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  • Review Article
  • Published:

Immunological biomarkers of tuberculosis

Nature Reviews Immunology volume 11, pages 343–354 (2011)Cite this article

Subjects

Key Points

  • Tuberculosis remains a serious global health problem and a lack of suitable biomarkers is holding back the evaluation of new tuberculosis vaccine candidates, the improvement of diagnostics and the development of more effective and shorter treatment regimens.

  • The stages of host–pathogen interactions include: an innate immune phase, during which Mycobacterium tuberculosis may be cleared without the sensitization of B or T cells; an adaptive immune phase in which immunological sensitization does take place; a quiescent infection phase during which the immune system prevents replication of M. tuberculosis in granulomas but fails to eradicate the organism; and a replicating phase, which can lead to clinical disease and the uncontrolled dissemination of bacteria.

  • Correlates of protection against tuberculosis and correlates of risk for tuberculosis will facilitate screening of new vaccine candidates, but there is currently a lack of such correlates. Several immune responses have been recognized as crucial for protection against tuberculosis, including interferon-γ production, CD4+ T cell responses (involving an optimal balance between TH1, TH2, TH17 and TReg cells) and polyfunctional T cell responses. However, these responses are not sufficient for protection and do not therefore represent correlates of risk or protection.

  • Combinations of host molecules, including cytokines, acute-phase proteins, proteins released during tissue damage and serological markers, may constitute diagnostic tests for active tuberculosis in the future.

  • Host immunological markers measured pretreatment, during early treatment and during the final months of treatment reflect bacterial burden and the level of inflammation and may aid new drug development and the clinical management of individual patients.

  • The lack of suitable biomarkers for tuberculosis suggests that appropriate markers should be sought through unbiased 'omics' approaches, followed by thorough hypothesis-driven investigations to develop qualified biomarkers.

Abstract

Currently there are no sufficiently validated biomarkers to aid the evaluation of new tuberculosis vaccine candidates, the improvement of tuberculosis diagnostics or the development of more effective and shorter treatment regimens. To date, the detection of Mycobacterium tuberculosis or its products has not been able to adequately address these needs. Understanding the interplay between the host immune system and M. tuberculosis may provide a platform for the identification of suitable biomarkers, through both unbiased and targeted hypothesis-driven approaches. Here, we review immunological markers, their relation to M. tuberculosis infection stages and their potential use in the fight against tuberculosis.

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Figure 1: Immune responses and potential host biomarkers of Mycobacterium tuberculosis exposure and infection.
Figure 2: Differential outcomes of tuberculosis treatment are associated with different infection phases.
Figure 3: Tuberculosis treatment and the potential role of biomarkers in clinical decision making and clinical trials.

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References

  1. World Health Organisation. Multidrug and extensively drug-resistant TB (M/XDR-TB): 2010 global report on surveillance and response. WHO [online], (2010).

  2. Corbett, E. L. et al. The growing burden of tuberculosis: global trends and interactions with the HIV epidemic. Arch. Intern. Med. 163, 1009–1021 (2003).

    Article  PubMed  Google Scholar 

  3. Abu-Raddad, L. J. et al. Epidemiological benefits of more-effective tuberculosis vaccines, drugs, and diagnostics. Proc. Natl Acad. Sci. USA 106, 13980–13985 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Davies, P. D. & Pai, M. The diagnosis and misdiagnosis of tuberculosis. Int. J. Tuberc. Lung Dis. 12, 1226–1234 (2008).

    CAS  PubMed  Google Scholar 

  5. Boehme, C. C. et al. Rapid molecular detection of tuberculosis and rifampin resistance. N. Engl. J. Med. 363, 1005–1015 (2010). This paper describes the biggest breakthrough in tuberculosis diagnostics in decades: a direct ex vivo M. tuberculosis gene amplification test for the diagnosis and detection of rifampicin resistance.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Black, G. F. et al. BCG-induced increase in interferon-γ response to mycobacterial antigens and efficacy of BCG vaccination in Malawi and the UK: two randomised controlled studies. Lancet 359, 1393–1401 (2002).

