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The aim of the present study was to determine whether there is a correlation between phylogenetic relationship and inflammatory response amongst a panel of clinical isolates representative of the global diversity of the human Mycobacterium tuberculosis Complex (MTBC). Measurement of cytokines from infected human peripheral blood monocyte-derived macrophages revealed a wide variation in the response to different strains. The same pattern of high or low response to individual strains was observed for different pro-inflammatory cytokines and chemokines, and was conserved across multiple human donors. Although each major phylogenetic lineage of MTBC included strains inducing a range of cytokine responses, we found that overall inflammatory phenotypes differed significantly across lineages. In particular, comparison of evolutionarily modern lineages demonstrated a significant skewing towards lower early inflammatory response. The differential response to ancient and modern lineages observed using GM-CSF derived macrophages was also observed in autologous monocyte-derived dendritic cells and murine bone marrow-derived macrophages, but not in human unfractionated peripheral blood mononuclear cells. We hypothesize that the reduced immune responses to modern lineages contribute to more rapid disease progression and transmission, which might be a selective advantage in the context of expanding human populations. In addition to the lineage effects, the large strain-to-strain variation in innate immune responses elicited by MTBC will need to be considered in tuberculosis vaccine development.

A previous study in Pará, Northern Brazil, described a strain of Mycobacterium tuberculosis with a unique genotype (SIT2517/T1) associated with multidrug-resistant tuberculosis (MDR-TB). To improve our understanding of MDR-TB transmission dynamics of these strains within this region, we performed phenotypic and genotypic drug susceptibility testing (pDST/gDST), 24-loci mycobacterial interspersed repetitive units (MIRU-VNTR) genotyping, whole-genome sequencing (WGS) and geo-epidemiology analysis. Of the 28 SIT2517/T1 isolates, 19 (67.9%) could be genotyped by 24-loci MIRU-VNTR and 15 by WGS. All belonged to sublineage 4.1.1.3, distinct from other representative Lineage 4 isolates identified in Brazil. The MDR phenotype determined by pDST was confirmed by gDST, the latter also demonstrating the presence of additional mutations conferring pre-extensively drug-resistance (pre-XDR). Discrepancies between gDST and pDST were observed for pyrazinamide and fluoroquinolones. Thirteen out of 15 isolates analyzed by WGS were clustered when applying a 12 single nucleotide polymorphisms (SNPs) cutoff. The SIT2517/T1 isolates were distributed across the metropolitan regions of Belém and Collares municipalities, showing no geographic clustering. WGS-transmission network analysis revealed a high likelihood of direct transmission and the formation of two closely linked transmission chains. This study highlights the need to implement TB genomic surveillance in the Brazilian Amazon region.

Persons living with HIV are known to be at increased risk of developing tuberculosis (TB) disease upon infection with Mycobacterium tuberculosis (Mtb). However, it has remained unclear how HIV co-infection affects subsequent Mtb transmission from these patients. Here, we customized a Bayesian phylodynamic framework to estimate the effects of HIV co-infection on the Mtb transmission dynamics from sequence data. We applied our model to four Mtb genomic datasets collected in sub-Saharan African countries with a generalized HIV epidemic. Our results confirm that HIV co-infection is a strong risk factor for developing active TB. Additionally, we demonstrate that HIV co-infection is associated with a reduced effective reproductive number for TB. Stratifying the population by CD4+ T-cell count yielded similar results, suggesting that, in this context, CD4+ T-cell count is not a better predictor of Mtb transmissibility than HIV infection status alone. Together, our genome-based analyses complement observational household contact studies, and more firmly establish the negative association between HIV co-infection and Mtb transmissibility.

