Whole genome analysis of Mycobacterium tuberculosis isolates from recurrent episodes of tuberculosis, Finland, 1995–2013

Abstract

Recurrent tuberculosis (TB) is caused by an endogenous re-activation of the same strain of Mycobacterium tuberculosis (relapse) or exogenous infection with a new strain (re-infection). Recurrence of TB in Finland was analysed in a population-based, 19-year study, and genotyping was used to define relapse and re-infection. The M. tuberculosis isolates from patients with suspected relapse were further analysed by whole genome sequencing (WGS) to determine the number and type of mutations occurring in the bacterial genome between the first and second disease episodes. In addition, publicly available tools (PhyResSE and SpolPred) were used to predict drug resistance and spoligotype profile from the WGS data. Of the 8299 notified TB cases, 48 (0.6%) patients had episodes classified as recurrent. Forty-two patients had more than one culture-confirmed TB episode, and isolates from two episodes in 21 patients were available for genotyping. In 18 patients, the M. tuberculosis isolates obtained from the first and second TB episodes had identical spoligotypes. The WGS analysis of the 36 M. tuberculosis isolates from the 18 suspected relapse patients (average time between isolates 2.8 years) revealed 0 to 38 single nucleotide polymorphisms (median 1, mean 3.78) between the first and second isolate. There seemed to be no direct relation between the number of years between the two isolates, or treatment outcome, and the number of single nucleotide polymorphisms. The results suggest that the mutation rate may depend on multiple host-, strain- and treatment-related factors.

Deciphering Within-Host Microevolution of Mycobacterium tuberculosis through Whole-Genome Sequencing: the Phenotypic Impact and Way Forward

“…In those studies, relapse and reinfection could clearly be distinguished, with SNV distances of Ͼ100 to Ͼ1,400 for reinfections compared to an SNV distance of Ͻ9 SNVs for relapse cases (21,36,41,42,44). Only two cases showed intermediate SNV distances of 38 (42) and 57 (44) SNV differences, complicating the classification into relapse or reinfection. Following intensified WGS analyses, the latter could be classified as a relapse, as an initially unidentified minority subclone could be detected in the first isolate (44).…”

“…WGS of serial isolates from a patient has also been used to establish transmission chains and to distinguish relapses from exogenous reinfections (21,(35)(36)(37)(40)(41)(42)(43)(44)(45). To infer direct transmission, the SNV distance between isolates of a transmission chain is determined.…”

“…Five studies used WGS to distinguish relapse from exogenous reinfection (21,36,41,42,44). In those studies, relapse and reinfection could clearly be distinguished, with SNV distances of Ͼ100 to Ͼ1,400 for reinfections compared to an SNV distance of Ͻ9 SNVs for relapse cases (21,36,41,42,44). Only two cases showed intermediate SNV distances of 38 (42) and 57 (44) SNV differences, complicating the classification into relapse or reinfection.…”

“…Faksri et al identified an emerging mutation in ettA (Rv2477c) that could represent a new marker for amikacin (AMI) and kanamycin (KAN) resistance (52) due to the encoded protein's drug efflux pump activity and the involvement in macrolide antibiotics transport across membranes (18,(55)(56)(57). Two other studies observed that ettA mutations emerged in an INH-monoresistant strain (53) and in an INH-and-streptomycin-resistant strain (42), one of which appeared simultaneously with an INH resistance-conferring katG mutation (53).…”

Beyond the SNP threshold: identifying outbreak clusters using inferred transmissions

“…The size of the population of bacteria within a host could also affect the number of SNPs observed between that host and those they infect. Unexplained larger variation is also encountered as documented in Korhonen et al (2016), though high SNP numbers could be a result of re-infection or mixed infection rather than in-host evolution. Where we know that selection or high substitution rates are likely to be present and detected, a higher rate is therefore likely to be appropriate for clustering, and this will affect the relationship between SNPs and transmission events.…”