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Nosocomial spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been widely reported, but the transmission pathways among patients and healthcare workers (HCWs) are unclear. Identifying the risk factors and drivers for these nosocomial transmissions is critical for infection prevention and control interventions. The main aim of our study was to quantify the relative importance of different transmission pathways of SARS-CoV-2 in the hospital setting. This is an observational cohort study using data from 4 teaching hospitals in Oxfordshire, United Kingdom, from January to October 2020. Associations between infectious SARS-CoV-2 individuals and infection risk were quantified using logistic, generalised additive and linear mixed models. Cases were classified as community- or hospital-acquired using likely incubation periods of 3 to 7 days. Of 66,184 patients who were hospitalised during the study period, 920 had a positive SARS-CoV-2 PCR test within the same period (1.4%). The mean age was 67.9 (±20.7) years, 49.2% were females, and 68.5% were from the white ethnic group. Out of these, 571 patients had their first positive PCR tests while hospitalised (62.1%), and 97 of these occurred at least 7 days after admission (10.5%). Among the 5,596 HCWs, 615 (11.0%) tested positive during the study period using PCR or serological tests. The mean age was 39.5 (±11.1) years, 78.9% were females, and 49.8% were nurses. For susceptible patients, 1 day in the same ward with another patient with hospital-acquired SARS-CoV-2 was associated with an additional 7.5 infections per 1,000 susceptible patients (95% credible interval (CrI) 5.5 to 9.5/1,000 susceptible patients/day) per day. Exposure to an infectious patient with community-acquired Coronavirus Disease 2019 (COVID-19) or to an infectious HCW was associated with substantially lower infection risks (2.0/1,000 susceptible patients/day, 95% CrI 1.6 to 2.2). As for HCW infections, exposure to an infectious patient with hospital-acquired SARS-CoV-2 or to an infectious HCW were both associated with an additional 0.8 infection per 1,000 susceptible HCWs per day (95% CrI 0.3 to 1.6 and 0.6 to 1.0, respectively). Exposure to an infectious patient with community-acquired SARS-CoV-2 was associated with less than half this risk (0.2/1,000 susceptible HCWs/day, 95% CrI 0.2 to 0.2). These assumptions were tested in sensitivity analysis, which showed broadly similar results. The main limitations were that the symptom onset dates and HCW absence days were not available. In this study, we observed that exposure to patients with hospital-acquired SARS-CoV-2 is associated with a substantial infection risk to both HCWs and other hospitalised patients. Infection control measures to limit nosocomial transmission must be optimised to protect both staff and patients from SARS-CoV-2 infection.

Whole genome sequencing (WGS) of Mycobacterium tuberculosis has rapidly progressed from a research tool to a clinical application for the diagnosis and management of tuberculosis and in public health surveillance. This development has been facilitated by drastic drops in cost, advances in technology and concerted efforts to translate sequencing data into actionable information. There is, however, a risk that, in the absence of a consensus and international standards, the widespread use of WGS technology may result in data and processes that lack harmonization, comparability and validation. In this Review, we outline the current landscape of WGS pipelines and applications, and set out best practices for M.tuberculosis WGS, including standards for bioinformatics pipelines, curated repositories of resistance-causing variants, phylogenetic analyses, quality control and standardized reporting.

The rise of antibiotic-resistant bacteria has led to an urgent need for rapid detection of drug resistance in clinical samples, and improvements in global surveillance. Here we show how de Bruijn graph representation of bacterial diversity can be used to identify species and resistance profiles of clinical isolates. We implement this method for Staphylococcus aureus and Mycobacterium tuberculosis in a software package (‘Mykrobe predictor’) that takes raw sequence data as input, and generates a clinician-friendly report within 3 minutes on a laptop. For S . aureus , the error rates of our method are comparable to gold-standard phenotypic methods, with sensitivity/specificity of 99.1%/99.6% across 12 antibiotics (using an independent validation set, n =470). For M . tuberculosis , our method predicts resistance with sensitivity/specificity of 82.6%/98.5% (independent validation set, n =1,609); sensitivity is lower here, probably because of limited understanding of the underlying genetic mechanisms. We give evidence that minor alleles improve detection of extremely drug-resistant strains, and demonstrate feasibility of the use of emerging single-molecule nanopore sequencing techniques for these purposes. The clinical application of new sequencing techniques is expected to accelerate pathogen identification. Here, Bradley et al . present a clinician-friendly software package that uses sequencing data for quick and accurate prediction of antibiotic resistance profiles for S. aureus and M. tuberculosis .