In settings with high tuberculosis (TB) endemicity, distinct genotypes of the Mycobacterium tuberculosis complex (MTBC) often differ in prevalence. However, the factors leading to these differences remain poorly understood. Here we studied the MTBC population in Dar es Salaam, Tanzania over a six-year period, using 1,082 unique patient-derived MTBC whole-genome sequences (WGS) and associated clinical data. We show that the TB epidemic in Dar es Salaam is dominated by multiple MTBC genotypes introduced to Tanzania from different parts of the world during the last 300 years. The most common MTBC genotypes deriving from these introductions exhibited differences in transmission rates and in the duration of the infectious period, but little differences in overall fitness, as measured by the effective reproductive number. Moreover, measures of disease severity and bacterial load indicated no differences in virulence between these genotypes during active TB. Instead, the combination of an early introduction and a high transmission rate accounted for the high prevalence of L3.1.1, the most dominant MTBC genotype in this setting. Yet, a longer co-existence with the host population did not always result in a higher transmission rate, suggesting that distinct life-history traits have evolved in the different MTBC genotypes. Taken together, our results point to bacterial factors as important determinants of the TB epidemic in Dar es Salaam.

Genome-wide association studies rely on the statistical inference of untyped variants, called imputation, to increase the coverage of genotyping arrays. However, the results are often suboptimal in populations underrepresented in existing reference panels and array designs, since the selected single nucleotide polymorphisms (SNPs) may fail to capture population-specific haplotype structures, hence the full extent of common genetic variation. Here, we propose to sequence the full genomes of a small subset of an underrepresented study cohort to inform the selection of population-specific add-on tag SNPs and to generate an internal population-specific imputation reference panel, such that the remaining array-genotyped cohort could be more accurately imputed. Using a Tanzania-based cohort as a proof-of-concept, we demonstrate the validity of our approach by showing improvements in imputation accuracy after the addition of our designed add-on tags to the base H3Africa array.

Abstract Background Large sequence datasets are difficult to visualize and handle. Additionally, they often do not represent a random subset of the natural diversity, but the result of uncoordinated and convenience sampling. Consequently, they can suffer from redundancy and sampling biases. Results Here we present Treemmer, a simple tool to evaluate the redundancy of phylogenetic trees and reduce their complexity by eliminating leaves that contribute the least to the tree diversity. Conclusions Treemmer can reduce the size of datasets with different phylogenetic structures and levels of redundancy while maintaining a sub-sample that is representative of the original diversity. Additionally, it is possible to fine-tune the behavior of Treemmer including any kind of meta-information, making Treemmer particularly useful for empirical studies.

Abstract Background Combination therapy is one of the most effective tools for limiting the emergence of drug resistance in pathogens. Despite the widespread adoption of combination therapy across diseases, drug resistance rates continue to rise, leading to failing treatment regimens. The mechanisms underlying treatment failure are well studied, but the processes governing successful combination therapy are poorly understood. We address this question by studying the population dynamics of Mycobacterium tuberculosis within tuberculosis patients undergoing treatment with different combinations of antibiotics. Results By combining very deep whole genome sequencing (~1000-fold genome-wide coverage) with sequential sputum sampling, we were able to detect transient genetic diversity driven by the apparently continuous turnover of minor alleles, which could serve as the source of drug-resistant bacteria. However, we report that treatment efficacy has a clear impact on the population dynamics: sufficient drug pressure bears a clear signature of purifying selection leading to apparent genetic stability. In contrast, M. tuberculosis populations subject to less drug pressure show markedly different dynamics, including cases of acquisition of additional drug resistance. Conclusions Our findings show that for a pathogen like M. tuberculosis, which is well adapted to the human host, purifying selection constrains the evolutionary trajectory to resistance in effectively treated individuals. Nonetheless, we also report a continuous turnover of minor variants, which could give rise to the emergence of drug resistance in cases of drug pressure weakening. Monitoring bacterial population dynamics could therefore provide an informative metric for assessing the efficacy of novel drug combinations.

The molecular clock and its phylogenetic applications to genomic data have changed how we study and understand one of the major human pathogens, Mycobacterium tuberculosis (MTB), the etiologic agent of tuberculosis. Genome sequences of MTB strains sampled at different times are increasingly used to infer when a particular outbreak begun, when a drug-resistant clone appeared and expanded, or when a strain was introduced into a specific region. Despite the growing importance of the molecular clock in tuberculosis research, there is a lack of consensus as to whether MTB displays a clocklike behavior and about its rate of evolution. Here we performed a systematic study of the molecular clock of MTB on a large genomic data set (6,285 strains), covering different epidemiological settings and most of the known global diversity. We found that sampling times below 15-20 years were often insufficient to calibrate the clock of MTB. For data sets where such calibration was possible, we obtained a clock rate between 1x10-8 and 5x10-7 nucleotide changes per-site-per-year (0.04-2.2 SNPs per-genome-per-year), with substantial differences between clades. These estimates were not strongly dependent on the time of the calibration points as they changed only marginally when we used epidemiological isolates (sampled in the last 40 years) or three ancient DNA samples (about 1,000 years old) to calibrate the tree. Additionally, the uncertainty and the discrepancies in the results of different methods were sometimes large, highlighting the importance of using different methods, and of considering carefully their assumptions and limitations.

