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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 an in-depth analysis of the ancient Indo-Oceanic 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 4939 pan-susceptible strains, and find 30 new genetically distinct clades that we validate in a dataset of 4645 independent isolates. We find a consistent geographically restricted or unrestricted pattern for 20 groups, including three groups of Lineage 1. The distribution of terminal branch lengths across the M. tuberculosis phylogeny supports the hypothesis of a higher transmissibility of Lineages 2 and 4, in comparison with Lineages 3 and 1, on a global scale. We define an expanded barcode of 95 single nucleotide substitutions that allows rapid identification of 69 M. tuberculosis sub-lineages and 26 additional internal groups. Our results paint a higher resolution picture of the M. tuberculosis phylogeny and biogeography., Mycobacterium tuberculosis is a clonal pathogen that has co-evolved with humans for millennia. Here, Freschi et al. reevaluate the population structure of M. tuberculosis, providing an in-depth analysis of the ancient Indo-Oceanic Lineage 1 and the modern Central Asian Lineage 3, and expanding our understanding of Lineages 2 and 4.

Summary Background Mycobacterium tuberculosis whole genome sequencing (WGS) data can provide insights into temporal and geographical trends in resistance acquisition and inform public health interventions. We aimed to use a large clinical collection of M tuberculosis WGS and resistance phenotype data to study how, when, and where resistance was acquired on a global scale. Methods We did a retrospective analysis of WGS data. We curated a set of clinical M tuberculosis isolates with high-quality sequencing and culture-based drug susceptibility data (spanning four lineages and 52 countries in Africa, Asia, the Americas, and Europe) using public databases and literature curation. For inclusion, sequence quality criteria and country of origin data were required. We constructed geographical and lineage specific M tuberculosis phylogenies and used Bayesian molecular dating with BEAST, version 1.10.4, to infer the most recent common susceptible ancestor age for 4869 instances of resistance to ten drugs. Findings Between Jan 1, 1987, and Sept 12, 2014, of 10 299 M tuberculosis clinical isolates, 8550 were curated, of which 6099 (71%) from 15 countries met criteria for molecular dating. The number of independent resistance acquisition events was lower than the number of resistant isolates across all countries, suggesting ongoing transmission of drug resistance. Ancestral age distributions supported the presence of old resistance, 20 years or more before, in most countries. A consistent order of resistance acquisition was observed globally starting with resistance to isoniazid, but resistance ancestral age varied by country. We found a direct correlation between gross domestic product per capita and resistance age (r2=0·47; p=0·014). Amplification of fluoroquinolone and second-line injectable resistance among multidrug-resistant isolates is estimated to have occurred very recently (median ancestral age 4·7 years [IQR 1·9–9·8] before sample collection). We found the sensitivity of commercial molecular diagnostics for second-line resistance to vary significantly by country (p Interpretation Our results highlight that both resistance transmission and amplification are contributing to disease burden globally but vary by country. The observation that wealthier nations are more likely to have old resistance (most recent common susceptible ancestor >20 years before isolation) suggests that programmatic improvements can reduce resistance amplification, but that fit resistant strains can circulate for decades subsequently implies the need for continued surveillance. Funding National Institutes of Health, a Harvard Institute of Global Health Burke Fellowship from the National Human Genome Research Institute, National Institutes of Health, the Bill & Melinda Gates Foundation, Boston Children's Hospital, Bushrod H Campbell and Adah F Hall Charity Fund, and Charles A King Trust Postdoctoral Fellowship.

Background Evolutionary pressures on bacterial pathogens can result in phenotypic change including increased virulence, drug resistance, and transmissibility. Understanding the evolution of these phenotypes in nature and the multiple genetic changes needed has historically been difficult due to sparse and contemporaneous sampling. A complete picture of the evolutionary routes frequently travelled by pathogens would allow us to better understand bacterial biology and potentially forecast pathogen population shifts. Methods In this work, we develop a phylogeny-based method to assess evolutionary dependency between mutations. We apply our method to a dataset of 31,428Mycobacterium tuberculosiscomplex (MTBC) genomes, a globally prevalent bacterial pathogen with increasing levels of antibiotic resistance. Results We find evolutionary dependency within simultaneously- and sequentially-acquired variation, and identify that genes with dependent sites are enriched in antibiotic resistance and antigenic function. We discover 20 mutations that potentiate the development of antibiotic resistance and 1,003 dependencies that evolve as a consequence antibiotic resistance. Varying by antibiotic, between 9% and 80% of resistant strains harbor a dependent mutation acquired after a resistance-conferring variant. We demonstrate that mutational dependence can not only improve prediction of phenotype (e.g. antibiotic resistance), but can also detect sequential environmental pressures on the pathogen (e.g. the pressures imposed by sequential antibiotic exposure during the course of standard multi-antibiotic treatment). Taken together, our results demonstrate the feasibility and utility of detecting dependent events in the evolution of natural populations. Data and code available at:https://github.com/farhat-lab/DependentMutations

Phase variation induced by insertions and deletions (INDELs) in genomic homopolymeric tracts (HT) can silence and regulate genes in pathogenic bacteria but this process is not characterized in MTBC adaptation. We leverage 31,428 diverse clinical isolates to identify genomic regions including phase-variants under positive selection. Of 87,651 INDEL events that emerge repeatedly across the phylogeny, 12.4% are phase-variants within HTs (0.02% of the genome by length). We estimated the in-vitro frameshift rate in a neutral HT at 100x the neutral substitution rate at 1.1 × 10−5 frameshifts/HT/year. Using neutral evolution simulations, we identified 4,098 substitutions and 45 phase-variants to be putatively adaptive to MTBC (PespA, a critical mediator of ESX-1 dependent virulence. Our evidence supports a new hypothesis that phase variation in the ESX-1 system of MTBC can act as a toggle between antigenicity and survival in the host.

