Genetic diversity and phenotypic variation within hatchery-produced oyster cohorts predict size and success in the field

The rapid growth of the aquaculture industry to meet global seafood demand offers both risks and opportunities for resource management and conservation. In particular, hatcheries hold promise for stock enhancement and restoration, yet cultivation practices may lead to enhanced variation between populations at the expense of variation within populations, with uncertain implications for performance and resilience. To date, few studies have assessed how production techniques impact genetic diversity and population structure, as well as resultant trait variation in and performance of cultivated offspring. We collaborated with a commercial hatchery to produce multiple cohorts of the eastern oyster (Crassostrea virginica) from field-collected broodstock using standard practices. We recorded key characteristics of the broodstock (male : female ratio, effective population size), quantified the genetic diversity of the resulting cohorts, and tested their trait variation and performance across multiple field sites and experimental conditions. Oyster cohorts produced under the same conditions in a single hatchery varied almost twofold in genetic diversity. In addition, cohort genetic diversity was a significant positive predictor of oyster performance traits, including initial size and survival in the field. Oyster cohorts produced in the hatchery had lower within-cohort genetic variation and higher among-cohort genetic structure than adults surveyed from the same source sites. These findings are consistent with “sweepstakes reproduction” in oysters, even when manually spawned. A readily measured characteristic of broodstock, the ratio of males to females, was positively correlated with within-cohort genetic diversity of the resulting offspring. Thus, this metric may offer a tractable way both to meet short-term production goals for seafood demand and to ensure the capacity of hatchery-produced stock to achieve conservation objectives, such as the recovery of self-sustaining wild populations

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification. Funding: UK Research and Innovation and National Institute for Health Research

Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain–gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

CMAP scan discontinuities: Automated detection and relation to motor unit loss

Objective: To evaluate an automated method that extracts motor unit (MU) information from the CMAP scan, a high-detail stimulus-response curve recorded with surface EMG. Discontinuities in the CMAP scan are hypothesized to result from MU loss and reinnervation. Methods: We introduce the parameter D50 to quantify CMAP scan discontinuities. D50 was compared with a previously developed manual score in 253 CMAP scans and with a simultaneously obtained motor unit number estimate (MUNE) in 173 CMAP scans. The effect of MU loss on D50 was determined with a simulation model. Results: We found a high agreement (sensitivity = 86.8%, specificity = 96.6%) between D50 and the manual score. D50 and MUNE were significantly correlated below 80 MUs (r = 0.65, n = 68, p < 0.001), but not when MUNE was larger than 120 MUs (r = 0.23, n = 59, p = 0.08). Conclusions: Discontinuities in the CMAP scan as expressed by a decreased D50 are related to significant MU loss. The determination of D50 is objective, quantitative, and less time-consuming than both manual scoring and many existing MUNE methods. Significance: D50 is potentially useful to monitor neurogenic disorders and moderate to severe MU loss. (C) 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved

CoXCS: A Coevolutionary Learning Classifier Based on Feature Space Partitioning

Learning classifier systems (LCSs) are a machine learning technique, which combine reinforcement learning and evolutionary algorithms to evolve a set of classifiers (or rules) for pattern classification tasks. Despite promising performance across a variety of data sets, the performance of LCS is often degraded when data sets of high dimensionality and relatively few instances are encountered, a common occurrence with gene expression data. In this paper, we propose a number of extensions to XCS, a widely used accuracy-based LCS, to tackle such problems. Our model, CoXCS, is a coevolutionary multi-population XCS. Isolated sub-populations evolve a set of classifiers based on a partitioning of the feature space in the data. Modifications to the base XCS framework are introduced including an algorithm to create the match set and a specialized crossover operator. Experimental results show that the accuracy of the proposed model is significantly better than other well-known classifiers when the ratio of data features to samples is extremely large