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  • Original Article
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Human gene expression profiles of susceptibility and resistance in tuberculosis

Abstract

Tuberculosis (TB) still poses a profound burden on global health, owing to significant morbidity and mortality worldwide. Although a fully functional immune system is essential for the control of Mycobacterium tuberculosis infection, the underlying mechanisms and reasons for failure in part of the infected population remain enigmatic. Here, whole-blood microarray gene expression analyses were performed in TB patients and in latently as well as uninfected healthy controls to define biomarkers predictive of susceptibility and resistance. Fc gamma receptor 1B (FCGRIB)was identified as the most differentially expressed gene, and, in combination with four other markers, produced a high degree of accuracy in discriminating TB patients and latently infected donors. We determined differentially expressed genes unique for active disease and identified profiles that correlated with susceptibility and resistance to TB. Elevated expression of innate immune-related genes in active TB and higher expression of particular gene clusters involved in apoptosis and natural killer cell activity in latently infected donors are likely to be the major distinctive factors determining failure or success in controlling M. tuberculosis infection. The gene expression profiles defined in this study provide valuable clues for better understanding of progression from latent infection to active disease and pave the way for defining predictive correlates of protection in TB.

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References

  1. Smith NH, Hewinson RG, Kremer K, Brosch R, Gordon SV . Myths and misconceptions: the origin and evolution of Mycobacterium tuberculosis. Nat Rev Microbiol 2009; 7: 537–544.

    Article  CAS  Google Scholar 

  2. Chan J, Flynn J . The immunological aspects of latency in tuberculosis. Clin Immunol 2004; 110: 2–12.

    Article  CAS  Google Scholar 

  3. Kaufmann SH, Cole ST, Mizrahi V, Rubin E, Nathan C . Mycobacterium tuberculosis and the host response. J Exp Med 2005; 201: 1693–1697.

    Article  CAS  Google Scholar 

  4. World Health Organization Annual Report 2009. Global Tuberculosis Control—Epidemiology, Strategy, Financing. WHO: Geneva, Switzerland, 2009.

  5. Comas I, Gagneux S . The past and future of tuberculosis research. PLoS Pathog 2009; 5: e1000600.

    Article  Google Scholar 

  6. Chaisson RE, Harrington M . How research can help control tuberculosis. Int J Tuberc Lung Dis 2009; 13: 558–568.

    CAS  PubMed  Google Scholar 

  7. Marais BJ, Raviglione M, Donald PR, Harries AD, Kritski AL, Graham SM et al. Scale up services and research priorities for TB diagnosis, management and control. Call to Action. Lancet 2010; 375: 2179–2191.

    Article  Google Scholar 

  8. Flynn FL, Chan J . Immunology of tuberculosis. Annu Rev Immunol 2001; 19: 93–129.

    Article  CAS  Google Scholar 

  9. Ulrichs T, Kaufmann SH . New insights into the function of granulomas in human tuberculosis. J Pathol 2006; 208: 261–269.

    Article  CAS  Google Scholar 

  10. Dorhoi A, Kaufmann SH . Fine-tuning of T cell responses during infection. Curr Opin Immunol 2009; 21: 367–377.

    Article  CAS  Google Scholar 

  11. Korbel DS, Schneider BE, Schaible UE . Innate immunity in tuberculosis: myths and truth. Microb Infect 2008; 10: 995–1004.

    Article  CAS  Google Scholar 

  12. Russell DG, Cardona P-J, Kim M-J, Allain S, Altare F . Foamy macrophages and the progression of the human tuberculosis granuloma. Nat Immunol 2009; 10: 943–948.

    Article  CAS  Google Scholar 

  13. Quesniaux V, Fremond C, Jacobs M, Parida S, Nicolle D, Yeremeev V et al. Toll-like receptor pathways in the immune responses to mycobacteria. Microbes Infect 2004; 6: 946–959.

    Article  CAS  Google Scholar 

  14. Kaufmann SH . Tuberculosis: deadly combination. Nature 2008; 15: 295–296.

    Article  Google Scholar 

  15. Brill KJ, Li Q, Larkin R, Canaday DH, Kaplan DR, Boom WH et al. Human natural killer cells mediate killing of intracellular Mycobacterium tuberculosis H37Rv via granule-independent mechanisms. Infect Immun 2001; 69: 1755–1765.

