A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction

The developmental and physiological complexity of the auditory system is likely reflected in the underlying set of genes involved in auditory function. In humans, over 150 non-syndromic loci have been identified, and there are more than 400 human genetic syndromes with a hearing loss component. Over 100 non-syndromic hearing loss genes have been identified in mouse and human, but we remain ignorant of the full extent of the genetic landscape involved in auditory dysfunction. As part of the International Mouse Phenotyping Consortium, we undertook a hearing loss screen in a cohort of 3006 mouse knockout strains. In total, we identify 67 candidate hearing loss genes. We detect known hearing loss genes, but the vast majority, 52, of the candidate genes were novel. Our analysis reveals a large and unexplored genetic landscape involved with auditory function

Mouse Mutants of Gpr37 and Gpr37l1 Receptor Genes: Disease Modeling Applications

The vertebrate G protein–coupled receptor 37 and G protein–coupled receptor 37-like 1 (GPR37 and GPR37L1) proteins have amino acid sequence homology to endothelin and bombesin-specific receptors. The prosaposin glycoprotein, its derived peptides, and analogues have been reported to interact with and activate both putative receptors. The GPR37 and GPR37L1 genes are highly expressed in human and rodent brains. GPR37 transcripts are most abundant in oligodendrocytes and in the neurons of the substantia nigra and hippocampus, while the GPR37L1 gene is markedly expressed in cerebellar Bergmann glia astrocytes. The human GPR37 protein is a substrate of parkin, and its insoluble form accumulates in brain samples from patients of inherited juvenile Parkinson’s disease. Several Gpr37 and Gpr37l1 mouse mutant strains have been produced and applied to extensive in vivo and ex vivo analyses of respective receptor functions and involvement in brain and other organ pathologies. The genotypic and phenotypic characteristics of the different mouse strains so far published are reported and discussed, and their current and proposed applications to human disease modeling are highlighted

“Be sustainable”: EOSC‐Life recommendations for implementation of FAIR principles in life science data handling

The main goals and challenges for the life science communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large‐scale data‐driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable life science resources based on the collaborative, cross‐disciplinary work done within the EOSC‐Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European life science research infrastructures, it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC‐Life provides a model for sustainable data management according to FAIR (findability, accessibility, interoperability, and reusability) principles, including solutions for sensitive‐ and industry‐related resources, by means of cross‐disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences

Preprint: "Be Sustainable", Recommendations for FAIR Resources in Life Sciences research: EOSC-Life's Lessons

"Be SURE - Be SUstainable REcommendations" The main goals and challenges for the Life Science (LS) communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable LS resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European LS Research Infrastructures (RIs), it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable FAIR data management, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences. IN PRESS EMBO Journal: https://www.embopress.org/journal/14602075This research is mainly a product of the EOSC-Life European programme funding from the European Union's Horizon Europe research and innovation programme under grant agreement Nº824087. Complementary support was provided through EU funded project AgroServ (grant agreement Nº101058020), EU funded project BY-COVID (grant agreement Nº101046203), EU funded project DANUBIUS-IP (grant agreement Nº101079778), EU funded project EMPHASIS-GO (grant agreement Nº101079772), EU funded project FAIRplus (IMI grant agreement Nº802750), EU funded project FAIRsharing (Wellcome grant agreement Nº212930/Z/18/Z), EU funded project ISIDORe (grant agreement Nº101046133), EU funded project Precision Toxicology (grant agreement Nº965406), UKRI DASH (grant agreement NºMR/V038966/1). Special thanks to T. Biro and her radical collaboration team from Research Data Alliance who gave us great inspiration on how to lead this radical collaboration work

“Be sustainable”: EOSC‐Life recommendations for implementation of FAIR principles in life science data handling

The main goals and challenges for the life science communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable life science resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European life science research infrastructures, it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable data management according to FAIR (findability, accessibility, interoperability, and reusability) principles, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences

EOSC-Life Report on the work of the Open Call Projects

This Deliverable 3.3 is a report on the Digital Life Sciences Open Call and two Internal Calls organised by EOSC-Life WP3. The organisation of these Calls followed the successful integration and support of 8 Demonstrator projects&nbsp;which provided the first concrete use cases in the initial phase of EOSC-Life. The three Calls overall supported 11 scientific user projects, selected to facilitate integration of concrete use-cases across Life Sciences domains into the European Open Science Cloud (EOSC)&nbsp;framework. Through the Calls, the practical goal was to facilitate co-creation of an open, digital collaborative space for life science research by developing FAIR&nbsp;tools, workflows, resources, infrastructures, and guidelines together with the EOSC-Life RIs experts and communities. We report in this Deliverable the following achievements: Organisation of the EOSC-Life Open and Internal Calls; Integrating and training the EOSC-Life WP3 Open Call&nbsp;and Internal Call&nbsp;project teams in EOSC-Life; Activities for connecting project teams with EOSC-Life and LS-RI communities and dissemination of projects outcomes to broader communities; Work done in the individual projects, their results, and impact of developed resources; Recommendations from the EOSC-Life WP3 project teams and the EOSC-Life community for future Open Calls. </ol

Author Correction: EMPReSS: standardized phenotype screens for functional annotation of the mouse genome

International audienceCorrection to: Nature Genetics, published online 1 November 2005.In the version of this article initially published, members of the Eumorphia Consortium appeared in the Supplementary Information but were not included in the main article. The full list of members appears below

Identification of genetic elements in metabolism by high-throughput mouse phenotyping

Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome