MmpR5 protein truncation and bedaquiline resistance in Mycobacterium tuberculosis isolates from South Africa: a genomic analysis.

BACKGROUND: The antibiotic bedaquiline is a key component of new WHO regimens for drug-resistant tuberculosis; however, predicting bedaquiline resistance from bacterial genotypes remains challenging. We aimed to understand the genetic mechanisms of bedaquiline resistance by analysing Mycobacterium tuberculosis isolates from South Africa. METHODS: For this genomic analysis, we conducted whole-genome sequencing of Mycobacterium tuberculosis samples collected at two referral laboratories in Cape Town and Johannesburg, covering regions of South Africa with a high prevalence of tuberculosis. We used the tool ARIBA to measure the status of predefined genes that are associated with bedaquiline resistance. To produce a broad genetic landscape of M tuberculosis in South Africa, we extended our analysis to include all publicly available isolates from the European Nucleotide Archive, including isolates obtained by the CRyPTIC consortium, for which minimum inhibitory concentrations of bedaquiline were available. FINDINGS: Between Jan 10, 2019, and July, 22, 2020, we sequenced 505 M tuberculosis isolates from 461 patients. Of the 64 isolates with mutations within the mmpR5 regulatory gene, we found 53 (83%) had independent acquisition of 31 different mutations, with a particular enrichment of truncated MmpR5 in bedaquiline-resistant isolates resulting from either frameshift mutations or the introduction of an insertion element. Truncation occurred across three M tuberculosis lineages, and were present in 66% of bedaquiline-resistant isolates. Although the distributions overlapped, the median minimum inhibitory concentration of bedaquiline was 0·25 mg/L (IQR 0·12-0·25) in mmpR5-disrupted isolates, compared with 0·06 mg/L (0·03-0·06) in wild-type M tuberculosis. INTERPRETATION: Reduction in the susceptibility of M tuberculosis to bedaquiline has evolved repeatedly across the phylogeny. In our data, we see no evidence that this reduction has led to the spread of a successful strain in South Africa. Binary phenotyping based on the bedaquiline breakpoint might be inappropriate to monitor resistance to this drug. We recommend the use of minimum inhibitory concentrations in addition to MmpR5 truncation screening to identify moderate increases in resistance to bedaquiline. FUNDING: US Centers for Disease Control and Prevention.

Implications of Race Adjustment in Lung-Function Equations.

BACKGROUND: Adjustment for race is discouraged in lung-function testing, but the implications of adopting race-neutral equations have not been comprehensively quantified. METHODS: We obtained longitudinal data from 369,077 participants in the National Health and Nutrition Examination Survey, U.K. Biobank, the Multi-Ethnic Study of Atherosclerosis, and the Organ Procurement and Transplantation Network. Using these data, we compared the race-based 2012 Global Lung Function Initiative (GLI-2012) equations with race-neutral equations introduced in 2022 (GLI-Global). Evaluated outcomes included national projections of clinical, occupational, and financial reclassifications; individual lung-allocation scores for transplantation priority; and concordance statistics (C statistics) for clinical prediction tasks. RESULTS: Among the 249 million persons in the United States between 6 and 79 years of age who are able to produce high-quality spirometric results, the use of GLI-Global equations may reclassify ventilatory impairment for 12.5 million persons, medical impairment ratings for 8.16 million, occupational eligibility for 2.28 million, grading of chronic obstructive pulmonary disease for 2.05 million, and military disability compensation for 413,000. These potential changes differed according to race; for example, classifications of nonobstructive ventilatory impairment may change dramatically, increasing 141% (95% confidence interval [CI], 113 to 169) among Black persons and decreasing 69% (95% CI, 63 to 74) among White persons. Annual disability payments may increase by more than $1 billion among Black veterans and decrease by $0.5 billion among White veterans. GLI-2012 and GLI-Global equations had similar discriminative accuracy with regard to respiratory symptoms, health care utilization, new-onset disease, death from any cause, death related to respiratory disease, and death among persons on a transplant waiting list, with differences in C statistics ranging from -0.008 to 0.011. CONCLUSIONS: The use of race-based and race-neutral equations generated similarly accurate predictions of respiratory outcomes but assigned different disease classifications, occupational eligibility, and disability compensation for millions of persons, with effects diverging according to race. (Funded by the National Heart Lung and Blood Institute and the National Institute of Environmental Health Sciences.).

