The Alliance of Genome Resources: Building a Modern Data Ecosystem for Model Organism Databases.

Model organisms are essential experimental platforms for discovering gene functions, defining protein and genetic networks, uncovering functional consequences of human genome variation, and for modeling human disease. For decades, researchers who use model organisms have relied on Model Organism Databases (MODs) and the Gene Ontology Consortium (GOC) for expertly curated annotations, and for access to integrated genomic and biological information obtained from the scientific literature and public data archives. Through the development and enforcement of data and semantic standards, these genome resources provide rapid access to the collected knowledge of model organisms in human readable and computation-ready formats that would otherwise require countless hours for individual researchers to assemble on their own. Since their inception, the MODs for the predominant biomedical model organisms [Mus sp (laboratory mouse), Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Danio rerio, and Rattus norvegicus] along with the GOC have operated as a network of independent, highly collaborative genome resources. In 2016, these six MODs and the GOC joined forces as the Alliance of Genome Resources (the Alliance). By implementing shared programmatic access methods and data-specific web pages with a unified look and feel, the Alliance is tackling barriers that have limited the ability of researchers to easily compare common data types and annotations across model organisms. To adapt to the rapidly changing landscape for evaluating and funding core data resources, the Alliance is building a modern, extensible, and operationally efficient knowledge commons for model organisms using shared, modular infrastructure

Gene regulation in activated microglia by adenosine A3 receptor agonists: a transcriptomics study

Most neurodegenerative disorders, including the two most common, Alzheimer’s disease (AD) and Parkinson’s disease (AD), course with activation of microglia, the resident innate immune cells of the central nervous system. A3 adenosine receptor (A3R) agonists have been proposed to be neuroprotective by regulating the phenotype of activated microglia. RNAseq was performed using samples isolated from lipopolysaccharide/interferon-γ activated microglia treated with 2-Cl-IB-MECA, a selective A3R agonist. The results showed that the number of negatively regulated genes in the presence of 2-Cl-IB-MECA was greater than the number of positively regulated genes. Gene ontology enrichment analysis showed regulation of genes participating in several cell processes, including those involved in immune-related events. Analysis of known and predicted protein-protein interactions showed that Smad3 and Sp1 are transcription factors whose genes are regulated by A3R activation. Under the conditions of cell activation and agonist treatment regimen, 2-Cl-IB-MECA did not lead to any tendency to favor the expression of genes related to neuroprotective microglia (M2). Supplementary Information The online version contains supplementary material available at 10.1007/s11302-022-09916-9. Keywords: Alzheimer’s disease, Parkinson’s disease, Neurodegeneration, Adenosine, Receptors, Microglia, Neuroinflammatio

Landscape of pleiotropic proteins causing human disease: structural and system biology insights

Pleiotropy is the phenomenon by which the same gene can result in multiple phenotypes. Pleiotropic proteins are emerging as important contributors to rare and common disorders. Nevertheless , little is known on the mechanisms underlying pleiotropy and the characteris tic of pleiotropic proteins. We analysed disease - causing proteins reported in Uni P rot and observed that 12% are pleiotropic ( variants in the same protein cause more than one disease). Pleiotropic proteins were enriched in deleterious and rare variants , bu t not in common variants . Pleiotropic proteins were more likely to be involved in the pathogenesis of n eoplasms, neurological and circulatory diseases, and congenital malformations, whereas non - pleiotropic proteins in endocrine and metabolic disorders . Pleiotropic proteins were more essential and ha d a higher number of interacting partners compared to non -pleiotropic proteins. S ignificantly more pleiotropic than non - pleiotropic proteins contained at least one intrinsically long disordered region (p<0.001 ). Deleterious variants occurring in structurally disordered regions were more commonly found in pleiotropic, rather than non - pleiotropic proteins. 14 In conclusion, pleiotropic proteins are an important contributor to human disease. They represent a biologi cally different class of proteins compared to non - pleiotropic proteins and a better understanding of their characteristics and genetic variants, can greatly aid in the interpretation of genetic studies and drug design

How to identify pathogenic mutations among all those variations: Variant annotation and filtration in the genome sequencing era

