Case studies to illustrate good practice in the management of non-tuberculous mycobacterial pulmonary disease

Pulmonary disease caused by non-tuberculous mycobacteria (NTM-PD) can be a complex condition for health care providers to manage, and delayed diagnosis and treatment failure are common. Here we present three case studies that illustrate key challenges in the diagnosis and treatment of NTM-PD, and provide guidance on these issues. In addition, we make recommendations on how the overall management of NTM-PD may be improved, through strategies such as physician education to recognise NTM-PD, and the development of multidisciplinary teams and patient-support groups

Deletion of cftr Leads to an Excessive Neutrophilic Response and Defective Tissue Repair in a Zebrafish Model of Sterile Inflammation.

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 in vivo 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

Epidemiology of nontuberculous mycobacteria (NTM) amongst individuals with cystic fibrosis (CF)

AbstractBackgroundInfection by nontuberculous mycobacteria (NTM) in patients with cystic fibrosis (CF) is often associated with significant morbidity. Limited, conflicting results are published regarding risk factors for pulmonary NTM disease. We analysed factors potentially associated with NTM in a large population of European patients with CF.MethodsWe investigated associations between presence of NTM and various factors for patients registered in the European Cystic Fibrosis Society Patient Registry.Results374 (2.75%) of 13,593 patients studied had at least one positive NTM culture within the study year. Age- and FEV1-adjusted odds of NTM infection was more than 2.5 times higher (95%CI: 1.79; 3.60) in patients infected by Stenotrophomonas maltophilia than in patients not infected (p<0.0001), 2.36 times higher (95%CI: 1.80;3.08) in patients with ABPA than without (p<0.0001), 1.79 times higher (95%CI: 1.34; 2.38) in patients who use bronchodilators than in patients who don't (p<0.0001), 1.49 times higher (95%CI: 1.18; 1.89) in patients who use inhaled antibiotics than in patients who don't (p=0.001), and 1.30 times higher (95%CI: 1.02; 1.66) in patients who use rhDNase than in patients who don't (p=0.032).ConclusionsNTM-positive cultures in individuals with CF are associated with distinct clinical variables. Improved data collection identifying risk factors for NTM infection will allow more focused screening strategies, and influence therapeutic choices and infection control measures in high-risk patients

Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic caused by the new coronavirus (SARS-CoV-2) is currently responsible for more than 3 million deaths in 219 countries across the world and with more than 140 million cases. The absence of FDA-approved drugs against SARS-CoV-2 has highlighted an urgent need to design new drugs. We developed an integrated model of the human cell and SARS-CoV-2 to provide insight into the virus' pathogenic mechanism and support current therapeutic strategies. We show the biochemical reactions required for the growth and general maintenance of the human cell, first, in its healthy state. We then demonstrate how the entry of SARS-CoV-2 into the human cell causes biochemical and structural changes, leading to a change of cell functions or cell death. A new computational method that predicts 20 unique reactions as drug targets from our models and provides a platform for future studies on viral entry inhibition, immune regulation, and drug optimisation strategies. The model is available in BioModels (https://www.ebi.ac.uk/biomodels/MODEL2007210001) and the software tool, findCPcli, that implements the computational method is available at https://github.com/findCP/findCPcli

A rapid unravelling of mycobacterial activity and of their susceptibility to antibiotics

The development of antibiotic-resistant bacteria is a worldwide health-related emergency that calls for new tools to study the bacterial metabolism and to obtain fast diagnoses. Indeed, the conventional analysis timescale is too long and affects our ability to fight infections. Slowly growing bacteria represent a bigger challenge, since their analysis may require up to months. Among these bacteria, Mycobacterium tuberculosis, the causative agent of tuberculosis has caused, only in 2016 more than 10 million new cases and 1.7 million deaths. We employed a particularly powerful nanomechanical oscillator, the nanomotion sensor, to characterize rapidly and in real time a tuberculous and a non-tuberculous bacterial species, Bacillus Calmette-Guérin and Mycobacterium abscessus exposed to different antibiotics. Here, we show how high speed and high sensitivity detectors, the nanomotion sensors, can provide a rapid and reliable analysis of different mycobacterial species, obtaining qualitative and quantitative information on their response to different drugs.Consiglio Nazionale delle Ricerche and Swiss National Grants 200021-144321 and 407240-167137 The Gerbert Ruf Stiftung GRS-024/1

Which microbial factors really are important in Pseudomonas aeruginosa infections?

Over the last two decades, tens of millions of dollars have been invested in understanding virulence in the human pathogen, Pseudomonas aeruginosa. However, the top 'hits' obtained in a recent TnSeq analysis aimed at identifying those genes that are conditionally essential for infection did not include most of the known virulence factors identified in these earlier studies. Instead, it seems that P. aeruginosa faces metabolic challenges in vivo, and unless it can overcome these, it fails to thrive and is cleared from the host. In this review, we look at the kinds of metabolic pathways that the pathogen seems to find essential, and comment on how this knowledge might be therapeutically exploited.Work in the MW laboratory is funded by the BBSRC (grant BB/M019411/1) and the EU (Marie Curie Educational Training Network “INTEGRATE”). AC is supported by the Cambridge Trusts. EM is funded by a studentship from the MRC. SB is supported by a Hershel Smith studentship. E-FU is a clinical research fellow funded by the CF Trust (UK), Papworth Hospital NHS Trust and the Wellcome Trust. YA is supported by a scholarship from the Yosef Jameel Foundation. YB is an EPSRC-funded PhD student. Work in the laboratory of AF is supported by the Wellcome Trust. Work in the DRS laboratory is supported by the EPSRC.This is the author accepted manuscript. The final version is available from Future Science Group via http://dx.doi.org/10.2217/fmb.15.10