    Article  PubMed  Google Scholar 

  7. Barry, C. E. et al. The spectrum of latent tuberculosis: rethinking the biology and intervention strategies. Nature Rev. Microbiol. 7, 845–855 (2009).

    Article  CAS  Google Scholar 

  8. Sudre, P., ten Dam, G. & Kochi, A. Tuberculosis: a global overview of the situation today. Bull. World Health Organ. 70, 149–159 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Lawn, S. D. & Churchyard, G. Epidemiology of HIV-associated tuberculosis. Curr. Opin. HIV AIDS 4, 325–333 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ottenhoff, T. H., Verreck, F. A., Hoeve, M. A. & van deVosse, E. Control of human host immunity to mycobacteria. Tuberculosis 85, 53–64 (2005).

    Article  CAS  PubMed  Google Scholar 

  11. Lawn, S. D., Myer, L., Edwards, D., Bekker, L. G. & Wood, R. Short-term and long-term risk of tuberculosis associated with CD4 cell recovery during antiretroviral therapy in South Africa. AIDS 23, 1717–1725 (2009).

    Article  PubMed  Google Scholar 

  12. Khader, S. A. et al. IL-23 and IL-17 in the establishment of protective pulmonary CD4+ T cell responses after vaccination and during Mycobacterium tuberculosis challenge. Nature Immunol. 8, 369–377 (2007).

    Article  CAS  Google Scholar 

  13. Vordermeier, H. M. et al. Viral booster vaccines improve Mycobacterium bovis BCG-induced protection against bovine tuberculosis. Infect. Immun. 77, 3364–3373 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Green, A. M. et al. CD4+ regulatory T cells in a cynomolgus macaque model of Mycobacterium tuberculosis infection. J. Infect. Dis. 202, 533–541 (2010).

    Article  CAS  PubMed  Google Scholar 

  15. Lazar-Molnar, E. et al. Programmed death-1 (PD-1)-deficient mice are extraordinarily sensitive to tuberculosis. Proc. Natl Acad. Sci. USA 107, 13402–13407 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Bruns, H. et al. Anti-TNF immunotherapy reduces CD8+ T cell-mediated antimicrobial activity against Mycobacterium tuberculosis in humans. J. Clin. Invest. 119, 1167–1177 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Stenger, S. et al. An antimicrobial activity of cytolytic T cells mediated by granulysin. Science 282, 121–125 (1998).

    Article  CAS  PubMed  Google Scholar 

  18. Cooper, A. M. Cell-mediated immune responses in tuberculosis. Annu. Rev. Immunol. 27, 393–422 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gallegos, A. M., Pamer, E. G. & Glickman, M. S. Delayed protection by ESAT-6-specific effector CD4+ T cells after airborne M. tuberculosis infection. J. Exp. Med. 205, 2359–2368 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Gonzalez-Juarrero, M. et al. Temporal and spatial arrangement of lymphocytes within lung granulomas induced by aerosol infection with Mycobacterium tuberculosis. Infect. Immun. 69, 1722–1728 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Qin, L., Gilbert, P. B., Corey, L., McElrath, M. J. & Self, S. G. A framework for assessing immunological correlates of protection in vaccine trials. J. Infect. Dis. 196, 1304–1312 (2007).

    Article  PubMed  Google Scholar 

  22. Comstock, G. W. Field trials of tuberculosis vaccines: how could we have done them better? Control. Clin. Trials 15, 247–276 (1994).

    Article  CAS  PubMed  Google Scholar 

  23. Kagina, B. M. et al. Specific T cell frequency and cytokine expression profile do not correlate with protection against tuberculosis after bacillus Calmette-Guerin vaccination of newborns. Am. J. Respir. Crit. Care Med. 182, 1073–1079 (2010). This study shows that polyfunctional T cells and cytokine expression do not correlate with BCG-induced protection against tuberculosis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Darrah, P. A. et al. Multifunctional TH1 cells define a correlate of vaccine-mediated protection against Leishmania major. Nature Med. 13, 843–850 (2007).

