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Cross Species Expression Analysis of Innate Immune Response

  • Yong Lu
  • Roni Rosenfeld
  • Gerard J. Nau
  • Ziv Bar-Joseph
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5541)

Abstract

The innate immune response is the first line of host defense against infections. This system employs a number of different types of cells which in turn activate different sets of genes. Microarray studies of human and mouse cells infected with various pathogens identified hundreds of differentially expressed genes. However, combining these datasets to identify common and unique response patterns remained a challenge. We developed methods based on probabilistic graphical models to combine expression experiments across species, cells and pathogens. Our method analyzes homologous genes in different species concurrently overcoming problems related to noise and orthology assignments. Using our method we identified both core immune response genes and genes that are activated in macrophages in both human and mouse but not in dendritic cells, and vice versa. Our results shed light on immune response mechanisms and on the differences between various types of cells that are used to fight infecting bacteria.

Supporting website: http://www.cs.cmu.edu/~lyongu/pub/immune/

Keywords

Dendritic Cell Innate Immune Response Edge Weight Markov Random Field Gene Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990)CrossRefPubMedGoogle Scholar
  2. 2.
    Bandyopadhyay, S., Sharan, R., Ideker, T.: Systematic identification of functional orthologs based on protein network comparison. Genome Res. 16, 428–435 (2006)CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Bishop, C.M.: Pattern Recognition and Machine Learning, pp. 383–392. Springer, Heidelberg (2006)Google Scholar
  4. 4.
    Bratt, J., Palmblad, J.: Cytokine-induced neutrophil-mediated injury of human endothelial cells. J. Immunol. 159, 912–918 (1997)PubMedGoogle Scholar
  5. 5.
    Chaussabel, D., Semnani, R.T., McDowell, M.A., Sacks, D., Sher, A., Nutman, T.B.: Unique gene expression profiles of human macrophages and dendritic cells to phylogenetically distinct parasites. Blood. 202, 672–681 (2003)CrossRefGoogle Scholar
  6. 6.
    Deng, M., Chen, T., Sun, F.: Integrated probabilistic model for functional prediction proteins. J. Comput. Biol. 11, 435–465 (2004)CrossRefGoogle Scholar
  7. 7.
    Detweiler, C.S., Cunanan, D.B., Falkow, S.: Host microarray analysis reveals a role for the Salmonella response regulator phoP in human macrophage cell death. Proc. Natl. Acad. Sci., USA 98, 5850–5855 (2001)CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Draper, D.W., Bethea, H.N., He, Y.W.: Toll-like receptor 2-dependent and -independent activation of macrophages by group B streptococci. Immunol. Lett. 102, 202–214 (2006)CrossRefPubMedGoogle Scholar
  9. 9.
    Enright, A.J., van Dongen, S., Ouzounis, C.A.: An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 30, 1575–1584 (2002)CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Eppig, J.T., Bult, C.J., Kadin, J.A., Richardson, J.E., Blake, J.A., the members of the Mouse Genome Database Group: The Mouse Genome Database (MGD): from genes to mice—a community resource for mouse biology. Nucleic Acids Res. 33, D471–D475 (2005)CrossRefGoogle Scholar
  11. 11.
    Ernst, J., Bar-Joseph, Z.: STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 7, 191 (2006)CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Fearon, D.T., Locksley, R.M.: The instructive role of innate immunity in the acquired immune response. Science 272, 50–54 (1996)CrossRefPubMedGoogle Scholar
  13. 13.
    Granucci, F., Vizzardelli, C., Pavelka, N., Feau, S., Persico, M., Virzi, E., Rescigno, M., Moro, G., Ricciardi-Castagnoli, P.: Inducible il-2 production by dendritic cells revealed by global gene expression analysis. Nat. Immunol. 2, 882–888 (2001)CrossRefPubMedGoogle Scholar
  14. 14.
    Grote, M.J., Huckle, T.: Parallel Preconditioning with Sparse Approximate Inverses. SIAM J. Sci. Comput. 18, 838–853 (1997)CrossRefGoogle Scholar
  15. 15.
    Hoffmann, J.A., Kafatos, F.C., Janeway Jr., C.A., Ezekowitz, R.A.B.: Phylogenetic perspectives in innate immunity. Science 284, 1313–1318 (1999)CrossRefPubMedGoogle Scholar
  16. 16.
    Hoffmann, R., van Erp, K., Trulzsch, K., Heesemann, J.: Transcriptional responses of murine macrophages to infection with yersinia enterocolitica. Cell Microbiol. 6, 377–390 (2004)CrossRefPubMedGoogle Scholar
  17. 17.
    Huang, Q., Liu, D., Majewski, P., Schulte, L.C., Korn, J.M., Young, R.A., Lander, E.S., Hacohen, N.: The Plasticity of Dendritic Cell Responses to Pathogens and Their Components. Science 294, 870–875 (2001)CrossRefPubMedGoogle Scholar
  18. 18.
    Jenner, R.G., Young, R.A.: Insights into host responses against pathogens from transcriptional profiling. Nat. Rev. Microbiol. 3, 281–294 (2005)CrossRefPubMedGoogle Scholar
  19. 19.
    Keller, G., Snodgrass, R.: Life span of multipotential hematopoietic stem cells in vivo. J. Exp. Med. 171, 1407–1418 (1990)CrossRefPubMedGoogle Scholar