    Article  CAS  Google Scholar 

  16. World Health Organization. The Stop TB Strategy, Building on and Enhancing DOTS to Meet the TB-Related Millennium Development Goals. WHO: Geneva, 2006 (WHO/HTM/TB/2006.368).

  17. Jacobsen M, Repsilber D, Gutschmidt A, Neher A, Feldmann K, Mollenkopf HJ et al. Candidate biomarkers for discrimination between infection and disease caused by Mycobacterium tuberculosis. J Mol Med 2007; 85: 613–621.

    Article  CAS  Google Scholar 

  18. Dennis Jr G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC et al. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol 2003; 4: P3.

    Article  Google Scholar 

  19. Huang DW, Sherman BT, Lempicki RA . Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protoc 2009; 4: 44–57.

    Article  CAS  Google Scholar 

  20. Brikos C, O’Neill LA . Signalling of toll-like receptors. Handb Exp Pharmacol 2008; 183: 21–50.

    Article  CAS  Google Scholar 

  21. Takeuchi O, Akira S . Pattern recognition receptors and inflammation. Cell 2010; 140: 805–820.

    Article  CAS  Google Scholar 

  22. Gaucher D, Therrien R, Kettaf N, Angermann BR, Boucher G, Filali-Mouhim A et al. Yellow fever vaccine induces integrated multilineage and polyfunctional immune responses. J Exp Med 2008; 205: 3119–3131.

    Article  CAS  Google Scholar 

  23. Jacobsen M, Mattow J, Repsilber D, Kaufmann SH . Novel strategies to identify biomarkers in tuberculosis. Biol Chem 2008; 389: 487–495.

    Article  CAS  Google Scholar 

  24. Parida SK, Kaufmann SH . The quest for biomarkers in tuberculosis. Drug Discov Today 2010; 15: 148–157.

    Article  CAS  Google Scholar 

  25. Mistry R, Cliff JM, Clayton CL, Beyers N, Mohamed YS, Wilson PA et al. Gene-expression patterns in whole blood identify subjects at risk for recurrent tuberculosis. J Infect Dis 2007; 195: 357–365.

    Article  CAS  Google Scholar 

  26. Glatman-Freedman A, Casadevall A . Serum therapy for tuberculosis revisited: reappraisal of the role of antibody-mediated immunity against Mycobacterium tuberculosis. Clin Microbiol Rev 1998; 11: 514–532.

    Article  CAS  Google Scholar 

  27. Abebe F, Bjune G . The protective role of antibody responses during Mycobacterium tuberculosis infection. Clin Exp Immunol 2009; 157: 235–243.

    Article  CAS  Google Scholar 

  28. Nimmerjahn F, Ravetch JV . Fcγ receptors as regulators of immune responses. Nat Rev Immunol 2008; 8: 34–47.

    Article  CAS  Google Scholar 

  29. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004; 5: R80.

    Article  Google Scholar 

  30. Smyth GK, Michaud J, Scott HS . Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 2005; 21: 2067–2075.

    Article  CAS  Google Scholar 

  31. Ritchie ME, Silver J, Oshlack A, Holmes M, Diyagama D, Holloway A et al. A comparison of background correction methods for two-colour microarrays. Bioinformatics 2007; 23: 2700–2707.

    Article  CAS  Google Scholar 

  32. Smyth GK, Speed T . Normalization of cDNA microarray data. Methods 2003; 31: 265–273.

    Article  CAS  Google Scholar 

  33. Efron B, Tibshirani R . Empirical Bayes methods and false discovery rates for microarrays. Genet Epidemiol 2002; 23: 70–86.

    Article  Google Scholar 

  34. Liaw A, Wiener M . Classification and regression by randomforest. R News 2002; 2: 18–22.

    Google Scholar 

Download references

Acknowledgements

We thank ML Grossman for thoroughly revising the paper, J Weiner for biostatistical support and the Microarray Core Facility unit at the Max Planck Institute for Infection Biology in Berlin for sample processing. This study was funded by Grant number 37772 from the Bill & Melinda Gates Foundation through the Grand Challenges in Global Health Initiative, to all authors except DR

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Correspondence to J Maertzdorf or S H E Kaufmann.

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Maertzdorf, J., Repsilber, D., Parida, S. et al. Human gene expression profiles of susceptibility and resistance in tuberculosis. Genes Immun 12, 15–22 (2011). https://doi.org/10.1038/gene.2010.51

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