    Article  CAS  PubMed  Google Scholar 

  25. Chen, C. Y. et al. A critical role for CD8 T cells in a nonhuman primate model of tuberculosis. PLoS Pathog. 5, e1000392 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Giri, P. K., Verma, I. & Khuller, G. K. Enhanced immunoprotective potential of Mycobacterium tuberculosis Ag85 complex protein based vaccine against airway Mycobacterium tuberculosis challenge following intranasal administration. FEMS Immunol. Med. Microbiol. 47, 233–241 (2006).

    Article  CAS  PubMed  Google Scholar 

  27. Agger, E. M. et al. Protective immunity to tuberculosis with Ag85B-ESAT-6 in a synthetic cationic adjuvant system IC31. Vaccine 24, 5452–5460 (2006).

    Article  CAS  PubMed  Google Scholar 

  28. Bennekov, T. et al. Alteration of epitope recognition pattern in Ag85B and ESAT-6 has a profound influence on vaccine-induced protection against Mycobacterium tuberculosis. Eur. J. Immunol. 36, 3346–3355 (2006).

    Article  CAS  PubMed  Google Scholar 

  29. Hoft, D. F. et al. Investigation of the relationships between immune-mediated inhibition of mycobacterial growth and other potential surrogate markers of protective Mycobacterium tuberculosis immunity. J. Infect. Dis. 186, 1448–1457 (2002).

    Article  CAS  PubMed  Google Scholar 

  30. Cheon, S. H. et al. Bactericidal activity in whole blood as a potential surrogate marker of immunity after vaccination against tuberculosis. Clin. Diagn. Lab. Immunol. 9, 901–907 (2002).

    PubMed  PubMed Central  Google Scholar 

  31. Spencer, C. T., Abate, G., Blazevic, A. & Hoft, D. F. Only a subset of phosphoantigen-responsive γ9δ2 T cells mediate protective tuberculosis immunity. J. Immunol. 181, 4471–4484 (2008).

    Article  CAS  PubMed  Google Scholar 

  32. Wallis, R. S. et al. Biomarkers and diagnostics for tuberculosis: progress, needs, and translation into practice. Lancet 375, 1920–1937 (2010). This is a comprehensive review of diagnostic and other biomarkers for tuberculosis.

    Article  CAS  PubMed  Google Scholar 

  33. Lalvani, A. et al. Enhanced contact tracing and spatial tracking of Mycobacterium tuberculosis infection by enumeration of antigen-specific T cells. Lancet 357, 2017–2021 (2001).

    Article  CAS  PubMed  Google Scholar 

  34. Mori, T. et al. Specific detection of tuberculosis infection: an interferon-γ-based assay using new antigens. Am. J. Respir. Crit. Care Med. 170, 59–64 (2004).

    Article  PubMed  Google Scholar 

  35. Ferrara, G. et al. Exploring the immune response against Mycobacterium tuberculosis for a better diagnosis of the infection. Arch. Immunol. Ther. Exp. 57, 425–433 (2009).

    Article  Google Scholar 

  36. Pai, M., Zwerling, A. & Menzies, D. Systematic review: T-cell-based assays for the diagnosis of latent tuberculosis infection: an update. Ann. Intern. Med. 149, 177–184 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Mazurek, G. H. et al. Prospective comparison of the tuberculin skin test and 2 whole-blood interferon-γ release assays in persons with suspected tuberculosis. Clin. Infect. Dis. 45, 837–845 (2007).

    Article  CAS  PubMed  Google Scholar 

  38. Janssens, J. P. Interferon-γ release assay tests to rule out active tuberculosis. Eur. Respir. J. 30, 183–184 (2007).

    Article  PubMed  Google Scholar 

  39. Losi, M. et al. Use of a T-cell interferon-γ release assay for the diagnosis of tuberculous pleurisy. Eur. Respir. J. 30, 1173–1179 (2007).

    Article  CAS  PubMed  Google Scholar 

  40. Thomas, M. M. et al. Rapid diagnosis of Mycobacterium tuberculosis meningitis by enumeration of cerebrospinal fluid antigen-specific T-cells. Int. J. Tuberc. Lung Dis. 12, 651–657 (2008).