[This corrects the article DOI: 10.1371/journal.ppat.1006939.].

Transition bias, an overabundance of transitions relative to transversions, has been widely reported among studies of the rates and spectra of spontaneous mutations. However, demonstrating the role of transition bias in adaptive evolution remains challenging. In particular, it is unclear whether such biases direct the evolution of bacterial pathogens adapting to treatment. We addressed this challenge by analyzing adaptive antibiotic-resistance mutations in the major human pathogen Mycobacterium tuberculosis (MTB). We found strong evidence for transition bias in two independently curated data sets comprising 152 and 208 antibiotic-resistance mutations. This was true at the level of mutational paths (distinct adaptive DNA sequence changes) and events (individual instances of the adaptive DNA sequence changes) and across different genes and gene promoters conferring resistance to a diversity of antibiotics. It was also true for mutations that do not code for amino acid changes (in gene promoters and the 16S ribosomal RNA gene rrs) and for mutations that are synonymous to each other and are therefore likely to have similar fitness effects, suggesting that transition bias can be caused by a bias in mutation supply. These results point to a central role for transition bias in determining which mutations drive adaptive antibiotic resistance evolution in a key pathogen.

Bacterial factors favoring the unprecedented multidrug-resistant tuberculosis (MDR-TB) epidemic in the former Soviet Union remain unclear. We utilized whole genome sequencing and Bayesian statistics to analyze the evolutionary history, temporal emergence of resistance and transmission networks of MDR Mycobacterium tuberculosis complex isolates from Karakalpakstan, Uzbekistan (2001–2006). One clade (termed Central Asian outbreak, CAO) dating back to 1974 (95% HPD 1969–1982) subsequently acquired resistance mediating mutations to eight anti-TB drugs. Introduction of standardized WHO-endorsed directly observed treatment, short-course in Karakalpakstan in 1998 likely selected for CAO-strains, comprising 75% of sampled MDR-TB isolates in 2005/2006. CAO-isolates were also identified in a published cohort from Russia (2008–2010). Similarly, the presence of mutations supposed to compensate bacterial fitness deficits was associated with transmission success and higher drug resistance rates. The genetic make-up of these MDR-strains threatens the success of both empirical and standardized MDR-TB therapies, including the newly WHO-endorsed short MDR-TB regimen in Uzbekistan.

Once considered a phenotypically monomorphic bacterium, there is a growing body of work demonstrating heterogeneity among Mycobacterium tuberculosis (Mtb) strains in clinically relevant characteristics, including virulence and response to antibiotics. However, the genetic and molecular basis for most phenotypic differences among Mtb strains remains unknown. To investigate the basis of strain variation in Mtb, we performed genome-wide transposon mutagenesis coupled with next-generation sequencing (TnSeq) for a panel of Mtb clinical isolates and the reference strain H37Rv to compare genetic requirements for in vitro growth across these strains. We developed an analytic approach to identify quantitative differences in genetic requirements between these genetically diverse strains, which vary in genomic structure and gene content. Using this methodology, we found differences between strains in their requirements for genes involved in fundamental cellular processes, including redox homeostasis and central carbon metabolism. Among the genes with differential requirements were katG, which encodes the activator of the first-line antitubercular agent isoniazid, and glcB, which encodes malate synthase, the target of a novel small-molecule inhibitor. Differences among strains in their requirement for katG and glcB predicted differences in their response to these antimicrobial agents. Importantly, these strain-specific differences in antibiotic response could not be predicted by genetic variants identified through whole genome sequencing or by gene expression analysis. Our results provide novel insight into the basis of variation among Mtb strains and demonstrate that TnSeq is a scalable method to predict clinically important phenotypic differences among Mtb strains.

