–250 (2008)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Fricke, W.F., Rasko, D.A., Ravel, J.: The Role of Genomics in the Identification, Prediction, and Prevention of Biological Threats. PLoS Biol. 7(10), e1000217 (2009)CrossRefGoogle Scholar
  20. 20.
    Crasta, O.R., et al.: Genome sequence of Brucella abortus vaccine strain S19 compared to virulent strains yields candidate virulence genes. PLoS ONE 3(5), e2193–e2193 (2008)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Shendure, J., Ji, H.: Next-generation DNA sequencing. Nat. Biotech. 26(10), 1135–1145 (2008)CrossRefGoogle Scholar
  22. 22.
    Ibarra, J.A., et al.: Induction of Salmonella pathogenicity island 1 under different growth conditions can affect Salmonella-host cell interactions in vitro. Microbiology 156(4), 1120–1133 (2010)CrossRefGoogle Scholar
  23. 23.
    Gao, Q., et al.: Gene expression diversity among Mycobacterium tuberculosis clinical isolates. Microbiology 151(1), 5–14 (2005)CrossRefGoogle Scholar
  24. 24.
    Wen, S., et al.: Inflammatory Gene Profiles in Gastric Mucosa during Helicobacter pylori Infection in Humans. The Journal of Immunology 172(4), 2595–2606 (2004)Google Scholar
  25. 25.
    Wang, Z., Gerstein, M., Snyder, M.: RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10(1), 57–63 (2009)CrossRefGoogle Scholar
  26. 26.
    Cossart, P., Archambaud, C.: The bacterial pathogen Listeria monocytogenes: an emerging model in prokaryotic transcriptomics. Journal of Biology 8(12), 107 (2009)CrossRefGoogle Scholar
  27. 27.
    Vinayavekhin, N., Homan, E.A., Saghatelian, A.: Exploring Disease through Metabolomics. ACS Chem. Biol. 5(1), 91–103 (2010)CrossRefGoogle Scholar
  28. 28.
    Bochner, B.R.: Global phenotypic characterization of bacteria. FEMS Microbiology Reviews 33(1), 191–205 (2009)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Benson, D.A., et al.: GenBank. Nucleic Acids Research 39(suppl. 1), D32–D37 (2011)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Boutet, E., et al.: UniProtKB/Swiss-Prot: The Manually Annotated Section of the UniProt KnowledgeBase. Methods Mol. Biol. 406, 89–112 (2007)Google Scholar
  31. 31.
    Kanehisa, M., et al.: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Research 38(suppl. 1), D355–D360 (2010)CrossRefGoogle Scholar
  32. 32.
    Barrett, T., et al.: NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Research 37(suppl. 1), D885–D890 (2009)CrossRefGoogle Scholar
  33. 33.
    Parkinson, H., et al.: ArrayExpress—a public repository for microarray gene expression data at the EBI. Nucleic Acids Research 33(suppl. 1), D553–D555 (2005)Google Scholar
  34. 34.
    Ji, L., et al.: NCBI Peptidome: a new repository for mass spectrometry proteomics data. Nucleic Acids Research 38(suppl. 1), D731–D735 (2010)CrossRefGoogle Scholar
  35. 35.
    Wang, Y., et al.: An overview of the PubChem BioAssay resource. Nucleic Acids Research 38(suppl. 1), D255–D266 (2010)CrossRefGoogle Scholar
  36. 36.
    Aurrecoechea, C., et al.: ApiDB: integrated resources for the apicomplexan bioinformatics resource center. Nucleic Acids Research 35(suppl. 1), D427–D430Google Scholar
  37. 37.
    Squires, B., et al.: BioHealthBase: informatics support in the elucidation of influenza virus host–pathogen interactions and virulence. Nucleic Acids Research 36(suppl. 1), D497–D503 (2008)Google Scholar
  38. 38.
    Lawson, D., et al.: VectorBase: a data resource for invertebrate vector genomics. Nucleic Acids Research 37(suppl. 1), D583–D587 (2009)CrossRefGoogle Scholar
  39. 39.
    Henry, C.S., et al.: High-throughput generation, optimization and analysis of genome-scale metabolic models. Nature Biotechnology 28(9), 969–974 (2010)CrossRefGoogle Scholar
  40. 40.
    Dyer, M.D., Murali, T.M., Sobral, B.W.: Computational prediction of host-pathogen protein–protein interactions. Bioinformatics 23(13), i159–i166 (2007)CrossRefGoogle Scholar
  41. 41.
    Bassaganya-Riera, J., Hontecillas, R.: CLA and n-3 PUFA differentially modulate clinical activity and colonic PPAR-responsive gene expression in a pig model of experimental IBD. Clinical Nutrition 25(3), 454–465 (2006)CrossRefGoogle Scholar
  42. 42.
    Ananiadou, S., Sullivan, D., Black, B., Levow, G.-A., Gillespie, J.J., Mao, C., Pyysalo, S., Kolluru, B., Tsujii, J., Sobral, B.: Named Entity Recognition for Bacterial Type IV Secretion Systems (2010) (submitted)Google Scholar
  43. 43.
    Pyysalo, S., Tomoko, O., Cho, H.-C., Sullivan, D., Mao, C., Sobral, B., Tsujii, J., Ananiadou, S.: Towards Event Extraction from Full Texts on Infectious Diseases. In: Proceedings of the 2010 Workshop on Biomedical Natural Language Processing. Association for Computational Linguistics, Uppsala (2010)Google Scholar
  44. 44.
    Noy, N.F., et al.: BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Research 37(suppl. 2), W170–W173 (2009)MathSciNetCrossRefGoogle Scholar
  45. 45.
    Sullivan, D.E., Gabbard Jr., J.L., Shukla, M., Sobral, B.: Data integration for dynamic and sustainable systems biology resources: challenges and lessons learned. Chemistry & Biodiversity 7(5), 1124–1141 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bruno Sobral
    • 1
  • Chunhong Mao
    • 1
  • Maulik Shukla
    • 1
  • Dan Sullivan
    • 1
  • Chengdong Zhang
    • 1
  1. 1.Virginia Bioinformatics Institute at Virginia TechBlacksburgU.S.A.

Personalised recommendations