    CAS  PubMed  Google Scholar 

  41. Chegou, N. N., Black, G. F., Kidd, M., van Helden, P. D. & Walzl, G. Host markers in QuantiFERON supernatants differentiate active TB from latent TB infection: preliminary report. BMC Pulm. Med. 9, 21 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wu, B. et al. Messenger RNA expression of IL-8, FOXP3, and IL-12β differentiates latent tuberculosis infection from disease. J. Immunol. 178, 3688–3694 (2007).

    Article  CAS  PubMed  Google Scholar 

  43. Djoba Siawaya, J. F. et al. Differential cytokine/chemokines and KL-6 profiles in patients with different forms of tuberculosis. Cytokine 47, 132–136 (2009).

    Article  CAS  PubMed  Google Scholar 

  44. Caccamo, N. et al. Multifunctional CD4+ T cells correlate with active Mycobacterium tuberculosis infection. Eur. J. Immunol. 40, 2211–2220 (2010).

    Article  CAS  PubMed  Google Scholar 

  45. Casey, R. et al. Enumeration of functional T-cell subsets by fluorescence-immunospot defines signatures of pathogen burden in tuberculosis. PLoS ONE 5, e15619 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Harari, A. et al. Dominant TNF-α+Mycobacterium tuberculosis-specific CD4+ T cell responses discriminate between latent infection and active disease. Nature Med. 17, 372–376 (2011).

    Article  CAS  PubMed  Google Scholar 

  47. Steingart, K. R. et al. A systematic review of commercial serological antibody detection tests for the diagnosis of extrapulmonary tuberculosis. Thorax 62, 911–918 (2007).

    PubMed  PubMed Central  Google Scholar 

  48. Steingart, K. R. et al. Performance of purified antigens for serodiagnosis of pulmonary tuberculosis: a meta-analysis. Clin. Vaccine Immunol. 16, 260–276 (2009).

    Article  CAS  PubMed  Google Scholar 

  49. Kunnath-Velayudhan, S. et al. Dynamic antibody responses to the Mycobacterium tuberculosis proteome. Proc. Natl Acad. Sci. USA 107, 14703–14708 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Hesseling, A. C. et al. Baseline sputum time to detection predicts month two culture conversion and relapse in non-HIV-infected patients. Int. J. Tuberc. Lung Dis. 14, 560–570 (2010).

    CAS  PubMed  Google Scholar 

  51. Johnson, J. L. et al. Shortening treatment in adults with noncavitary tuberculosis and 2-month culture conversion. Am. J. Respir. Crit. Care Med. 180, 558–563 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Chee, C. B. et al. Tuberculosis treatment effect on T-cell interferon-γ responses to Mycobacterium tuberculosis-specific antigens. Eur. Respir. J. 36, 355–361 (2010).

    Article  CAS  PubMed  Google Scholar 

  53. Sai Priya, V. H., Latha, G. S., Hasnain, S. E., Murthy, K. J. & Valluri, V. L. Enhanced T cell responsiveness to Mycobacterium bovis BCG r32-kDa Ag correlates with successful anti-tuberculosis treatment in humans. Cytokine 52, 190–193 (2010).

    Article  CAS  PubMed  Google Scholar 

  54. Wassie, L. et al. Ex vivo cytokine mRNA levels correlate with changing clinical status of Ethiopian TB patients and their contacts over time. PLoS ONE 3, e1522 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Millington, K. A. et al. Dynamic relationship between IFN-γ and IL-2 profile of Mycobacterium tuberculosis-specific T cells and antigen load. J. Immunol. 178, 5217–5226 (2007).

    Article  CAS  PubMed  Google Scholar 

  56. Millington, K. A., Gooding, S., Hinks, T. S., Reynolds, D. J. & Lalvani, A. Mycobacterium tuberculosis-specific cellular immune profiles suggest bacillary persistence decades after spontaneous cure in untreated tuberculosis. J. Infect. Dis. 202, 1685–1689 (2010). This paper demonstrates that in some individuals T EM cells persist more than 50 years after spontaneous clinical cure of tuberculosis, suggesting the persistence of antigen. In some cured patients, however, only T CM cells are present, and this is consistent with sterilizing cure.