Molecular epidemiological assessments, drug treatment optimization, and development of immunological interventions all depend on understanding pathogen adaptation and genetic variation, which differ for specific pathogens. Mycobacterium tuberculosis is an exceptionally successful human pathogen, yet beyond knowledge that this bacterium has low overall genomic variation but acquires drug resistance mutations, little is known of the factors that drive its population genomic characteristics. Here, we compared the genetic diversity of the bacteria that established infection to the bacterial populations obtained from infected tissues during murine M. tuberculosis pulmonary infection and human disseminated M. bovis BCG infection. We found that new mutations accumulate during in vitro culture, but that in vivo, purifying selection against new mutations dominates, indicating that M. tuberculosis follows a dominant lineage model of evolution. Comparing bacterial populations passaged in T cell-deficient and immunocompetent mice, we found that the presence of T cells is associated with an increase in the diversity of the M. tuberculosis genome. Together, our findings put M. tuberculosis genetic evolution in a new perspective and clarify the impact of T cells on sequence diversity of M. tuberculosis.

Renewed efforts in tuberculosis (TB) research have led to important new insights into the biology and epidemiology of this devastating disease. Yet, in the face of the modern epidemics of HIV/AIDS, diabetes, and multidrug resistance--all of which contribute to susceptibility to TB--global control of the disease will remain a formidable challenge for years to come. New high-throughput genomics technologies are already contributing to studies of TB's epidemiology, comparative genomics, evolution, and host-pathogen interaction. We argue here, however, that new multidisciplinary approaches--especially the integration of epidemiology with systems biology in what we call "systems epidemiology"--will be required to eliminate TB.

Mycobacterium tuberculosis infects one third of the human world population and kills someone every 15 seconds. For more than a century, scientists and clinicians have been distinguishing between the human- and animal-adapted members of the M. tuberculosis complex (MTBC). However, all human-adapted strains of MTBC have traditionally been considered to be essentially identical. We surveyed sequence diversity within a global collection of strains belonging to MTBC using seven megabase pairs of DNA sequence data. We show that the members of MTBC affecting humans are more genetically diverse than generally assumed, and that this diversity can be linked to human demographic and migratory events. We further demonstrate that these organisms are under extremely reduced purifying selection and that, as a result of increased genetic drift, much of this genetic diversity is likely to have functional consequences. Our findings suggest that the current increases in human population, urbanization, and global travel, combined with the population genetic characteristics of M. tuberculosis described here, could contribute to the emergence and spread of drug-resistant tuberculosis.

The factors that govern the development of tuberculosis disease are incompletely understood. We hypothesized that some strains of Mycobacterium tuberculosis (M. tuberculosis) are more capable of causing disseminated disease than others and may be associated with polymorphisms in host genes responsible for the innate immune response to infection. We compared the host and bacterial genotype in 187 Vietnamese adults with tuberculous meningitis (TBM) and 237 Vietnamese adults with uncomplicated pulmonary tuberculosis. The host genotype of tuberculosis cases was also compared with the genotype of 392 cord blood controls from the same population. Isolates of M. tuberculosis were genotyped by large sequence polymorphisms. The hosts were defined by polymorphisms in genes encoding Toll-interleukin 1 receptor domain containing adaptor protein (TIRAP) and Toll-like receptor-2 (TLR-2). We found a significant protective association between the Euro-American lineage of M. tuberculosis and pulmonary rather than meningeal tuberculosis (Odds ratio (OR) for causing TBM 0.395, 95% confidence intervals (C.I.) 0.193-0.806, P = 0.009), suggesting these strains are less capable of extra-pulmonary dissemination than others in the study population. We also found that individuals with the C allele of TLR-2 T597C allele were more likely to have tuberculosis caused by the East-Asian/Beijing genotype (OR = 1.57 [95% C.I. 1.15-2.15]) than other individuals. The study provides evidence that M. tuberculosis genotype influences clinical disease phenotype and demonstrates, for the first time, a significant interaction between host and bacterial genotypes and the development of tuberculosis.