The Integrated Assessment as the main goal for achieving an Ecosystem Approach to Management in the Western European Shelf Seas

Providing regional integrated ecosystem assessments (IEA) is a key challenge identified in the ICES Strategic Plan (2014-2018). IEAs are seen as a fundamental link between advice and ecosystem science inachieving Ecosystem Based Management (EBM).While EBM is not a new concept, difficulties in achieving such an ambitious goal have been highlighted by the extensive work conducted in this area. The implementation of new regulation policies, such as the Marine Strategy Framework Directive (MSFD) and the reformed Common Fisheries Policy (CFP) in Europe,have challenged the scientific community to rapidly react despite these difficulties and provide scientific advice to support management decisions concerning these policies. RegionalICES groups have been tasked with developing methods and tools for IEA in their corresponding ecoregions; this is the case of the Working Group on Ecosystem Assessment of Western European Shelf Seas (WGEAWESS). The role of this group is to implement, and test tools and methods for the advisory process, focusing on the North Atlantic European continental shelf, including Celtic Seas, Bay of Biscay and Iberian Waters. In this presentation we show the progress made within this WG during its initial three years of activity, in relation to some of the terms of reference already addressed. An adaptation of the ODEMM framework has been selected as a tool for identifying a) links between components, processes, pressures and states, and b) gaps in data availability and indicator implementation. Some preliminary results of a first IEA exercise will also been shownwith emphasis onthe MSFD descriptors D1 (biological diversity) and D4 (food webs)

NCAM140 Interacts with the Focal Adhesion Kinase p125 fak and the SRC-related Tyrosine Kinase p59 fyn

Axonal growth cones respond to adhesion molecules and extracellular matrix components by rapid morphological changes and growth rate modification. Neurite outgrowth mediated by the neural cell adhesion molecule (NCAM) requires the src family tyrosine kinase p59(fyn) in nerve growth cones, but the molecular basis for this interaction has not been defined. The NCAM140 isoform, which is found in migrating growth cones, selectively co-immunoprecipitated with p59(fyn) from nonionic detergent (Brij 96) extracts of early postnatal mouse cerebellum and transfected rat B35 neuroblastoma and COS-7 cells. p59(fyn) did not associate significantly with the NCAM180 isoform, which is found at sites of stable neural cell contacts, or with the glycophosphatidylinositol-linked NCAM120 isoform. pp60(c-)src, a tyrosine kinase that promotes neurite growth on the neuronal cell adhesion molecule L1, did not interact with any NCAM isoform. Whereas p59(fyn) was constitutively associated with NCAM140, the focal adhesion kinase p125(fak), a nonreceptor tyrosine kinase known to mediate integrin-dependent signaling, became recruited to the NCAM140-p59(fyn) complex when cells were reacted with antibodies against the extracellular region of NCAM. Treatment of cells with a soluble NCAM fusion protein or with NCAM antibodies caused a rapid and transient increase in tyrosine phosphorylation of p125(fak) and p59(fyn). These results suggest that NCAM140 binding interactions at the cell surface induce the assembly of a molecular complex of NCAM140, p125(fak), and p59(fyn) and activate the catalytic function of these tyrosine kinases, initiating a signaling cascade that may modulate growth cone migration

Refining fisheries advice with stock-specific ecosystem information

Although frequently suggested as a goal for ecosystem-based fisheries management, incorporating ecosystem information into fisheries stock assessments has proven challenging. The uncertainty of input data, coupled with the structural uncertainty of complex multi-species models, currently makes the use of absolute values from such models contentious for short-term single-species fisheries management advice. Here, we propose a different approach where the standard assessment methodologies can be enhanced using ecosystem model derived information. Using a case study of the Irish Sea, we illustrate how stock-specific ecosystem indicators can be used to set an ecosystem-based fishing mortality reference point (FECO) within the “Pretty Good Yield” ranges for fishing mortality which form the present precautionary approach adopted in Europe by the International Council for the Exploration of the Sea (ICES). We propose that this new target, FECO, can be used to scale fishing mortality down when the ecosystem conditions for the stock are poor and up when conditions are good. This approach provides a streamlined quantitative way of incorporating ecosystem information into catch advice and provides an opportunity to operationalize ecosystem models and empirical indicators, while retaining the integrity of current assessment models and the FMSY-based advice process.publishedVersio

Mutation update and genotype-phenotype correlations of novel and previously described mutations in TPM2 and TPM3 causing congenital myopathies