    Article  CAS  PubMed  Google Scholar 

  57. Bahk, Y. Y. et al. Antigens secreted from Mycobacterium tuberculosis: identification by proteomics approach and test for diagnostic marker. Proteomics 4, 3299–3307 (2004).

    Article  CAS  PubMed  Google Scholar 

  58. Berry, M. P. et al. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature 466, 973–977 (2010). This work illustrates a role for neutrophil-driven type I IFN signalling in M. tuberculosis pathogenesis and the importance of cell-specific transcriptome analysis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Jacobsen, M. et al. Candidate biomarkers for discrimination between infection and disease caused by Mycobacterium tuberculosis. J. Mol. Med. 85, 613–621 (2007).

    Article  CAS  PubMed  Google Scholar 

  60. Mistry, R. et al. Gene-expression patterns in whole blood identify subjects at risk for recurrent tuberculosis. J. Infect. Dis. 195, 357–365 (2007).

    Article  CAS  PubMed  Google Scholar 

  61. Maertzdorf, J. et al. Human gene expression profiles of susceptibility and resistance in tuberculosis. Genes Immun. 12, 15–22 (2011).

    Article  CAS  PubMed  Google Scholar 

  62. Repsilber, D. et al. Biomarker discovery in heterogeneous tissue samples – taking the in-silico deconfounding approach. BMC Bioinformatics 11, 27 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Sartain, M. J., Slayden, R. A., Singh, K. K., Laal, S. & Belisle, J. T. Disease state differentiation and identification of tuberculosis biomarkers via native antigen array profiling. Mol. Cell. Proteomics 5, 2102–2113 (2006).

    Article  CAS  PubMed  Google Scholar 

  64. O'Connell, R. M., Rao, D. S., Chaudhuri, A. A. & Baltimore, D. Physiological and pathological roles for microRNAs in the immune system. Nature Rev. Immunol. 10, 111–122 (2010).

    Article  CAS  Google Scholar 

  65. Liu, Q. et al. Serum protein profiling of smear-positive and smear-negative pulmonary tuberculosis using SELDI-TOF mass spectrometry. Lung 188, 15–23 (2010).

    Article  CAS  PubMed  Google Scholar 

  66. Agranoff, D. et al. Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum. Lancet 368, 1012–1021 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. de Carvalho, L. P. et al. Activity-based metabolomic profiling of enzymatic function: identification of Rv1248c as a mycobacterial 2-hydroxy-3-oxoadipate synthase. Chem. Biol. 17, 323–332 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Koulman, A., Lane, G. A., Harrison, S. J. & Volmer, D. A. From differentiating metabolites to biomarkers. Anal. Bioanal. Chem. 394, 663–670 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. World Health Organisation. Treatment of tuberculosis: guidelines 4th edn. WHO [online], (2010).

  70. Diel, R., Loddenkemper, R., Meywald-Walter, K., Niemann, S. & Nienhaus, A. Predictive value of a whole blood IFN-γ assay for the development of active tuberculosis disease after recent infection with Mycobacterium tuberculosis. Am. J. Respir. Crit. Care Med. 177, 1164–1170 (2008).

    Article  PubMed  Google Scholar 

  71. Doherty, T. M. et al. Immune responses to the Mycobacterium tuberculosis-specific antigen ESAT-6 signal subclinical infection among contacts of tuberculosis patients. J. Clin. Microbiol. 40, 704–706 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Mattos, A. M. et al. Increased IgG1, IFN-γ, TNF-α and IL-6 responses to Mycobacterium tuberculosis antigens in patients with tuberculosis are lower after chemotherapy. Int. Immunol. 22, 775–782 (2010).

    Article  CAS  PubMed  Google Scholar 

  73. Veenstra, H. et al. Changes in the kinetics of intracellular IFN-γ production in TB patients during treatment. Clin. Immunol. 124, 336–344 (2007).

