Alterations in the intestinal microbiota associated with active tuberculosis and latent tuberculosis infection.

Objectives: To study the characteristics of intestinal microbiota at different stages of Mycobacterium tuberculosis infection. Methods: Fecal samples of 19 active tuberculosis (ATB) patients, 21 latent tuberculosis infection (LTBI) individuals, and 20 healthy controls (HC) were collected. Gut microbiota of all the participants were analyzed by 16S rDNA sequencing. Clinical information of ATB patients was also collected and analyzed. Results: Both ATB and LTBI groups showed significant decreases in microbial diversity and decline of Clostridia. For ATB patients, bacteria within phylum Proteobacteria increased. While for LTBI individuals, genera Prevotella and Rosburia enriched. The abundance of Faecalibacterium, Clostridia and Gammaproteobacteria has the potential to diagnose ATB, with the area under the curve (AUC) of 0.808, 0.784 and 0.717. And Prevotella and Rosburia has the potential to diagnose LTBI, with the AUC of 0.689 and 0.689. Notably, in ATB patients, the relative abundance of Blautia was negatively correlated with the proportions of peripheral T cells and CD8+T cells. And serum direct bilirubin was positively correlated with Bacteroidales, while negatively correlated with Clostridiales in ATB patients. Conclusions: The specifically changed bacteria are promising markers for ATB and LTBI diagnosis. Some gut bacteria contribute to anti-MTB immunity through interactions with T cells and bilirubin.

Excluding Participants With Mycobacteria Infections From Clinical Trials: A Critical Consideration in Evaluating the Efficacy of BCG Against COVID-19.

In the context of the coronavirus disease 2019 (COVID-19) pandemic, Bacillus Calmette-Guérin (BCG), a tuberculosis (TB) vaccine, has been investigated for its potential to prevent COVID-19 with conflicting outcomes. Currently, over 50 clinical trials have been conducted to assess the effectiveness of BCG in preventing COVID-19, but the results have shown considerable variations. After scrutinizing the data, it was discovered that some trials had enrolled individuals with active TB, latent TB infection, or a history of TB. This finding raises concerns about the reliability and validity of the trial outcomes. In this study, we explore the potential consequences of including these participants in clinical trials, including impaired host immunity, immune exhaustion, and the potential masking of the BCG vaccine's protective efficacy against COVID-19 by persistent mycobacterial infections. We also put forth several suggestions for future clinical trials. Our study underscores the criticality of excluding individuals with active or latent TB from clinical trials evaluating the efficacy of BCG in preventing COVID-19.

High Dimensional Immune Profiling Reveals Different Response Patterns in Active and Latent Tuberculosis Following Stimulation With Mycobacterial Glycolipids.

Upon infection with Mycobacterium tuberculosis (Mtb) the host immune response might clear the bacteria, control its growth leading to latent tuberculosis (LTB), or fail to control its growth resulting in active TB (ATB). There is however no clear understanding of the features underlying a more or less effective response. Mtb glycolipids are abundant in the bacterial cell envelope and modulate the immune response to Mtb, but the patterns of response to glycolipids are still underexplored. To identify the CD45+ leukocyte activation landscape induced by Mtb glycolipids in peripheral blood of ATB and LTB, we performed a detailed assessment of the immune response of PBMCs to the Mtb glycolipids lipoarabinomannan (LAM) and its biosynthetic precursor phosphatidyl-inositol mannoside (PIM), and purified-protein derivate (PPD). At 24 h of stimulation, cell profiling and secretome analysis was done using mass cytometry and high-multiplex immunoassay. PIM induced a diverse cytokine response, mainly affecting antigen-presenting cells to produce both pro-inflammatory and anti-inflammatory cytokines, but not IFN-γ, contrasting with PPD that was a strong inducer of IFN-γ. The effect of PIM on the antigen-presenting cells was partly TLR2-dependent. Expansion of monocyte subsets in response to PIM or LAM was reduced primarily in LTB as compared to healthy controls, suggesting a hyporesponsive/tolerance pattern derived from Mtb infection.

Serum Biomarker Profile Including CCL1, CXCL10, VEGF, and Adenosine Deaminase Activity Distinguishes Active From Remotely Acquired Latent Tuberculosis.

