The RNA-binding proteins Zfp36l1 and Zfp36l2 act redundantly in myogenesis.

BACKGROUND: Members of the ZFP36 family of RNA-binding proteins regulate gene expression post-transcriptionally by binding to AU-rich elements in the 3'UTR of mRNA and stimulating mRNA degradation. The proteins within this family target different transcripts in different tissues. In particular, ZFP36 targets myogenic transcripts and may have a role in adult muscle stem cell quiescence. Our study examined the requirement of ZFP36L1 and ZFP36L2 in adult muscle cell fate regulation. METHODS: We generated single and double conditional knockout mice in which Zfp36l1 and/or Zfp36l2 were deleted in Pax7-expressing cells. Immunostained muscle sections were used to analyse resting skeletal muscle, and a cardiotoxin-induced injury model was used to determine the regenerative capacity of muscle. RESULTS: We show that ZFP36L1 and ZFP36L2 proteins are expressed in satellite cells. Mice lacking the two proteins in Pax7-expressing cells have reduced body weight and have reduced skeletal muscle mass. Furthermore, the number of satellite cells is reduced in adult skeletal muscle and the capacity of this muscle to regenerate following muscle injury is diminished. CONCLUSION: ZFP36L1 and ZFP36L2 act redundantly in myogenesis. These findings add further intricacy to the regulation of the cell fate of Pax7-expressing cells in skeletal muscle by RNA-binding proteins

Complex Portal 2022:New curation frontiers

International audienceThe Complex Portal (www.ebi.ac.uk/complexportal) is a manually curated, encyclopaedic database of macromolecular complexes with known function from a range of model organisms. It summarizes complex composition, topology and function along with links to a large range of domain-specific resources (i.e. wwPDB, EMDB and Reactome). Since the last update in 2019, we have produced a first draft complexome for Escherichia coli, maintained and updated that of Saccharomyces cerevisiae, added over 40 coronavirus complexes and increased the human complexome to over 1100 complexes that include approximately 200 complexes that act as targets for viral proteins or are part of the immune system. The display of protein features in ComplexViewer has been improved and the participant table is now colour-coordinated with the nodes in ComplexViewer. Community collaboration has expanded, for example by contributing to an analysis of putative transcription cofactors and providing data accessible to semantic web tools through Wikidata which is now populated with manually curated Complex Portal content through a new bot. Our data license is now CC0 to encourage data reuse. Users are encouraged to get in touch, provide us with feedback and send curation requests through the ‘Support’ link

The Gene Ontology knowledgebase in 2023

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project

On expert curation and scalability: UniProtKB/Swiss-Prot as a case study

Abstract Motivation Biological knowledgebases, such as UniProtKB/Swiss-Prot, constitute an essential component of daily scientific research by offering distilled, summarized and computable knowledge extracted from the literature by expert curators. While knowledgebases play an increasingly important role in the scientific community, their ability to keep up with the growth of biomedical literature is under scrutiny. Using UniProtKB/Swiss-Prot as a case study, we address this concern via multiple literature triage approaches. Results With the assistance of the PubTator text-mining tool, we tagged more than 10 000 articles to assess the ratio of papers relevant for curation. We first show that curators read and evaluate many more papers than they curate, and that measuring the number of curated publications is insufficient to provide a complete picture as demonstrated by the fact that 8000-10 000 papers are curated in UniProt each year while curators evaluate 50 000-70 000 papers per year. We show that 90% of the papers in PubMed are out of the scope of UniProt, that a maximum of 2-3% of the papers indexed in PubMed each year are relevant for UniProt curation, and that, despite appearances, expert curation in UniProt is scalable. Availability and implementation UniProt is freely available at http://www.uniprot.org/. Supplementary information Supplementary data are available at Bioinformatics online

Physiological Characterization of Muscle Strength With Variable Levels of Dystrophin Restoration in mdx Mice Following Local Antisense Therapy

Antisense-induced exon skipping can restore the open reading frame, and thus correct the dystrophin deficiency that causes Duchenne muscular dystrophy (DMD), a lethal muscle wasting condition. Successful proof-of-principle in preclinical models has led to human clinical trials. However, it is still not known what percentage of dystrophin-positive fibers and what level of expression is necessary for functional improvement. This study directly address these key questions in the mdx mouse model of DMD. To achieve a significant variation in dystrophin expression, we locally administered into tibialis anterior muscles various doses of a phosphorodiamidate morpholino oligomer (PMO) designed to skip the mutated exon 23 from the mRNA of murine dystrophin. We found a highly significant correlation between the number of dystrophin-positive fibers and resistance to contraction-induced injury, with a minimum of 20% of dystrophin-positive fibers required for meaningful improvement. Furthermore, our results also indicate that a relatively low level of dystrophin expression in muscle fibers may have significant clinical benefits. In contrast, improvements in muscle force were not correlated with either the number of positive fibers or total dystrophin levels, which highlight the need to conduct appropriate functional assessments in preclinical testing using the mdx mouse

The Gene Ontology Knowledgebase in 2023

: The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and non-coding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains and updates the GO knowledgebase. The GO knowledgebase consists of three components: 1) the Gene Ontology - a computational knowledge structure describing functional characteristics of genes; 2) GO annotations - evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and 3) GO Causal Activity Models (GO-CAMs) - mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised and updated in response to newly published discoveries, and receives extensive QA checks, reviews and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, as well as guidance on how users can best make use of the data we provide. We conclude with future directions for the project

The Gene Ontology knowledgebase in 2023.

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project