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The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2.
The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 led to increased sampling of sarbecoviruses circulating in horseshoe bats. Employing phylogenetic inference while accounting for recombination of bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed less than a decade prior to their emergence in humans. Phylogeographic analyses show bat sarbecoviruses traveled at rates approximating their horseshoe bat hosts and circulated in Asia for millennia. We find that the direct ancestors of SARS-CoV and SARS-CoV-2 are unlikely to have reached their respective sites of emergence via dispersal in the bat reservoir alone, supporting interactions with intermediate hosts through wildlife trade playing a role in zoonotic spillover. These results can guide future sampling efforts and demonstrate that viral genomic regions extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses.
Dominant variants in major spliceosome U4 and U5 small nuclear RNA genes cause neurodevelopmental disorders through splicing disruption.
The major spliceosome contains five small nuclear RNAs (snRNAs; U1, U2, U4, U5 and U6) essential for splicing. Variants in RNU4-2, encoding U4, cause a neurodevelopmental disorder called ReNU syndrome. We investigated de novo variants in 50 snRNA-encoding genes in a French cohort of 23,649 individuals with rare disorders and gathered additional cases through international collaborations. Altogether, we identified 145 previously unreported probands with (likely) pathogenic variants in RNU4-2 and 21 individuals with de novo and/or recurrent variants in RNU5B-1 and RNU5A-1, encoding U5. Pathogenic variants typically arose de novo on the maternal allele and cluster in regions critical for splicing. RNU4-2 variants mainly localize to two structures, the stem III and T-loop/quasi-pseudoknot, which position the U6 ACAGAGA box for 5' splice site recognition and associate with different phenotypic severity. RNU4-2 variants result in specific defects in alternative 5' splice site usage and methylation patterns (episignatures) that correlate with variant location and clinical severity. This study establishes RNU5B-1 as a neurodevelopmental disorder gene, suggests RNU5A-1 as a strong candidate and highlights the role of de novo variants in snRNAs.
Systematic identification of disease-causing promoter and untranslated region variants in 8040 undiagnosed individuals with rare disease.
BackgroundBoth promoters and untranslated regions (UTRs) have critical regulatory roles, yet variants in these regions are largely excluded from clinical genetic testing due to difficulty in interpreting pathogenicity. The extent to which these regions may harbour diagnoses for individuals with rare disease is currently unknown.MethodsWe present a framework for the identification and annotation of potentially deleterious proximal promoter and UTR variants in known dominant disease genes. We use this framework to annotate de novo variants (DNVs) in 8040 undiagnosed individuals in the Genomics England 100,000 genomes project, which were subject to strict region-based filtering, clinical review, and validation studies where possible. In addition, we performed region and variant annotation-based burden testing in 7862 unrelated probands against matched unaffected controls.ResultsWe prioritised eleven DNVs and identified an additional variant overlapping one of the eleven. Ten of these twelve variants (82%) are in genes that are a strong match to the individual's phenotype and six had not previously been identified. Through burden testing, we did not observe a significant enrichment of potentially deleterious promoter and/or UTR variants in individuals with rare disease collectively across any of our region or variant annotations.ConclusionsWhilst screening promoters and UTRs can uncover additional diagnoses for individuals with rare disease, including these regions in diagnostic pipelines is not likely to dramatically increase diagnostic yield. Nevertheless, we provide a framework to aid identification of these variants.
Incongruent Multimodal Federated Learning for Medical Vision and Language-based Multi-label Disease Detection
Federated Learning (FL) in healthcare ensures patient privacy by allowing hospitals to collaboratively train machine learning models while keeping sensitive medical data secure and localized. Most existing research in FL has concentrated on unimodal scenarios, where all healthcare institutes share the same type of data. However, in real-world healthcare situations, some clients may have access to multiple types of data pertaining to the same disease. Multimodal Federated Learning (MMFL) utilizes multiple modalities to build a more powerful FL model than its unimodal counterpart. However, the impact of missing modality in different clients, called modality incongruity, has been greatly overlooked. This paper, for the first time, analyses the impact of modality incongruity and reveals its connection with data heterogeneity across participating clients. We particularly inspect whether incongruent MMFL with unimodal and multimodal clients is more beneficial than unimodal FL. Furthermore, we examine three potential routes of addressing this issue. Firstly, we study the effectiveness of various self-attention mechanisms towards incongruity-agnostic information fusion in MMFL. Secondly, we introduce a modality imputation network (MIN) pre-trained in a multimodal client for modality translation in unimodal clients and investigate its potential towards mitigating the missing modality problem. Thirdly, we introduce several client-level and server-level regularization techniques including Modality-aware knowledge Distillation (MAD) and Leave-one-out teacher (LOOT) towards mitigating modality incongruity effects. Experiments are conducted with Chest X-Ray and radiology reports under several MMFL settings on two publicly available real-world datasets, MIMIC-CXR and Open-I.
Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities
Segmentation models for brain lesions in MRI are commonly developed for a specific disease and trained on data with a predefined set of MRI modalities. Such models cannot segment the disease using data with a different set of MRI modalities, nor can they segment other types of diseases. Moreover, this training paradigm prevents a model from using the advantages of learning from heterogeneous databases that may contain scans and segmentation labels for different brain pathologies and diverse sets of MRI modalities. Additionally, the confidentiality of patient data often prevents central data aggregation, necessitating a decentralized approach. Is it feasible to use Federated Learning (FL) to train a single model on client databases that contain scans and labels of different brain pathologies and diverse sets of MRI modalities? We demonstrate promising results by combining appropriate, simple, and practical modifications to the model and training strategy: Designing a model with input channels that cover the whole set of modalities available across clients, training with random modality drop, and exploring the effects of feature normalization methods. Evaluation on 7 brain MRI databases with 5 different diseases shows that this FL framework can train a single model that achieves very promising results in segmenting all disease types seen during training. Importantly, it can segment these diseases in new databases that contain sets of modalities different from those in training clients. These results demonstrate, for the first time, the feasibility and effectiveness of using FL to train a single 3D segmentation model on decentralised data with diverse brain diseases and MRI modalities, a necessary step towards leveraging heterogeneous real-world databases. Code: https://github.com/FelixWag/FedUniBrain
Molecular clock complexities of Clostridioides difficile.
ObjectivesReconstruct the phylogenetic status of a collection of historical Clostridioides difficile isolates and evaluate the congruence of their evolutionary trajectories with established molecular clock models.MethodsPhylogenetic analysis was performed on Illumina sequence reads from previously analysed historic C. difficile isolates (1980-86; n=75) demonstrating multiple antimicrobial resistances. Data was grouped by ribotype (RT), including comparators from European surveillance (2012-13) and phylogenetic studies (1985-2010). Reads were mapped to CD630/CD196 reference genomes and compared using recombination-adjusted maximum likelihood trees. Prediction intervals for expected SNP differences by age were calculated using a Poisson distribution and molecular clock estimates (0.74 SNPs per genome/per year). Root-to-tip analysis was performed to determine the date of most common recent ancestor of genomes sharing a ribotype.ResultsMoxifloxacin-resistant (>16 mg/L) RT027 isolate JV67 (1986) was two SNPs distinct from a 2006 genome, fewer than the expected lower estimate (4.4 SNPs) under current molecular clock calculations; (p=3.93x10-5). For isolate JV02 (1981), the 13 SNP divergence from a 2008 isolate was consistent with expectations (5.9 SNPs; p=0.07). JV73 (1983) demonstrated an 8 SNP difference, which although above the expected lower limit (5.5 SNPs), was outside the 95% prediction interval; (p= 4.51x10-3). Only sixty-nine percent of historical genomes fit within the prediction interval for the number of SNPs expected compared to recent isolates, with fewer SNPs observed more frequently than expected. Root-to-tip analysis demonstrated only a weak linear correlation.ConclusionsC. difficile molecular clock estimations may be more complex than previously considered, with periods of spore quiescence potentially complicating analyses.
Grey-Matter Structure Markers of Alzheimer's Disease, Alzheimer's Conversion, Functioning and Cognition: A Meta-Analysis Across 11 Cohorts.
Alzheimer's disease (AD) brain markers are needed to select people with early-stage AD for clinical trials and as quantitative endpoint measures in trials. Using 10 clinical cohorts (N = 9140) and the community volunteer UK Biobank (N = 37,664) we performed region of interest (ROI) and vertex-wise analyses of grey-matter structure (thickness, surface area and volume). We identified 94 trait-ROI significant associations, and 307 distinct cluster of vertex-associations, which partly overlap the ROI associations. For AD versus controls, smaller hippocampus, amygdala and of the medial temporal lobe (fusiform and parahippocampal gyri) was confirmed and the vertex-wise results provided unprecedented localisation of some of the associated region. We replicated AD associated differences in several subcortical (putamen, accumbens) and cortical regions (inferior parietal, postcentral, middle temporal, transverse temporal, inferior temporal, paracentral, superior frontal). These grey-matter regions and their relative effect sizes can help refine our understanding of the brain regions that may drive or precede the widespread brain atrophy observed in AD. An AD grey-matter score evaluated in independent cohorts was significantly associated with cognition, MCI status, AD conversion (progression from cognitively normal or MCI to AD), genetic risk, and tau concentration in individuals with none or mild cognitive impairments (AUC in 0.54-0.70, p-value
Rapid identification of bacterial isolates using microfluidic adaptive channels and multiplexed fluorescence microscopy.