BackgroundMycobacterium tuberculosis (Mtb) transmissibility may vary between lineages (or variants) and this may contribute to the slow decline of tuberculosis (TB) incidence. The objective of our study was to compare transmissibility across four major lineages (L1-4) of Mtb among participants from two cohort studies in Pune, India.MethodsWe performed whole-genome sequencing (WGS) of Mtb sputum culture-positive isolates from participants in two prospective cohort studies of adults with pulmonary TB seeking care at public treatment centers in Pune, Maharashtra. We performed genotypic susceptibility prediction for both first- and second-line drugs using a previously validated random forest model. We used single nucleotide substitutions (SNS) and maximum likelihood estimation to build isolate phylogenies by lineage. We used Bayesian molecular dating to estimate ancestral node ages and compared tree characteristics using a two-sample Kolmogorov-Smirnov (KS) test.ResultsOf the 642 isolates from distinct study participants that underwent WGS, 612 met sequence quality criteria. The median age of the 612 participants was 31 years (IQR 24.4-44.2), the majority were male (64.7%) and sputum smear-positive (83.3%), and 6.7% had co-infection with HIV. Most isolates belonged to L3 (44.6%). The majority (61.1%) of multidrug-resistant isolates (MDR, resistant to isoniazid and rifampin) belonged to L2 (P < 0.001 [Fisher’s Exact]). There was no significant difference in host characteristics between participants infected with the four major lineages. In phylogenetic analysis, we measured shorter terminal branch lengths in the L2 tree compared to L1 and L3 trees indicating less time elapsing between transmission and sampling and higher transmissibility (median branch lengths: L2 - 3.3, L3 - 7.8, p ConclusionModern Mtb lineages (L2 and L4) were more recently introduced in western India, compared to older lineages (L1 and L3). L2 shows a higher frequency of drug-resistance and higher transmissibility. Our findings highlight the need for contact tracing around cases of TB due to L2, and heightened surveillance of TB antibiotic resistance in India.

Long diagnostic wait times hinder international efforts to address multi-drug resistance in M. tuberculosis. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution. However, generalizability and clinical adoption have been limited in part by a lack of interpretability and verifiability, especially in deep learning methods. Here, we present a deep convolutional neural network (CNN) that predicts the antibiotic resistance phenotypes of M. tuberculosis isolates. The CNN performs with state-of-the-art levels of predictive accuracy. Evaluation of salient sequence features permits biologically meaningful interpretation and validation of the CNN’s predictions, with promising repercussions for functional variant discovery, clinical applicability, and translation to phenotype prediction in other organisms.

Antibiotic resistance among bacterial pathogens poses a major global health threat. Mycobacterium tuberculosis complex (MTBC) is estimated to have the highest resistance rates of any pathogen globally. Given the low growth rate and the need for a biosafety level 3 laboratory, the only realistic avenue to scale up drug susceptibility testing (DST) for this pathogen is to rely on genotypic techniques. This raises the fundamental question of whether a mutation is a reliable surrogate for phenotypic resistance or whether the presence of a second mutation can completely counteract its effect, resulting in major diagnostic errors (i.e., systematic false resistance results). To date, such epistatic interactions have only been reported for streptomycin that is now rarely used. By analyzing more than 31,000 MTBC genomes, we demonstrated that the eis C-14T promoter mutation, which is interrogated by several genotypic DST assays endorsed by the World Health Organization, cannot confer resistance to amikacin and kanamycin if it coincides with loss-of-function (LoF) mutations in the coding region of eis. To our knowledge, this represents the first definitive example of antibiotic reversion in MTBC. Moreover, we raise the possibility that mmpR (Rv0678) mutations are not valid markers of resistance to bedaquiline and clofazimine if these coincide with an LoF mutation in the efflux pump encoded by mmpS5 (Rv0677c) and mmpL5 (Rv0676c).

BackgroundGlobal tuberculosis (TB) drug resistance (DR) surveillance is largely focused on the drug rifampicin. We leveraged public and surveillance M. tuberculosis (Mtb) whole genome sequencing (WGS) data, to generate more comprehensive country-level resistance prevalence estimates (antibiograms) using in silico resistance prediction.MethodsWe curated and quality-controlled Mtb WGS data. We used a validated random forest model to predict phenotypic resistance to twelve drugs and bias-corrected for model performance, outbreak sampling, and resistance oversampling. We validated our estimates using a national DR survey conducted in South Africa.ResultsMtb isolates from 29 countries (n=19,149) met sequence quality criteria. Marginal genotypic resistance estimates overlapped with the South African DR survey for all drugs except isoniazid and second-line injectables that were underestimated (n=3,134); among multi-drug resistant (MDR) TB, estimates overlapped for pyrazinamide and the fluoroquinolones. Globally, mono-resistance to isoniazid was estimated at 10.9% (95% CI: 10.2-11.7%, n = 14,012. Mono-levofloxacin resistance rates were highest in South Asia (Pakistan 3.4% [0.1-11%], n=111 and India 2.8% [0.08-9.4%], n=114). Rates of resistance discordance between isoniazid and ethionamide were high with 74.4% (IQR: 64.5-79.7%) of isoniazid resistant isolates predicted to be ethionamide susceptible. The global susceptibility rate to pyrazinamide and levofloxacin among MDR was 15.1% (95% CI: 10.2-19.9%, n=3,964).ConclusionsThis is the first attempt at global Mtb antibiogram estimation. DR prevalence in Mtb can be reliably estimated using public WGS and phenotypic resistance prediction for key antibiotics. Our results raise concerns about the empiric use of short-course fluoroquinolone regimens for drug susceptible TB in South Asia and suggest that ethionamide is an under-utilized drug in MDR treatment.

