Novel gene function revealed by mouse mutagenesis screens for models of age-related disease

Determining the genetic bases of age-related disease remains a major challenge requiring a spectrum of approaches from human and clinical genetics to the utilization of model organism studies. Here we report a large-scale genetic screen in mice employing a phenotype-driven discovery platform to identify mutations resulting in age-related disease, both late-onset and progressive. We have utilized N-ethyl-N-nitrosourea mutagenesis to generate pedigrees of mutagenized mice that were subject to recurrent screens for mutant phenotypes as the mice aged. In total, we identify 105 distinct mutant lines from 157 pedigrees analysed, out of which 27 are late-onset phenotypes across a range of physiological systems. Using whole-genome sequencing we uncover the underlying genes for 44 of these mutant phenotypes, including 12 late-onset phenotypes. These genes reveal a number of novel pathways involved with age-related disease. We illustrate our findings by the recovery and characterization of a novel mouse model of age-related hearing loss

Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation

A novel method for quantifying the similarity between phenotypes by the use of ontologies can be used to search for candidate genes, pathway members, and human disease models on the basis of phenotypes alone

Mapping the gap: curation of phenotype-driven gene discovery in congenital heart disease research

The goal of translational research is to improve public health by accelerating basic science discovery to human application and clinical practice. The NHLBI Bench-to-Bassinet (B2B) program promotes this goal through its translational research initiative. Together with other collaborators of the B2B program, the University of Pittsburgh mutagenesis screen strives to elucidate the underlying genetic and developmental processes of congenital heart disease (CHD), which is a significant source of morbidity and mortality in the population. The screen investigators have curated over 200 mouse models of CHD on the Jackson Laboratory (JAX) Mouse Genome Database (MGD) through a multi-tiered strategy of phenotypic and genetic analyses. Within the translational research paradigm, this screen has contributed to the improvement of public health and patient care by enabling the identification of 107 pathogenic mutations in 68 unique genes as well as providing 62 models of human disease for future research and development of therapies. Two mutant mouse lines, lines 1702 and 2407, will be thoroughly discussed with regard to their significance to research. However, analysis of the screen curation protocol demonstrated inefficiencies representative of problems across the entirety of the translational research continuum. Within this continuum, data must be translated and readily shared between databases in each domain. Research is currently scattered across disconnected, autonomous databases, which prevents data integration and comprehensive retrieval of information from a single platform. Moreover, data are represented as a combination of discordant ontologies and free-text annotations, which further impede cross-species or cross-domain comparisons and database integration. Although ontology mapping endeavors have achieved some success, the process is flawed with unequivocal alignments or inaccuracies and requires extensive manual validation. Harmonization of ontologies through, ideally, a standardized, relational framework, is necessary to improve the efficacy and utility of translational research. In summary, the future progress of translational research, as exemplified by the University of Pittsburgh B2B program, and its potential in improving public health depends on the acceleration of basic discovery to clinical application through a network of integrated databases supported by a unified ontological system

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

Redefining Androgen Receptor Function: Clinical Implications in Understanding Prostate Cancer Progression and Therapeutic Resistance

The current description of the function of the human androgen receptor (AR), as a transcription factor directing androgen responsive gene expression, is limited in scope and thus is unable to account for the varied cellular and physiological transformation observed in the development and progression of prostate cancer (CaP). The chapter will focus on four important aspects of AR and CaP investigations: (1) a description of AR somatic mutations and the perils of AR-directed therapeutics; (2) our characterization of AR protein interactors that have imbued new functional properties for AR linked to prostatic disease; (3) review of the advances made and shortcomings of AR mouse models in describing CaP onset and progression; and (4) speculate as to the mechanisms by which new mutations can originate and initiate disease onset

'There and Back Again'-Forward Genetics and Reverse Phenotyping in Pulmonary Arterial Hypertension.

Although the invention of right heart catheterisation in the 1950s enabled accurate clinical diagnosis of pulmonary arterial hypertension (PAH), it was not until 2000 when the landmark discovery of the causative role of bone morphogenetic protein receptor type II (BMPR2) mutations shed new light on the pathogenesis of PAH. Since then several genes have been discovered, which now account for around 25% of cases with the clinical diagnosis of idiopathic PAH. Despite the ongoing efforts, in the majority of patients the cause of the disease remains elusive, a phenomenon often referred to as "missing heritability". In this review, we discuss research approaches to uncover the genetic architecture of PAH starting with forward phenotyping, which in a research setting should focus on stable intermediate phenotypes, forward and reverse genetics, and finally reverse phenotyping. We then discuss potential sources of "missing heritability" and how functional genomics and multi-omics methods are employed to tackle this problem

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 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