Drug resistance and epidemiological success of modern Mycobacterium tuberculosis lineages in western India.

BACKGROUND: Transmission is contributing to the slow decline of tuberculosis (TB) incidence globally. Drivers of TB transmission in India, the country estimated to carry a quarter of the World's burden, are not well studied. We conducted a genomic epidemiology study to compare epidemiological success, host factors and drug resistance (DR) among the four major Mycobacterium tuberculosis (Mtb) lineages (L1-4) circulating in Pune, India. METHODS: We performed whole-genome sequencing (WGS) of Mtb sputum culture-positive isolates from participants in two prospective cohort studies and predicted genotypic susceptibility using a validated random forest model. We used maximum likelihood estimation to build phylogenies. We compared lineage specific phylogenetic and time-scaled metrics to assess epidemiological success. RESULTS: Of the 642 isolates that underwent WGS, 612 met sequence quality criteria. Most isolates belonged to L3 (44.6%). The majority (61.1%) of multidrug-resistant isolates belonged to L2 (P < 0.001). In molecular dating, L2 demonstrated a higher rate and more recent resistance acquisition. We measured higher clustering, and time-scaled haplotypic density (THD) for L4 and L2 compared to L3 and/or L1 suggesting higher epidemiological success. L4 demonstrated higher THD and clustering (OR 5.1 (95% CI 2.3-12.3) in multivariate models controlling for host factors and DR. CONCLUSION: L2 shows a higher frequency of DR and both L2 and L4 demonstrate evidence of higher epidemiological success than L3 or L1 in the study setting. Our findings highlight the need for contact tracing around TB cases, and heightened surveillance of TB DR in India.

Identification of bacterial determinants of tuberculosis infection and treatment outcomes: a phenogenomic analysis of clinical strains.

BACKGROUND: Bacterial diversity could contribute to the diversity of tuberculosis infection and treatment outcomes observed clinically, but the biological basis of this association is poorly understood. The aim of this study was to identify associations between phenogenomic variation in Mycobacterium tuberculosis and tuberculosis clinical features. METHODS: We developed a high-throughput platform to define phenotype-genotype relationships in M tuberculosis clinical isolates, which we tested on a set of 158 drug-sensitive M tuberculosis strains sampled from a large tuberculosis clinical study in Ho Chi Minh City, Viet Nam. We tagged the strains with unique genetic barcodes in multiplicate, allowing us to pool the strains for in-vitro competitive fitness assays across 16 host-relevant antibiotic and metabolic conditions. Relative fitness was quantified by deep sequencing, enumerating output barcode read counts relative to input normalised values. We performed a genome-wide association study to identify phylogenetically linked and monogenic mutations associated with the in-vitro fitness phenotypes. These genetic determinants were further associated with relevant clinical outcomes (cavitary disease and treatment failure) by calculating odds ratios (ORs) with binomial logistic regressions. We also assessed the population-level transmission of strains associated with cavitary disease and treatment failure using terminal branch length analysis of the phylogenetic data. FINDINGS: M tuberculosis clinical strains had diverse growth characteristics in host-like metabolic and drug conditions. These fitness phenotypes were highly heritable, and we identified monogenic and phylogenetically linked variants associated with the fitness phenotypes. These data enabled us to define two genetic features that were associated with clinical outcomes. First, mutations in Rv1339, a phosphodiesterase, which were associated with slow growth in glycerol, were further associated with treatment failure (OR 5·34, 95% CI 1·21-23·58, p=0·027). Second, we identified a phenotypically distinct slow-growing subclade of lineage 1 strains (L1.1.1.1) that was associated with cavitary disease (OR 2·49, 1·11-5·59, p=0·027) and treatment failure (OR 4·76, 1·53-14·78, p=0·0069), and which had shorter terminal branch lengths on the phylogenetic tree, suggesting increased transmission. INTERPRETATION: Slow growth under various antibiotic and metabolic conditions served as in-vitro intermediate phenotypes underlying the association between M tuberculosis monogenic and phylogenetically linked mutations and outcomes such as cavitary disease, treatment failure, and transmission potential. These data suggest that M tuberculosis growth regulation is an adaptive advantage for bacterial success in human populations, at least in some circumstances. These data further suggest markers for the underlying bacterial processes that contribute to these clinical outcomes. FUNDING: National Health and Medical Research Council/A∗STAR, National Institutes of Allergy and Infectious Diseases, National Institute of Child Health and Human Development, and the Wellcome Trust Fellowship in Public Health and Tropical Medicine.

