Transmission patterns of rifampicin resistant Mycobacterium tuberculosis complex strains in Cameroon: a genomic epidemiological study

Transmission of multidrug-resistant tuberculosis in Jiangxi, China, and associated risk factors

In order to effectively combat the urgent threat of multidrug-resistant tuberculosis (MDR-TB), it is imperative to gain a comprehensive understanding of the drug-resistant profiles, transmission dynamics, and associated risk factors. Our study encompassed a population-based retrospective analysis with 130 MDR-TB patients from 2018 to 2021. The research methodology incorporated whole-genome sequencing, drug susceptibility testing , and logistic regression analysis to discern the risk factors of genomic clustering linked to recent transmission. The findings from phenotypic drug resistance assessments revealed notable resistance rates: ethambutol at 62.3% (81/130), streptomycin at 72.3% (94/130), levofloxacin at 51.5% (67/130), and moxifloxacin at 50.0% (65/130). Furthermore, among all patients, 38 individuals (29.23%, 38/130) were found to be part of 17 clusters, indicating instances of recent MDR-TB transmission. The genomic clustering patients were deeply investigated. Lineage 2.2.1 was established as the primary sub-lineage (86.15%, 112/130), followed by lineage 4 (9.23%, 12/130). Moreover, the logistic regression analysis underscored that unemployment, farming occupations, and prior TB treatment were identified as significant risk factors for recent transmission.

IMPORTANCE

The high prevalence of multidrug-resistant tuberculosis (MDR-TB) in Jiangxi Province highlights the importance of understanding the genetic background and drug resistance patterns of these strains. This knowledge is crucial for developing effective control methods. Furthermore, in light of the significance of preventing transmission among tuberculosis patients, whole-genome sequencing was utilized to investigate the recent transmission of MDR-TB and identify associated risk factors. The findings revealed that individuals in the farming sector, those who are unemployed, and patients with a history of tuberculosis treatment are at elevated risk. Consequently, targeted public interventions for these at-risk groups are imperative.

Delineating the evolutionary pathway to multidrug-resistant outbreaks of a Mycobacterium tuberculosis L4.1.2.1/Haarlem sublineage

OBJECTIVES

We sought to capture the evolutionary itinerary of the Mycobacterium tuberculosis L4.1.2.1/Haarlem sublineage in northern Tunisia, where it caused a major multidrug-resistant (MDR) tuberculosis outbreak in a context strictly negative for HIV infection.

METHODS

We combined whole genome sequencing and Bayesian approaches using a representative collection of drug-susceptible and drug-resistant L4.1.2.1/Haarlem clinical strains (n = 121) recovered from the outbreak region over 16 years.

RESULTS

In the absence of drug resistance, the L4.1.2.1/Haarlem sublineage showed a propensity for rapid transmission as witnessed by the high clustering (44.6%) and recent transmission rates (25%), as well as the reduced mean distance between genome pairs. The entire pool of L4.1.2.1/Haarlem MDR strains was found to be linked to either the aforementioned major outbreak (68 individuals, 2001-2016) or to a minor, newly uncovered outbreak (six cases, 2001-2011). Strikingly, the two outbreaks descended independently from a common ancestor that can be dated back to 1886.

CONCLUSIONS

Our data point to the intrinsic propensity for rapid transmission of the M. tuberculosis L4.1.2.1/Haarlem sublineage in northern Tunisia, linking the overall MDR tuberculosis epidemic to a single ancestor. These findings bring out the important role of the bacillus' genetic background in the emergence of successful MDR M. tuberculosis clones.

Risk factors of MDR-TB and impacts of COVID-19 pandemic on escalating of MDR-TB incidence in lower-middle-income countries: A scoping review

The coronavirus disease 2019 (COVID-19) pandemic is affecting tuberculosis (TB) treatment in many ways that might lead to increasing the prevalence of multi-drugs-resistance tuberculosis (MDR-TB), especially in lower-middle-income-countries (LMICs). This scoping review aimed to identify the risk factors of MDR-TB and to determine the impacts of the COVID-19 pandemic on MDR-TB prevalence in LMICs. This study was reported according to the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) guideline. The relevant keywords were used to search studies in three databases (PubMed, ScienceDirect and SpringerLink) to identify the related articles. The English-written articles published from January 2012 to December 2022 that explored risk factors or causes of MDR-TB in LMICs were included. Out of 1,542 identified articles, 17 retrospective, prospective, case-control and cross-sectional studies from ten LMICs met were included in this scoping review. Twenty-one risk factors were discovered, with prior TB treatment (relapsed cases), diabetes, living area, living condition, smoking and low socioeconomic status were the main factors in developing MDR-TB during COVID-19 pandemic. The pandemic increased the MDR-TB prevalence through drug resistance transmission inside households, the distance between home and healthcare facilities and low socioeconomic status. This scoping review demonstrates how the COVID-19 pandemic has affected the rising incidence of MDR-TB in LMICs.

