Metformin promotes smear conversion in tuberculosis-diabetes comorbidity and construction of prediction models.

BACKGROUND: The comorbidity of tuberculosis (TB) and diabetes mellitus (DM) is a global health concern. Metformin is commonly used in DM but the potential effectiveness in comorbid patients is uncertain. This retrospective study aims to investigate the effect of metformin on TB-DM comorbidity and construct prediction models. METHODS: Patients diagnosed with TB-DM in West China Hospital were retrospectively enrolled from Nov 2013 to Sep 2019. Electronic health records of patients were extracted. Two-month smear conversion (2SC) was considered an outcome indicator of TB. Univariate and multivariate logistic regression (LR) were used to assess the role of metformin and other independent predictors. Meanwhile, prediction models were built by LR, elastic net regression, support vector machine, k-nearest neighbors, and random forest. RESULTS: A total of 927 individuals were recruited, among which 408 (44.01%) were metformin-exposed patients. A higher 2SC rate was observed in the metformin users. Other impact factors such as smoking, glucose, and creatinine levels were also identified. Multivariable models were then constructed using filtered variables. The support vector machine model yields the highest AUC (0.808, 95% CI: 0.767-0.849) and specificity (83.24%). LR model outperformed others in terms of sensitivity (69.71%). CONCLUSION: This retrospective study of a large population from southwestern China provides strong clinical evidence for the positive effects of metformin in TB-DM. Metformin is associated with a better therapeutic outcome and promising for the adjuvant therapy of TB-DM. Furthermore, a combination of support vector machine and LR models is recommended to discriminate the patients with poor treatment outcomes.

From tuberculosis bedside to bench: UBE2B splicing as a potential biomarker and its regulatory mechanism.

Alternative splicing (AS) is an important approach for pathogens and hosts to remodel transcriptome. However, tuberculosis (TB)-related AS has not been sufficiently explored. Here we presented the first landscape of TB-related AS by long-read sequencing, and screened four AS events (S100A8-intron1-retention intron, RPS20-exon1-alternaitve promoter, KIF13B-exon4-skipping exon (SE) and UBE2B-exon7-SE) as potential biomarkers in an in-house cohort-1. The validations in an in-house cohort-2 (2274 samples) and public datasets (1557 samples) indicated that the latter three AS events are potential promising biomarkers for TB diagnosis, but not for TB progression and prognosis. The excellent performance of classifiers further underscored the diagnostic value of these three biomarkers. Subgroup analyses indicated that UBE2B-exon7-SE splicing was not affected by confounding factors and thus had relatively stable performance. The splicing of UBE2B-exon7-SE can be changed by heat-killed mycobacterium tuberculosis through inhibiting SRSF1 expression. After heat-killed mycobacterium tuberculosis stimulation, 231 ubiquitination proteins in macrophages were differentially expressed, and most of them are apoptosis-related proteins. Taken together, we depicted a global TB-associated splicing profile, developed TB-related AS biomarkers, demonstrated an optimal application scope of target biomarkers and preliminarily elucidated mycobacterium tuberculosis-host interaction from the perspective of splicing, offering a novel insight into the pathophysiology of TB.