Precision Phenotyping for Curating Research Cohorts of Patients with Post-Acute Sequelae of COVID-19 (PASC) as a Diagnosis of Exclusion.

Scalable identification of patients with the post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms and the suboptimal accuracy, demographic biases, and underestimation of the PASC diagnosis code (ICD-10 U09.9). In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying research cohorts of PASC patients, defined as a diagnosis of exclusion. We used longitudinal electronic health records (EHR) data from over 295 thousand patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to exclude sequelae that prior conditions can explain. We performed independent chart reviews to tune and validate our precision phenotyping algorithm. Our PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying Long COVID patients compared to the U09.9 diagnosis code. Our algorithm identified a PASC research cohort of over 24 thousand patients (compared to about 6 thousand when using the U09.9 diagnosis code), with a 79.9 percent precision (compared to 77.8 percent from the U09.9 diagnosis code). Our estimated prevalence of PASC was 22.8 percent, which is close to the national estimates for the region. We also provide an in-depth analysis outlining the clinical attributes, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC. The PASC phenotyping method presented in this study boasts superior precision, accurately gauges the prevalence of PASC without underestimating it, and exhibits less bias in pinpointing Long COVID patients. The PASC cohort derived from our algorithm will serve as a springboard for delving into Long COVID's genetic, metabolomic, and clinical intricacies, surmounting the constraints of recent PASC cohort studies, which were hampered by their limited size and available outcome data.

Analysis of the limited M. tuberculosis accessory genome reveals potential pitfalls of pan-genome analysis approaches.

Pan-genome analysis is a fundamental tool for studying bacterial genome evolution; however, the variety of methods used to define and measure the pan-genome poses challenges to the interpretation and reliability of results. To quantify sources of bias and error related to common pan-genome analysis approaches, we evaluated different approaches applied to curated collection of 151 Mycobacterium tuberculosis ( Mtb ) isolates. Mtb is characterized by its clonal evolution, absence of horizontal gene transfer, and limited accessory genome, making it an ideal test case for this study. Using a state-of-the-art graph-genome approach, we found that a majority of the structural variation observed in Mtb originates from rearrangement, deletion, and duplication of redundant nucleotide sequences. In contrast, we found that pan-genome analyses that focus on comparison of coding sequences (at the amino acid level) can yield surprisingly variable results, driven by differences in assembly quality and the softwares used. Upon closer inspection, we found that coding sequence annotation discrepancies were a major contributor to inflated Mtb accessory genome estimates. To address this, we developed panqc, a software that detects annotation discrepancies and collapses nucleotide redundancy in pan-genome estimates. When applied to Mtb and E. coli pan-genomes, panqc exposed distinct biases influenced by the genomic diversity of the population studied. Our findings underscore the need for careful methodological selection and quality control to accurately map the evolutionary dynamics of a bacterial species.

Transcriptomic responses to antibiotic exposure in Mycobacterium tuberculosis.

Transcriptional responses in bacteria following antibiotic exposure offer insights into antibiotic mechanism of action, bacterial responses, and characterization of antimicrobial resistance. We aimed to define the transcriptional antibiotic response (TAR) in Mycobacterium tuberculosis (Mtb) isolates for clinically relevant drugs by pooling and analyzing Mtb microarray and RNA-seq data sets. We generated 99 antibiotic transcription profiles across 17 antibiotics, with 76% of profiles generated using 3-24 hours of antibiotic exposure and 49% within one doubling of the WHO antibiotic critical concentration. TAR genes were time-dependent, and largely specific to the antibiotic mechanism of action. TAR signatures performed well at predicting antibiotic exposure, with the area under the receiver operating curve (AUC) ranging from 0.84-1.00 (TAR <6 hours of antibiotic exposure) and 0.76-1.00 (>6 hours of antibiotic exposure) for upregulated genes and 0.57-0.90 and 0.87-1.00, respectfully, for downregulated genes. This work desmonstrates that transcriptomics allows for the assessment of antibiotic activity in Mtb within 6 hours of exposure.

For Tuberculosis, Not "To Screen or Not to Screen?" but "Who?" and "How?"

Active case finding leveraging new molecular diagnostics and chest X-rays with automated interpretation algorithms is increasingly being developed for high-risk populations to drive down tuberculosis incidence. We consider why such an approach did not deliver a decline in tuberculosis prevalence in Brazilian prison populations and what to consider next.

Exploring gene content with pangene graphs.

Motivation: The gene content regulates the biology of an organism. It varies between species and between individuals of the same species. Although tools have been developed to identify gene content changes in bacterial genomes, none is applicable to collections of large eukaryotic genomes such as the human pangenome. Results: We developed pangene, a computational tool to identify gene orientation, gene order and gene copy-number changes in a collection of genomes. Pangene aligns a set of input protein sequences to the genomes, resolves redundancies between protein sequences and constructs a gene graph with each genome represented as a walk in the graph. It additionally finds subgraphs, which we call bibubbles, that capture gene content changes. Applied to the human pangenome, pangene identifies known gene-level variations and reveals complex haplotypes that are not well studied before. Pangene also works with high-quality bacterial pangenome and reports similar numbers of core and accessory genes in comparison to existing tools. Availability and implementation: Source code at https://github.com/lh3/pangene; pre-built pangene graphs can be downloaded from https://zenodo.org/records/8118576 and visualized at https://pangene.bioinweb.org.

Evaluating generalizability of artificial intelligence models for molecular datasets.

Deep learning has made rapid advances in modeling molecular sequencing data. Despite achieving high performance on benchmarks, it remains unclear to what extent deep learning models learn general principles and generalize to previously unseen sequences. Benchmarks traditionally interrogate model generalizability by generating metadata based (MB) or sequence-similarity based (SB) train and test splits of input data before assessing model performance. Here, we show that this approach mischaracterizes model generalizability by failing to consider the full spectrum of cross-split overlap, i.e., similarity between train and test splits. We introduce Spectra, a spectral framework for comprehensive model evaluation. For a given model and input data, Spectra plots model performance as a function of decreasing cross-split overlap and reports the area under this curve as a measure of generalizability. We apply Spectra to 18 sequencing datasets with associated phenotypes ranging from antibiotic resistance in tuberculosis to protein-ligand binding to evaluate the generalizability of 19 state-of-the-art deep learning models, including large language models, graph neural networks, diffusion models, and convolutional neural networks. We show that SB and MB splits provide an incomplete assessment of model generalizability. With Spectra, we find as cross-split overlap decreases, deep learning models consistently exhibit a reduction in performance in a task- and model-dependent manner. Although no model consistently achieved the highest performance across all tasks, we show that deep learning models can generalize to previously unseen sequences on specific tasks. Spectra paves the way toward a better understanding of how foundation models generalize in biology.

Drug-resistant tuberculosis: a persistent global health concern.

Drug-resistant tuberculosis (TB) is estimated to cause 13% of all antimicrobial resistance-attributable deaths worldwide and is driven by both ongoing resistance acquisition and person-to-person transmission. Poor outcomes are exacerbated by late diagnosis and inadequate access to effective treatment. Advances in rapid molecular testing have recently improved the diagnosis of TB and drug resistance. Next-generation sequencing of Mycobacterium tuberculosis has increased our understanding of genetic resistance mechanisms and can now detect mutations associated with resistance phenotypes. All-oral, shorter drug regimens that can achieve high cure rates of drug-resistant TB within 6-9 months are now available and recommended but have yet to be scaled to global clinical use. Promising regimens for the prevention of drug-resistant TB among high-risk contacts are supported by early clinical trial data but final results are pending. A person-centred approach is crucial in managing drug-resistant TB to reduce the risk of poor treatment outcomes, side effects, stigma and mental health burden associated with the diagnosis. In this Review, we describe current surveillance of drug-resistant TB and the causes, risk factors and determinants of drug resistance as well as the stigma and mental health considerations associated with it. We discuss recent advances in diagnostics and drug-susceptibility testing and outline the progress in developing better treatment and preventive therapies.

Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning.

BACKGROUND: Global tuberculosis (TB) drug resistance (DR) surveillance focuses on rifampicin. We examined the potential of public and surveillance Mycobacterium tuberculosis (Mtb) whole-genome sequencing (WGS) data, to generate expanded country-level resistance prevalence estimates (antibiograms) using in silico resistance prediction. METHODS: We curated and quality-controlled Mtb WGS data. We used a validated random forest model to predict phenotypic resistance to 12 drugs and bias-corrected for model performance, outbreak sampling and rifampicin resistance oversampling. Validation leveraged a national DR survey conducted in South Africa. RESULTS: Mtb isolates from 29 countries (n=19 149) met sequence quality criteria. Global marginal genotypic resistance among mono-resistant TB estimates overlapped with the South African DR survey, except for isoniazid, ethionamide and second-line injectables, which were underestimated (n=3134). Among multidrug resistant (MDR) TB (n=268), estimates overlapped for the fluoroquinolones but overestimated other drugs. Globally pooled mono-resistance to isoniazid was 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). Given the recent interest in drugs enhancing ethionamide activity and their expected activity against isolates with resistance discordance between isoniazid and ethionamide, we measured this rate and found it to be high at 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=3964). CONCLUSIONS: This 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, but public WGS data demonstrates oversampling of isolates with higher resistance levels than MDR. Nevertheless, our results raise concerns about the empiric use of short-course fluoroquinolone regimens for drug-susceptible TB in South Asia and indicate underutilisation of ethionamide in MDR treatment.

Genotype-Phenotype Characterization of Serial Mycobacterium tuberculosis Isolates in Bedaquiline-Resistant Tuberculosis.

BACKGROUND: Emerging resistance to bedaquiline (BDQ) threatens to undermine advances in the treatment of drug-resistant tuberculosis (DRTB). Characterizing serial Mycobacterium tuberculosis (Mtb) isolates collected during BDQ-based treatment can provide insights into the etiologies of BDQ resistance in this important group of DRTB patients. METHODS: We measured mycobacteria growth indicator tube (MGIT)-based BDQ minimum inhibitory concentrations (MICs) of Mtb isolates collected from 195 individuals with no prior BDQ exposure who were receiving BDQ-based treatment for DRTB. We conducted whole-genome sequencing on serial Mtb isolates from all participants who had any isolate with a BDQ MIC >1 collected before or after starting treatment (95 total Mtb isolates from 24 participants). RESULTS: Sixteen of 24 participants had BDQ-resistant TB (MGIT MIC ≥4 µg/mL) and 8 had BDQ-intermediate infections (MGIT MIC = 2 µg/mL). Participants with pre-existing resistance outnumbered those with resistance acquired during treatment, and 8 of 24 participants had polyclonal infections. BDQ resistance was observed across multiple Mtb strain types and involved a diverse catalog of mmpR5 (Rv0678) mutations, but no mutations in atpE or pepQ. Nine pairs of participants shared genetically similar isolates separated by <5 single nucleotide polymorphisms, concerning for potential transmitted BDQ resistance. CONCLUSIONS: BDQ-resistant TB can arise via multiple, overlapping processes, including transmission of strains with pre-existing resistance. Capturing the within-host diversity of these infections could potentially improve clinical diagnosis, population-level surveillance, and molecular diagnostic test development.

Multimodal learning with graphs.

Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics. However, the increasingly heterogeneous graph datasets call for multimodal methods that can combine different inductive biases-the set of assumptions that algorithms use to make predictions for inputs they have not encountered during training. Learning on multimodal datasets presents fundamental challenges because the inductive biases can vary by data modality and graphs might not be explicitly given in the input. To address these challenges, multimodal graph AI methods combine different modalities while leveraging cross-modal dependencies using graphs. Diverse datasets are combined using graphs and fed into sophisticated multimodal architectures, specified as image-intensive, knowledge-grounded and language-intensive models. Using this categorization, we introduce a blueprint for multimodal graph learning, use it to study existing methods and provide guidelines to design new models.

Addressing patients' unmet needs related to multidrug-resistant tuberculosis (MDR-TB) care: A qualitative research study from Pune city, India.

