Mycobacterium kansasii can cause serious pulmonary disease. It belongs to a group of closely-related species of non-tuberculous mycobacteria known as the M. kansasii complex (MKC). Here, we report a population genomics analysis of 358 MKC isolates from worldwide water and clinical sources. We find that recombination, likely mediated by distributive conjugative transfer, has contributed to speciation and on-going diversification of the MKC. Our analyses support municipal water as a main source of MKC infections. Furthermore, nearly 80% of the MKC infections are due to closely-related M. kansasii strains, forming a main cluster that apparently originated in the 1900s and subsequently expanded globally. Bioinformatic analyses indicate that several genes involved in metabolism (e.g., maintenance of the methylcitrate cycle), ESX-I secretion, metal ion homeostasis and cell surface remodelling may have contributed to M. kansasii’s success and its ongoing adaptation to the human host.

Mixed infection by multiple Mycobacterium tuberculosis (MTB) strains is associated with poor treatment outcome of tuberculosis (TB). Traditional genotyping methods have been used to detect mixed infections of MTB, however, their sensitivity and resolution are limited. Deep whole-genome sequencing (WGS) has been proved highly sensitive and discriminative for studying population heterogeneity of MTB. Here, we developed a phylogenetic-based method to detect MTB mixed infections using WGS data. We collected published WGS data of 782 global MTB strains from public database. We called homogeneous and heterogeneous single nucleotide variations (SNVs) of individual strains by mapping short reads to the ancestral MTB reference genome. We constructed a phylogenomic database based on 68,639 homogeneous SNVs of 652 MTB strains. Mixed infections were determined if multiple evolutionary paths were identified by mapping the SNVs of individual samples to the phylogenomic database. By simulation, our method could specifically detect mixed infections when the sequencing depth of minor strains was as low as 1× coverage, and when the genomic distance of two mixed strains was as small as 16 SNVs. By applying our methods to all 782 samples, we detected 47 mixed infections and 45 of them were caused by locally endemic strains. The results indicate that our method is highly sensitive and discriminative for identifying mixed infections from deep WGS data of MTB isolates.

Pathogens can elicit high selective pressure on hosts, potentially altering genetic diversity over short evolutionary timescales. Intraspecific variation in immune response is observable as variable survivability from specific infections. The great gerbil (Rhombomys opimus) is a rodent plague host with a heterogenic but highly resistant phenotype. Here, we investigate the genomic basis for plague-resistant phenotypes by exposing wild-caught great gerbils to plague (Yersinia pestis). Whole genome sequencing of ten survivors and ten moribund individuals revealed a subset of genomic regions showing elevated differentiation. Gene ontology (GO) analysis of candidate genes in these regions demonstrated enrichment of genes directly involved in immune functions, cellular metabolism and the regulation of apoptosis as well as pathways involved in transcription, translation and gene regulation. Transcriptomic analysis revealed that the early activated great gerbil immune response to plague consisted of classical components of the innate immune system. Our approach combining challenge experiments with transcriptomics and population level sequencing, provides new insight into the genetic background of plague-resistance and confirms its complex nature, most likely involving multiple genes and pathways of both the immune system and regulation of basic cellular functions.