    Article  CAS  PubMed  Google Scholar 

  74. Goletti, D. et al. Is IP-10 an accurate marker for detecting M. tuberculosis-specific response in HIV-infected persons? PLoS ONE 5, e12577 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Ruhwald, M. et al. Evaluating the potential of IP-10 and MCP-2 as biomarkers for the diagnosis of tuberculosis. Eur. Respir. J. 32, 1607–1615 (2008).

    Article  CAS  PubMed  Google Scholar 

  76. Ruhwald, M., Bjerregaard-Andersen, M., Rabna, P., Eugen-Olsen, J. & Ravn, P. IP-10, MCP-1, MCP-2, MCP-3, and IL-1RA hold promise as biomarkers for infection with M. tuberculosis in a whole blood based T-cell assay. BMC Res. Notes 2, 19 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Smith, S. G. et al. Mycobacterium tuberculosis PPD-induced immune biomarkers measurable in vitro following BCG vaccination of UK adolescents by multiplex bead array and intracellular cytokine staining. BMC Immunol. 11, 35 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Demissie, A. et al. Healthy individuals that control a latent infection with Mycobacterium tuberculosis express high levels of Th1 cytokines and the IL-4 antagonist IL-4δ2. J. Immunol. 172, 6938–6943 (2004).

    Article  CAS  PubMed  Google Scholar 

  79. Ordway, D. J. et al. Increased Interleukin-4 production by CD8 and γδ T cells in health-care workers is associated with the subsequent development of active tuberculosis. J. Infect. Dis. 190, 756–766 (2004).

    Article  CAS  PubMed  Google Scholar 

  80. Lee, J. H. & Chang, J. H. Changes of plasma interleukin-1 receptor antagonist, interleukin-8 and other serologic markers during chemotherapy in patients with active pulmonary tuberculosis. Korean J. Intern. Med. 18, 138–145 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Sutherland, J. S., de Jong, B. C., Jeffries, D. J., Adetifa, I. M. & Ota, M. O. Production of TNF-α, IL-12(p40) and IL-17 can discriminate between active TB disease and latent infection in a West African cohort. PLoS ONE 5, e12365 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Djoba Siawaya, J. F. et al. Differential expression of interleukin-4 (IL-4) and IL-4δ2 mRNA, but not transforming growth factor beta (TGF-β), TGF-βRII, Foxp3, gamma interferon, T-bet, or GATA-3 mRNA, in patients with fast and slow responses to antituberculosis treatment. Clin. Vaccine Immunol. 15, 1165–1170 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Djoba Siawaya, J. F. et al. Immune parameters as markers of tuberculosis extent of disease and early prediction of anti-tuberculosis chemotherapy response. J. Infect. 56, 340–347 (2008).

    Article  PubMed  Google Scholar 

  84. Eugen-Olsen, J. et al. The serum level of soluble urokinase receptor is elevated in tuberculosis patients and predicts mortality during treatment: a community study from Guinea-Bissau. Int. J. Tuberc. Lung Dis. 6, 686–692 (2002).

    CAS  PubMed  Google Scholar 

  85. Demir, T., Yalcinoz, C., Keskinel, I., Demiroz, F. & Yildirim, N. sICAM-1 as a serum marker in the diagnosis and follow-up of treatment of pulmonary tuberculosis. Int. J. Tuberc. Lung Dis. 6, 155–159 (2002).

    CAS  PubMed  Google Scholar 

  86. Mukae, H. et al. Elevated levels of circulating adhesion molecules in patients with active pulmonary tuberculosis. Respirology 8, 326–331 (2003).

    Article  PubMed  Google Scholar 

  87. Chan, C. H., Lai, C. K., Leung, J. C., Ho, A. S. & Lai, K. N. Elevated interleukin-2 receptor level in patients with active pulmonary tuberculosis and the changes following anti-tuberculosis chemotherapy. Eur. Respir. J. 8, 70–73 (1995).

    Article  CAS  PubMed  Google Scholar 

  88. Tsao, T. C. et al. Imbalances between tumor necrosis factor-α and its soluble receptor forms, and interleukin-1β and interleukin-1 receptor antagonist in BAL fluid of cavitary pulmonary tuberculosis. Chest 117, 103–109 (2000).

    Article  CAS  PubMed  Google Scholar 

  89. Djoba Siawaya, J. F., Ruhwald, M., Eugen-Olsen, J. & Walzl, G. Correlates for disease progression and prognosis during concurrent HIV/TB infection. Int. J. Infect. Dis. 11, 289–299 (2007).

    Article  CAS  PubMed  Google Scholar 

  90. Rosas-Taraco, A. G. et al. Expression of CDllc in blood monocytes as biomarker for favorable response to antituberculosis treatment. Arch. Med. Res. 40, 128–131 (2009).

    Article  CAS  PubMed  Google Scholar 

  91. Wolday, D. et al. Expression of chemokine receptors CCR5 and CXCR4 on CD4+ T cells and plasma chemokine levels during treatment of active tuberculosis in HIV-1-coinfected patients. J. Acquir. Immune Defic. Syndr. 39, 265–271 (2005).

    Article  CAS  PubMed  Google Scholar 

  92. Hosp, M. et al. Neopterin, β2-microglobulin, and acute phase proteins in HIV-1-seropositive and -seronegative Zambian patients with tuberculosis. Lung 175, 265–275 (1997).

    Article  CAS  PubMed  Google Scholar 

  93. Schleicher, G. K. et al. Procalcitonin and C-reactive protein levels in HIV-positive subjects with tuberculosis and pneumonia. Eur. Respir. J. 25, 688–692 (2005).

    Article  CAS  PubMed  Google Scholar 

  94. Krenke, R. & Korczynski, P. Use of pleural fluid levels of adenosine deaminase and interferon γ in the diagnosis of tuberculous pleuritis. Curr. Opin. Pulm. Med. 16, 367–375 (2010).

    Article  CAS  PubMed  Google Scholar 

  95. Abel, B. et al. The novel tuberculosis vaccine, AERAS-402, induces robust and polyfunctional CD4+ and CD8+ T cells in adults. Am. J. Respir. Crit. Care Med. 181, 1407–1417 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Hohn, H. et al. MHC class II tetramer guided detection of Mycobacterium tuberculosis-specific CD4+ T cells in peripheral blood from patients with pulmonary tuberculosis. Scand. J. Immunol. 65, 467–478 (2007).

    Article  CAS  PubMed  Google Scholar 

  97. Axelsson-Robertson, R. et al. Extensive major histocompatibility complex class I binding promiscuity for Mycobacterium tuberculosis TB10.4 peptides and immune dominance of human leucocyte antigen (HLA)-B*0702 and HLA-B*0801 alleles in TB10.4 CD8 T-cell responses. Immunology 129, 496–505 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Veenstra, H. et al. Changes in leucocyte and lymphocyte subsets during tuberculosis treatment; prominence of CD3dimCD56+ natural killer T cells in fast treatment responders. Clin. Exp. Immunol. 145, 252–260 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Azzurri, A. et al. Serological markers of pulmonary tuberculosis and of response to anti-tuberculosis treatment in a patient population in Guinea. Int. J. Immunopathol. Pharmacol. 19, 199–208 (2006).

    Article  CAS  PubMed  Google Scholar 

  100. Esquivel-Valerio, J. A. et al. Antineutrophil cytoplasm autoantibodies in patients with tuberculosis are directed against bactericidal/permeability increasing protein and are detected after treatment initiation. Clin. Exp. Rheumatol. 28, 35–39 (2010).

    PubMed  Google Scholar 

  101. Jacobsen, M. et al. Suppressor of cytokine signaling (SOCS)-3 is affected in T cells from TB patients. Clin. Microbiol. Infect. 29 Jul 2010 (doi:10.1111/j.1469-0691.2010.03326.x).

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Authors and Affiliations

  1. Division of Molecular Biology and Human Genetics, Immunology Research Group, NRF/DST Centre of Excellence for TB Biomedical Research, Faculty of Health Sciences, Stellenbosch University, South Africa

    Gerhard Walzl & Katharina Ronacher

  2. South African Tuberculosis Vaccine Initiative, Institute of Infectious Diseases and School of Child and Adolescent Health, University of Cape Town, South Africa

    Willem Hanekom & Thomas J. Scriba

  3. Division of Infection and Immunity, University College London Medical School, London, UK

    Alimuddin Zumla

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Glossary

Latent M. tuberculosis infection

Latent infection with M. tuberculosis indicates the presence of live M. tuberculosis organisms in a human host who is asymptomatic. It is detected by demonstrating immune responsiveness of the host to M. tuberculosis antigens (using the tuberculin skin test or interferon-γ release assays). Latent infection can last a lifetime.

Active tuberculosis

The symptomatic disease caused by M. tuberculosis infection. Approximately 10% of infected individuals develop active disease in their lifetime owing to a loss of immune control over the pathogen. The disease manifests mainly in the lungs but can be extrapulmonary or disseminated.

Tuberculosis biomarker

An ideal tuberculosis biomarker should: differentiate between patients with active tuberculosis and individuals with latent M. tuberculosis infection; return to normal levels during treatment; reproducibly predict clinical outcomes (for example, cure, relapse risk or eradication of M. tuberculosis infection) in diverse patient populations; and predict vaccine efficacy and provide end points for clinical trials.

Sputum smear test

Quantification of mycobacteria in stained sputum preparations by microscopic examination. Traditionally, this test is used for diagnosis and after the 2-month intensive phase of tuberculosis treatment to assess treatment response.

Sputum culture test

Assessment of the growth of M. tuberculosis from sputum in (currently mostly liquid) culture medium. Sputum culture conversion is used to assess treatment success. Successful treatment is determined by a lack of M. tuberculosis growth in a sample from an individual whose previous sputum culture test was positive.

Correlates of risk

Markers whose presence is associated with a low risk of disease, or whose absence is associated with a high risk of disease.

Correlates of protection

Several terms are used for this concept, including surrogates of protection. These markers reliably predict the level of protective efficacy induced by a vaccine on the basis of differences in the immunological measurements of vaccinated and unvaccinated groups.

Effector memory T cell

A terminally differentiated T cell that lacks lymph node-homing receptors but expresses receptors that enable it to home to inflamed tissues. Effector memory T cells can exert immediate effector functions without the need for further differentiation.

Central memory T cell

An antigen-experienced T cell that expresses cell-surface receptors for homing to secondary lymphoid organs. These cells are generally thought to be long-lived and can serve as the precursors for effector T cells in recall responses.

Relapse

A recurrent episode of tuberculosis after initial cure, resulting from incomplete clearance of the original infection. The same bacterial strain is involved at both episodes.

Tuberculin skin test reaction

A delayed-type hypersensitivity reaction following intradermal injection of purified M. tuberculosis-derived proteins. The tuberculin skin test is also known as the Mantoux test and is used as a diagnostic tool for latent M. tuberculosis infection.

γδ T cells

T cells that express the γδ T cell receptor. These cells are present in the skin, vagina and intestinal epithelium as intraepithelial lymphocytes. Although the exact function of γδ T cells is unknown, it has been suggested that mucosal γδ T cells are involved in innate immune responses.

Meta-analysis

A statistical approach that combines results from multiple related studies to define a composite effect. When applied to genome-wide association studies, more modest association effects can be identified.

Baseline biomarkers

Markers that can be measured at diagnosis of tuberculosis disease before the commencement of treatment.

Time-to-detection in liquid culture

The number of days until growth of M. tuberculosis is detected in liquid culture medium.

Cured tuberculosis patient

A patient whose sputum smear tests (and sputum culture tests, if available) are negative in both the last month of treatment (conventionally month 6) and on at least one previous occasion. This does not necessarily equate to sterilizing cure.

MicroRNAs

Small RNA molecules that regulate the expression of genes by binding to the 3′-untranslated regions (3′-UTRs) of specific mRNAs.

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Walzl, G., Ronacher, K., Hanekom, W. et al. Immunological biomarkers of tuberculosis. Nat Rev Immunol 11, 343–354 (2011). https://doi.org/10.1038/nri2960

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