Abstract:The clinical phenotype of zoonotic tuberculosis and its contribution to the global burden of disease are poorly understood and probably underestimated. This shortcoming is partly because of the inability of currently available laboratory and in silico tools to accurately identify all subspecies of the
Mycobacterium tuberculosis
complex (MTBC). We present SNPs to Identify TB (SNP-IT), a single-nucleotide polymorphism–based tool to identify all members of MTBC, including animal clades. By … Show more
“…bovis (not intrinsically resistant to pyrazinamide)." M. orygis has been isolated from many different animals, and there is a growing recognition that it is a zoonotic source of human TB (27). Our in silico typing approach confirmed that M. orygis could be specifically identified by a mutation at codon 329 of gyrB (8).…”
Using 894 phylogenetically diverse genomes of the Mycobacterium tuberculosis complex (MTBC), we simulated in silico the ability of the Hain Lifescience GenoType MTBC assay to differentiate the causative agents of tuberculosis. Here, we propose a revised interpretation of this assay to reflect its strengths (e.g., it can distinguish some strains of Mycobacterium canettii and variants of Mycobacterium bovis that are not intrinsically resistant to pyrazinamide) and limitations (e.g., Mycobacterium orygis cannot be differentiated from Mycobacterium africanum).
“…bovis (not intrinsically resistant to pyrazinamide)." M. orygis has been isolated from many different animals, and there is a growing recognition that it is a zoonotic source of human TB (27). Our in silico typing approach confirmed that M. orygis could be specifically identified by a mutation at codon 329 of gyrB (8).…”
Using 894 phylogenetically diverse genomes of the Mycobacterium tuberculosis complex (MTBC), we simulated in silico the ability of the Hain Lifescience GenoType MTBC assay to differentiate the causative agents of tuberculosis. Here, we propose a revised interpretation of this assay to reflect its strengths (e.g., it can distinguish some strains of Mycobacterium canettii and variants of Mycobacterium bovis that are not intrinsically resistant to pyrazinamide) and limitations (e.g., Mycobacterium orygis cannot be differentiated from Mycobacterium africanum).
Little is known about the physiology of latent Mycobacterium tuberculosis infection. We studied the mutational rates of 24 index tuberculosis (TB) cases and their latently infected household contacts who developed active TB up to 5.25 years later, as an indication of bacterial physiological state and possible generation times during latent TB infection in humans. Here we report that the rate of new mutations in the M. tuberculosis genome decline dramatically after two years of latent infection (two-sided p < 0.001, assuming an 18 h generation time equal to log phase M. tuberculosis, with latency period modeled as a continuous variable). Alternatively, assuming a fixed mutation rate, the generation time increases over the latency duration. Mutations indicative of oxidative stress do not increase with increasing latency duration suggesting a lack of host or bacterial derived mutational stress. These results suggest that M. tuberculosis enters a quiescent state during latency, decreasing the risk for mutational drug resistance and increasing generation time, but potentially increasing bacterial tolerance to drugs that target actively growing bacteria.
“…In particular, we improve the resolution of known sub-lineages, for instance 4 We define a SNS barcode (95 SNS) that allows the rapid assignment of Mtb sub-lineage designations with detailed semantic and hierarchical sub-lineage naming schema from genomic data (.vcf files) [8][9][10] . Along with this, we provide a software package and comparative tables / figures to facilitate the interchange between five SNS schemas and three Mtb sub-lineage naming systems [8][9][10]20 . We expect these tools to facilitate the use of the expanded Mtb classification by scholars of Mtb evolution and public health practitioners alike for applications such as the rapid assessment of potential outbreaks and isolate triage for detailed phylogenetic analyses.…”
Section: Discussionmentioning
confidence: 99%
“…We developed a python module (https://github.com/farhat-lab/fast-lineage-caller) that takes as input a .vcf file and returns the lineage / sub-lineage calls. We provide five SNS schemes for Mtb : Coll et al 8 (all lineages), the Shitikov et al 9 (L2), Stucki et al 10 (L4, geographically bounded and unbounded sub-lineages), Lipworth et al 20 ( Mtb complex species, Mtb main lineages) and the one proposed here (L1-4).…”
Mycobacterium tuberculosis is a clonal pathogen proposed to have co-evolved with its human host for millennia, yet our understanding of its genomic diversity and biogeography remains incomplete. Here we use a combination of phylogenetics and dimensionality reduction to reevaluate the population structure of M. tuberculosis, providing the first in-depth analysis of the ancient East African Indian Lineage 1 and the modern Central Asian Lineage 3 and expanding our understanding of Lineages 2 and 4. We assess sub-lineages using genomic sequences from 4,939 pan-susceptible strains and find 30 new genetically distinct clades that we validate in a dataset of 4,645 independent isolates. We characterize sub-lineage geographic distributions and demonstrate a consistent geographically restricted and unrestricted pattern for 20 groups, including three groups of Lineage 1. We assess the transmissibility of the four major lineages by examining the distribution of terminal branch lengths across the M. tuberculosis phylogeny and identify evidence supporting higher transmissibility in Lineages 2 and 4 than 3 and 1 on a global scale. We define a robust expanded barcode of 95 single nucleotide substitutions (SNS) that allows for the rapid identification of 69 Mtb sub-lineages and 26 additional internal groups. Our results paint a higher resolution picture of the Mtb phylogeny and biogeography.
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