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Population-level comparisons of prokaryotic genomes must take into account the substantial differences in gene content resulting from horizontal gene transfer, gene duplication and gene loss. However, the automated annotation of prokaryotic genomes is imperfect, and errors due to fragmented assemblies, contamination, diverse gene families and mis-assemblies accumulate over the population, leading to profound consequences when analysing the set of all genes found in a species. Here, we introduce Panaroo, a graph-based pangenome clustering tool that is able to account for many of the sources of error introduced during the annotation of prokaryotic genome assemblies. Panaroo is available at https://github.com/gtonkinhill/panaroo .

The new BTS Guidelines for the management of non-tuberculous mycobacterial pulmonary disease combine the best available evidence with expert consensus to generate a set of pragmatic Guidelines, published as a supplement to this issue of Thorax, to assist in the management of these challenging infections.

Soluble aggregates of the microtubule-associated protein tau have been challenging to assemble and characterize, despite their important role in the development of tauopathies. We found that sequential hyperphosphorylation by protein kinase A in conjugation with either glycogen synthase kinase 3β or stress activated protein kinase 4 enabled recombinant wild-type tau of isoform 0N4R to spontaneously polymerize into small amorphous aggregates in vitro. We employed tandem mass spectrometry to determine the phosphorylation sites, high-resolution native mass spectrometry to measure the degree of phosphorylation, and super-resolution microscopy and electron microscopy to characterize the morphology of aggregates formed. Functionally, compared with the unmodified aggregates, which require heparin induction to assemble, these self-assembled hyperphosphorylated tau aggregates more efficiently disrupt membrane bilayers and induce Toll-like receptor 4-dependent responses in human macrophages. Together, our results demonstrate that hyperphosphorylated tau aggregates are potentially damaging to cells, suggesting a mechanism for how hyperphosphorylation could drive neuroinflammation in tauopathies. In this work, the authors report that hyperphosphorylated recombinant tau spontaneously assembles into small, amorphous aggregates, which disrupt membranes and induce Toll-like receptor 4-dependent responses in human macrophages.

Increasing numbers of individuals with cystic fibrosis are becoming infected with the multidrug-resistant non-tuberculous mycobacterium (NTM) Mycobacterium abscessus, which causes progressive lung damage and is extremely challenging to treat. How this organism is acquired is not currently known, but there is growing concern that person-to-person transmission could occur. We aimed to define the mechanisms of acquisition of M abscessus in individuals with cystic fibrosis. Whole genome sequencing and antimicrobial susceptibility testing were done on 168 consecutive isolates of M abscessus from 31 patients attending an adult cystic fibrosis centre in the UK between 2007 and 2011. In parallel, we undertook detailed environmental testing for NTM and defined potential opportunities for transmission between patients both in and out of hospital using epidemiological data and social network analysis. Phylogenetic analysis revealed two clustered outbreaks of near-identical isolates of the M abscessus subspecies massiliense (from 11 patients), differing by less than ten base pairs. This variation represents less diversity than that seen within isolates from a single individual, strongly indicating between-patient transmission. All patients within these clusters had numerous opportunities for within-hospital transmission from other individuals, while comprehensive environmental sampling, initiated during the outbreak, failed to detect any potential point source of NTM infection. The clusters of M abscessus subspecies massiliense showed evidence of transmission of mutations acquired during infection of an individual to other patients. Thus, isolates with constitutive resistance to amikacin and clarithromycin were isolated from several individuals never previously exposed to long-term macrolides or aminoglycosides, further indicating cross-infection. Whole genome sequencing has revealed frequent transmission of multidrug resistant NTM between patients with cystic fibrosis despite conventional cross-infection measures. Although the exact transmission route is yet to be established, our epidemiological analysis suggests that it could be indirect. The Wellcome Trust, Papworth Hospital, NIHR Cambridge Biomedical Research Centre, UK Health Protection Agency, Medical Research Council, and the UKCRC Translational Infection Research Initiative.

Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection, given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics and patterns of recovery could bring insights and lead to more timely treatment or treatment adjustment, as well as better resource management in health care systems. The primary objective of this study is to explore the potential of longitudinal audio samples over time for COVID-19 progression prediction and, especially, recovery trend prediction using sequential deep learning techniques. Crowdsourced respiratory audio data, including breathing, cough, and voice samples, from 212 individuals over 5-385 days were analyzed, alongside their self-reported COVID-19 test results. We developed and validated a deep learning-enabled tracking tool using gated recurrent units (GRUs) to detect COVID-19 progression by exploring the audio dynamics of the individuals' historical audio biomarkers. The investigation comprised 2 parts: (1) COVID-19 detection in terms of positive and negative (healthy) tests using sequential audio signals, which was primarily assessed in terms of the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with 95% CIs, and (2) longitudinal disease progression prediction over time in terms of probability of positive tests, which was evaluated using the correlation between the predicted probability trajectory and self-reported labels. We first explored the benefits of capturing longitudinal dynamics of audio biomarkers for COVID-19 detection. The strong performance, yielding an AUROC of 0.79, a sensitivity of 0.75, and a specificity of 0.71 supported the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examined the predicted disease progression trajectory, which displayed high consistency with longitudinal test results with a correlation of 0.75 in the test cohort and 0.86 in a subset of the test cohort with 12 (57.1%) of 21 COVID-19-positive participants who reported disease recovery. Our findings suggest that monitoring COVID-19 evolution via longitudinal audio data has potential in the tracking of individuals' disease progression and recovery. An audio-based COVID-19 progression monitoring system was developed using deep learning techniques, with strong performance showing high consistency between the predicted trajectory and the test results over time, especially for recovery trend predictions. This has good potential in the postpeak and postpandemic era that can help guide medical treatment and optimize hospital resource allocations. The changes in longitudinal audio samples, referred to as audio dynamics, are associated with COVID-19 progression; thus, modeling the audio dynamics can potentially capture the underlying disease progression process and further aid COVID-19 progression prediction. This framework provides a flexible, affordable, and timely tool for COVID-19 tracking, and more importantly, it also provides a proof of concept of how telemonitoring could be applicable to respiratory diseases monitoring, in general.

Mycobacterium abscessus , a multidrug-resistant nontuberculous mycobacterium, has emerged as a major pathogen affecting people with cystic fibrosis (CF). Although originally thought to be acquired independently from the environment, most individuals are infected with one of several dominant circulating clones (DCCs), indicating the presence of global transmission networks of M. abscessus . How and when these clones emerged and spread globally is unclear. Here, we use evolutionary analyses of isolates from individuals both with and without CF to reconstruct the population history, spatiotemporal spread and recent transmission networks of the DCCs. We demonstrate synchronous expansion of six unrelated DCCs in the 1960s, a period associated with major changes in CF care and survival. Each of these clones has spread globally as a result of rare intercontinental transmission events. We show that the DCCs, but not environmentally acquired isolates, exhibit a specific smoking-associated mutational signature and that current transmission networks include individuals both with and without CF. We therefore propose that the DCCs initially emerged in non-CF populations but were then amplified and spread through the CF community. While individuals with CF are probably the most permissive host, non-CF individuals continue to play a key role in transmission networks and may facilitate long-distance transmission. In this Article, the authors perform evolutionary analyses of M. abscessus clinical isolates and report the emergence of dominant circulating clones (DCCs) in non-cystic fibrosis (CF) individuals followed by amplification in the CF community.

Inflammation-related progressive lung destruction is the leading causes of premature death in cystic fibrosis (CF), a genetic disorder caused by a defective cystic fibrosis transmembrane conductance regulator (CFTR). However, therapeutic targeting of inflammation has been hampered by a lack of understanding of the links between a dysfunctional CFTR and the deleterious innate immune response in CF. Herein, we used a CFTR-depleted zebrafish larva, as an innovative vertebrate model, to understand how CFTR dysfunction leads to abnormal inflammatory status in CF. We show that impaired CFTR-mediated inflammation correlates with an exuberant neutrophilic response after injury: CF zebrafish exhibit enhanced and sustained accumulation of neutrophils at wounds. Excessive epithelial oxidative responses drive enhanced neutrophil recruitment towards wounds. Persistence of neutrophils at inflamed sites is associated with impaired reverse migration of neutrophils and reduction in neutrophil apoptosis. As a consequence, the increased number of neutrophils at wound sites causes tissue damage and abnormal tissue repair. Importantly, the molecule Tanshinone IIA successfully accelerates inflammation resolution and improves tissue repair in CF animal. Our findings bring important new understanding of the mechanisms underlying the inflammatory pathology in CF, which could be addressed therapeutically to prevent inflammatory lung damage in CF patients with potential improvements in disease outcomes.

As observed in cancers, individual mutagens and defects in DNA repair create distinctive mutational signatures that combine to form context-specific spectra within cells. We reasoned that similar processes must occur in bacterial lineages, potentially allowing decomposition analysis to detect both disruption of DNA repair processes and exposure to niche-specific mutagens. Here we reconstruct mutational spectra for 84 clades from 31 diverse bacterial species and find distinct mutational patterns. We extract signatures driven by specific DNA repair defects using hypermutator lineages, and further deconvolute the spectra into multiple signatures operating within different clades. We show that these signatures are explained by both bacterial phylogeny and replication niche. By comparing mutational spectra of clades from different environmental and biological locations, we identify niche-associated mutational signatures, and then employ these signatures to infer the predominant replication niches for several clades where this was previously obscure. Our results show that mutational spectra may be associated with sites of bacterial replication when mutagen exposures differ, and can be used in these cases to infer transmission routes for established and emergent human bacterial pathogens. Mutagens and DNA repair defects generate context-specific mutational signatures in cancer cells. Here, Ruis et al. provide evidence of similar processes in bacteria, showing that mutational spectra may be associated with sites of bacterial replication when mutagen exposures differ, and can be used in these cases to infer transmission routes.