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

The Gene Ontology resource: enriching a GOld mine

The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations

A genomic-systems biology map for cardiovascular function.

With the draft sequence of the human genome available, there is a need to better define gene function in the context of systems biology. We studied 239 cardiovascular and renal phenotypes in 113 male rats derived from an F2 intercross and mapped 81 of these traits onto the genome. Aggregates of traits were identified on chromosomes 1, 2, 7, and 18. Systems biology was assessed by examining patterns of correlations ( physiological profiles ) that can be used for gene hunting, mechanism-based physiological studies, and, with comparative genomics, translating these data to the human genome

Modulation of the diet and gastrointestinal microbiota normalizes systemic inflammation and β-cell chemokine expression associated with autoimmune diabetes susceptibility

<div><p>Environmental changes associated with modern lifestyles may underlie the rising incidence of Type 1 diabetes (T1D). Our previous studies of T1D families and the BioBreeding (BB) rat model have identified a peripheral inflammatory state that is associated with diabetes susceptibility, consistent with pattern recognition receptor ligation, but is independent of disease progression. Here, compared to control strains, islets of spontaneously diabetic BB DR<i>lyp/lyp</i> and diabetes inducible BB DR+/+ weanlings provided a standard cereal diet expressed a robust proinflammatory transcriptional program consistent with microbial antigen exposure that included numerous cytokines/chemokines. The dependence of this phenotype on diet and gastrointestinal microbiota was investigated by transitioning DR+/+ weanlings to a gluten-free hydrolyzed casein diet (HCD) or treating them with antibiotics to alter/reduce pattern recognition receptor ligand exposure. Bacterial 16S rRNA gene sequencing revealed that these treatments altered the ileal and cecal microbiota, increasing the Firmicutes:Bacteriodetes ratio and the relative abundances of lactobacilli and butyrate producing taxa. While these conditions did not normalize the inherent hyper-responsiveness of DR+/+ rat leukocytes to <i>ex vivo</i> TLR stimulation, they normalized plasma cytokine levels, plasma TLR4 activity levels, the proinflammatory islet transcriptome, and β-cell chemokine expression. In lymphopenic DR<i>lyp/lyp</i> rats, HCD reduced T1D incidence, and the introduction of gluten to this diet induced islet chemokine expression and abrogated protection from diabetes. Overall, these studies link BB rat islet-level immunocyte recruiting potential, as measured by β-cell chemokine expression, to a genetically controlled immune hyper-responsiveness and innate inflammatory state that can be modulated by diet and the intestinal microbiota.</p></div