Introduction: There is an urgent medical need to differentiate active tuberculosis (ATB) from latent tuberculosis infection (LTBI) and prevent undertreatment and overtreatment. The aim of this study was to identify biomarker profiles that may support the differentiation between ATB and LTBI and to validate these signatures. Materials and Methods: The discovery cohort included adult individuals classified in four groups: ATB (n = 20), LTBI without prophylaxis (untreated LTBI; n = 20), LTBI after completion of prophylaxis (treated LTBI; n = 20), and healthy controls (HC; n = 20). Their sera were analyzed for 40 cytokines/chemokines and activity of adenosine deaminase (ADA) isozymes. A prediction model was designed to differentiate ATB from untreated LTBI using sparse partial least squares (sPLS) and logistic regression analyses. Serum samples of two independent cohorts (national and international) were used for validation. Results: sPLS regression analyses identified C-C motif chemokine ligand 1 (CCL1), C-reactive protein (CRP), C-X-C motif chemokine ligand 10 (CXCL10), and vascular endothelial growth factor (VEGF) as the most discriminating biomarkers. These markers and ADA(2) activity were significantly increased in ATB compared to untreated LTBI (p ≤ 0.007). Combining CCL1, CXCL10, VEGF, and ADA2 activity yielded a sensitivity and specificity of 95% and 90%, respectively, in differentiating ATB from untreated LTBI. These findings were confirmed in the validation cohort including remotely acquired untreated LTBI participants. Conclusion: The biomarker signature of CCL1, CXCL10, VEGF, and ADA2 activity provides a promising tool for differentiating patients with ATB from non-treated LTBI individuals.

Developing and verifying automatic detection of active pulmonary tuberculosis from multi-slice spiral CT images based on deep learning.

OBJECTIVE: Diagnosis of tuberculosis (TB) in multi-slice spiral computed tomography (CT) images is a difficult task in many TB prevalent locations in which experienced radiologists are lacking. To address this difficulty, we develop an automated detection system based on artificial intelligence (AI) in this study to simplify the diagnostic process of active tuberculosis (ATB) and improve the diagnostic accuracy using CT images. DATA: A CT image dataset of 846 patients is retrospectively collected from a large teaching hospital. The gold standard for ATB patients is sputum smear, and the gold standard for normal and pneumonia patients is the CT report result. The dataset is divided into independent training and testing data subsets. The training data contains 337 ATB, 110 pneumonia, and 120 normal cases, while the testing data contains 139 ATB, 40 pneumonia, and 100 normal cases, respectively. METHODS: A U-Net deep learning algorithm was applied for automatic detection and segmentation of ATB lesions. Image processing methods are then applied to CT layers diagnosed as ATB lesions by U-Net, which can detect potentially misdiagnosed layers, and can turn 2D ATB lesions into 3D lesions based on consecutive U-Net annotations. Finally, independent test data is used to evaluate the performance of the developed AI tool. RESULTS: For an independent test, the AI tool yields an AUC value of 0.980. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value are 0.968, 0.964, 0.971, 0.971, and 0.964, respectively, which shows that the AI tool performs well for detection of ATB and differential diagnosis of non-ATB (i.e. pneumonia and normal cases). CONCLUSION: An AI tool for automatic detection of ATB in chest CT is successfully developed in this study. The AI tool can accurately detect ATB patients, and distinguish between ATB and non- ATB cases, which simplifies the diagnosis process and lays a solid foundation for the next step of AI in CT diagnosis of ATB in clinical application.

Characterization of plasma proteins in children of different Mycobacterium tuberculosis infection status using label-free quantitative proteomics.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is an infectious disease found worldwide. Children infected with MTB are more likely to progress to active TB (ATB); however, the molecular mechanism behind this process has long been a mystery. We employed the label-free quantitative proteomic technology to identify and characterize differences in plasma proteins between ATB and latent TB infection (LTBI) in children. To detect differences that are indicative of MTB infection, we first selected proteins whose expressions were markedly different between the ATB and LTBI groups and the control groups (inflammatory disease control (IDC) and healthy control (HC) groups). A total of 521 proteins differed (> 1.5-fold or < 0.6-fold) in the LTBI group, and 318 proteins in the ATB group when compared with the control groups. Of these, 49 overlapping proteins were differentially expressed between LTBI and ATB. Gene Ontology (GO) analysis revealed most proteins had a cellular and organelle distribution. The MTB infection status was mainly related to differences in binding, cellular and metabolic processes. XRCC4, PCF11, SEMA4A and ATP11A were selected and further verified by qPCR and western blot. At the mRNA level, the expression of XRCC4, PCF11and SEMA4A presented an increased trend in ATB group compare with LTBI. At the protein level, the expression of all these proteins by western blot in ATB/LTBI was consistent with the trends from proteomic detection. Our results provide important data for future mechanism studies and biomarker selection for MTB infection in children.