E3 ligases: a ubiquitous link between DNA repair, DNA replication and human disease.

Maintenance of genome stability is of paramount importance for the survival of an organism. However, genomic integrity is constantly being challenged by various endogenous and exogenous processes that damage DNA. Therefore, cells are heavily reliant on DNA repair pathways that have evolved to deal with every type of genotoxic insult that threatens to compromise genome stability. Notably, inherited mutations in genes encoding proteins involved in these protective pathways trigger the onset of disease that is driven by chromosome instability e.g. neurodevelopmental abnormalities, neurodegeneration, premature ageing, immunodeficiency and cancer development. The ability of cells to regulate the recruitment of specific DNA repair proteins to sites of DNA damage is extremely complex but is primarily mediated by protein post-translational modifications (PTMs). Ubiquitylation is one such PTM, which controls genome stability by regulating protein localisation, protein turnover, protein-protein interactions and intra-cellular signalling. Over the past two decades, numerous ubiquitin (Ub) E3 ligases have been identified to play a crucial role not only in the initiation of DNA replication and DNA damage repair but also in the efficient termination of these processes. In this review, we discuss our current understanding of how different Ub E3 ligases (RNF168, TRAIP, HUWE1, TRIP12, FANCL, BRCA1, RFWD3) function to regulate DNA repair and replication and the pathological consequences arising from inheriting deleterious mutations that compromise the Ub-dependent DNA damage response.

Species-level, metagenomic and proteomic analysis of microbe-immune interactions in severe asthma.

BACKGROUND: The airway microbiome in severe asthma has not been characterised at species-level by metagenomic sequencing, nor have the relationships between specific species and mucosal immune responses in 'type-2 low', neutrophilic asthma been defined. We performed an integrated species-level metagenomic data with inflammatory mediators to characterise prevalence of dominant potentially pathogenic organisms and host immune responses. METHODS: Sputum and nasal lavage samples were analysed using long-read metagenomic sequencing with Nanopore and qPCR in two cross-sectional adult severe asthma cohorts, Wessex (n = 66) and Oxford (n = 30). We integrated species-level data with clinical parameters and 39 selected airway proteins measured by immunoassay and O-link. RESULTS: The sputum microbiome in health and mild asthma displayed comparable microbial diversity. By contrast, 23% (19/81) of severe asthma microbiomes were dominated by a single respiratory pathogen, namely H. influenzae (n = 10), M. catarrhalis (n = 4), S. pneumoniae (n = 4) and P. aeruginosa (n = 1). Neutrophilic asthma was associated with H. influenzae, M. catarrhalis, S. pneumoniae and T. whipplei with elevated type-1 cytokines and proteases; eosinophilic asthma with higher M. catarrhalis, but lower H. influenzae, and S. pneumoniae abundance. H. influenzae load correlated with Eosinophil Cationic Protein, elastase and IL-10. R. mucilaginosa associated positively with IL-6 and negatively with FGF. Bayesian network analysis also revealed close and distinct relationships of H. influenzae and M. catarrhalis with type-1 airway inflammation. The microbiomes and cytokine milieu were distinct between upper and lower airways. CONCLUSIONS: This species-level integrated analysis reveals central, but distinct associations between potentially pathogenic bacteria and airways inflammation in severe asthma.

Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning.

INTRODUCTION: Spirometry is the gold standard for COPD diagnosis and severity determination, but is technique-dependent, nonspecific, and requires administration by a trained healthcare professional. There is a need for a fast, reliable, and precise alternative diagnostic test. This study's aim was to use interpretable machine learning to diagnose COPD and assess severity using 75-second carbon dioxide (CO2) breath records captured with TidalSense's N-TidalTM capnometer. METHOD: For COPD diagnosis, machine learning algorithms were trained and evaluated on 294 COPD (including GOLD stages 1-4) and 705 non-COPD participants. A logistic regression model was also trained to distinguish GOLD 1 from GOLD 4 COPD with the output probability used as an index of severity. RESULTS: The best diagnostic model achieved an AUROC of 0.890, sensitivity of 0.771, specificity of 0.850 and positive predictive value (PPV) of 0.834. Evaluating performance on all test capnograms that were confidently ruled in or out yielded PPV of 0.930 and NPV of 0.890. The severity determination model yielded an AUROC of 0.980, sensitivity of 0.958, specificity of 0.961 and PPV of 0.958 in distinguishing GOLD 1 from GOLD 4. Output probabilities from the severity determination model produced a correlation of 0.71 with percentage predicted FEV1. CONCLUSION: The N-TidalTM device could be used alongside interpretable machine learning as an accurate, point-of-care diagnostic test for COPD, particularly in primary care as a rapid rule-in or rule-out test. N-TidalTM also could be effective in monitoring disease progression, providing a possible alternative to spirometry for disease monitoring.

Respiratory Pathogens at Exacerbation in Chronic Bronchitis With Airway Bacterial Colonisation: A Cohort Study.

BACKGROUND AND OBJECTIVE: COPD and bronchiectasis are common causes of morbidity, particularly around exacerbation. Colonisation with respiratory pathogens can increase the frequency and severity of exacerbations. However, bacterial and viral presence at exacerbation in people with airway colonisation has not been well studied. METHODS: A 6-month cohort study of participants (n = 30) with chronic bronchitis due to bronchiectasis (n = 26) and/or COPD (n = 13) and colonisation with Pseudomonas aeruginosa or Haemophilus influenzae was proven on two sputum cultures at exacerbation in the previous 12 months. Participants were provided self-management education and collected sputum samples daily. Sputum samples at baseline (at least 14 days before or after an exacerbation) and at each exacerbation were examined for a panel of 34 respiratory pathogens using commercially available RT-PCR kits and compared to results obtained using culture methods for the detection of bacteria. RESULTS: Participants provided 29 baseline samples and 71 samples at exacerbation. In 17/29 baseline samples, RT-PCR analysis confirmed the organism demonstrated by culture, while 12 samples showed a discrepancy from culture results. Most exacerbations (57.7%) were not associated with acquiring new bacteria or viruses, while 19.8% showed new bacteria, 15.7% new viruses and 7% both new viruses and bacteria. CONCLUSION: Over half of exacerbations were not associated with new organisms in this cohort of participants with chronic bronchitis and colonisation. However, 26.8% demonstrated a new bacterial species in sputum, which is relevant for antibiotic therapy. Baseline RT-PCR and culture results were discordant in one-third of participants.

MISSION ABC: transforming respiratory care through one-stop multidisciplinary clinics - an observational study.

OBJECTIVES: The Modern Innovative Solutions to Improve Outcomes in Asthma, Breathlessness and Chronic Obstructive Pulmonary Disease (COPD) (MABC) service aimed to enhance disease management for chronic respiratory conditions through specialist multidisciplinary clinics, predominantly in the community. This study assesses the outcomes of these clinics. DESIGN: This study used a prospective, longitudinal, participatory action research approach. SETTING: The study was conducted in primary care practices across Hampshire, UK. PARTICIPANTS: Adults aged 16 years and above with poorly controlled asthma or COPD, as well as those with undifferentiated breathlessness not under specialist care, were included. INTERVENTIONS: Participants received care through the multidisciplinary, specialist-led MABC clinics. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes included disease activity, quality of life and healthcare utilisation. Secondary outcomes encompassed clinic attendance, diagnostic changes, patient activation, participant and healthcare professional experiences and cost-effectiveness. RESULTS: A total of 441 participants from 11 general practitioner practices were recruited. Ninety-six per cent of participants would recommend MABC clinics. MABC assessments led to diagnosis changes for 64 (17%) participants with asthma and COPD and treatment adjustments for 252 participants (57%). Exacerbations decreased significantly from 236 to 30 after attending the clinics (p<0.005), with a mean reduction of 0.53 exacerbation events per participant. Reductions were also seen in unscheduled and out-of-hours primary care attendance, emergency department visits and hospital admissions (all p<0.005). Cost savings from reduced exacerbations and healthcare utilisation offset increased medication costs and clinic expenses. CONCLUSIONS: Specialist-supported multidisciplinary teams in MABC clinics improved diagnosis accuracy and adherence to guidelines. High patient satisfaction, disease control improvements and reduced exacerbations resulted in decreased unscheduled healthcare use and cost savings. TRIAL REGISTRATION NUMBER: NCT03096509.

