The Rat Genome Database (RGD) facilitates genomic and phenotypic data integration across multiple species for biomedical research

A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats

The seventh iteration of the reference genome assembly for Rattus norvegicus-mRatBN7.2-corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared with its predecessor. Gene annotations are now more complete, improving the mapping precision of genomic, transcriptomic, and proteomics datasets. We jointly analyzed 163 short-read whole-genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined ∼20.0 million sequence variations, of which 18,700 are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.

The Ontology of Biological Attributes (OBA)-computational traits for the life sciences

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

Rare disease research resources at the Rat Genome Database

Rare diseases individually affect relatively few people, but as a group they impact considerable numbers of people. The Rat Genome Database (https://rgd.mcw.edu) is a knowledgebase that offers resources for rare disease research. This includes disease definitions, genes, quantitative trail loci (QTLs), genetic variants, annotations to published literature, links to external resources, and more. One important resource is identifying relevant cell lines and rat strains that serve as models for disease research. Diseases, genes, and strains have report pages with consolidated data, and links to analysis tools. Utilizing these globally accessible resources for rare disease research, potentiating discovery of mechanisms and new treatments, can point researchers toward solutions to alleviate the suffering of those afflicted with these diseases.

2022 updates to the Rat Genome Database: a Findable, Accessible, Interoperable, and Reusable (FAIR) resource

The Rat Genome Database (RGD, https://rgd.mcw.edu) has evolved from simply a resource for rat genetic markers, maps, and genes, by adding multiple genomic data types and extensive disease and phenotype annotations and developing tools to effectively mine, analyze, and visualize the available data, to empower investigators in their hypothesis-driven research. Leveraging its robust and flexible infrastructure, RGD has added data for human and eight other model organisms (mouse, 13-lined ground squirrel, chinchilla, naked mole-rat, dog, pig, African green monkey/vervet, and bonobo) besides rat to enhance its translational aspect. This article presents an overview of the database with the most recent additions to RGD's genome, variant, and quantitative phenotype data. We also briefly introduce Virtual Comparative Map (VCMap), an updated tool that explores synteny between species as an improvement to RGD's suite of tools, followed by a discussion regarding the refinements to the existing PhenoMiner tool that assists researchers in finding and comparing quantitative data across rat strains. Collectively, RGD focuses on providing a continuously improving, consistent, and high-quality data resource for researchers while advancing data reproducibility and fulfilling Findable, Accessible, Interoperable, and Reusable (FAIR) data principles.

The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

Ontological Analysis of Coronavirus Associated Human Genes at the COVID-19 Disease Portal

The COVID-19 pandemic stemmed a parallel upsurge in the scientific literature about SARS-CoV-2 infection and its health burden. The Rat Genome Database (RGD) created a COVID-19 Disease Portal to leverage information from the scientific literature. In the COVID-19 Portal, gene-disease associations are established by manual curation of PubMed literature. The portal contains data for nine ontologies related to COVID-19, an embedded enrichment analysis tool, as well as links to a toolkit. Using these information and tools, we performed analyses on the curated COVID-19 disease genes. As expected, Disease Ontology enrichment analysis showed that the COVID-19 gene set is highly enriched with coronavirus infectious disease and related diseases. However, other less related diseases were also highly enriched, such as liver and rheumatic diseases. Using the comparison heatmap tool, we found nearly 60 percent of the COVID-19 genes were associated with nervous system disease and 40 percent were associated with gastrointestinal disease. Our analysis confirms the role of the immune system in COVID-19 pathogenesis as shown by substantial enrichment of immune system related Gene Ontology terms. The information in RGD's COVID-19 disease portal can generate new hypotheses to potentiate novel therapies and prevention of acute and long-term complications of COVID-19.

The importance of being the HGNC

The HUGO Gene Nomenclature Committee (HGNC) has been providing standardized symbols and names for human genes since the late 1970s. As funding agencies change their priorities, finding financial support for critical biomedical resources such as the HGNC becomes ever more challenging. In this article, we outline the key roles the HGNC currently plays in aiding communication and the need for these activities to be maintained.