Background Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly and accurate resistance prediction tools are needed to enable public health and clinical practitioners to rapidly diagnose resistance and inform treatment regimens. Results We present Translational Genomics platform for Tuberculosis (GenTB), a free and open web-based application to predict antibiotic resistance from next-generation sequence data. The user can choose between two potential predictors, a Random Forest (RF) classifier and a Wide and Deep Neural Network (WDNN) to predict phenotypic resistance to 13 and 10 anti-tuberculosis drugs, respectively. We benchmark GenTB’s predictive performance along with leading TB resistance prediction tools (Mykrobe and TB-Profiler) using a ground truth dataset of 20,408 isolates with laboratory-based drug susceptibility data. All four tools reliably predicted resistance to first-line tuberculosis drugs but had varying performance for second-line drugs. The mean sensitivities for GenTB-RF and GenTB-WDNN across the nine shared drugs were 77.6% (95% CI 76.6–78.5%) and 75.4% (95% CI 74.5–76.4%), respectively, and marginally higher than the sensitivities of TB-Profiler at 74.4% (95% CI 73.4–75.3%) and Mykrobe at 71.9% (95% CI 70.9–72.9%). The higher sensitivities were at an expense of ≤ 1.5% lower specificity: Mykrobe 97.6% (95% CI 97.5–97.7%), TB-Profiler 96.9% (95% CI 96.7 to 97.0%), GenTB-WDNN 96.2% (95% CI 96.0 to 96.4%), and GenTB-RF 96.1% (95% CI 96.0 to 96.3%). Averaged across the four tools, genotypic resistance sensitivity was 11% and 9% lower for isoniazid and rifampicin respectively, on isolates sequenced at low depth (< 10× across 95% of the genome) emphasizing the need to quality control input sequence data before prediction. We discuss differences between tools in reporting results to the user including variants underlying the resistance calls and any novel or indeterminate variants Conclusions GenTB is an easy-to-use online tool to rapidly and accurately predict resistance to anti-tuberculosis drugs. GenTB can be accessed online at https://gentb.hms.harvard.edu, and the source code is available at https://github.com/farhat-lab/gentb-site.

Antibiotic resistance among bacterial pathogens poses a major global health threat. M. tuberculosis complex (MTBC) is estimated to have the highest resistance rates of any pathogen globally. Given the slow growth rate and the need for a biosafety level 3 laboratory, the only realistic avenue to scale up drug-susceptibility testing (DST) for this pathogen is to rely on genotypic techniques. This raises the fundamental question of whether a mutation is a reliable surrogate for phenotypic resistance or whether the presence of a second mutation can completely counteract its effect, resulting in major diagnostic errors (i.e. systematic false resistance results). To date, such epistatic interactions have only been reported for streptomycin that is now rarely used. By analyzing more than 31,000 MTBC genomes, we demonstrated that eis C-14T promoter mutation, which is interrogated by several genotypic DST assays endorsed by the World Health Organization, cannot confer resistance to amikacin and kanamycin if it coincides with loss-of-function (LoF) mutations in the coding region of eis. To our knowledge, this represents the first definitive example of antibiotic reversion in MTBC. Moreover, we raise the possibility that mmpR (Rv0678) mutations are not valid markers of resistance to bedaquiline and clofazimine if these coincide with LoF mutation in the efflux pump encoded by mmpS5 (Rv0677c) and mmpL5 (Rv0676c).

The Liverpool epidemic strain (LES) is an important transmissible clonal lineage of Pseudomonas aeruginosa that chronically infects the lungs of people with cystic fibrosis (CF). Previous studies have focused on the genomics of the LES in a limited number of isolates, mostly from one CF centre in the UK, and from studies highlighting identification of the LES in Canada. Here we significantly extend the current LES genome database by genome sequencing 91 isolates from multiple CF centres across the UK, and we describe the comparative genomics of this large collection of LES isolates from the UK and Canada. Phylogenetic analysis revealed that the 145 LES genomes analysed formed a distinct clonal lineage when compared with the wider P. aeruginosa population. Notably, the isolates formed two clades: one associated with isolates from Canada, and the other associated with UK isolates. Further analysis of the UK LES isolates revealed clustering by clinic geography. Where isolates clustered closely together, the association was often supported by clinical data linking isolates or patients. When compared with the earliest known isolate, LESB58 (from 1988), many UK LES isolates shared common loss-of-function mutations, such as in genes gltR and fleR. Other loss-of-function mutations identified in previous studies as common adaptations during CF chronic lung infections were also identified in multiple LES isolates. Analysis of the LES accessory genome (including genomic islands and prophages) revealed variations in the carriage of large genomic regions, with some evidence for shared genomic island/prophage complement according to clinic location. Our study reveals divergence and adaptation during the spread of the LES, within the UK and between continents.

The Liverpool epidemic strain (LES) is an important transmissible clonal lineage ofPseudomonas aeruginosathat chronically infects the lungs of people with cystic fibrosis (CF). Previous studies have focused on the genomics of the LES in a limited number of isolates, mostly from one CF centre in the UK, and from studies highlighting identification of the LES in Canada. Here we significantly extend the current LES genome database by genome sequencing 91 isolates from multiple CF centres across the UK, and we describe the comparative genomics of this large collection of LES isolates from the UK and Canada. Phylogenetic analysis revealed that the 145 LES genomes analysed formed a distinct clonal lineage when compared with the widerP. aeruginosapopulation. Notably, the isolates formed two clades: one associated with isolates from Canada, and the other associated with UK isolates. Further analysis of the UK LES isolates revealed clustering by clinic geography. Where isolates clustered closely together, the association was often supported by clinical data linking isolates or patients. When compared with the earliest known isolate, LESB58 (from 1988), many UK LES isolates shared common loss-of-function mutations, such as in genesgltRandfleR. Other loss-of-function mutations identified in previous studies as common adaptations during CF chronic lung infections were also identified in multiple LES isolates. Analysis of the LES accessory genome (including genomic islands and prophages) revealed variations in the carriage of large genomic regions, with some evidence for shared genomic island/prophage complement according to clinic location. Our study reveals divergence and adaptation during the spread of the LES, within the UK and between continents.

Tuberculosis (TB) is a leading cause of death globally. Understanding the population dynamics of TB’s causative agent Mycobacterium tuberculosis complex (Mtbc) in-host is vital for understanding the efficacy of antibiotic treatment. We use longitudinally collected clinical Mtbc isolates that underwent Whole-Genome Sequencing from the sputa of 200 patients to investigate Mtbc diversity during the course of active TB disease after excluding 107 cases suspected of reinfection, mixed infection or contamination. Of the 178/200 patients with persistent clonal infection >2 months, 27 developed new resistance mutations between sampling with 20/27 occurring in patients with pre-existing resistance. Low abundance resistance variants at a purity of ≥19% in the first isolate predict fixation in the subsequent sample. We identify significant in-host variation in 27 genes, including antibiotic resistance genes, metabolic genes and genes known to modulate host innate immunity and confirm several to be under positive selection by assessing phylogenetic convergence across a genetically diverse sample of 20,352 isolates.

The emergence of multidrug-resistant bacterial strains worldwide has become a serious problem for public health over recent decades. The increase in antimicrobial resistance has been expanding via plasmids as mobile genetic elements encoding antimicrobial resistance (AMR) genes that are transferred vertically and horizontally. This study focuses on Salmonella enterica, one of the leading foodborne pathogens in industrialized countries. S. enterica is known to carry several plasmids involved not only in virulence but also in AMR. In the current paper, we present an integrated strategy to detect plasmid scaffolds in whole genome sequencing (WGS) assemblies. We developed a two-step procedure to predict plasmids based on i) the presence of essential elements for plasmid replication and mobility, as well as ii) sequence similarity to a reference plasmid. Next, to confirm the accuracy of the prediction in 1750 S. enterica short-read sequencing data, we combined Oxford Nanopore MinION long-read sequencing with Illumina MiSeq short-read sequencing in hybrid assemblies for 84 isolates to evaluate the proportion of plasmid that has been detected. At least one scaffold with an origin of replication (ORI) was predicted in 61.3% of the Salmonella isolates tested. The results indicated that IncFII and IncI1 ORIs were distributed in many S. enterica serotypes and were the most prevalent AMR genes carrier, whereas IncHI2A/IncHI2 and IncA/C2 were more serotype restricted but bore several AMR genes. Comparison between hybrid and short-read assemblies revealed that 81.1% of plasmids were found in the short-read sequencing using our pipeline. Through this process, we established that plasmids are prevalent in S. enterica and we also substantially expand the AMR genes in the resistome of this species.

The huge increase in the availability of bacterial genomes led us to a point in which we can investigate and query pan-genomes, for example, the full set of genes of a given bacterial species or clade. Here, we used a data set of 1,311 high-quality genomes from the human pathogen Pseudomonas aeruginosa, 619 of which were newly sequenced, to show that a pan-genomic approach can greatly refine the population structure of bacterial species, provide new insights to define species boundaries, and generate hypotheses on the evolution of pathogenicity. The 665-gene P. aeruginosa core genome presented here, which constitutes only 1% of the entire pan-genome, is the first to be in the same order of magnitude as the minimal bacterial genome and represents a conservative estimate of the actual core genome. Moreover, the phylogeny based on this core genome provides strong evidence for a five-group population structure that includes two previously undescribed groups of isolates. Comparative genomics focusing on antimicrobial resistance and virulence genes showed that variation among isolates was partly linked to this population structure. Finally, we hypothesized that horizontal gene transfer had an important role in this respect, and found a total of 3,010 putative complete and fragmented plasmids, 5% and 12% of which contained resistance or virulence genes, respectively. This work provides data and strategies to study the evolutionary trajectories of resistance and virulence in P. aeruginosa.

Fresh produce is increasingly implicated in foodborne outbreaks and most fresh produce is consumed raw, emphasizing the need to develop non-thermal methods to control foodborne pathogens. This study investigates bacterial cell lysis through induction of prophages as a novel approach to control foodborne bacterial pathogens on fresh produce. Shiga toxin producing Escherichia coli (STEC) and Salmonella enterica isolates were exposed to different prophage inducers (i.e. mitomycin C or streptonigrin) and growth of the cells was monitored by measuring the optical density (OD600) during incubation at 37 °C. Beginning at three hours after addition of the inducer, all concentrations (0.5, 1, 2 μg/mL) of mitomycin C, or 2 μg/mL streptonigrin significantly reduced the OD600 in broth cultures, in a concentration dependent manner, relative to cultures where no inducer was added. PCR confirmed bacterial release of induced bacteriophages and demonstrated that a single compound could successfully induce multiple types of prophages. The ability of mitomycin C to induce prophages in STEC O157:H7 and in S. enterica (serovars Typhimurium and Newport) on fresh produce was evaluated by inoculating red greenhouse tomatoes or spinach leaves with 5 × 107 and 5 × 108 colony forming units, respectively. After allowing time for the inoculum to dry on the fresh produce samples, 6 μg/mL mitomycin C was sprayed onto each sample, while control samples were sprayed with water. Following overnight incubation at 4 °C, the bacterial cells were recovered and plate counts were performed. A 3 log reduction in STEC O157:H7 cells was observed on tomatoes sprayed with mitomycin C compared to those sprayed with water, while a 1 log reduction was obtained on spinach. Similarly, spraying mitomycin C on tomatoes and spinach inoculated with S. enterica isolates resulted in a 1-1.5 log and 2 log reduction, respectively. These findings serve as a proof of concept that prophage induction can effectively control bacterial foodborne pathogens on fresh produce.

The gypsy moth, Lymantria dispar L., is one of the most destructive forest pests in the world. While the subspecies established in North America is the European gypsy moth (L. dispar dispar), whose females are flightless, the two Asian subspecies, L. dispar asiatica and L. dispar japonica, have flight-capable females, enhancing their invasiveness and warranting precautionary measures to prevent their permanent establishment in North America. Various molecular tools have been developed to help distinguish European from Asian subspecies, several of which are based on the mitochondrial barcode region. In an effort to identify additional informative markers, we undertook the sequencing and analysis of the mitogenomes of 10 geographic variants of L. dispar, including two or more variants of each subspecies, plus the closely related L. umbrosa as outgroup. Several regions of the gypsy moth mitogenomes displayed nucleotide substitutions with potential usefulness for the identification of subspecies and/or geographic origins. Interestingly, the mitogenome of one geographic variant displayed significant divergence relative to the remaining variants, raising questions about its taxonomic status. Phylogenetic analyses placed this population from northern Iran as basal to the L. dispar clades. The present findings will help improve diagnostic tests aimed at limiting risks of AGM invasions.

Antibiotic resistance is a worldwide health issue spreading quickly among human and animal pathogens, as well as environmental bacteria. Misuse of antibiotics has an impact on the selection of resistant bacteria, thus contributing to an increase in the occurrence of resistant genotypes that emerge via spontaneous mutation or are acquired by horizontal gene transfer. There is a specific and urgent need not only to detect antimicrobial resistance but also to predict antibiotic resistance in silico. We now have the capability to sequence hundreds of bacterial genomes per week, including assembly and annotation. Novel and forthcoming bioinformatics tools can predict the resistome and the mobilome with a level of sophistication not previously possible. Coupled with bacterial strain collections and databases containing strain metadata, prediction of antibiotic resistance and the potential for virulence are moving rapidly toward a novel approach in molecular epidemiology. Here, we present a model system in antibiotic-resistance prediction, along with its promises and limitations. As it is commonly multidrug resistant, Pseudomonas aeruginosa causes infections that are often difficult to eradicate. We review novel approaches for genotype prediction of antibiotic resistance. We discuss the generation of microbial sequence data for real-time patient management and the prediction of antimicrobial resistance.

Over the past decade, there has been a rising interest in Achromobacter sp., an emerging opportunistic pathogen responsible for nosocomial and cystic fibrosis lung infections. Species of this genus are ubiquitous in the environment, can outcompete resident microbiota, and are resistant to commonly used disinfectants as well as antibiotics. Nevertheless, the Achromobacter genus suffers from difficulties in diagnosis, unresolved taxonomy and limited understanding of how it adapts to the cystic fibrosis lung, not to mention other host environments. The goals of this first genus-wide comparative genomics study were to clarify the taxonomy of this genus and identify genomic features associated with pathogenicity and host adaptation. This was done with a widely applicable approach based on pan-genome analysis. First, using all publicly available genomes, a combination of phylogenetic analysis based on 1,780 conserved genes with average nucleotide identity and accessory genome composition allowed the identification of a largely clinical lineage composed of Achromobacter xylosoxidans, Achromobacter insuavis, Achromobacter dolens, and Achromobacter ruhlandii. Within this lineage, we identified 35 positively selected genes involved in metabolism, regulation and efflux-mediated antibiotic resistance. Second, resistome analysis showed that this clinical lineage carried additional antibiotic resistance genes compared with other isolates. Finally, we identified putative mobile elements that contribute 53% of the genus’s resistome and support horizontal gene transfer between Achromobacter and other ecologically similar genera. This study provides strong phylogenetic and pan-genomic bases to motivate further research on Achromobacter, and contributes to the understanding of opportunistic pathogen evolution.

BackgroundMycobacterium tuberculosis(MTB) whole genome sequencing data can provide insights into temporal and geographic trends in resistance acquisition and inform public health interventions.MethodsWe curated a set of clinical MTB isolates with high quality sequencing and culture-based drug susceptibility data spanning four lineages and more than 20 countries. We constructed geographic and lineage specific MTB phylogenies and used Bayesian molecular dating to infer the most-recent-common-susceptible-ancestor age for 4,869 instances of resistance to 10 drugs.FindingsOf 8,550 isolates curated, 6,099 from 15 countries met criteria for molecular dating. The number of independent resistance acquisition events was lower than the number of resistant isolates across all countries, suggesting ongoing transmission of drug resistance. Ancestral age distributions supported the presence of old resistance, ≥20 years prior, in the majority of countries. A consistent order of resistance acquisition was observed globally starting with resistance to isoniazid, but resistance ancestral age varied by country. We found a direct correlation between country wealth and resistance age (R2= 0.47, P-value= 0.014). Amplification of fluoroquinolone and second-line injectable resistance among multidrug-resistant isolates is estimated to have occurred very recently (median ancestral age 4.7 years IQR 1.9-9.8 prior to sample collection). We found the sensitivity of commercial molecular diagnostics for second-line resistance to vary significantly by country (P-value InterpretationOur results highlight that both resistance transmission and amplification are contributing to disease burden globally but are variable by country. The observation that wealthier nations are more likely to have old resistance suggests that programmatic improvements can reduce resistance amplification, but that fit resistant strains can circulate for decades subsequently.FundingThis work was supported by the NIH BD2K grant K01 ES026835, a Harvard Institute of Global Health Burke Fellowship (MF), Boston Children’s Hospital OFD/BTREC/CTREC Faculty Career Development Fellowship and Bushrod H. Campbell and Adah F. Hall Charity Fund/Charles A. King Trust Postdoctoral Fellowship (AD).Research in contextEvidence before this studyAcquisition and spread of drug-resistance byMycobacterium tuberculosis(MTB) varies across countries. Local factors driving evolution of drug resistance in MTB are not well studied.Added value of this studyWe applied molecular dating to 6,099 global MTB patient isolates and found the order of resistance acquisition to be consistent across the countries examined,i.e.acquisition of isoniazid resistance first followed by rifampicin and streptomycin followed by resistance to other drugs. In all countries with data available there was evidence for transmission of resistant strains from patient-to-patient and in the majority for extended periods of time (>20 years).Countries with lower gross wealth indices were found to have more recent resistance acquisition to the drug rifampicin. Based on the resistance patterns identified in our study we estimate that commercial diagnostic tests vary considerably in sensitivity for second-line resistance diagnosis by country.Implications of all available evidenceThe longevity of resistant MTB in many parts of the world emphasizes its fitness for transmission and its continued threat to public health. The association between country wealth and recent resistance acquisition emphasizes the need for continued investment in TB care delivery and surveillance programs. Geographically relevant diagnostics that take into account a country’s unique distribution of resistance are necessary.