We demonstrate the rapid capture, enrichment, and identification of bacterial pathogens using Adaptive Channel Bacterial Capture (ACBC) devices. Using controlled tuning of device backpressure in polydimethylsiloxane (PDMS) devices, we enable the controlled formation of capture regions capable of trapping bacteria from low cell density samples with near 100% capture efficiency. The technical demands to prepare such devices are much lower compared to conventional methods for bacterial trapping and can be achieved with simple benchtop fabrication methods. We demonstrate the capture and identification of seven species of bacteria with bacterial concentrations lower than 1000 cells per mL, including common Gram-negative and Gram-positive pathogens such as Escherichia coli and Staphylococcus aureus. We further demonstrate that species identification of the trapped bacteria can be undertaken in the order of one-hour using multiplexed 16S rRNA-FISH with identification accuracies of 70-98% with unsupervised classification methods across 7 species of bacteria. Finally, by using the bacterial capture capabilities of the ACBC chip with an ultra-rapid antimicrobial susceptibility testing method employing fluorescence imaging and convolutional neural network (CNN) classification, we demonstrate that we can use the ACBC chip as an imaging flow cytometer that can predict the antibiotic susceptibility of E. coli cells after identification.
Infection Inspection: using the power of citizen science for image-based prediction of antibiotic resistance in Escherichia coli treated with ciprofloxacin.
Antibiotic resistance is an urgent global health challenge, necessitating rapid diagnostic tools to combat its threat. This study uses citizen science and image feature analysis to profile the cellular features associated with antibiotic resistance in Escherichia coli. Between February and April 2023, we conducted the Infection Inspection project, in which 5273 volunteers made 1,045,199 classifications of single-cell images from five E. coli strains, labelling them as antibiotic-sensitive or antibiotic-resistant based on their response to the antibiotic ciprofloxacin. User accuracy in image classification reached 66.8 ± 0.1%, lower than our deep learning model's performance at 75.3 ± 0.4%, but both users and the model were more accurate when classifying cells treated at a concentration greater than the strain's own minimum inhibitory concentration. We used the users' classifications to elucidate which visual features influence classification decisions, most importantly the degree of DNA compaction and heterogeneity. We paired our classification data with an image feature analysis which showed that most of the incorrect classifications happened when cellular features varied from the expected response. This understanding informs ongoing efforts to enhance the robustness of our diagnostic methodology. Infection Inspection is another demonstration of the potential for public participation in research, specifically increasing public awareness of antibiotic resistance.
Assessments and developments in constructing a National Health Index for policy-making, in the UK
Composite indicators are useful for summarizing and comparing changes among different communities. The UK Office for National Statistics has created an annual England Health Index (2015-2018) comprised of three main health domains - lives, places, and people - to monitor health over time and across different geographical areas and evaluate the nation's health. We reviewed the conceptual coherence and statistical requirements, focusing on three main steps: correlation analysis at different levels, comparison of the implemented weights, and a sensitivity and uncertainty analysis. Based on the results, we have highlighted features that have improved the statistical requirements of the forthcoming UK Health Index.
Delineating Mpl-dependent and -independent phenotypes of Jak2 V617F- positive MPNs in vivo.
The Jak2 V617F mutation stands as the main driver of myeloproliferative neoplasms (MPNs) by constitutively activating signaling of several type I cytokine receptors, namely those for erythropoietin (EpoR), thrombopoietin (TpoR), and Granulocyte Colony Stimulating Factor (G-CSFR). Among these, TpoR assumes a pivotal role in hematopoietic stem cell renewal and differentiation, being positioned as a key driver of MPNs alongside mutated Jak2. However, the impact of TpoR/MPL absence in the context of Jak2 V617F in vivo has been explored only through a transgenic Jak2 V617F mouse model, where regulation of Jak2 expression does not depend on its natural promoter. In this study, we use a novel mouse model expressing Jak2 V617F under its endogenous promoter at the heterozygous state within a Mpl knock-out background. Our findings indicate that erythrocytosis, leukocytosis and moderate splenomegaly with mild spleen peri-vascular fibrosis persist even in the absence of Mpl expression. Notably, the inherent growth-stimulating effect induced by Jak2 V617F remains consistent across diverse early hematopoietic progenitor populations in the absence of Mpl but is reduced at the stem cell level and does not allow clonal expansion in competitive transplantation. Our results delineate Mpl-dependent and -independent phenotypes induced by Jak2 V617F and confirm that inhibiting Mpl expression at the stem cell level negates the long-term advantage of the mutant clone. Consequently, while MPL emerges as a major player in Jak2 V617F positive MPNs, our study underscores that it is not the exclusive contributor, broadening the spectrum for therapeutic intervention.