Identified Gefitinib Metabolism-Related lncRNAs can be Applied to Predict Prognosis, Tumor Microenvironment, and Drug Sensitivity in Non-Small Cell Lung Cancer

Gefitinib has shown promising efficacy in the treatment of patients with locally advanced or metastatic EGFR-mutated non-small cell lung cancer (NSCLC). Molecular biomarkers for gefitinib metabolism-related lncRNAs have not yet been elucidated. Here, we downloaded relevant genes and matched them to relevant lncRNAs. We then used univariate, LASSO, and multivariate regression to screen for significant genes to construct prognostic models. We investigated TME and drug sensitivity by risk score data. All lncRNAs with differential expression were selected for GO/KEGG analysis. Imvigor210 cohort was used to validate the value of the prognostic model. Finally, we performed a stemness indices difference analysis. lncRNA-constructed prognostic models were significant in the high-risk and low-risk subgroups. Immune pathways were identified in both groups at low risk. The higher the risk score the greater the value of exclusion, MDSC, and CAF. PRRophetic algorithm screened a total of 58 compounds. In conclusion, the prognostic model we constructed can accurately predict OS in NSCLC patients. Two groups of low-risk immune pathways are beneficial to patients. Gefitinib metabolism was again validated to be related to cytochrome P450 and lipid metabolism. Finally, drugs that might be used to treat NSCLC patients were screened.

RhoBTB1 reverses established arterial stiffness in angiotensin-II hypertension by promoting actin depolymerization

Arterial stiffness predicts cardiovascular disease and all-cause mortality, but its treatment remains challenging. Mice treated with angiotensin II (Ang II) develop hypertension, arterial stiffness, vascular dysfunction, and a downregulation of Rho-related BTB domain–containing protein 1 (RhoBTB1) in the vasculature. RhoBTB1 is associated with blood pressure regulation, but its function is poorly understood. We tested the hypothesis that restoring RhoBTB1 can attenuate arterial stiffness, hypertension, and vascular dysfunction in Ang II–treated mice. Genetic complementation of RhoBTB1 in the vasculature was achieved using mice expressing a tamoxifen-inducible, smooth muscle–specific RhoBTB1 transgene. RhoBTB1 restoration efficiently and rapidly alleviated arterial stiffness but not hypertension or vascular dysfunction. Mechanistic studies revealed that RhoBTB1 had no substantial effect on several classical arterial stiffness contributors, such as collagen deposition, elastin content, and vascular smooth muscle remodeling. Instead, Ang II increased actin polymerization in the aorta, which was reversed by RhoBTB1. Changes in the levels of 2 regulators of actin polymerization, cofilin and vasodilator-stimulated phosphoprotein, in response to RhoBTB1 were consistent with an actin depolymerization mechanism. Our study reveals an important function of RhoBTB1, demonstrates its vital role in antagonizing established arterial stiffness, and further supports a functional and mechanistic separation among hypertension, vascular dysfunction, and arterial stiffness.

Laboratory Codes in Nomenclature and Scientific Communication (Advancing Organism Nomenclature in Scientific Communication to Improve Research Reporting and Reproducibility)

Laboratory registration codes, also known as laboratory codes or lab codes, are a key element in standardized laboratory animal and genetic nomenclature. As such they are critical to accurate scientific communication and to research reproducibility and integrity. The original committee on Mouse Genetic Nomenclature published nomenclature conventions for mice genetics in 1940, and then conventions for inbred strains in 1952. Unique designations were needed, and have been in use since the 1950s, for the sources of animals and substrains, for the laboratories that identified new alleles or mutations, and then for developers of transgenes and induced mutations. Current laboratory codes are typically a 2- to 4-letter acronym for an institution or an investigator. Unique codes are assigned from the International Laboratory Code Registry, which was developed and is maintained by ILAR in the National Academies (National Academies of Sciences Engineering and Medicine and previously National Academy of Sciences). As a resource for the global research community, the registry has been online since 1997. Since 2003 mouse and rat genetic and strain nomenclature rules have been reviewed and updated annually as a joint effort of the International Committee on Standardized Genetic Nomenclature for Mice and the Rat Genome and Nomenclature Committee. The current nomenclature conventions (particularly conventions for non-inbred animals) are applicable beyond rodents, although not widely adopted. Ongoing recognition, since at least the 1930s, of the research relevance of genetic backgrounds and origins of animals, and of spontaneous and induced genetic variants speaks to the need for broader application of standardized nomenclature for animals in research, particularly given the increasing numbers and complexities of genetically modified swine, nonhuman primates, fish, and other species.