Mutations affecting skeletal muscle isoforms of the tropomyosin genes may cause nemaline myopathy, cap myopathy, core-rod myopathy, congenital fiber-type disproportion, distal arthrogryposes, and Escobar syndrome. We correlate the clinical picture of these diseases with novel (19) and previously reported (31) mutations of the TPM2 and TPM3 genes. Included are altogether 93 families: 53 with TPM2 mutations and 40 with TPM3 mutations. Thirty distinct pathogenic variants of TPM2 and 20 of TPM3 have been published or listed in the Leiden Open Variant Database (http://www.dmd.nl/). Most are heterozygous changes associated with autosomal-dominant disease. Patients with TPM2 mutations tended to present with milder symptoms than those with TPM3 mutations, DA being present only in the TPM2 group. Previous studies have shown that five of the mutations in TPM2 and one in TPM3 cause increased Ca2+ sensitivity resulting in a hypercontractile molecular phenotype. Patients with hypercontractile phenotype more often had contractures of the limb joints (18/19) and jaw (6/19) than those with nonhypercontractile ones (2/22 and 1/22), whereas patients with the non-hypercontractile molecular phenotype more often (19/22) had axial contractures than the hypercontractile group (7/19). Our in silico predictions show that most mutations affect tropomyosin–actin association or tropomyosin head-to-tail binding

COVID-19 booster vaccination uptake and infection breakthrough amongst health care workers in Wales:A national prospective cohort study

Background: From September 2021, Health Care Workers (HCWs) in Wales began receiving a COVID-19 booster vaccination. This is the first dose beyond the primary vaccination schedule. Given the emergence of new variants, vaccine waning vaccine, and increasing vaccination hesitancy, there is a need to understand booster vaccine uptake and subsequent breakthrough in this high-risk population. Methods: We conducted a prospective, national-scale, observational cohort study of HCWs in Wales using anonymised, linked data from the SAIL Databank. We analysed uptake of COVID-19 booster vaccinations from September 2021 to February 2022, with comparisons against uptake of the initial primary vaccination schedule. We also analysed booster breakthrough, in the form of PCR-confirmed SARS-Cov-2 infection, comparing to the second primary dose. Cox proportional hazard models were used to estimate associations for vaccination uptake and breakthrough regarding staff roles, socio-demographics, household composition, and other factors. Results: We derived a cohort of 73,030 HCWs living in Wales (78% female, 60% 18–49 years old). Uptake was quickest amongst HCWs aged 60 + years old (aHR 2.54, 95%CI 2.45–2.63), compared with those aged 18–29. Asian HCWs had quicker uptake (aHR 1.18, 95%CI 1.14–1.22), whilst Black HCWs had slower uptake (aHR 0.67, 95%CI 0.61–0.74), compared to white HCWs. HCWs residing in the least deprived areas were slightly quicker to have received a booster dose (aHR 1.12, 95%CI 1.09–1.16), compared with those in the most deprived areas. Strongest associations with breakthrough infections were found for those living with children (aHR 1.52, 95%CI 1.41–1.63), compared to two-adult only households. HCWs aged 60 + years old were less likely to get breakthrough infections, compared to those aged 18–29 (aHR 0.42, 95%CI 0.38–0.47). Conclusion: Vaccination uptake was consistently lower among black HCWs, as well as those from deprived areas. Whilst breakthrough infections were highest in households with children

Genetics and geography of leukocyte telomere length in sub-Saharan Africans

Leukocyte telomere length (LTL) might be causal in cardiovascular disease and major cancers. To elucidate the roles of genetics and geography in LTL variability across humans, we compared LTL measured in 1295 sub-Saharan Africans (SSAs) with 559 African-Americans (AAms) and 2464 European-Americans (EAms). LTL differed significantly across SSAs (P = 0.003), with the San from Botswana (with the oldest genomic ancestry) having the longest LTL and populations from Ethiopia having the shortest LTL. SSAs had significantly longer LTL than AAms [P = 6.5(e-16)] whose LTL was significantly longer than EAms [P = 2.5(e-7)]. Genetic variation in SSAs explained 52% of LTL variance versus 27% in AAms and 34% in EAms. Adjustment for genetic variation removed the LTL differences among SSAs. LTL genetic variation among SSAs, with the longest LTL in the San, supports the hypothesis that longer LTL was ancestral in humans. Identifying factors driving LTL variation in Africa may have important ramifications for LTL-associated diseases

Mutations in the Satellite Cell Gene MEGF10 Cause a Recessive Congenital Myopathy with Minicores

We ascertained a nuclear family in which three of four siblings were affected with an unclassified autosomal recessive myopathy characterized by severe weakness, respiratory impairment, scoliosis, joint contractures, and an unusual combination of dystrophic and myopathic features on muscle biopsy. Whole genome sequence from one affected subject was filtered using linkage data and variant databases. A single gene, MEGF10, contained nonsynonymous mutations that co-segregated with the phenotype. Affected subjects were compound heterozygous for missense mutations c.976T > C (p.C326R) and c.2320T > C (p.C774R). Screening the MEGF10 open reading frame in 190 patients with genetically unexplained myopathies revealed a heterozygous mutation, c.211C > T (p.R71W), in one additional subject with a similar clinical and histological presentation as the discovery family. All three mutations were absent from at least 645 genotyped unaffected control subjects. MEGF10 contains 17 atypical epidermal growth factor-like domains, each of which contains eight cysteine residues that likely form disulfide bonds. Both the p.C326R and p.C774R mutations alter one of these residues, which are completely conserved in vertebrates. Previous work showed that murine Megf10 is required for preserving the undifferentiated, proliferative potential of satellite cells, myogenic precursors that regenerate skeletal muscle in response to injury or disease. Here, knockdown of megf10 in zebrafish by four different morpholinos resulted in abnormal phenotypes including unhatched eggs, curved tails, impaired motility, and disorganized muscle tissue, corroborating the pathogenicity of the human mutations. Our data establish the importance of MEGF10 in human skeletal muscle and suggest satellite cell dysfunction as a novel myopathic mechanism

Exponential growth, high prevalence of SARS-CoV-2, and vaccine effectiveness associated with the Delta variant.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections were rising during early summer 2021 in many countries as a result of the Delta variant. We assessed reverse transcription polymerase chain reaction swab positivity in the Real-time Assessment of Community Transmission–1 (REACT-1) study in England. During June and July 2021, we observed sustained exponential growth with an average doubling time of 25 days, driven by complete replacement of the Alpha variant by Delta and by high prevalence at younger, less-vaccinated ages. Prevalence among unvaccinated people [1.21% (95% credible interval 1.03%, 1.41%)] was three times that among double-vaccinated people [0.40% (95% credible interval 0.34%, 0.48%)]. However, after adjusting for age and other variables, vaccine effectiveness for double-vaccinated people was estimated at between ~50% and ~60% during this period in England. Increased social mixing in the presence of Delta had the potential to generate sustained growth in infections, even at high levels of vaccination.The study was funded by the Department of Health and Social Care in England. Sequencing was provided through funding from the COVID-19 Genomics UK (COG-UK) Consortium. P.E. is Director of the Medical Research Council (MRC) Centre for Environment and Health (MR/L01341X/1, MR/S019669/1). P.E. acknowledges support from Health Data Research UK (HDR UK); the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre; NIHR Health Protection Research Units (HPRUs) in Chemical and Radiation Threats and Hazards, and Environmental Exposures and Health; the British Heart Foundation Centre for Research Excellence at Imperial College London (RE/18/4/34215); and the UK Dementia Research Institute at Imperial (MC_PC_17114). S.R., C.A.D. acknowledge support: MRC Centre for Global Infectious Disease Analysis, NIHR HPRU in Modelling and Health Economics, Wellcome Trust (200861/Z/16/Z, 200187/Z/15/Z), and Centres for Disease Control and Prevention (US, U01CK0005-01-02). G.C. is supported by an NIHR Professorship. H.War. acknowledges support from an NIHR Senior Investigator Award and the Wellcome Trust (205456/Z/16/Z). We thank The Huo Family Foundation for their support of our work on COVID-19. Quadram authors gratefully acknowledge the support of the Biotechnology and Biological Sciences Research Council (BBSRC); their research was funded by the BBSRC Institute Strategic Programme Microbes in the Food Chain BB/R012504/1 and its constituent project BBS/E/F/000PR10352. We thank members of the COVID-19 Genomics Consortium UK (COG-UK) for their contributions to generating the genomic data used in this study. COG-UK is supported by funding from the MRC, part of UK Research & Innovation (UKRI), NIHR and Genome Research Limited, operating as the Wellcome Sanger Institute

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. Design Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. Results Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. Conclusion This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

OBJECTIVE Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. DESIGN Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. RESULTS Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. CONCLUSION This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows