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The role of CD39 pathway in Th1 cell function in tuberculosis (TB) is rarely elucidated. The present study aims to investigate the modulating mechanism of CD39 pathway during Mycobacterium tuberculosis (MTB) infection. CD39 expression was examined on host immune cells among patients with TB. The relationship between CD39 expression and Th1 cell function was analysed. Patients with TB displayed dramatically higher CD39 expression on Th1 cells than healthy controls, and a significantly increased expression of surface markers, including activation, exhaustion and apoptosis markers, were noted in CD39 + Th1 cells in comparison with CD39 - Th1 cells. Conversely, CD39 expression on Th1 cells was associated with diminished number of polyfunctional cells producing Th1-type cytokines, and CD39 + Th1 cells showed obviously lower proliferation potential. Notably, tetramer analysis demonstrated a predominant CD39 expression on TB-specific CD4 + cells, which was associated with higher apoptosis and lower cytokine-producing ability. Transcriptome sequencing identified 27 genes that were differentially expressed between CD39 + and CD39 - Th1 cells, such as IL32, DUSP4 and RGS1. Inhibition of CD39 pathway could enhance the activation, proliferation and cytokine-producing ability of Th1 cells. Furthermore, there was a significantly negative correlation between CD39 expression on Th1 cells and nutritional status indicators such as lymphocyte count and albumin levels, and we observed a significant decline in CD39 expression on Th1 cells after anti-TB treatment. CD39 is predominantly expressed on TB-specific Th1 cells and correlated with their exhausted function, which suggests that CD39 could serve as a prominent target for TB therapy., (© 2022 The Authors. Immunology published by John Wiley & Sons Ltd.)

Background: Novel approaches for tuberculosis (TB) diagnosis, especially for distinguishing active TB (ATB) from latent TB infection (LTBI), are urgently warranted. The present study aims to determine whether the combination of HLA-DR on Mycobacterium tuberculosis (MTB)-specific cells and TB antigen/phytohemagglutinin (TBAg/PHA) ratio could facilitate MTB infection status discrimination., Methods: Between June 2020 and June 2021, participants with ATB and LTBI were recruited from Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort), respectively. The detection of HLA-DR on MTB-specific cells upon TB antigen stimulation and T-SPOT assay were simultaneously performed on all subjects., Results: A total of 116 (54 ATB and 62 LTBI) and another 84 (43 ATB and 41 LTBI) cases were respectively enrolled from Qiaokou cohort and Caidian cohort. Both HLA-DR on IFN-γ + TNF-α + cells and TBAg/PHA ratio showed discriminatory value in distinguishing between ATB and LTBI. Receiver operator characteristic (ROC) curve analysis showed that HLA-DR on IFN-γ + TNF-α + cells produced an area under the ROC curve (AUC) of 0.886. Besides, TBAg/PHA ratio yield an AUC of 0.736. Furthermore, the combination of these two indicators resulted in the accurate discrimination with an AUC of 0.937. When the threshold was set as 0.36, the diagnostic model could differentiate ATB from LTBI with a sensitivity of 92.00% and a specificity of 81.82%. The performance obtained in Qiaokou cohort was further validated in Caidian cohort., Conclusions: The combination of HLA-DR on MTB-specific cells and TBAg/PHA ratio could serve as a robust tool to determine TB disease states., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Luo, Xue, Tang, Lin, Song, Liu, Yin, Huang, Wei, Mao, Wang and Sun.)

Background: Rapid and effective discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains a challenge. There is an urgent need for developing practical and affordable approaches targeting this issue., Methods: Participants with ATB and LTBI were recruited at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort) based on positive T-SPOT results from June 2020 to January 2021. The expression of activation markers including HLA-DR, CD38, CD69, and CD25 was examined on Mycobacterium tuberculosis (MTB)-specific CD4 + T cells defined by IFN-γ, TNF-α, and IL-2 expression upon MTB antigen stimulation., Results: A total of 90 (40 ATB and 50 LTBI) and another 64 (29 ATB and 35 LTBI) subjects were recruited from the Qiaokou cohort and Caidian cohort, respectively. The expression patterns of Th1 cytokines including IFN-γ, TNF-α, and IL-2 upon MTB antigen stimulation could not differentiate ATB patients from LTBI individuals well. However, both HLA-DR and CD38 on MTB-specific cells showed discriminatory value in distinguishing between ATB patients and LTBI individuals. As for developing a single candidate biomarker, HLA-DR had the advantage over CD38. Moreover, HLA-DR on TNF-α + or IL-2 + cells had superiority over that on IFN-γ + cells in differentiating ATB patients from LTBI individuals. Besides, HLA-DR on MTB-specific cells defined by multiple cytokine co-expression had a higher ability to discriminate patients with ATB from LTBI individuals than that of MTB-specific cells defined by one kind of cytokine expression. Specially, HLA-DR on TNF-α + IL-2 + cells produced an AUC of 0.901 (95% CI, 0.833-0.969), with a sensitivity of 93.75% (95% CI, 79.85-98.27%) and specificity of 72.97% (95% CI, 57.02-84.60%) as a threshold of 44% was used. Furthermore, the performance of HLA-DR on TNF-α + IL-2 + cells for differential diagnosis was obtained with validation cohort data: 90.91% (95% CI, 72.19-97.47%) sensitivity and 68.97% (95% CI, 50.77-82.73%) specificity., Conclusions: We demonstrated that HLA-DR on MTB-specific cells was a potentially useful biomarker for accurate discrimination between ATB and LTBI., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Luo, Xue, Mao, Lin, Tang, Song, Liu, Tong, Hou, Huang, Ouyang, Wang and Sun.)

