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NATURE MEDICINE | LETTER
Dominant TNF-α+ Mycobacterium tuberculosis–specific CD4+ T cell responses discriminate between latent infection and active disease
Alexandre Harari, Virginie Rozot, Felicitas Bellutti Enders, Matthieu Perreau, Jesica Mazza Stalder, Laurent P Nicod, Matthias Cavassini, Thierry Calandra, Catherine Lazor Blanchet, Katia Jaton, Mohamed Faouzi, Cheryl L Day, Willem A Hanekom, Pierre-Alexandre Bart & Giuseppe Pantaleo
AffiliationsContributionsCorresponding author
Nature Medicine (2011) doi:10.1038/nm.2299
Received 09 November 2010 Accepted 05 January 2011 Published online 20 February 2011
Rapid diagnosis of active Mycobacterium tuberculosis (Mtb) infection remains a clinical and laboratory challenge. We have analyzed the cytokine profile (interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α) and interleukin-2 (IL-2)) of Mtb-specific T cells by polychromatic flow cytometry. We studied Mtb-specific CD4+ T cell responses in subjects with latent Mtb infection and active tuberculosis disease. The results showed substantial increase in the proportion of single-positive TNF-α Mtb-specific CD4+ T cells in subjects with active disease, and this parameter was the strongest predictor of diagnosis of active disease versus latent infection. We validated the use of this parameter in a cohort of 101 subjects with tuberculosis diagnosis unknown to the investigator. The sensitivity and specificity of the flow cytometry–based assay were 67% and 92%, respectively, the positive predictive value was 80% and the negative predictive value was 92.4%. Therefore, the proportion of single-positive TNF-α Mtb-specific CD4+ T cells is a new tool for the rapid diagnosis of active tuberculosis disease.
Main Methods References Acknowledgments Author information Supplementary information
Cellular immunity, particularly of CD4+ T cells, IFN-γ and TNF-α, has a central role in the control of and protection against Mycobacterium tuberculosis (Mtb) infection1, 2. Diagnosis of Mtb infection remains complex and requires several clinical, radiological, histopathological, bacteriological and molecular parameters. IFN-γ release assays measure responses to antigens (for example, 6-kDa early secretory antigenic target (ESAT-6) or 10-kDa culture filtrate antigen (CFP-10)) expressed by Mtb itself and discriminate between infection by Mtb and immunity induced by vaccination with Bacille Calmette-Guérin (BCG)3, 4 but not between active disease and latent infection5, 6.
Studies in the field of antiviral immunity have shown that polyfunctional (IFN-γ+IL-2+TNF-α+) profiles of virus-specific T cell responses, and not IFN-γ production alone, correlated with disease activity7, 8, 9, 10.
Therefore, we have used the same strategy, polychromatic flow cytometry, to functionally characterize Mtb-specific T cells in subjects with latent Mtb infection or active tuberculosis disease and tested the hypothesis that different cytokine profiles of pathogen-specific T cells may discriminate between active tuberculosis disease and latent Mtb infection.
We enrolled an initial cohort of 283 individuals with known diagnosis of Mtb infection in Switzerland and termed it the 'test cohort' (Supplementary Fig. 1). Subjects were selected on the basis of positive IFN-γ ELISPOT responses against CFP-10, ESAT-6 or both. Among the 283 subjects, active tuberculosis disease was diagnosed in 11 subjects on the basis of clinical signs (for example, cough, weight loss and lymphadenopathy), sputum stain for acid-fast bacilli (AFB), culture and PCR for Mtb and chest radiography6 (the Online Methods and Supplementary Table 1 contain detailed clinical parameters). The remaining 272 participants were diagnosed with latent Mtb infection. We first assessed the magnitude of Mtb-specific T cell responses by IFN-γ ELISPOT after stimulation with pools of peptides encompassing CFP-10 or ESAT-6 proteins. In agreement with previous studies11, 12, Mtb-specific T cell responses were similar in subjects with latent infection and active disease (Fig. 1a).
Figure 1: Quantitative and qualitative analysis of Mtb-specific T cell responses in the test cohort.
(a) IFN-γ ELISPOT responses after stimulation with ESAT-6 or CFP-10 peptide pools in a cohort of 283 participants with latent Mtb infection (n = 272) or active tuberculosis disease (n = 11, Supplementary Fig. 1). Shown are the numbers of spot-forming units (SFU) per 106 mononuclear cells. Statistical significance (P values) of the results was calculated by unpaired two-tailed Student's t test using GraphPad Prism 5. Bonferroni's correction for multiples analyses was applied. (b) Qualitative analysis of Mtb-specific CD4+ T cell responses by polychromatic flow cytometry. Shown are representative flow cytometry analyses of the functional profile of Mtb-specific CD4+ T cell responses in participants with either latent Mtb infection (Subject L5, left) or active tuberculosis disease (Subject A2, right). Profiles are gated on live CD3+CD4+ T cells, and the various combinations of IFN-γ, IL-2 and TNF-α are shown following stimulation with ESAT-6 and CFP-10 peptide pools or PPD. NS, not significant; Neg, negative control (unstimulated). (c) Simultaneous analysis of the functional profile of Mtb-specific CD4+ T cells on the basis of IFN-γ, IL-2 or TNF-α production. ESAT-6–, CFP-10– and PPD-specific CD4+ T cell responses are shown (as indicated by the six colored boxes to the right of the panel) from 48 participants with latent Mtb infection and eight participants with active tuberculosis (TB) disease. Representative examples from subject L5 and subject A2 are also identified. All the possible combinations of the various functions are shown on the x axis, whereas the percentages of the distinct cytokine-producing cell subsets within Mtb-specific CD4+ T cells are shown on the y axis. The pie charts summarize the data, and each slice corresponds to the proportion of Mtb-specific CD4+ T cells positive for a certain combination of functions. Colors in the pie charts are indicated by the seven colored boxes at the bottom of the panel. (d) Distribution of CFP-10– and/or ESAT-6–specific CD4+ T cell responses among subjects with latent Mtb infection or active tuberculosis disease.
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We then assessed the functional profile of Mtb-specific T cell responses by polychromatic flow cytometry and a panel of markers including a viability marker and antibodies specific for CD3, CD4, CD8, IL-2, TNF-α and IFN-γ. Owing to blood specimen availability or quality (see flowchart in Supplementary Fig. 1), this analysis was performed in 48 subjects with latent infection and eight subjects with active disease (Supplementary Table 1). Within the group with latent infection, five were investigated for suspected tuberculosis disease but had negative sputum AFB staining and culture and PCR for Mtb. Twenty-three were health-care workers screened for Mtb infection as part of routine surveillance at the Centre Hospitalier Universitaire Vaudois (CHUV; Supplementary Fig. 1). Twenty were investigated for Mtb infection before the initiation of anti-TNF-α antibody treatment and had negative chest radiographs (Supplementary Fig. 1). In agreement with previous studies11, 12, Mtb-specific CD4+ T cell responses in a representative subject with latent Mtb infection (subject L5) were mostly (>70%) polyfunctional (Fig. 1b), that is, producing IFN-γ, IL-2 and TNF-α. In contrast, a representative subject with active tuberculosis disease (subject A2) (Fig. 1b) showed a dominant (>70% of CD4+ T cells) TNF-α–only response. In these two participants, the functional profile of Mtb-specific CD4+ T cells was similar regardless of the stimulus, that is, ESAT-6 or CFP-10 peptide pools or tuberculin purified protein derivative (PPD). Of note, Mtb-specific T cell responses (analyzed by either IFN-γ ELISPOT or flow cytometry) from the 20 subjects analyzed before the initiation of TNF-α–specific antibody treatment were not different from T cell responses in the remaining 28 subjects also diagnosed with latent infection (Supplementary Fig. 2). The marked difference between the functional profile of Mtb-specific CD4+ T cell responses in latent infection versus active disease was confirmed in a total of 142 Mtb-specific CD4+ T cell responses (all P <>90%) also responded to PPD. Of the 142 responses, 21 were detected in subjects with active disease and 121 in subjects with latent infection (Fig. 1c). Of note, we confirmed the differences in the profile of cytokines between active disease and latent infection when we expressed the data as absolute frequency of cytokine-producing CD4+ T cells (Supplementary Fig. 3). The frequency of single-positive TNF-α–producing CD4+ T cells was higher in individuals with active disease (Supplementary Fig. 3).
In summary, in an opportunistically selected (on the basis of a sufficient number of mononuclear cells; Supplementary Data 1) subgroup of individuals subjected to detailed intracellular cytokine staining (ICS) studies, a functional profile (single-positive TNF-α Mtb-specific CD4+ T cells) was associated with disease activity and so might be helpful for rapid diagnosis of active disease as compare to the conventional culture tuberculosis tests, which require up to several weeks.
We then calculated which parameter (that is, functional T cell subset) was the strongest predictive measure of discrimination between active disease and latent infection. For these purposes, because CFP-10 was more frequently recognized than ESAT-6 (Fig. 1d), we focused the analysis on CFP-10–specific CD4+ T cell responses and included ESAT-6–specific CD4+ T cell responses only when CFP-10 responses were negative. We observed the latter scenario in only one individual with active disease and one individual with latent infection (Fig. 1d).
On the basis of the logistic regression analysis of the multiple functionally distinct T cell subsets, the proportion of TNF-α single-positive Mtb-specific CD4+ T cells was the strongest predictive measure of discrimination between active disease and latent infection (area under the curve (AUC) = 0.995 (95% confidence interval: 0.984–1); odds ratio = 1.45; Supplementary Fig. 4). In addition, a cutoff of 37.4% of single-positive TNF-α–producing CD4+ T cells was calculated as the value allowing the best (sensitivity of 100% and specificity of 96%) separation between latent infection and active disease (Supplementary Fig. 4).
A limitation of these results was that the laboratory investigators were not blinded to the diagnosis of latent infection or active disease. We therefore examined peripheral blood mononuclear cells from a second independent cohort termed the 'validation cohort', whose clinical status was unknown to the investigators. We tested whether the proportion of TNF-α single-positive Mtb-specific CD4+ T cells, and particularly the cutoff at 37.4%, could discriminate between latent infection and active disease.
One hundred and fourteen participants from both Switzerland (n = 72) and South Africa (n = 42) were enrolled between 2009 and 2010 to confirm the functional profile also in persons from a setting (South Africa) where tuberculosis is prevalent (Supplementary Fig. 5). Participants from South Africa were enrolled from clinics in the public health sector in Cape Town and Worcester, both in the Western Cape province of South Africa. Participants from Switzerland in the validation cohort were not included in the test cohort described above. Subjects were selected on the basis of the following criteria: positive Mtb-specific IFN-γ ELISPOT responses, absence of Mtb-specific treatment, seronegative for HIV and good general health status (the Online Methods and Supplementary Fig. 5 contain a full description of the subjects). Active tuberculosis disease diagnosis in subjects from both Switzerland and South Africa was based on clinical signs (for example, cough, weight loss and lymphadenopathy), sputum stain for AFB, culture and PCR for Mtb and chest radiography6 (the Online Methods and Supplementary Table 2 contain detailed clinical parameters). Flow cytometry analyses were performed on the 101 subjects from the validation cohort with positive Mtb-specific CD4+ T cell responses (Supplementary Fig. 5).
IFN-γ ELISPOT and CD4+ T cell specific cytokine expression in response to CFP-10, ESAT-6 or both were evaluated, and the data were provided to the biostatistics facility of CHUV. Later, unblinding of the Mtb clinical status allowed us to confirm that IFN-γ ELISPOT responses were not significantly different between latent infection and active disease (Fig. 2a). Of note, the magnitude of Mtb-specific IFN-γ ELISPOT responses (Fig. 2b) and the distribution of CFP-10– and/or ESAT-6–specific CD4+ T cell responses among subjects with latent Mtb infection or active disease were similar between subjects from Switzerland and South Africa (Fig. 2c).
Figure 2: Analysis of Mtb-specific T cell responses in the validation cohort after unblinding of the clinical status.
(a) IFN-γ ELISPOT responses after stimulation with ESAT-6 or CFP-10 peptide pools. Shown are the numbers of SFU per 106 mononuclear cells. Statistical significance (P values) of the results was calculated by unpaired two-tailed Student's t test in GraphPad Prism 5. Bonferroni's correction for multiples analyses was applied. (b) Analysis of Mtb-specific IFN-γ ELISPOT T cell responses in subjects enrolled in Switzerland (CH) and SA. (c) Distribution of CFP-10– and/or ESAT-6–specific CD4+ T cell responses among subjects from the validation cohort with positive and concordant Mtb-specific CD4+ T cell responses (Supplementary Fig. 5).
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With regard to the polychromatic flow cytometric cytokine profile, 15 participants had a dominant TNF-α single-positive Mtb-specific CD4+ T cell response, that is, >37.4%, considered predictive of active disease in the test cohort (Supplementary Fig. 4). After unblinding, active disease was confirmed in 12 of these 15 participants (Fig. 3a). Along the same line, 79 participants had polyfunctional Mtb-specific CD4+ T cells, including a TNF-α single-positive proportion of <37.4%,>37.4% and the other response <37.4%)> 0.05 for positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity), thus providing evidence that the combined analysis of Swiss and South African cohorts is valid. On the basis of the analysis on the combined cohorts, the global performance of the assay was as follows: PPV = 80%, NPV = 92.4%, sensitivity = 66.67% and specificity = 92.41% (Supplementary Fig. 7). Overall, the cytokine profile predicted the clinical diagnosis in 90% of cases. Of note, these values apply to subjects with detectable ICS responses. When subjects with discordant ESAT-6 and CFP-10 responses were also included in the analysis, the correct clinical diagnosis was made in 84% of subjects.
Figure 3: Percentages of CFP-10– or ESAT-6–specific single-positive TNF-α–producing CD4+ T cells of the 94 subjects from the validation cohort with concordant responses against CFP-10 and ESAT-6.
Dashed line represents the cutoff of 37.4% of single-positive TNF-α. (a) Subjects with active disease or latent infection are identified with blue and red dots, respectively. (b) Subjects from South Africa (SA) or Switzerland (CH) are identified with orange and green dots, respectively.
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We then investigated whether the percentage of Mtb-specific TNF-α–producing CD4+ T cells was the parameter with the strongest predictive value of the clinical status in the validation cohort. On the basis of the logistic regression analysis of the multiple functionally distinct T cell subsets, the proportion of TNF-α single-positive Mtb-specific CD4+ T cells was indeed the strongest predictive measure of discrimination between active disease and latent infection (AUC = 0.825 (95% confidence interval: 0.683–0.968); odds ratio = 1.10; Supplementary Fig. 7). In addition, a cutoff of 38.8% (as compared to 37.4% obtained in the test cohort) of single-positive TNF-α–producing CD4+ T cells was calculated as the value allowing the best separation between latent infection and active disease (Supplementary Fig. 7).
We also had the opportunity to analyze T cell cytokine profiles in five participants during untreated active tuberculosis disease and then after tuberculosis treatment (Fig. 4). In agreement with the above data, the percentage of single-positive TNF-α–producing CD4+ T cells was >37.4% in individuals with active tuberculosis disease. We observed a shift to a polyfunctional profile (single-positive TNF-α–producing CD4+ T cells < n =" 283)" n =" 114)">80% purity). Tuberculin Purified Protein Derivative (PPD RT 23) was purchased from Statens Serum Institute.
IFN-γ ELISPOT assays.
ELISPOT assays were performed per the manufacturer's instructions (Becton Dickinson). Briefly, cryopreserved blood mononuclear cells were rested for 8 h at 37 °C, and then 2 × 105 cells were stimulated with peptide pools (1 μg of each single peptide) in 100 μl of complete medium (RPMI + 10%FBS) in quadruplicate conditions as previously described18. Medium only was used as negative control. Staphylococcal enterotoxin B (SEB; Sigma; 200 ng ml−1) was used as a positive control on 50,000 cells. Results are expressed as the mean number of SFU per 106 cells from quadruplicate assays. Only cell samples with >80% viability after thawing were analyzed, and only assays with <50>500 SFU per 106 cells after SEB stimulation were considered as valid. An ELISPOT result was defined as positive if the number of SFUs was ≥55 SFU per 106 cells and more than fourfold higher than the negative control.
Flow cytometry analysis.
For ICS, cryopreserved blood mononuclear cells (1–2 × 106) were rested for 6–8 h and then stimulated overnight in 1 ml of complete medium containing Golgiplug (1 μl ml−1, Becton Dickinson) and CD28-specific antibodies (0.5 μg ml−1, Becton Dickinson) as previously described19. For stimulation of blood mononuclear cells, peptide pools were used at 1 μl ml−1 for each peptide. SEB stimulation (200 ng ml−1) served as positive control. At the end of the stimulation period, cells were stained for dead cells (LIVE/DEAD kit, Invitrogen), permeabilized (Cytofix/Cytoperm, Becton Dickinson) and then stained with antibodies specific for CD3, CD4, CD8, IFN-γ, TNF-α and IL-2. All antibodies but those specific for CD3 (Invitrogen) and CD4 and CD19 (VWR International) were purchased from Becton Dickinson. Cells were then fixed, acquired on an LSRII SORP (four lasers) and analyzed with FlowJo 8.8.2 and SPICE 4.2.3 (developed by M. Roederer, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, US National Institutes of Health) as previously described18. The number of lymphocyte-gated events ranged between 105 and 106 in the flow cytometry experiments shown.
Statistical analyses.
Comparisons of categorical variables were made with Fisher's exact test. Statistical significance (P values) of the magnitude of ELISPOT responses was calculated by unpaired two-tailed Student's t test using GraphPad Prism 5. Bonferroni's correction for multiples analyses was applied. The selection of the optimal parameters to discriminate between cases of latent infection and cases of active disease was performed using a logistic regression analysis followed by a receiver operating characteristic (ROC) curve analysis20, 21, 22 to evaluate the diagnostic performance of each parameter. Results for the optimal parameter (single-positive TNF-α) are summarized as a contingency table giving sensitivity, specificity and positive and negative predictive values (PPV and NPV). Analyses provided include a ROC-curve graph and a sensitivity and specificity graph as a function of the probability cutoff.
References
Acknowledgments
Author information
Main Methods References Acknowledgments Author information Supplementary information
Affiliations
Division of Immunology and Allergy, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland.
Alexandre Harari, Virginie Rozot, Felicitas Bellutti Enders, Matthieu Perreau, Pierre-Alexandre Bart & Giuseppe Pantaleo
Swiss Vaccine Research Institute, Lausanne, Switzerland.
Alexandre Harari & Giuseppe Pantaleo
Division of Pneumology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland.
Jesica Mazza Stalder & Laurent P Nicod
Division of Infectious Diseases, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland.
Matthias Cavassini & Thierry Calandra
Division of Occupational Medicine, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland.
Catherine Lazor Blanchet
Institute of Microbiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland.
Katia Jaton
Center of Clinical Epidemiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland.
Mohamed Faouzi
South African Tuberculosis Vaccine Initiative, University of Cape Town, Cape Town, South Africa.
Cheryl L Day & Willem A Hanekom
Contributions
A.H. designed the study, performed the analyses and wrote the manuscript; V.R., F.B.E. and M.P. generated data and performed analyses; J.M.S., L.P.N., M.C., T.C., C.L.B., C.L.D. and W.A.H. recruited study participants; K.J. performed analyses; M.F. performed the statistical analyses; P.-A.B. contributed to the design of the study, performed analyses and wrote the manuscript; G.P. designed the study, supervised the analyses and wrote the paper. All authors have read and approved the final manuscript.
Competing financial interests
The authors declare no competing financial interests.
Corresponding author
Correspondence to: Giuseppe Pantaleo
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