Whole-Genome Sequencing to Predict Mycobacterium tuberculosis Drug Resistance: A Retrospective Observational Study in Eastern China

Pulmonary tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (MTB). Whole-genome sequencing (WGS) holds great promise as an advanced technology for accurately predicting anti-TB drug resistance. The development of a reliable method for detecting drug resistance is crucial in order to standardize anti-TB treatments, enhance patient prognosis, and effectively reduce the risk of transmission. In this study, our primary objective was to explore and determine the potential of WGS for assessing drug resistance based on genetic variants recommended by the World Health Organization (WHO). A total of 1105 MTB strains were selected from samples collected from 2014-2018 in Zhejiang Province, China. Phenotypic drug sensitivity tests (DST) of the anti-TB drugs were conducted for isoniazid (INH), rifampicin (RFP), streptomycin, ethambutol, fluoroquinolones (levofloxacin and moxifloxacin), amikacin, kanamycin, and capreomycin, and the drug-resistance rates were calculated. The clean WGS data of the 1105 strains were acquired and analyzed. The predictive performance of WGS was evaluated by the comparison between genotypic and phenotypic DST results. For all anti-TB drugs, WGS achieved good specificity values (>90%). The sensitivity values for INH and RFP were 91.78% and 82.26%, respectively; however, they were ≤60% for other drugs. The positive predictive values for anti-TB drugs were >80%, except for ethambutol and moxifloxacin, and the negative predictive values were >90% for all drugs. In light of the findings from our study, we draw the conclusion that WGS is a valuable tool for identifying genome-wide variants. Leveraging the genetic variants recommended by the WHO, WGS proves to be effective in detecting resistance to RFP and INH, enabling the identification of multi-drug resistant TB patients. However, it is evident that the genetic variants recommended for predicting resistance to other anti-TB drugs require further optimization and improvement.

Whole-Genome Sequencing and Epidemiological Investigation of Tuberculosis Outbreaks in High Schools in Hunan, China

Background

Tuberculosis (TB) seriously threatens individual and public health. Recently, TB outbreaks in schools have been reported more frequently in China and have attracted widespread attention. We reported three TB outbreaks in high schools in Hunan Province, China.

Methods

When a tuberculosis patient was reported in a school, we carried out field epidemiological investigations, including tuberculin skin testing (TST), chest X-ray (CXR) and laboratory test for all close contacts, and whole-genome sequencing (WGS) analyses to understand the transmission patterns, the causes and the risk factors for the outbreaks, thereby providing a foundation for the control of TB epidemics in schools.

Results

A total of 49 students with TB patients were identified in the three schools where TB outbreaks occurred, including nine patients in School A, 14 patients in School B, and 26 patients in School C. In Schools A, B and C, the putative attack rates in the classes of the index case were 13.8% (8/58), 7.6% (5/66), and 40.4% (21/52), while the putative attack rates of expanding screening in the school were 0.3% (1/361), 0.2% (9/3955), and 0.2% (5/2080), respectively. Thirteen patients had patient delay, with a median delay interval of 69 days (IQR 30.5–113 days). Twelve patients had a healthcare diagnostic delay with a median delay interval of 32 days (IQR 24–82 days). Phylogenetic analysis of culture-positive patients revealed that most of them shared a small genetic distance (≤12 SNPs), with three separate genetic clusters (including one MDR-TB genomic cluster), indicating the recent transmission of Mycobacterium tuberculosis strains.

Conclusion

This combination of field investigation and WGS analysis revealed the transmission of three TB outbreaks in schools. Reinforced implementation is needed to improve timely case finding and reduce diagnosis delay in routine TB control in the school population.

Understanding drivers of phylogenetic clustering and terminal branch lengths distribution in epidemics of Mycobacterium tuberculosis

Detecting factors associated with transmission is important to understand disease epidemics, and to design effective public health measures. Clustering and terminal branch lengths (TBL) analyses are commonly applied to genomic data sets of Mycobacterium tuberculosis (MTB) to identify sub-populations with increased transmission. Here, I used a simulation-based approach to investigate what epidemiological processes influence the results of clustering and TBL analyses, and whether differences in transmission can be detected with these methods. I simulated MTB epidemics with different dynamics (latency, infectious period, transmission rate, basic reproductive number R0, sampling proportion, sampling period, and molecular clock), and found that all considered factors, except for the length of the infectious period, affect the results of clustering and TBL distributions. I show that standard interpretations of this type of analyses ignore two main caveats: (1) clustering results and TBL depend on many factors that have nothing to do with transmission, (2) clustering results and TBL do not tell anything about whether the epidemic is stable, growing, or shrinking, unless all the additional parameters that influence these metrics are known, or assumed identical between sub-populations. An important consequence is that the optimal SNP threshold for clustering depends on the epidemiological conditions, and that sub-populations with different epidemiological characteristics should not be analyzed with the same threshold. Finally, these results suggest that different clustering rates and TBL distributions, that are found consistently between different MTB lineages, are probably due to intrinsic bacterial factors, and do not indicate necessarily differences in transmission or evolutionary success.