Background: Easily accessible tools that reliably stratify Mycobacterium tuberculosis (MTB) infection are needed to facilitate the improvement of clinical management. The current study attempts to reveal lymphocyte-related immune characteristics of active tuberculosis (ATB) patients and establish immunodiagnostic model for discriminating ATB from latent tuberculosis infection (LTBI) and healthy controls (HC)., Methods: A total of 171 subjects consisted of 54 ATB, 57 LTBI, and 60 HC were consecutively recruited at Tongji hospital from January 2019 to January 2021. All participants were tested for lymphocyte subsets, phenotype, and function. Other examination including T-SPOT and microbiological detection for MTB were performed simultaneously., Results: Compared with LTBI and HC, ATB patients exhibited significantly lower number and function of lymphocytes including CD4 + T cells, CD8 + T cells and NK cells, and significantly higher T cell activation represented by HLA-DR and proportion of immunosuppressive cells represented by Treg. An immunodiagnostic model based on the combination of NK cell number, HLA-DR + CD3 + T cells, Treg, CD4 + T cell function, and NK cell function was built using logistic regression. Based on receiver operating characteristic curve analysis, the area under the curve (AUC) of the diagnostic model was 0.920 (95% CI, 0.867-0.973) in distinguishing ATB from LTBI, while the cut-off value of 0.676 produced a sensitivity of 81.48% (95% CI, 69.16%-89.62%) and specificity of 91.23% (95% CI, 81.06%-96.20%). Meanwhile, AUC analysis between ATB and HC according to the diagnostic model was 0.911 (95% CI, 0.855-0.967), with a sensitivity of 81.48% (95% CI, 69.16%-89.62%) and a specificity of 90.00% (95% CI, 79.85%-95.34%)., Conclusions: Our study demonstrated that the immunodiagnostic model established by the combination of lymphocyte-related indicators could facilitate the status differentiation of MTB infection., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Luo, Xue, Tang, Cai, Yuan, Lin, Song, Liu, Mao, Zhou, Chen, Zhu, Liu, Wu, Wang and Sun.)

Background: Distinguishing between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging., Methods: Between 2013 and 2019, 2,059 (1,097 ATB and 962 LTBI) and another 883 (372 ATB and 511 LTBI) participants were recruited based on positive T-SPOT.TB (T-SPOT) results from Qiaokou (training) and Caidian (validation) cohorts, respectively. Blood routine examination (BRE) was performed simultaneously. Diagnostic model was established according to multivariate logistic regression., Results: Significant differences were observed in all indicators of BRE and T-SPOT assay between ATB and LTBI. Diagnostic model built on BRE showed area under the curve (AUC) of 0.846 and 0.850 for discriminating ATB from LTBI in the training and validation cohorts, respectively. Meanwhile, TB-specific antigens spot-forming cells (SFC) (the larger of early secreted antigenic target 6 and culture filtrate protein 10 SFC in T-SPOT assay) produced lower AUC of 0.775 and 0.800 in the training and validation cohorts, respectively. The diagnostic model based on combination of BRE and T-SPOT showed an AUC of 0.909 for differentiating ATB from LTBI, with 78.03% sensitivity and 90.23% specificity when a cutoff value of 0.587 was used in the training cohort. Application of the model to the validation cohort showed similar performance. The AUC, sensitivity, and specificity were 0.910, 78.23%, and 90.02%, respectively. Furthermore, we also assessed the performance of our model in differentiating ATB from LTBI with lung lesions. Receiver operating characteristic analysis showed that the AUC of established model was 0.885, while a threshold of 0.587 yield a sensitivity of 78.03% and a specificity of 85.69%, respectively., Conclusions: The diagnostic model based on combination of BRE and T-SPOT could provide a reliable differentiation between ATB and LTBI., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Luo, Tang, Yuan, Lin, Mao, Song, Xue, Wu, Ouyang, Hou, Wang and Sun.)

Background: Given that there is no rapid and effective method for distinguishing active tuberculosis (ATB) from latent tuberculosis infection (LTBI), the discrimination between these two statuses remains challenging. This study sought to investigate the value of nutritional indexes and tuberculosis-specific antigen/phytohemagglutinin ratio (TBAg/PHA ratio) for distinguishing ATB from LTBI., Methods: Participants were consecutively recruited based on positive T-SPOT.TB results between January 2018 and January 2020. ATB was diagnosed by positive mycobacterial culture and/or positive GeneXpert MTB/RIF, with clinical symptoms and radiological characteristics suggestive of ATB. Individuals with positive T-SPOT.TB but without the evidence of ATB were defined as LTBI. Patients younger than 17 years and undergoing anti-TB treatment were excluded., Results: A total of 709 (312 ATB and 397 LTBI) and another 309 (120 ATB and 189 LTBI) subjects were respectively recruited from Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The level of prealbumin was significantly lower in ATB than in LTBI. With a cut-off value of 139 mg/L, the sensitivity and specificity of prealbumin in distinguishing ATB from LTBI were 50.96% (45.41%-56.51%) and 91.69% (88.97%-94.40%). Meanwhile, TBAg/PHA ratio was found statistically higher in ATB compared with LTBI. If using the threshold of 0.29, the sensitivity and specificity of TBAg/PHA ratio were 65.71% (60.44%-70.97%) and 90.93% (88.11%-93.76%), respectively. Moreover, the combination of prealbumin and TBAg/PHA ratio (obtaining by diagnostic model) yielded better specificity (90.18%, [87.25%-93.10%]) and sensitivity (87.18%, [83.47%-90.89%]), while the clinical utility index (CUI) positive and CUI negative were respectively 0.76 and 0.81. After anti-TB treatment, TBAg/PHA ratio was declined while the level of prealbumin was restored (Wilcoxon test, P < 0.001). Furthermore, the performance of diagnostic model obtained in Qiaokou cohort was confirmed in Caidian cohort., Conclusions: The diagnostic model based on combination of prealbumin and TBAg/PHA ratio is a rapid and accurate tool for discriminating ATB from LTBI., (© 2020 The Authors. International Journal of Clinical Practice published by John Wiley & Sons Ltd.)

Background: Discriminating active tuberculosis (ATB) from latent tuberculosis infection (LTBI) remains challenging. This study aimed to investigate a diagnostic model based on a combination of iron metabolism and the TB-specific antigen/phytohemagglutinin ratio (TBAg/PHA ratio) in T-SPOT.TB assay for differentiation between ATB and LTBI., Methods: A total of 345 participants with ATB (n=191) and LTBI (n=154) were recruited based on positive T-SPOT.TB results at Tongji hospital between January 2017 and January 2020. Iron metabolism analysis was performed simultaneously. A diagnostic model for distinguishing ATB from LTBI was established according to multivariate logistic regression., Results: The TBAg/PHA ratio showed 64.00% sensitivity and 90.10% specificity in distinguishing ATB from LTBI when a threshold of 0.22 was used. All iron metabolism biomarkers in the ATB group were significantly different from those in the LTBI group. Specifically, serum ferritin and soluble transferrin receptor in ATB were significantly higher than LTBI. On the contrary, serum iron, transferrin, total iron binding capacity, and unsaturated iron binding capacity in ATB were significantly lower than LTBI. The combination of iron metabolism indicators accurately predicted 60.00% of ATB cases and 91.09% of LTBI subjects, respectively. Moreover, the combination of iron metabolism indexes and TBAg/PHA ratio resulted in a sensitivity of 88.80% and specificity of 90.10%. Furthermore, the performance of models established in the Qiaokou cohort was confirmed in the Caidian cohort., Conclusions: The data suggest that the combination of iron metabolism indexes and TBAg/PHA ratio could serve as a biomarker to distinguish ATB from LTBI in T-SPOT-positive individuals., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objectives: Distinguishing between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging., Methods: The modified T-SPOT.TB assay was performed in 499 participants (243 ATB and 256 LTBI) and another 322 participants (162 ATB and 160 LTBI) who were diagnosed in Qiaokou (training) and Caidian (validation) cohort respectively., Results: The mean spot sizes (MSS) of early secreted antigenic target 6 (ESAT-6) spot-forming cells (SFC) of T-SPOT.TB assay in ATB patients was significantly higher than that in LTBI individuals. 1.0 × 10 5 was the optimal number of cells added to phytohaemagglutinin (PHA) well for obtaining more accurate TB-specific antigen to phytohaemagglutinin (TBAg/PHA) ratio. The area under the curve of the diagnostic model by combination of ESAT-6 SFC MSS and modified TBAg/PHA ratio in distinguishing ATB from LTBI was 0.959 in training cohort, with a sensitivity of 90.12% and a specificity of 91.02% when a cutoff value of 0.46 was used. This diagnostic model showed similar performance in the validation cohort. The area under the curve, sensitivity, and specificity were 0.962, 93.21%, and 90.00%, respectively. Further flow cytometry analysis showed that ESAT-6 stimulation induced a significantly higher mean fluorescence intensity of IFN-γ + cells in lymphocytes compared with culture filtrate protein 10 (CFP-10) stimulation. In contrast, CFP-10 stimulation induced a significantly higher percentage of IFN-γ + cells in lymphocytes compared with ESAT-6 stimulation., Conclusions: The combination of the MSS of ESAT-6 SFC and the modified TBAg/PHA ratio of T-SPOT.TB assay showed great value in discriminating ATB from LTBI., Competing Interests: Declaration of Competing Interest None declared., (Copyright © 2020. Published by Elsevier Ltd.)

Objectives: CD4 + CXCR5 + T cells have previously been established. However, their decreased frequency during tuberculosis (TB) disease is only partially understood. The aim of this study was to explore the depletion of CD4 + CXCR5 + T cells in human TB., Methods: The frequency and function of CD4 + CXCR5 + T cells were evaluated in active TB (ATB) patients and healthy control subjects. The function of CD4 + CXCR5 + T cells was determined after blockade of inhibitory receptors., Results: The frequency of CD4 + CXCR5 + T cells was decreased in ATB patients. The expression of activation markers (HLA-DR and ICOS) and inhibitory receptors (Tim-3 and PD-1) on CD4 + CXCR5 + T cells was increased in the ATB group. TB-specific antigen stimulation induced higher expression of inhibitory receptors than phytohemagglutinin stimulation in the ATB group. In contrast, TB antigen stimulation did not induce a significantly increased expression of IL-21 and Ki-67 on CD4 + CXCR5 + T cells. However, blockade of inhibitory receptors Tim-3 and PD-1 not only increased the frequency of CD4 + CXCR5 + T cells, but also restored their proliferation and cytokine secretion potential., Conclusions: The increased expression of inhibitory receptors involves the depletion of CD4 + CXCR5 + T cells, and blockade of inhibitory receptors can restore the function of CD4 + CXCR5 + T cells in ATB patients., (Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Rapid and effective discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains a challenge. There is an urgent need for developing practical and affordable approaches targeting this issue. Methods: Participants with ATB and LTBI were recruited at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort) based on positive T-SPOT results from June 2020 to January 2021. The expression of activation markers including HLA-DR, CD38, CD69, and CD25 was examined on Mycobacterium tuberculosis (MTB)-specific CD4+ T cells defined by IFN-γ, TNF-α, and IL-2 expression upon MTB antigen stimulation. Results: A total of 90 (40 ATB and 50 LTBI) and another 64 (29 ATB and 35 LTBI) subjects were recruited from the Qiaokou cohort and Caidian cohort, respectively. The expression patterns of Th1 cytokines including IFN-γ, TNF-α, and IL-2 upon MTB antigen stimulation could not differentiate ATB patients from LTBI individuals well. However, both HLA-DR and CD38 on MTB-specific cells showed discriminatory value in distinguishing between ATB patients and LTBI individuals. As for developing a single candidate biomarker, HLA-DR had the advantage over CD38. Moreover, HLA-DR on TNF-α+ or IL-2+ cells had superiority over that on IFN-γ+ cells in differentiating ATB patients from LTBI individuals. Besides, HLA-DR on MTB-specific cells defined by multiple cytokine co-expression had a higher ability to discriminate patients with ATB from LTBI individuals than that of MTB-specific cells defined by one kind of cytokine expression. Specially, HLA-DR on TNF-α+IL-2+ cells produced an AUC of 0.901 (95% CI, 0.833–0.969), with a sensitivity of 93.75% (95% CI, 79.85–98.27%) and specificity of 72.97% (95% CI, 57.02–84.60%) as a threshold of 44% was used. Furthermore, the performance of HLA-DR on TNF-α+IL-2+ cells for differential diagnosis was obtained with validation cohort data: 90.91% (95% CI, 72.19–97.47%) sensitivity and 68.97% (95% CI, 50.77–82.73%) specificity. Conclusions: We demonstrated that HLA-DR on MTB-specific cells was a potentially useful biomarker for accurate discrimination between ATB and LTBI. [ABSTRACT FROM AUTHOR]

Background: Inadequate tuberculosis (TB) diagnostics, especially for discrimination between active TB (ATB) and latent TB infection (LTBI), are major hurdle in the reduction of the disease burden. The present study aims to investigate the role of lymphocyte non-specific function detection for TB diagnosis in clinical practice. Methods: A total of 208 participants including 49 ATB patients, 64 LTBI individuals, and 95 healthy controls were recruited at Tongji hospital from January 2019 to October 2020. All subjects were tested with lymphocyte non-specific function detection and T-SPOT assay. Results: Significantly positive correlation existed between lymphocyte non-specific function and phytohemagglutinin (PHA) spot number. CD4+ T cell non-specific function showed the potential for differentiating patients with negative T-SPOT results from those with positive T-SPOT results with an area under the curve (AUC) of 0.732 (95% CI, 0.572-0.893). The non-specific function of CD4+ T cells, CD8+ T cells, and NK cells was found significantly lower in ATB patients than in LTBI individuals. The AUCs presented by CD4+ T cell non-specific function, CD8+ T cell non-specific function, and NK cell non-specific function for discriminating ATB patients from LTBI individuals were 0.845 (95% CI, 0.767-0.925), 0.770 (95% CI, 0.683-0.857), and 0.691 (95% CI, 0.593-0.789), respectively. Application of multivariable logistic regression resulted in the combination of CD4+ T cell non-specific function, NK cell non-specific function, and culture filtrate protein-10 (CFP-10) spot number as the optimally diagnostic model for differentiating ATB from LTBI. The AUC of the model in distinguishing between ATB and LTBI was 0.939 (95% CI, 0.898-0.981). The sensitivity and specificity were 83.67% (95% CI, 70.96%-91.49%) and 90.63% (95% CI, 81.02%-95.63%) with the threshold as 0.57. Our established model showed superior performance to TB-specific antigen (TBAg)/PHA ratio in stratifying TB infection status. Conclusions: Lymphocyte non-specific function detection offers an attractive alternative to facilitate TB diagnosis. The three-index diagnostic model was proved to be a potent tool for the identification of different events involved in TB infection, which is helpful for the treatment and management of patients. [ABSTRACT FROM AUTHOR]

• PF T-SPOT shows good diagnostic utility for TP diagnosis. • The performance of PF T-SPOT is comparable to that of IFN-γ in diagnosing TP. • The combination of PF T-SPOT and IFN-γ can show a better performance in diagnosing TP. Early and accurate diagnosis of tuberculous pleurisy (TP) remains a challenge. The aim of the present study is to evaluate the performance of the pleural fluid (PF) T-SPOT and interferon-gamma (IFN-γ) for TP diagnosis in high tuberculosis (TB) burden settings. In total, 214 and 217 subjects suspected of TP were prospectively enrolled in the Wuhan (training) cohort and Changchun (validation) cohort, respectively. All patients were examined with PF T-SPOT, IFN-γ, and other traditional tests simultaneously. The receiver-operating characteristic (ROC) curve analysis showed that the area under the curve (AUC), sensitivity, and specificity of TB-specific antigen (TBAg) spot-forming cells (SFC) (the larger of early secreted antigenic target 6 and culture filtrate protein 10 SFC in PF T-SPOT assay) for TP diagnosis were 0.972, 92.86%, and 92.16%, respectively, with a cutoff value of 35 in the Wuhan cohort. Meanwhile, when a threshold value of 95 ng/mL was set, the AUC, sensitivity, and specificity of IFN-γ to diagnose TP were 0.951, 86.61%, and 90.20%, respectively. Moreover, the diagnostic model based on the combination of TBAg SFC and IFN-γ showed an AUC of 0.983 for differentiating TP from non-TP, with 95.54% sensitivity and 95.10% specificity when a cutoff value of 0.32 was used in the Wuhan cohort. Excellent diagnostic accuracy was also observed in the Changchun cohort. When applying the cutoff value obtained from the Wuhan cohort, the AUC, sensitivity, and specificity of the diagnostic model were 0.995, 95.08%, and 97.89%, respectively. The performance of PF T-SPOT was comparable to IFN-γ in diagnosing TP. However, using the diagnostic model established by the combination of these two assays can achieve a more accurate diagnosis of TP. [ABSTRACT FROM AUTHOR]