Human gene expression profiles of susceptibility and resistance in tuberculosis

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.

[1]  S. Kaufmann,et al.  The quest for biomarkers in tuberculosis. , 2010, Drug discovery today.

[2]  Terry Speed,et al.  Normalization of cDNA microarray data. , 2003, Methods.

[3]  U. Schaible,et al.  Innate immunity in tuberculosis: myths and truth. , 2008, Microbes and infection.

[4]  S. Kaufmann,et al.  Mycobacterium tuberculosis and the host response , 2005, The Journal of experimental medicine.

[5]  L. O’Neill,et al.  Signalling of toll-like receptors. , 2008, Handbook of experimental pharmacology.

[6]  S. Kaufmann Tuberculosis: Deadly combination , 2008, Nature.

[7]  Bastian R. Angermann,et al.  Yellow fever vaccine induces integrated multilineage and polyfunctional immune responses , 2008, The Journal of experimental medicine.

[8]  A. Casadevall,et al.  Serum Therapy for Tuberculosis Revisited: Reappraisal of the Role of Antibody-Mediated Immunity againstMycobacterium tuberculosis , 1998, Clinical Microbiology Reviews.

[9]  J. Flynn,et al.  The immunological aspects of latency in tuberculosis. , 2004, Clinical immunology.

[10]  Gordon K. Smyth,et al.  Use of within-array replicate spots for assessing differential expression in microarray experiments , 2005, Bioinform..

[11]  S. Kaufmann,et al.  Fine-tuning of T cell responses during infection. , 2009, Current opinion in immunology.

[12]  Gordon K. Smyth,et al.  A comparison of background correction methods for two-colour microarrays , 2007, Bioinform..

[13]  J. Ravetch,et al.  Fcgamma receptors as regulators of immune responses. , 2008, Nature reviews. Immunology.

[14]  Alimuddin Zumla,et al.  Scale-up of services and research priorities for diagnosis, management, and control of tuberculosis: a call to action , 2010, The Lancet.

[15]  R. Tibshirani,et al.  Empirical bayes methods and false discovery rates for microarrays , 2002, Genetic epidemiology.

[16]  D. Kaplan,et al.  Human Natural Killer Cells Mediate Killing of Intracellular Mycobacterium tuberculosis H37Rv via Granule-Independent Mechanisms , 2001, Infection and Immunity.

[17]  Mi‐jeong Kim,et al.  Foamy macrophages and the progression of the human tuberculosis granuloma , 2009, Nature Immunology.

[18]  B. Ryffel,et al.  Toll-like receptor pathways in the immune responses to mycobacteria. , 2004, Microbes and infection.

[19]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[20]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[21]  G. Bjune,et al.  The protective role of antibody responses during Mycobacterium tuberculosis infection , 2009, Clinical and experimental immunology.

[22]  S. Kaufmann,et al.  New insights into the function of granulomas in human tuberculosis , 2006, The Journal of pathology.

[23]  Brad T. Sherman,et al.  DAVID: Database for Annotation, Visualization, and Integrated Discovery , 2003, Genome Biology.

[24]  I. Comas,et al.  The Past and Future of Tuberculosis Research , 2009, PLoS pathogens.

[25]  Dirk Repsilber,et al.  Candidate biomarkers for discrimination between infection and disease caused by Mycobacterium tuberculosis , 2007, Journal of Molecular Medicine.

[26]  Dirk Repsilber,et al.  Novel strategies to identify biomarkers in tuberculosis , 2008, Biological chemistry.

[27]  Paul D van Helden,et al.  Gene-expression patterns in whole blood identify subjects at risk for recurrent tuberculosis. , 2007, The Journal of infectious diseases.

[28]  R. Chaisson,et al.  How research can help control tuberculosis. , 2009, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[29]  K. Floyd,et al.  The Stop TB Strategy: building on and enhancing DOTS to meet the TB-related Millennium Development Goals. , 2006 .

[30]  S. Akira,et al.  Pattern Recognition Receptors and Inflammation , 2010, Cell.

[31]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[32]  J. Flynn,et al.  Immunology of tuberculosis. , 2003, Annual review of immunology.

[33]  N. Smith,et al.  Myths and misconceptions: the origin and evolution of Mycobacterium tuberculosis , 2009, Nature Reviews Microbiology.