  20. 20.
    Kelley, J., Bono, B.D., Trowsdale, J.: IRIS: a database surveying known human immune system genes. Genomics 85, 503–511 (2005)CrossRefPubMedGoogle Scholar
  21. 21.
    Lammers, K.M., Brigidi, P., Vitali, B., Gionchetti, P., Rizzello, F., Caramelli, E., Matteuzzi, D., Campieri, M.: Immunomodulatory effects of probiotic bacteria DNA: IL-1 and IL-10 response in human peripheral blood mononuclear cells. FEMS Immunol Med. Microbiol. 22, 165–172 (2003)CrossRefGoogle Scholar
  22. 22.
    Lang, R., Patel, D., Morris, J.J., Rutschman, R.L., Murray, P.J.: Shaping Gene Expression in Activated and Resting PrimaryMacrophages by IL-10. J. Immunol. 169, 2253–2263 (2002)CrossRefPubMedGoogle Scholar
  23. 23.
    Lee, H.C., Goodman, J.L.: Anaplasma phagocytophilum causes global induction of antiapoptosis in human neutrophils. Genomics 88, 496–503 (2006)CrossRefPubMedGoogle Scholar
  24. 24.
    Letovsky, S., Kasif, S.: Predicting protein function from protein/protein interaction data: a probabilistic approach. Bioinformatics 19(suppl. 1), i197–i204 (2003)CrossRefGoogle Scholar
  25. 25.
    Liu, M., Liberzon, A., Kong, S.W., Lai, W.R., Park, P.J., Kohane, I.S., Kasif, S.: Network-Based Analysis of Affected Biological Processes in Type 2 Diabetes Models. PLoS Genet. 3, e96 (2007)CrossRefGoogle Scholar
  26. 26.
    Lu, Y., Rosenfeld, R., Bar-Joseph, Z.: Identifying Cycling Genes by Combining Sequence Homology and Expression Data. Bioinformatics 22, e314–e322 (2006)CrossRefGoogle Scholar
  27. 27.
    Lu, Y., Mahony, S., Benos, P.V., Rosenfeld, R., Simon, I., Breeden, L.L., Bar-Joseph, Z.: Combined Analysis Reveals a Core Set of Cycling Genes. Genome Biol. 8, R146 (2007)CrossRefGoogle Scholar
  28. 28.
    Lukacs, N.W., Strieter, R.M., Chensue, S.W., Widmer, M., Kunkel, S.L.: TNF-alpha mediates recruitment of neutrophils and eosinophils during airway inflammation. J. Immunol. 154, 5411–5417 (1995)PubMedGoogle Scholar
  29. 29.
    McCaffrey, R.L., Fawcett, P., O’Riordan, M., Lee, K.-D., Havell, E.A., Brown, P.O., Portnoy, D.A.: From the Cover: A specific gene expression program triggered by Grampositive bacteria in the cytosol. Proc. Natl. Acad. Sci. USA 101, 11386–11391 (2004)CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Mignon, C., Okada, A., Mattei, M.G., Basset, P.: Assignment of the human membrane-type matrix metalloproteinase (MMP14) gene to 14q11-q12 by in situ hybridization. Genomics 28, 360–361 (1995)CrossRefPubMedGoogle Scholar
  31. 31.
    Mizutani, H., Schechter, N., Lazarus, G., Black, R.A., Kupper, T.S.: Rapid and specific conversion of precursor interleukin 1 beta (IL-1 beta) to an active IL-1 species by human mast cell chymase. J. Exp. Med. 174, 821–825 (1991)CrossRefPubMedGoogle Scholar
  32. 32.
    Nau, G.J., Richmond, J.F.L., Schlesinger, A., Jennings, E.G., Lander, E.S., Young, R.A.: Human macrophage activation programs induced by bacterial pathogens. Proc. Natl. Acad. Sci. USA 99, 1503–1508 (2002)CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Nelder, J.A., Mead, R.: A Simplex Method for Function Minimization. Comput. J. 7, 308–313 (1965)CrossRefGoogle Scholar
  34. 34.
    Sun, Z., Andersson, R.: NF-kappaB activation and inhibition: a review. Shock 18, 99–106 (2002)CrossRefPubMedGoogle Scholar
  35. 35.
    Valbuena, G., Bradford, W., Walker, D.H.: Expression analysis of the T-cell-targeting chemokines CXCL9 and CXCL10 in mice and humans with endothelial infections caused by rickettsiae of the spotted fever group. Am. J. Pathol. 163, 1357–1369 (2003)CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Van Erp, K., Dach, K., Koch, I., Heesemann, J., Hoffmann, R.: Role of strain differences on host resistance and the transcriptional response of macrophages to infection with Yersinia enterocolitica. Physiol Genomics 25, 75–84 (2006)CrossRefPubMedGoogle Scholar
  37. 37.
    Wolpe, S.D., Davatelis, G., Sherry, B., Beutler, B., Hesse, D.G., Nguyen, H.T., Moldawer, L.L., Nathan, C.F., Lowry, S.F., Cerami, A.: Macrophages secrete a novel heparin-binding protein with inflammatory and neutrophil chemokinetic properties. J. Exp. Med. 167, 570–581 (1988)CrossRefPubMedGoogle Scholar
  38. 38.
    Yedidia, J.S., Freeman, W.T., Weiss, Y.: Understanding belief propagation and its generalizations. In: Exploring Artificial Intelligence in the New Millennium, pp. 236–239. Morgan Kaufmann Publishers Inc., San Francisco (2003)Google Scholar
  39. 39.
    Zhu, X.: Semi-Supervised Learning with Graphs. Ph.D. Thesis, Carnegie Mellon University, CMU-LTI-05-192 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yong Lu
    • 1
    • 3
  • Roni Rosenfeld
    • 1
  • Gerard J. Nau
    • 2
  • Ziv Bar-Joseph
    • 1
  1. 1.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department of Molecular Genetics and BiochemistryUniversity of Pittsburgh Medical SchoolPittsburghUSA
  3. 3.Present address: Department of Biological Chemistry and Molecular PharmacologyHarvard Medical SchoolBostonUSA

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