A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.

BACKGROUND: Multicentre training could reduce biases in medical artificial intelligence (AI); however, ethical, legal, and technical considerations can constrain the ability of hospitals to share data. Federated learning enables institutions to participate in algorithm development while retaining custody of their data but uptake in hospitals has been limited, possibly as deployment requires specialist software and technical expertise at each site. We previously developed an artificial intelligence-driven screening test for COVID-19 in emergency departments, known as CURIAL-Lab, which uses vital signs and blood tests that are routinely available within 1 h of a patient's arrival. Here we aimed to federate our COVID-19 screening test by developing an easy-to-use embedded system-which we introduce as full-stack federated learning-to train and evaluate machine learning models across four UK hospital groups without centralising patient data. METHODS: We supplied a Raspberry Pi 4 Model B preloaded with our federated learning software pipeline to four National Health Service (NHS) hospital groups in the UK: Oxford University Hospitals NHS Foundation Trust (OUH; through the locally linked research University, University of Oxford), University Hospitals Birmingham NHS Foundation Trust (UHB), Bedfordshire Hospitals NHS Foundation Trust (BH), and Portsmouth Hospitals University NHS Trust (PUH). OUH, PUH, and UHB participated in federated training, training a deep neural network and logistic regressor over 150 rounds to form and calibrate a global model to predict COVID-19 status, using clinical data from patients admitted before the pandemic (COVID-19-negative) and testing positive for COVID-19 during the first wave of the pandemic. We conducted a federated evaluation of the global model for admissions during the second wave of the pandemic at OUH, PUH, and externally at BH. For OUH and PUH, we additionally performed local fine-tuning of the global model using the sites' individual training data, forming a site-tuned model, and evaluated the resultant model for admissions during the second wave of the pandemic. This study included data collected between Dec 1, 2018, and March 1, 2021; the exact date ranges used varied by site. The primary outcome was overall model performance, measured as the area under the receiver operating characteristic curve (AUROC). Removable micro secure digital (microSD) storage was destroyed on study completion. FINDINGS: Clinical data from 130 941 patients (1772 COVID-19-positive), routinely collected across three hospital groups (OUH, PUH, and UHB), were included in federated training. The evaluation step included data from 32 986 patients (3549 COVID-19-positive) attending OUH, PUH, or BH during the second wave of the pandemic. Federated training of a global deep neural network classifier improved upon performance of models trained locally in terms of AUROC by a mean of 27·6% (SD 2·2): AUROC increased from 0·574 (95% CI 0·560-0·589) at OUH and 0·622 (0·608-0·637) at PUH using the locally trained models to 0·872 (0·862-0·882) at OUH and 0·876 (0·865-0·886) at PUH using the federated global model. Performance improvement was smaller for a logistic regression model, with a mean increase in AUROC of 13·9% (0·5%). During federated external evaluation at BH, AUROC for the global deep neural network model was 0·917 (0·893-0·942), with 89·7% sensitivity (83·6-93·6) and 76·6% specificity (73·9-79·1). Site-specific tuning of the global model did not significantly improve performance (change in AUROC <0·01). INTERPRETATION: We developed an embedded system for federated learning, using microcomputing to optimise for ease of deployment. We deployed full-stack federated learning across four UK hospital groups to develop a COVID-19 screening test without centralising patient data. Federation improved model performance, and the resultant global models were generalisable. Full-stack federated learning could enable hospitals to contribute to AI development at low cost and without specialist technical expertise at each site. FUNDING: The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund.