Machine learning reveals that Mycobacterium tuberculosis genotypes and anatomic disease site impacts drug resistance and disease transmission among patients with proven extra-pulmonary tuberculosis

Exploring the impact of socioeconomic and natural factors on pulmonary tuberculosis incidence in China (2013-2019) using explainable machine learning: A nationwide study

Pulmonary tuberculosis (PTB) stands as a significant and prevalent infectious disease in China. Integrating 13 natural and socioeconomic factors, we conduct nine machine learning (ML) models alongside the Tree-Structured Parzen Estimator to predict the monthly PTB incidence rate from 2013 to 2019 in mainland China. With explainable ML techniques, our research highlights that population size, per capita GDP, and PM10 concentration emerge as the primary determinants influencing the PTB incidence rate. We delineate both the independent and interactive impacts of these factors on the PTB incidence rate. Furthermore, crucial thresholds associated with factors influencing the PTB incidence rate are identified. Taking factors that have a positive effect on reducing the incidence rate of PTB as an example, the thresholds at which the effects of factors PM2.5, PM10, O3, and RH on the incidence rate change from increase to decrease are 105.5 µg/m3, 75.5 µg/m3, 90.8 µg/m3, and 72.3 % respectively. Our work will contribute valuable insights for public health interventions.

Molecular investigations of Mycobacterium tuberculosis genotypes among baseline and follow-up strains circulating in four regions of Eswatini

BACKGROUND

The tuberculosis (TB) epidemic remains a major global health problem and Eswatini is not excluded. Our study investigated the circulating genotypes in Eswatini and compared them at baseline (start of treatment) and follow-up during TB treatment.

METHODS

Three hundred and ninety (n = 390) participants were prospectively enrolled from referral clinics and patients who met the inclusion criteria, were included in the study. A total of 103 participants provided specimens at baseline and follow-up within six months. Mycobacterium tuberculosis (M.tb) strains were detected by GeneXpert® MTB/RIF assay (Cephied, USA) and Ziehl -Neelsen (ZN) microscopy respectively at baseline and follow-up time-points respectively. The 206 collected specimens were decontaminated and cultured on BACTEC™ MGIT™ 960 Mycobacteria Culture System (Becton Dickinson, USA). Drug sensitivity testing was performed at both baseline and follow-up time points. Spoligotyping was performed on both baseline and follow-up strains after DNA extraction.

RESULTS

Resistance to at least one first line drug was detected higher at baseline compared to follow-up specimens with most of them developing into multidrug-resistant (MDR)-TB. A total of four lineages and twenty genotypes were detected. The distribution of the lineages varied among the different regions in Eswatini. The Euro-American lineage was the most prevalent with 46.12% (95/206) followed by the East Asian with 24.27% (50/206); Indo-Oceanic at 9.71% (20/206) and Central Asian at 1.94% (4/206). Furthermore, a high proportion of the Beijing genotype at 24.27% (50/206) and S genotype at 16.50% (34/206) were detected. The Beijing genotype was predominant in follow-up specimens collected from the Manzini region with 48.9% (23/47) (p = 0.001). A significant proportion of follow-up specimens developed MDR-TB (p = 0.001) with Beijing being the major genotype in most follow-up specimens (p < 0.000).

CONCLUSION

Eswatini has a high M.tb genotypic diversity. A significant proportion of the TB infected participants had the Beijing genotype associated with MDR-TB in follow-up specimens and thus indicate community wide transmission.

Cutaneous Manifestations of Mycobacterium tuberculosis: A Literature Review

Tuberculosis is an ancient disease that humanity struggled with for centuries and continues to struggle with. The bacteria Mycobacterium tuberculosis often infects the lungs through respiratory transmission and manifests itself through various symptoms, including cutaneous infections. Cutaneous tuberculosis (CTB) comprises about 1% to 1.5% of all extrapulmonary manifestations and is often accompanied by polymorphous lesions, including papules, nodules, plaques, ulcers, gummas, and verrucous lesions. CTB is most commonly observed in low-income, HIV, and immunosuppressed populations, similar to intrapulmonary manifestations. The main pathogen for CTB is M. tuberculosis but less commonly with M. bovis and BCG vaccine, and the modes of transmission are largely classified into exogenous and endogenous CTB. Current treatment options for CTB include oral therapy of antibiotic medications such as rifampicin, streptomycin, ethambutol, isoniazid, and pyrazinamide, which is occasionally combined with surgical intervention.

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

Genetic diversity and drug resistance profiles of Mycobacterium tuberculosis complex isolates from patients with extrapulmonary tuberculosis in Ghana and their associated host immune responses

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Pleural TB and TB lymphadenitis are the most common forms of extrapulmonary TB in Ghana.

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M. tb sub-lineage Cameroon is associated with decreased serum IL-1β, IL-17A, and IFN-α.

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A significant association was observed between age and M. tb complex lineages.

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Generally, M. bovis contributes minimally to human extrapulmonary TB in Ghana.

Objectives

This study sought to determine the genetic diversity and drug resistance profiles of Mycobacterium tuberculosis complex (MTBC) isolates from extrapulmonary tuberculosis (EPTB) patients in Ghana, and their associated immune responses.

Methods

Spoligotyping was performed on 102 MTBC isolates from EPTB patients. Lineages/sub-lineages were assigned by comparing spoligotyping patterns primarily with the SITVIT2 database and subsequently with the TB-Lineage online tool for unknown isolates in SITVIT2. Drug susceptibility testing was performed using MGIT (BD BACTEC 960), Lowenstein-Jensen media (indirect proportion method), and GenoType MTBDRplus/MTBDRsl assays. Differential cytokine levels in the serum of 20 EPTB patients infected with MTBC lineage 4 were determined using the Luminex multiplex immunoassay.

Results

Around 95% (97/102) of isolates were Mycobacterium tuberculosis, predominantly lineage 4 (95%; 92/97). Of the lineage 4 isolates, the majority were sub-lineage Cameroon (37%, 34/92). Prevalence was significantly higher in the 15–34 years age group among EPTB patients infected with lineage 4 strains (p = 0.024). Fifteen isolates were resistant to at least one anti-TB drug tested. Decreased levels of IL-1β, IL-17A, and IFN-α were observed in individuals infected with Cameroon sub-lineages compared with other lineage 4 sub-lineages.

Conclusions

Our study confirms Cameroon (SIT61) as the most common spoligotype causing human EPTB in Ghana, and that it is associated with decreased serum IL-1β, IL-17A, and IFN-α.