Pediatric Outcomes Data Collection Instrument is a Useful Patient-Reported Outcome Measure for Physical Function in Children with Osteogenesis Imperfecta.
PurposePatient-reported outcome measures (PROMs) are increasingly recognized as valuable endpoints in clinical trials. The Pediatric Outcomes Data Collection Instrument (PODCI) is a PROM utilized in children with musculoskeletal disorders. We evaluated the validity and reliability of PODCI in children with osteogenesis imperfecta (OI).MethodsPhysical functioning and psychological well-being were assessed using PODCI in a large cohort of children enrolled in a multicenter study conducted by the Brittle Bone Disorders Consortium. Physical function scores were correlated with a validated, observer-rated scale, Brief Assessment of Motor Function (BAMF), and with psychological well-being scores. We calculated sample sizes required to detect clinically meaningful differences in physical function.ResultsFour hundred seventeen children with OI types I, III, and IV were enrolled. Physical function scores in OI type III were significantly lower than those in OI types I and IV. There were no significant differences in psychological well-being. PODCI physical function scores showed moderate-to-strong correlation with BAMF. The Global Functioning Scale, a composite of physical function, did not consistently correlate with psychological well-being.ConclusionPODCI can be a reliable measure of physical functioning in children with OI and offers valuable information about patient-reported health status and new ways to examine the utility of interventions in this population.
Familial coaggregation and shared familiality of functional and internalizing disorders in the Lifelines cohort.
BackgroundFunctional disorders (FDs) are characterized by persistent somatic symptoms and are highly comorbid with internalizing disorders (IDs). To provide much-needed insight into FD etiology, we evaluated FD and ID familial coaggregation and shared familiality.MethodsLifelines is a three-generation cohort study, which assessed three FDs (myalgic encephalomyelitis/chronic fatigue syndrome [ME/CFS], irritable bowel syndrome [IBS], and fibromyalgia [FM]) and six IDs (major depressive disorder [MDD], dysthymia [DYS], generalized anxiety disorder [GAD], agoraphobia [AGPH], social phobia [SPH], and panic disorder [PD]) according to diagnostic criteria. Based on 153,803 individuals, including 90,397 with a first-degree relative in Lifelines, we calculated recurrence risk ratios (λRs) and tetrachoric correlations to evaluate familial aggregation and coaggregation of these disorders in first-degree relatives. We then estimated their familiality and familial correlations.ResultsFamilial aggregation was observed across disorders, with λR ranging from 1.45 to 2.23 within disorders and from 1.17 to 1.94 across disorders. Familiality estimates ranged from 22% (95% confidence interval [CI]: 16-29) for IBS to 42% (95% CI: 33-50) for ME/CFS. Familial correlations ranged from +0.37 (95% CI: 0.24-0.51) between FM and AGPH to +0.97 (95% CI: 0.80-1) between ME/CFS and FM. The highest familial correlation between an ID and FD was +0.83 (95% CI: 0.66-0.99) for MDD and ME/CFS.ConclusionsThere is a clear familial component to FDs, which is partially shared with IDs. This suggests that IDs and FDs share both genetic and family-environmental risk factors. Of the FDs, ME/CFS is most closely related to IDs.
Reply to: What evidence is required to justify the NHS Health Check programme?
Medications that reduce blood pressure and cholesterol are among the most cost-effective healthcare interventions available, but their delivery remains suboptimal. In 2009, the National Health Service (NHS) Health Check was introduced to increase the detection and treatment of major cardiovascular risk factors in people aged 40-74 years. In a prospective observational study using the UK Biobank, we compared health outcomes between NHS Health Check attenders and matched non-attenders. Attenders exhibited higher early rates of new hypertension, hyperlipidaemia, and chronic kidney disease, followed by significantly lower long-term multi-system disease and mortality. A recent critique of our work raises questions regarding observational design limitations, self-selection bias, and discrepancies with randomised controlled trials (RCTs). However, RCTs face ethical and feasibility challenges in large-scale public health interventions and are not immune to self-selection. Furthermore, the argument that self-selection explains our findings is inconsistent with our results. If healthier individuals were disproportionately attending NHS Health Checks, we would expect lower risk across all outcomes. However, we observed an initial increase in new diagnoses suggesting that NHS Health Checks are detecting pre-existing conditions earlier rather than merely attracting healthier individuals. Additionally, the cited RCTs predate modern antihypertensive and statin treatments, and considered heterogenous non-validated interventions. In summary, this critique relies on selective citation of outdated and inappropriate RCTs, an overstatement of selection bias, and an underappreciation of the role of observational research in shaping public health improvements. Our findings indicate that NHS Health Checks contribute to the prevention of multi-system morbidity and mortality, warranting continued investment.