AIM: We aimed to identify and describe the unmet needs of patients with multidrug-resistant tuberculosis (MDR-TB). METHODS: As a part of larger cross-sectional mixed-methods (qualitative and quantitative data) study on pathways to MDR-TB care, here we present the qualitative component. We interviewed 128 (56 men and 72 women) individuals who had MDR-TB, aged > = 15 years, registered and treated under the National TB Elimination Program (NTEP) in Pune city of India. We carried out thematic analysis of participants' narratives. RESULTS: We found that delays in diagnosis, lack of counseling, late referral to the NTEP and unwarranted expenditure were the main barriers to care that study participants experienced in the private sector. Provider dismissal of symptoms, non-courteous behavior, lack of hygiene in the referral centers, forced stay with other patients and lack of support for psychological/psychiatric problems were identified as a few additional challenges that participants faced at the NTEP care centers. CONCLUSION: Using qualitative data from experiences of participants with MDR-TB, we identify patients' several unmet needs, attention to which can improve MDR-TB care. Educating private providers about MDR-TB risk and available rapid molecular assays can help the timely diagnosis of MDR-TB and reduce patients' out of pocket costs. At the RNTCP/NTEP, measures such as training health workers to build rapport with patients, maintaining hygienic environments in the health centers with adequate isolation of participants with MDR from other serious cases, referral of patients with psychiatric symptoms to mental health specialists and monitoring drug shortages can help in improving care delivery.

Phase variation as a major mechanism of adaptation in Mycobacterium tuberculosis complex.

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 (Mycobacterium tuberculosis complex) 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 100× the neutral substitution rate at [Formula: see text] frameshifts/HT/year. Using neutral evolution simulations, we identified 4,098 substitutions and 45 phase-variants to be putatively adaptive to MTBC (P < 0.002). We experimentally confirm that a putatively adaptive phase-variant alters the expression of espA, a critical mediator of ESX-1-dependent virulence. Our evidence supports the hypothesis that phase variation in the ESX-1 system of MTBC can act as a toggle between antigenicity and survival in the host.

Genomic Insights into Virulence Factors and Multi-Drug Resistance in Clostridium perfringens IRMC2505A.

Clostridium perfringens is a spore-forming, Gram-positive anaerobic pathogen that causes several disorders in humans and animals. A multidrug-resistant Clostridium strain was isolated from the fecal sample of a patient who was clinically suspected of gastrointestinal infection and had a recent history of antibiotic exposure and diarrhea. The strain was identified by 16s rRNA sequencing as Clostridium perfringens. The strain's pathogenesis was analyzed through its complete genome, specifically antimicrobial resistance-related genes. The Clostridium perfringens IRMC2505A genome contains 19 (Alr, Ddl, dxr, EF-G, EF-Tu, folA, Dfr, folP, gyrA, gyrB, Iso-tRNA, kasA, MurA, rho, rpoB, rpoC, S10p, and S12p) antibiotic-susceptible genetic species according to the k-mer-based detection of antimicrobial resistance genes. Genome mapping using CARD and VFDB databases revealed significant (p-value = 1 × 10-26) genes with aligned reads against antibiotic-resistant genes or virulence factors, including phospholipase C, perfringolysin O, collagenase, hyaluronidase, alpha-clostripain, exo-alpha-sialidase, and sialidase activity. In conclusion, this is the first report on C. perfringens from Saudi Arabia that conducted whole genome sequencing of IRMC2505A and confirmed the strain as an MDR bacterium with several virulence factors. Developing control strategies requires a detailed understanding of the epidemiology of C. perfringens, its virulence factors, and regional antimicrobial resistance patterns.

Analysis of Genome-Wide Mutational Dependence in Naturally Evolving Mycobacterium tuberculosis Populations.

Pathogenic microorganisms are in a perpetual struggle for survival in changing host environments, where host pressures necessitate changes in pathogen virulence, antibiotic resistance, or transmissibility. The genetic basis of phenotypic adaptation by pathogens is difficult to study in vivo. In this work, we develop a phylogenetic method to detect genetic dependencies that promote pathogen adaptation using 31,428 in vivo sampled Mycobacterium tuberculosis genomes, a globally prevalent bacterial pathogen with increasing levels of antibiotic resistance. We find that dependencies between mutations are enriched in antigenic and antibiotic resistance functions and discover 23 mutations that potentiate the development of antibiotic resistance. Between 11% and 92% of resistant strains harbor a dependent mutation acquired after a resistance-conferring variant. We demonstrate the pervasiveness of genetic dependency in adaptation of naturally evolving populations and the utility of the proposed computational approach.