Tuberculosis incidence in the UK has risen in the past decade. Disease control depends on epidemiological data, which can be difficult to obtain. Whole-genome sequencing can detect microevolution within Mycobacterium tuberculosis strains. We aimed to estimate the genetic diversity of related M tuberculosis strains in the UK Midlands and to investigate how this measurement might be used to investigate community outbreaks. In a retrospective observational study, we used Illumina technology to sequence M tuberculosis genomes from an archive of frozen cultures. We characterised isolates into four groups: cross-sectional, longitudinal, household, and community. We measured pairwise nucleotide differences within hosts and between hosts in household outbreaks and estimated the rate of change in DNA sequences. We used the findings to interpret network diagrams constructed from 11 community clusters derived from mycobacterial interspersed repetitive-unit–variable-number tandem-repeat data. We sequenced 390 separate isolates from 254 patients, including representatives from all five major lineages of M tuberculosis. The estimated rate of change in DNA sequences was 0·5 single nucleotide polymorphisms (SNPs) per genome per year (95% CI 0·3–0·7) in longitudinal isolates from 30 individuals and 25 families. Divergence is rarely higher than five SNPs in 3 years. 109 (96%) of 114 paired isolates from individuals and households differed by five or fewer SNPs. More than five SNPs separated isolates from none of 69 epidemiologically linked patients, two (15%) of 13 possibly linked patients, and 13 (17%) of 75 epidemiologically unlinked patients (three-way comparison exact p<0·0001). Genetic trees and clinical and epidemiological data suggest that super-spreaders were present in two community clusters. Whole-genome sequencing can delineate outbreaks of tuberculosis and allows inference about direction of transmission between cases. The technique could identify super-spreaders and predict the existence of undiagnosed cases, potentially leading to early treatment of infectious patients and their contacts. Medical Research Council, Wellcome Trust, National Institute for Health Research, and the Health Protection Agency.

Rapid genotype-based drug susceptibility testing for the Mycobacterium tuberculosis complex (MTBC) relies on a comprehensive knowledgebase of the genetic determinants of resistance. Here we present a catalogue of resistance-associated mutations using a regression-based approach and benchmark it against the 2nd edition of the World Health Organisation (WHO) mutation catalogue. We train multivariate logistic regression models on over 52,000 MTBC isolates to associate binary resistance phenotypes for 15 antitubercular drugs with variants extracted from candidate resistance genes. Regression detects 450/457 (98%) resistance-associated variants identified using the existing method ( a.k.a , SOLO method) and grades 221 (29%) more total variants than SOLO. The regression-based catalogue achieves higher sensitivity on average (+3.2 percentage points, pp) than SOLO with smaller average decreases in specificity (−1.0 pp) and positive predictive value (−1.6 pp). Sensitivity gains are highest for ethambutol, clofazimine, streptomycin, and ethionamide as regression graded considerably more resistance-associated variants than SOLO for these drugs. There is no difference between SOLO and regression with regards to meeting the target product profiles set by the WHO for genetic drug susceptibility testing, except for rifampicin, for which regression specificity is below the threshold of 98% at 97%. The regression pipeline also detects isoniazid resistance compensatory mutations in ahpC and variants linked to bedaquiline and aminoglycoside hypersusceptibility. These results inform the continued development of targeted next generation sequencing, whole genome sequencing, and other commercial molecular assays for diagnosing resistance in the MTBC. Here the authors train multivariate logistic regression models on over 52,000 MTBC isolates to associate binary resistance phenotypes for 15 antitubercular drugs with variants extracted from candidate resistance genes, and generate a regression-based catalogue of resistance-associated mutations that achieves higher sensitivity on average than the gold standard with smaller average decreases in specificity and positive predictive value.

Preventive therapy remains key to the elimination of tuberculosis and is typically offered to people with presumptive Mycobacterium tuberculosis infection to prevent active disease. Although the duration of tuberculosis preventive therapy has been reduced substantially over time, it remains long in absolute terms, and uptake remains low. Treatment-shortening trials using non-inferiority designs have so far led to the implementation of effective regimens of 1–4 months’ duration. Such regimens are a substantial improvement on the previous 6–9 months’ duration standard of care but still far too long given potential toxicity and the very low baseline risk of disease for most individuals. The efficacy of even shorter tuberculosis preventive therapy regimens, including ultra-short regimens shorter than 2 weeks’ duration, is yet to be explored, but optimal public health outcomes might be achieved even if the efficacy of such regimens is lower than that of the standard of care. Greater acceptability could lead to higher population uptake, and, potentially, to more cases of tuberculosis avoided. Nonetheless, the optimal duration of ultra-short tuberculosis preventive therapy regimens cannot be explored through classic two-arm non-inferiority trials. Instead, the relationship between different durations and efficacy of tuberculosis preventive therapy will need to be characterised, requiring some participants to be randomly assigned to no (or delayed) therapy in order to characterise the number of tuberculosis cases averted by the shortest options. We argue that such trials are needed to identify the optimal trade-off between efficacy and acceptability and would be ethically acceptable provided there were appropriate risk mitigation measures for participants, including careful monitoring for the development of active disease. In this Personal View, we discuss some of the scientific and ethical considerations around the investigation of ultra-short-course preventive therapy for tuberculosis.