How artificial intelligence and machine learning can help healthcare systems respond to COVID-19

The COVID-19 global pandemic is a threat not only to the health of millions of individuals, but also to the stability of infrastructure and economies around the world. The disease will inevitably place an overwhelming burden on healthcare systems that cannot be effectively dealt with by existing facilities or responses based on conventional approaches. We believe that a rigorous clinical and societal response can only be mounted by using intelligence derived from a variety of data sources to better utilize scarce healthcare resources, provide per- sonalized patient management plans, inform policy, and expedite clinical trials. In this paper, we introduce five of the most important challenges in responding to COVID-19 and show how each of them can be addressed by recent develop- ments in machine learning (ML) and artificial intelligence (AI). We argue that the integration of these techniques into local, national, and international healthcare systems will save lives, and propose specific methods by which implementation can happen swiftly and efficiently. We offer to extend these resources and knowledge to assist policymakers seeking to implement these techniques

Sounds of COVID-19: exploring realistic performance of audio-based digital testing.

To identify Coronavirus disease (COVID-19) cases efficiently, affordably, and at scale, recent work has shown how audio (including cough, breathing and voice) based approaches can be used for testing. However, there is a lack of exploration of how biases and methodological decisions impact these tools' performance in practice. In this paper, we explore the realistic performance of audio-based digital testing of COVID-19. To investigate this, we collected a large crowdsourced respiratory audio dataset through a mobile app, alongside symptoms and COVID-19 test results. Within the collected dataset, we selected 5240 samples from 2478 English-speaking participants and split them into participant-independent sets for model development and validation. In addition to controlling the language, we also balanced demographics for model training to avoid potential acoustic bias. We used these audio samples to construct an audio-based COVID-19 prediction model. The unbiased model took features extracted from breathing, coughs and voice signals as predictors and yielded an AUC-ROC of 0.71 (95% CI: 0.65-0.77). We further explored several scenarios with different types of unbalanced data distributions to demonstrate how biases and participant splits affect the performance. With these different, but less appropriate, evaluation strategies, the performance could be overestimated, reaching an AUC up to 0.90 (95% CI: 0.85-0.95) in some circumstances. We found that an unrealistic experimental setting can result in misleading, sometimes over-optimistic, performance. Instead, we reported complete and reliable results on crowd-sourced data, which would allow medical professionals and policy makers to accurately assess the value of this technology and facilitate its deployment

Dissemination of Mycobacterium abscessus via global transmission networks.

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.Funding for this work was provided by The Wellcome Trust (investigator award no. 107032/Z/15/Z to R.A.F.), Fondation Botnar (Programme grant no. 6063) and the UK CF Trust (Innovation Hub award no. 001; Strategic Research Centre award no. 010). M.S., N.A.H. and R.M.D. acknowledge the Cystic Fibrosis Foundation for funding

Structure-guided fragment-based drug discovery at the synchrotron: screening binding sites and correlations with hotspot mapping.

Structure-guided drug discovery emerged in the 1970s and 1980s, stimulated by the three-dimensional structures of protein targets that became available, mainly through X-ray crystal structure analysis, assisted by the development of synchrotron radiation sources. Structures of known drugs or inhibitors were used to guide the development of leads. The growth of high-throughput screening during the late 1980s and the early 1990s in the pharmaceutical industry of chemical libraries of hundreds of thousands of compounds of molecular weight of approximately 500 Da was impressive but still explored only a tiny fraction of the chemical space of the predicted 1040 drug-like compounds. The use of fragments with molecular weights less than 300 Da in drug discovery not only decreased the chemical space needing exploration but also increased promiscuity in binding targets. Here we discuss advances in X-ray fragment screening and the challenge of identifying sites where fragments not only bind but can be chemically elaborated while retaining their positions and binding modes. We first describe the analysis of fragment binding using conventional X-ray difference Fourier techniques, with Mycobacterium abscessus SAICAR synthetase (PurC) as an example. We observe that all fragments occupy positions predicted by computational hotspot mapping. We compare this with fragment screening at Diamond Synchrotron Light Source XChem facility using PanDDA software, which identifies many more fragment hits, only some of which bind to the predicted hotspots. Many low occupancy sites identified may not support elaboration to give adequate ligand affinity, although they will likely be useful in drug discovery as 'warm spots' for guiding elaboration of fragments bound at hotspots. We discuss implications of these observations for fragment screening at the synchrotron sources. This article is part of the theme issue 'Fifty years of synchrotron science: achievements and opportunities'.The Botnar Foundation (grant number: 6063), the Cystic Fibrosis Trust (Strategic Research Centre Awards 002, 010 & 201) and the Bill and Melinda Gates Foundation, Shorten-TB Award