Mutation rates and adaptive variation among the clinically dominant clusters of Mycobacterium abscessus.

Mycobacterium abscessus (Mab) is a multidrug-resistant pathogen increasingly responsible for severe pulmonary infections. Analysis of whole-genome sequences (WGS) of Mab demonstrates dense genetic clustering of clinical isolates collected from disparate geographic locations. This has been interpreted as supporting patient-to-patient transmission, but epidemiological studies have contradicted this interpretation. Here, we present evidence for a slowing of the Mab molecular clock rate coincident with the emergence of phylogenetic clusters. We performed phylogenetic inference using publicly available WGS from 483 Mab patient isolates. We implement a subsampling approach in combination with coalescent analysis to estimate the molecular clock rate along the long internal branches of the tree, indicating a faster long-term molecular clock rate compared to branches within phylogenetic clusters. We used ancestry simulation to predict the effects of clock rate variation on phylogenetic clustering and found that the degree of clustering in the observed phylogeny is more easily explained by a clock rate slowdown than by transmission. We also find that phylogenetic clusters are enriched in mutations affecting DNA repair machinery and report that clustered isolates have lower spontaneous mutation rates in vitro. We propose that Mab adaptation to the host environment through variation in DNA repair genes affects the organism's mutation rate and that this manifests as phylogenetic clustering. These results challenge the model that phylogenetic clustering in Mab is explained by person-to-person transmission and inform our understanding of transmission inference in emerging, facultative pathogens.

High-throughput phenogenotyping of Mycobacteria tuberculosis clinical strains reveals bacterial determinants of treatment outcomes.

Background: Combatting the tuberculosis (TB) epidemic caused by Mycobacterium tuberculosis ( Mtb ) necessitates a better understanding of the factors contributing to patient clinical outcomes and transmission. While host and environmental factors have been evaluated, the impact of Mtb genetic background and phenotypic diversity is underexplored. Previous work has made associations between Mtb genetic lineages and some clinical and epidemiological features, but the bacterial traits underlying these connections are largely unknown. Methods: We developed a high-throughput functional genomics platform for defining genotype-phenotype relationships across a panel of Mtb clinical isolates. These phenotypic fitness profiles function as intermediate traits which can be linked to Mtb genetic variants and associated with clinical and epidemiological outcomes. We applied this approach to a collection of 158 Mtb strains from a study of Mtb transmission in Ho Chi Minh City, Vietnam. Mtb strains were genetically tagged in multiplicate, which allowed us to pool the strains and assess in vitro competitive fitness using deep sequencing across a set of 14 host-relevant antibiotic and metabolic conditions. Phylogenetic and monogenic associations with these intermediate traits were identified and then associated with clinical outcomes. Findings: Mtb clinical strains have a broad range of growth and drug response dynamics that can be clustered by their phylogenetic relationships. We identified novel monogenic associations with Mtb fitness in various metabolic and antibiotic conditions. Among these, we find that mutations in Rv1339 , a phosphodiesterase, which were identified through their association with slow growth in glycerol, are further associated with treatment failure. We also identify a previously uncharacterized subclade of Lineage 1 strains (L1.1.1.1) that is phenotypically distinguished by slow growth under most antibiotic and metabolic stress conditions in vitro . This clade is associated with cavitary disease, treatment failure, and demonstrates increased transmission potential. Interpretation: High-throughput phenogenotyping of Mtb clinical strains enabled bacterial intermediate trait identification that can provide a mechanistic link between Mtb genetic variation and patient clinical outcomes. Mtb strains associated with cavitary disease, treatment failure, and transmission potential display intermediate phenotypes distinguished by slow growth under various antibiotic and metabolic conditions. These data suggest that Mtb growth regulation is an adaptive advantage for host bacterial success in human populations, in at least some circumstances. These data further suggest markers for the underlying bacterial processes that govern these clinical outcomes. Funding: National Institutes of Allergy and Infectious Diseases: P01 AI132130 (SS, SMF); P01 AI143575 (XW, SMF); U19 AI142793 (QL, SMF); 5T32AI132120-03 (SS); 5T32AI132120-04 (SS); 5T32AI049928-17 (SS) Wellcome Trust Fellowship in Public Health and Tropical Medicine: 097124/Z/11/Z (NTTT) National Health and Medical Research Council (NHMRC)/A*STAR joint call: APP1056689 (SJD) The funding sources had no involvement in study methodology, data collection, analysis, and interpretation nor in the writing or submission of the manuscript. Research in context: Evidence before this study: We used different combinations of the words mycobacterium tuberculosis, tuberculosis, clinical strains, intermediate phenotypes, genetic barcoding, phenogenomics, cavitary disease, treatment failure, and transmission to search the PubMed database for all studies published up until January 20 th , 2022. We only considered English language publications, which biases our search. Previous work linking Mtb determinants to clinical or epidemiological data has made associations between bacterial lineage, or less frequently, genetic polymorphisms to in vitro or in vivo models of pathogenesis, transmission, and clinical outcomes such as cavitary disease, treatment failure, delayed culture conversion, and severity. Many of these studies focus on the global pandemic Lineage 2 and Lineage 4 Mtb strains due in part to a deletion in a polyketide synthase implicated in host-pathogen interactions. There are a number of Mtb GWAS studies that have led to novel genetic determinants of in vitro drug resistance and tolerance. Previous Mtb GWAS analyses with clinical outcomes did not experimentally test any predicted phenotypes of the clinical strains. Published laboratory-based studies of Mtb clinical strains involve relatively small numbers of strains, do not identify the genetic basis of relevant phenotypes, or link findings to the corresponding clinical outcomes. There are two recent studies of other pathogens that describe phenogenomic analyses. One study of 331 M. abscessus clinical strains performed one-by-one phenotyping to identify bacterial features associated with clearance of infection and another details a competition experiment utilizing three barcoded Plasmodium falciparum clinical isolates to assay antimalarial fitness and resistance. Added value of this study: We developed a functional genomics platform to perform high-throughput phenotyping of Mtb clinical strains. We then used these phenotypes as intermediate traits to identify novel bacterial genetic features associated with clinical outcomes. We leveraged this platform with a sample of 158 Mtb clinical strains from a cross sectional study of Mtb transmission in Ho Chi Minh City, Vietnam. To enable high-throughput phenotyping of large numbers of Mtb clinical isolates, we applied a DNA barcoding approach that has not been previously utilized for the high-throughput analysis of Mtb clinical strains. This approach allowed us to perform pooled competitive fitness assays, tracking strain fitness using deep sequencing. We measured the replicative fitness of the clinical strains in multiplicate under 14 metabolic and antibiotic stress condition. To our knowledge, this is the largest phenotypic screen of Mtb clinical isolates to date. We performed bacterial GWAS to delineate the Mtb genetic variants associated with each fitness phenotype, identifying monogenic associations with several conditions. We then defined Mtb phenotypic and genetic features associated with clinical outcomes. We find that a subclade of Mtb strains, defined by variants largely involved in fatty acid metabolic pathways, share a universal slow growth phenotype that is associated with cavitary disease, treatment failure and increased transmission potential in Vietnam. We also find that mutations in Rv1339 , a poorly characterized phosphodiesterase, also associate with slow growth in vitro and with treatment failure in patients. Implications of all the available evidence: Phenogenomic profiling demonstrates that Mtb strains exhibit distinct growth characteristics under metabolic and antibiotic stress conditions. These fitness profiles can serve as intermediate traits for GWAS and association with clinical outcomes. Intermediate phenotyping allows us to examine potential processes by which bacterial strain differences contribute to clinical outcomes. Our study identifies clinical strains with slow growth phenotypes under in vitro models of antibiotic and host-like metabolic conditions that are associated with adverse clinical outcomes. It is possible that the bacterial intermediate phenotypes we identified are directly related to the mechanisms of these outcomes, or they may serve as markers for the causal yet unidentified bacterial determinants. Via the intermediate phenotyping, we also discovered a surprising diversity in Mtb responses to the new anti-mycobacterial drugs that target central metabolic processes, which will be important in considering roll-out of these new agents. Our study and others that have identified Mtb determinants of TB clinical and epidemiological phenotypes should inform efforts to improve diagnostics and drug regimen design.