BackgroundTuberculosis (TB) is a leading cause of death globally from an infectious agent. Understanding the population dynamics of TB’s causative agentMycobacterium tuberculosis(Mtb) in-host is vital for understanding the efficacy of antibiotic treatment. Here we use longitudinally collected clinical Mtb isolates that underwent Whole-Genome Sequencing (WGS) from the sputa of 307 subjects to investigate Mtb diversity during the course of active TB disease.Methods and findingsWe excluded cases suspected of reinfection or contamination to analyze data from 200 subjects, 167 of which met microbiological criteria for delayed culture conversion, treatment failure or relapse. Using technical and biological replicate samples, we defined an allele frequency threshold attributable to in-host evolution. Of the 167 subjects with unsuccessful treatment outcome, 27 (16%) developed new resistance mutations between sampling with 20/27 (74%) occurring in patients with pre-existing antibiotic resistance. Low abundance resistance variants at a purity of ≥19% in the first isolate predicts fixation of these variants in the subsequent sample with 27.0% sensitivity and 95.8% specificity. We identify significant in-host variation in seven genes associated with antibiotic resistance and twenty other genes, including metabolic genes and genes known to modulate host innate immunity by interacting with TLR2. We confirm Rv0095c, Rv1944c,PPE18, PPE54andPPE60to be under positive selection by assessing phylogenetic convergence across a global and genetically diverse independent sample of 20,352 isolates.ConclusionsOur large sample provides a comprehensive picture of the mutational dynamicsin-hostduring active TB disease. We demonstrate a framework to study temporal changes in Mtb population diversity using average depth WGS data. We show that minor variants can be used to inform antibiotic treatment regimens in patients with TB. Furthermore, we detect a signature of positive selection in-host, possibly stemming from innate immune pressure and informing our understanding of host-pathogen interactions.

Background Pseudomonas aeruginosa is an opportunistic pathogen and a major cause of infections. Widespread resistance in human infections are increasing the use of last resort antimicrobials such as polymyxins. However, these have been used for decades in veterinary medicine. Companion animals are an understudied source of antimicrobial resistant P. aeruginosa isolates. This study evaluated the susceptibility of P. aeruginosa veterinary isolates to polymyxins to determine whether the veterinary niche represents a potential reservoir of resistance genes for pathogenic bacteria in both animals and humans. Methods and results Clinical P. aeruginosa isolates (n=24) from UK companion animals were compared for antimicrobial susceptibility to a panel of human-associated isolates (n=37). Minimum inhibitory concentration (MIC) values for polymyxin B and colistin in the companion animals was significantly higher than in human isolates (P=0.033 and P=0.013, respectively). Genotyping revealed that the veterinary isolates were spread throughout the P. aeruginosa population, with shared array types from human infections such as keratitis and respiratory infections, suggesting the potential for zoonotic transmission. Whole genome sequencing revealed mutations in genes associated with polymyxin resistance and other antimicrobial resistance-related genes. Conclusion The high levels of resistance to polymyxin shown here, along with genetic similarities between some human and animal isolates, together suggest a need for sustained surveillance of this veterinary niche as a potential reservoir for resistant, clinically relevant bacteria in both animals and humans.

Drug resistance diagnostics that rely on the detection of resistance-related mutations could expedite patient care and TB eradication. We perform minimum inhibitory concentration testing for 12 anti-TB drugs together with Illumina whole-genome sequencing on 1452 clinical Mycobacterium tuberculosis (MTB) isolates. We evaluate genome-wide associations between mutations in MTB genes or non-coding regions and resistance, followed by validation in an independent data set of 792 patient isolates. We confirm associations at 13 non-canonical loci, with two involving non-coding regions. Promoter mutations are measured to have smaller average effects on resistance than gene body mutations. We estimate the heritability of the resistance phenotype to 11 anti-TB drugs and identify a lower than expected contribution from known resistance genes. This study highlights the complexity of the genomic mechanisms associated with the MTB resistance phenotype, including the relatively large number of potentially causal loci, and emphasizes the contribution of the non-coding portion of the genome., Resistance to antibiotics hampers the treatment of infectious diseases such as tuberculosis (TB). Here, Farhat et al. perform genome-wide association testing for minimum inhibitory concentration (MIC) of 12 anti-TB drugs in whole-genome sequenced clinical M. tuberculosis isolates and identify 13 genomic loci.

The Canadian province of Quebec has among the highest prevalence rates of Cystic Fibrosis (CF) in the world, with an average of 1 in 2,500 newborns (cysticfibrosis.ca, 1), and up to 1 in 902 in the region of Saguenay-Lac-Saint-Jean (2-4).…

Background The diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of clinical Mycobacterium tuberculosis isolates promises to circumvent the long wait times and limited scope of conventional phenotypic antimicrobial susceptibility, but gaps remain for predicting phenotype accurately from genotypic data especially for certain drugs. Our primary aim was to perform an exploration of statistical learning algorithms and genetic predictor sets using a rich dataset to build a high performing and fast predicting model to detect anti-tuberculosis drug resistance. Methods We collected targeted or whole genome sequencing and conventional drug resistance phenotyping data from 3601 Mycobacterium tuberculosis strains enriched for resistance to first- and second-line drugs, with 1228 multidrug resistant strains. We investigated the utility of (1) rare variants and variants known to be determinants of resistance for at least one drug and (2) machine and statistical learning architectures in predicting phenotypic drug resistance to 10 anti-tuberculosis drugs. Specifically, we investigated multitask and single task wide and deep neural networks, a multilayer perceptron, regularized logistic regression, and random forest classifiers. Findings The highest performing machine and statistical learning methods included both rare variants and those known to be causal of resistance for at least one drug. Both simpler L2 penalized regression and complex machine learning models had high predictive performance. The average AUCs for our highest performing model was 0.979 for first-line drugs and 0.936 for second-line drugs during repeated cross-validation. On an independent validation set, the highest performing model showed average AUCs, sensitivities, and specificities, respectively, of 0.937, 87.9%, and 92.7% for first-line drugs and 0.891, 82.0% and 90.1% for second-line drugs. Our method outperforms existing approaches based on direct association, with increased sum of sensitivity and specificity of 11.7% on first line drugs and 3.2% on second line drugs. Our method has higher predictive performance compared to previously reported machine learning models during cross-validation, with higher AUCs for 8 of 10 drugs. Interpretation Statistical models, especially those that are trained using both frequent and less frequent variants, significantly improve the accuracy of resistance prediction and hold promise in bringing sequencing technologies closer to the bedside.

Pseudomonas aeruginosa is an environmental bacterium and opportunistic pathogen. Here, we present draft genome sequences for 161 isolates from diverse clinical and environmental sources., Pseudomonas aeruginosa is an environmental bacterium and opportunistic pathogen. Here, we present draft genome sequences for 161 isolates from diverse clinical and environmental sources. This set of genome sequences complements other major public data releases from the International Pseudomonas Consortium Database.

Whole genome sequencing (WGS) can elucidate Mycobacterium tuberculosis (Mtb) transmission patterns but more data is needed to guide its use in high-burden settings. In a household-based TB transmissibility study in Peru, we identified a large MIRU-VNTR Mtb cluster (148 isolates) with a range of resistance phenotypes, and studied host and bacterial factors contributing to its spread. WGS was performed on 61 of the 148 isolates. We compared transmission link inference using epidemiological or genomic data and estimated the dates of emergence of the cluster and antimicrobial drug resistance (DR) acquisition events by generating a time-calibrated phylogeny. Using a set of 12,032 public Mtb genomes, we determined bacterial factors characterizing this cluster and under positive selection in other Mtb lineages. Four of the 61 isolates were distantly related and the remaining 57 isolates diverged ca. 1968 (95%HPD: 1945–1985). Isoniazid resistance arose once and rifampin resistance emerged subsequently at least three times. Emergence of other DR types occurred as recently as within the last year of sampling. We identified five cluster-defining SNPs potentially contributing to transmissibility. In conclusion, clusters (as defined by MIRU-VNTR typing) may be circulating for decades in a high-burden setting. WGS allows for an enhanced understanding of transmission, drug resistance, and bacterial fitness factors.

Background Improved estimates of drug resistant tuberculosis (TB) burden are needed to aid control efforts. The World Health Organization (WHO) currently reports estimates for rifampin resistance (RR) or multidrug resistance (MDR) at the national level. Resistance rates to other first-line and second-line agents, e.g. ethambutol, pyrazinamide, and aminoglycosides, are rarely available, even at the country level. Our objective was to generate country and drug specific resistance prevalence estimates (antibiograms) using in silico phenotype prediction and curated public and surveillance Mycobacterium tuberculosis (MTB) genomic data. Methods We curated MTB genomes either by sequencing or from published literature and excluded genomes that did not meet our quality criteria (i.e. at least 10X depth in >95% of the genome). A machine learning model previously trained to predict phenotypic resistance in MTB with high accuracy, a wide and deep neural net (WDNN), was used to predict resistance to ten drugs. We corrected for resistance oversampling in genomic data by conditioning on RR and using country specific surveillance MDR/RR rates reported by the WHO. Results Of the 49,851 MTB genomes curated, 33,873 isolates met quality criteria. Of these, geographic data was available for 22,838 genomes. Antibiograms were generated for nine first- and second-line drugs for 36 countries. Among countries with at least 100 isolates, a high rate of resistance to fluoroquinolones and second line injectables was seen among isolates from the Republic of Moldova (15.4% [CI = 13.7-16.7%] moxifloxacin resistant, 6.3% [CI = 5.5-6.8%] kanamycin resistant, n = 330) and Russian Federation (9.3% [CI = 9.1-9.4] moxifloxacin resistant, 5.4% [CI = 5.3-5.5%] kanamycin resistant, n = 1011) (Figure 1). Figure 1: Antibiograms created using genotypic data for isolates from Republic of Moldova (n=330, rifampin-resistance rate correction: 29%, range 26-31% among new tuberculosis cases);and Russian Federation (n=1011, rifampin-resistance rate correction 35%, range 34-35%, among new tuberculosis cases. rif: rifampin, inh: isoniazid, pza: pyrazinamide, emb: ethambutol, str: streptomycin, cap: capreomycin, amk: amikacin, kan: kanamycin, moxi: moxifloxacin Conclusion The estimation of antibiotic resistance prevalence in MTB for pyrazinamide, ethambutol and second-line agents can be aided by the use of in silico models of drug resistance. A high rate of resistance to second-line drugs precludes large scale roll out of short-course WHO regimens for treatment of MDR-TB for empiric use in certain countries. The use of whole genome sequencing for resistance surveillance can inform policy on optimal national regimen choice for TB treatment. Disclosures All Authors: No reported disclosures

Drug resistance is threatening attempts at tuberculosis epidemic control. Molecular diagnostics for drug resistance that rely on the detection of resistance-related mutations could expedite patient care and accelerate progress in TB eradication. We performed minimum inhibitory concentration testing for 12 anti-TB drugs together with Illumina whole genome sequencing on 1452 clinical Mycobacterium tuberculosis (MTB) isolates. We then used a linear mixed model to evaluate genome wide associations between mutations in MTB genes or noncoding regions and drug resistance, followed by validation of our findings in an independent dataset of 792 patient isolates. Novel associations at 13 genomic loci were confirmed in the validation set, with 2 involving noncoding regions. We found promoter mutations to have smaller average effects on resistance levels than gene body mutations in genes where both can contribute to resistance. Enabled by a quantitative measure of resistance, we estimated the heritability of the resistance phenotype to 11 anti-TB drugs and identify a lower than expected contribution from known resistance genes. We also report the proportion of variation in resistance levels explained by the novel loci identified here. This study highlights the complexity of the genomic mechanisms associated with the MTB resistance phenotype, including the relatively large number of potentially causative or compensatory loci, and emphasizes the contribution of the noncoding portion of the genome.

BackgroundWhole genome sequencing (WGS) can elucidateMycobacterium tuberculosis(Mtb) transmission patterns but more data is needed to guide its use in high-burden settings. In a household-based transmissibility study of 4,000 TB patients in Lima, Peru, we identified a large MIRU-VNTR Mtb cluster with a range of resistance phenotypes and studied host and bacterial factors contributing to its spread.MethodsWGS was performed on 61 of 148 isolates in the cluster. We compared transmission link inference using epidemiological or genomic data with and without the inclusion of controversial variants, and estimated the dates of emergence of the cluster and antimicrobial drug resistance acquisition events by generating a time-calibrated phylogeny. We validated our findings in genomic data from an outbreak of 325 TB cases in London. Using a larger set of 12,032 public Mtb genomes, we determined bacterial factors characterizing this cluster and under positive selection in other Mtb lineages.FindingsFour isolates were distantly related and the remaining 57 isolates diverged ca. 1968 (95% HPD: 1945-1985). Isoniazid resistance arose once, whereas rifampicin resistance emerged subsequently at least three times. Amplification of other drug resistance occurred as recently as within the last year of sampling. High quality PE/PPE variants and indels added information for transmission inference. We identified five cluster-defining SNPs, includingesxVS23L to be potentially contributing to transmissibility.InterpretationClusters defined by MIRU-VNTR typing, could be circulating for decades in a high-burden setting. WGS allows for an improved understanding of transmission, as well as bacterial resistance and fitness factors.FundingThe study was funded by the National Institutes of Health (Peru Epi study U19-AI076217 and K01-ES026835 to MRF). The funding sources had no role in any aspect of the study, manuscript or decision to submit it for publication.Research in contextEvidence before this studyUse of whole genome sequencing (WGS) to study tuberculosis (TB) transmission has proven to have higher resolution that traditional typing methods in low-burden settings. The implications of its use in high-burden settings are not well understood.Added value of this studyUsing WGS, we found that TB clusters defined by traditional typing methods may be circulating for several decades. Genomic regions typically excluded from WGS analysis contain large amount of genetic variation that may affect interpretation of transmission events. We also identified five bacterial mutations that may contribute to transmission fitness.Implications of all the available evidenceAdded value of WGS for understanding TB transmission may be even higher in high-burden vs. low-burden settings. Methods integrating variants found in polymorphic sites and insertions and deletions are likely to have higher resolution. Several host and bacterial factors may be responsible for higher transmissibility that can be targets of intervention to interrupt TB transmission in communities.

ClinicalThe high levels of resistance to polymyxin shown here, along with genetic similarities between some human and animal isolates, together suggest a need for sustained surveillance of this veterinary niche as a potential reservoir for resistant, clinically relevant bacteria in both animals and humans.

Pseudomonas aeruginosa is an important opportunistic pathogen, especially in the context of infections of cystic fibrosis (CF). In order to facilitate coordinated study of this pathogen, an international reference panel of P. aeruginosa isolates was assembled. Here we report the genome sequencing and analysis of 33 of these isolates and 7 reference genomes to further characterise this panel. Core genome single nucleotide variant phylogeny demonstrated that the panel strains are widely distributed amongst the P. aeruginosa population. Common loss-of-function mutations reported as adaptive during CF (such as in mucA and mexA) were identified amongst isolates from chronic respiratory infections. From the 40 strains analysed, 37 unique resistomes were predicted, based on the Resistance Gene Identifier method using the Comprehensive Antibiotic Resistance Database. Notably, hierarchical clustering and phylogenetic reconstructions based on the presence/absence of genomic islands (GIs), prophages and other regions of genome plasticity (RGPs) supported the subdivision of P. aeruginosa into two main groups. This is the largest, most diverse analysis of GIs and associated RGPs to date, and the results suggest that, at least at the largest clade grouping level (group 1 vs group 2), each group may be drawing upon distinct mobile gene pools.

BackgroundThe diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of clinicalMycobacteriumtuberculosis isolates promises to circumvent the long wait times and limited scope of conventional phenotypic antimicrobial susceptibility, but gaps remain for predicting phenotype accurately from genotypic data.Methods and FindingsUsing targeted or whole genome sequencing and conventional drug resistance phenotyping data from 3,601Mycobacterium tuberculosisstrains, 1,228 of which were multidrug resistant, we investigated the use of machine learning to predict phenotypic drug resistance to 10 anti-tuberculosis drugs. The final model, a multitask wide and deep neural network (MD-WDNN), achieved improved high predictive performance: the average AUCs were 0.979 for first-line drugs and 0.936 for second-line drugs during repeated cross-validation. On an independent validation set, the MD-WDNN showed average AUCs, sensitivities, and specificities, respectively, of 0.937, 87.9%, and 92.7% for first-line drugs and 0.891, 82.0% and 90.1% for second-line drugs. In addition to being able to learn from samples that have only been partially phenotyped, our proposed multidrug architecture shares information across different anti-tuberculosis drugs and genes to provide a more accurate phenotypic prediction. We uset-distributed Stochastic Neighbor Embedding (t-SNE) visualization and feature importance analyses to examine inter-drug similarities.ConclusionsMachine learning is capable of accurately predicting resistant status using genomic information and holds promise in bringing sequencing technologies closer to the bedside.

International audience; The bacterium Pseudomonas aeruginosa is well known to have a remarkable adaptive capacity allowing it to colonise many environments. A recent study on environmental isolates from dental unit waterlines (DUWLs) hinted at a genetic clustering into two groups. Isolates from one of these groups, named cluster III, were shown to have unusual phenotypes for environmental isolates, such as an increased biofilm production. To have a better ecological view, more specifically on isolates from cluster III, the complete genomes of 39 isolates including 16 from DUWLs were sequenced. In addition to an investigation of antibiotic resistance and secretion system gene content, a molecular phylogeny allowed confirmation of the split of the 16 environmental isolates in two groups and also sheds light on a correlation between the phylogenetic positions and the serotypes of the isolates. Isolates from cluster III harboured insertion sequences ISPa11 inserted into the O-specific antigen biosynthesis clusters and the gene lasR, encoding for a master regulator of the quorum sensing. Investigation of key regulators revealed another truncated gene, gacS. Alteration in lasR and gacS genes was consistent with phenotypic assays confirming their inactivation. These results bring new perspectives regarding the ecological adaptive potential of P. aeruginosa.