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Rationale: Isoniazid-resistant tuberculosis (Hr-TB) is often overlooked in diagnostic algorithms because of reliance on first-line molecular assays testing only for rifampicin resistance. Objectives: To determine the prevalence, outcomes, and molecular mechanisms associated with rifampin-susceptible, isoniazid-resistant TB (Hr-TB) in the Eastern Cape, South Africa. Methods: Between April 2016 and October 2017, sputum samples were collected from patients with rifampin-susceptible TB at baseline and at Weeks 7 and 23 of drug-susceptible TB treatment. We performed isoniazid phenotypic and genotypic drug susceptibility testing, including FluoroTypeMTBDR, Sanger sequencing, targeted next-generation sequencing, and whole-genome sequencing. Results: We analyzed baseline isolates from 766 patients with rifampin-susceptible TB. Of 89 patients (11.7%) who were found to have Hr-TB, 39 (44%) had canonical katG or inhA promoter mutations; 35 (39%) had noncanonical katG mutations (including 5 with underlying large deletions); 4 (5%) had mutations in other candidate genes associated with isoniazid resistance. For 11 (12.4%), no cause of resistance was found. Conclusions: Among patients with rifampin-susceptible TB who were diagnosed using first-line molecular TB assays, there is a high prevalence of Hr-TB. Phenotypic drug susceptibility testing remains the gold standard. To improve the performance of genetic-based phenotyping tests, all isoniazid resistance–associated regions should be included, and such tests should have the ability to identify underlying mutations. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB), caused by the human pathogen Mycobacterium tuberculosis, is a recognized disease in human-managed and wild Asian elephants (Elephas maximus) and African elephants (Loxodonta africana) (1-3). Previous findings demonstrate [...]

Background South Africa has the highest tuberculosis incidence globally (781/100,000), with an estimated 4.3% of cases being rifampicin resistant (RR). Control and elimination strategies will require detailed spatial information to understand where drug-resistant tuberculosis exists and why it persists in those communities. We demonstrate a method to enable drug-resistant tuberculosis monitoring by identifying high-burden communities in the Western Cape Province using routinely collected laboratory data. Methods and findings We retrospectively identified cases of microbiologically confirmed tuberculosis and RR-tuberculosis from all biological samples submitted for tuberculosis testing (n = 2,219,891) to the Western Cape National Health Laboratory Services (NHLS) between January 1, 2008, and June 30, 2013. Because the NHLS database lacks unique patient identifiers, we performed a series of record-linking processes to match specimen records to individual patients. We counted an individual as having a single disease episode if their positive samples came from within two years of each other. Cases were aggregated by clinic location (n = 302) to estimate the percentage of tuberculosis cases with rifampicin resistance per clinic. We used inverse distance weighting (IDW) to produce heatmaps of the RR-tuberculosis percentage across the province. Regression was used to estimate annual changes in the RR-tuberculosis percentage by clinic, and estimated average size and direction of change was mapped. We identified 799,779 individuals who had specimens submitted from mappable clinics for testing, of whom 222,735 (27.8%) had microbiologically confirmed tuberculosis. The study population was 43% female, the median age was 36 years (IQR 27-44), and 10,255 (4.6%, 95% CI: 4.6-4.7) cases had documented rifampicin resistance. Among individuals with microbiologically confirmed tuberculosis, 8,947 (4.0%) had more than one disease episode during the study period. The percentage of tuberculosis cases with rifampicin resistance documented among these individuals was 11.4% (95% CI: 10.7-12.0). Overall, the percentage of tuberculosis cases that were RR-tuberculosis was spatially heterogeneous, ranging from 0% to 25% across the province. Our maps reveal significant yearly fluctuations in RR-tuberculosis percentages at several locations. Additionally, the directions of change over time in RR-tuberculosis percentage were not uniform. The main limitation of this study is the lack of unique patient identifiers in the NHLS database, rendering findings to be estimates reliant on the accuracy of the person-matching algorithm. Conclusions Our maps reveal striking spatial and temporal heterogeneity in RR-tuberculosis percentages across this province. We demonstrate the potential to monitor RR-tuberculosis spatially and temporally with routinely collected laboratory data, enabling improved resource targeting and more rapid locally appropriate interventions., Author(s): Avery I. McIntosh 1, Helen E. Jenkins 1, Laura F. White 1, Marinus Barnard 2, Dana R. Thomson 3, Tania Dolby 2, John Simpson 2, Elizabeth M. Streicher 4, [...]

The emergence of drug-resistant tuberculosis (TB) is often attributed to the failure to implement proper TB control programs and correctly manage TB cases (1,2). Consequently, >450,000 multidrug-resistant (MDR) TB cases [...]

Multidrug-resistant tuberculosis was diagnosed in 21 HIV-negative, nonhospitalized male patients residing in northern Tunisia. A detailed investigation showed accelerated transmission of a Mycobacterium tuberculosis clone of the Haarlem type in [...]

Abstract Background The Beijing genotype of M. tuberculosis is a virulent strain that is disseminating worldwide and has a strong association with drug resistance. In the Western Cape of South Africa, epidemiological studies have identified the R220 cluster of the Beijing genotype as a major contributor to a recent outbreak of drug-resistant tuberculosis. Although the outbreak is considered to be due to clonal transmission, the relationship among drug resistant isolates has not yet been established. Results To better understand the evolution of drug resistance among these strains, 14 drug-resistant clinical isolates of the Beijing genotype were sequenced by whole-genome sequencing, including eight from R220 and six from a more ancestral Beijing cluster, R86, for comparison. While each cluster shares a distinct resistance mutation for isoniazid, mapping of other drug-resistance mutations onto a phylogenetic tree constructed from single nucleotide polymorphisms shows that resistance mutations to many drugs have arisen multiple times independently within each cluster of isolates. Thus, drug resistance among these isolates appears to be acquired, not clonally derived. This observation suggests that, although the Beijing genotype as a whole might have selective advantages enabling its rapid dissemination, the XDR isolates are relatively less fit and do not propagate well. Although it has been hypothesized that the increased frequency of drug resistance in some Beijing lineages might be caused by a mutator phenotype, no significant shift in synonymous substitution patterns is observed in the genomes. Conclusion While MDR-TB is spreading by transmission in the Western Cape, our data suggests that further drug resistance (i.e. XDR-TB) at this stage is acquired.