[...]mutations can be used to predict different treatment outcomes. [...]by also considering phylogenetic benign mutations that do not confer resistance, a comprehensive molecular drug susceptibility profile could be inferred for a pathogen-tailored individualized treatment regimen in the future. cgMLST,core genome multilocus sequencing type; TB, tuberculosis. https://doi.org/10.1371/journal.ppat.1007297.g001 These examples of how genomic data inform treatment choice illustrate the concept of precision medicine in infectious diseases, in which prevention and treatment strategies take information from systems biology and individual variability into account [5]. First clinical data are accumulating, demonstrating how pathogen-based genetic information provides insights on potential treatment course and outcome. [...]therapeutic drug monitoring using dried blood-spots provides information on the drug level at a certain time point. [...]insights on the phylogenetic lineage of the individual and infecting MTBC strain, coupled with their virulence and transmission properties, may inform and further individualize the treatment course.

Contact between humans and wildlife presents a risk for both zoonotic and anthropozoonotic disease transmission. In this study we report the detection of human strains of Mycobacterium tuberculosis in sun bears and an Asiatic black bear in a wildlife rescue centre in Cambodia, confirming for the first time the susceptibility of these bear species to tuberculosis when in close contact with humans. After genotyping revealed two different strains of M. tuberculosis from cases occurring between 2009 and 2019, 100 isolates from 30 sun bear cases, a single Asiatic black bear case, and a human case were subjected to whole genome sequencing. We combined single nucleotide polymorphism analysis and exploration of mixed base calls with epidemiological data to indicate the evolution of each outbreak. Our results confirmed two concurrent yet separate tuberculosis outbreaks and established a likely transmission route in one outbreak where the human case acted as an intermediatory between bear cases. In both outbreaks, we observed high rates of transmission and progression to active disease, suggesting that sun bears are highly susceptible to tuberculosis if exposed under these conditions. Overall, our findings highlight the risk of bi-directional transmission of tuberculosis between humans and captive bears in high human tuberculosis burden regions, with implied considerations for veterinary and public health. We also demonstrate the use of standard genomic approaches to better understand disease outbreaks in captive wildlife settings and to inform control and prevention measures.

SummaryThe rapid emergence of resistance to novel tuberculosis drugs, such as bedaquiline, is a key threat to the long-term effectiveness of novel regimens. Given that the introduction of these agents has enabled the introduction of an all-oral regimen for rifampicin-resistant and multidrug-resistant tuberculosis, the rise of resistance underscores the urgent need to safeguard their efficacy and responsible use. A major barrier is the delay in developing reliable tools to detect resistance to novel compounds, which limits clinical decision-making and surveillance efforts. Herein, we outline a framework for integrating the development of drug susceptibility testing alongside tuberculosis drug development, including early stage resistance profiling and defining appropriate epidemiological cutoff values. We highlight key gaps, including the need for structured partnerships between drug developers, diagnostic manufacturers, regulators, research institutions, funders, and policy makers. We propose a roadmap to accelerate drug susceptibility testing and development of new tuberculosis regimens, ensuring that resistance detection maintains pace with the introduction of novel drugs. Establishing collaborative platforms for data sharing, genomic analysis, and diagnostic innovation will help ensure that resistance detection evolves in step with drug development, thereby preserving novel treatments and improving global tuberculosis care.

Two billion people are infected with Mycobacterium tuberculosis , leading to 10 million new cases of active tuberculosis and 1.5 million deaths annually. Universal access to drug susceptibility testing (DST) has become a World Health Organization priority. We previously developed a software tool, Mykrobe predictor , which provided offline species identification and drug resistance predictions for M. tuberculosis from whole genome sequencing (WGS) data. Performance was insufficient to support the use of WGS as an alternative to conventional phenotype-based DST, due to mutation catalogue limitations.  Here we present a new tool, Mykrobe , which provides the same functionality based on a new software implementation. Improvements include i) an updated mutation catalogue giving greater sensitivity to detect pyrazinamide resistance, ii) support for user-defined resistance catalogues, iii) improved identification of non-tuberculous mycobacterial species, and iv) an updated statistical model for Oxford Nanopore Technologies sequencing data. Mykrobe is released under MIT license at https://github.com/mykrobe-tools/mykrobe. We incorporate mutation catalogues from the CRyPTIC consortium et al. (2018) and from Walker et al. (2015), and make improvements based on performance on an initial set of 3206 and an independent set of 5845 M. tuberculosis Illumina sequences. To give estimates of error rates, we use a prospectively collected dataset of 4362 M. tuberculosis isolates . Using culture based DST as the reference, we estimate Mykrobe to be 100%, 95%, 82%, 99% sensitive and 99%, 100%, 99%, 99% specific for rifampicin, isoniazid, pyrazinamide and ethambutol resistance prediction respectively. We benchmark against four other tools on 10207 (=5845+4362) samples, and also show that Mykrobe gives concordant results with nanopore data.  We measure the ability of Mykrobe -based DST to guide personalized therapeutic regimen design in the context of complex drug susceptibility profiles, showing 94% concordance of implied regimen with that driven by phenotypic DST, higher than all other benchmarked tools.