High-throughput sequencing technologies have become fundamental for the identification of disease-causing mutations in human genetic diseases both in research and clinical testing contexts. The cumulative number of genes linked to rare diseases is now close to 3,500 with more than 1,000 genes identified between 2010 and 2014 because of the early adoption of Exome Sequencing technologies. However, despite these encouraging figures, the success rate of clinical exome diagnosis remains low due to several factors including wrong variant annotation and nonoptimal filtration practices, which may lead to misinterpretation of disease-causing mutations. In this review, we describe the critical steps of variant annotation and filtration processes to highlight a handful of potential disease-causing mutations for downstream analysis. We report the key annotation elements to gather at multiple levels for each mutation, and which systems are designed to help in collecting this mandatory information. We describe the filtration options, their efficiency, and limits and provide a generic filtration workflow and highlight potential pitfalls through a use case

Evolutionary trajectories of snake genes and genomes revealed by comparative analyses of five-pacer viper

Snakes have numerous features distinctive from other tetrapods and a rich history of genome evolution that is still obscure. Here, we report the high-quality genome of the five-pacer viper, Deinagkistrodon acutus, and comparative analyses with other representative snake and lizard genomes. We map the evolutionary trajectories of transposable elements (TEs), developmental genes and sex chromosomes onto the snake phylogeny. TEs exhibit dynamic lineage-specific expansion, and many viper TEs show brain-specific gene expression along with their nearby genes. We detect signatures of adaptive evolution in olfactory, venom and thermal-sensing genes and also functional degeneration of genes associated with vision and hearing. Lineage-specific relaxation of functional constraints on respective Hox and Tbx limb-patterning genes supports fossil evidence for a successive loss of forelimbs then hindlimbs during snake evolution. Finally, we infer that the ZW sex chromosome pair had undergone at least three recombination suppression events in the ancestor of advanced snakes. These results altogether forge a framework for our deep understanding into snakes' history of molecular evolution

Proteomic analysis of six- and twelve-month hippocampus and cerebellum in a murine Down syndrome model

This study was designed to investigate the brain proteome of the Ts65Dn mouse model of Down syndrome. We profiled the cerebellum and hippocampus proteomes of 6- and 12-month-old trisomic and disomic mice by difference gel electrophoresis. We quantified levels of 2082 protein spots and identified 272 (170 unique UniProt accessions) by mass spectrometry. Four identified proteins are encoded by genes trisomic in the Ts65Dn mouse. Three of these (CRYZL11, EZR, and SOD1) were elevated with p-value \u3c0.05, and 2 proteins encoded by disomic genes (MAPRE3 and PHB) were reduced. Intergel comparisons based on age (6 vs. 12 months) and brain region (cerebellum vs. hippocampus) revealed numerous differences. Specifically, 132 identified proteins were different between age groups, and 141 identified proteins were different between the 2 brain regions. Our results suggest that compensatory mechanisms exist, which ameliorate the effect of trisomy in the Ts65Dn mice. Differences observed during aging may play a role in the accelerated deterioration of learning and memory seen in Ts65Dn mice

Exploring autophagy with Gene Ontology.

Autophagy is a fundamental cellular process that is well conserved among eukaryotes. It is one of the strategies that cells use to catabolize substances in a controlled way. Autophagy is used for recycling cellular components, responding to cellular stresses and ridding cells of foreign material. Perturbations in autophagy have been implicated in a number of pathological conditions such as neurodegeneration, cardiac disease and cancer. The growing knowledge about autophagic mechanisms needs to be collected in a computable and shareable format to allow its use in data representation and interpretation. The Gene Ontology (GO) is a freely available resource that describes how and where gene products function in biological systems. It consists of 3 interrelated structured vocabularies that outline what gene products do at the biochemical level, where they act in a cell and the overall biological objectives to which their actions contribute. It also consists of \u27annotations\u27 that associate gene products with the terms. Here we describe how we represent autophagy in GO, how we create and define terms relevant to autophagy researchers and how we interrelate those terms to generate a coherent view of the process, therefore allowing an interoperable description of its biological aspects. We also describe how annotation of gene products with GO terms improves data analysis and interpretation, hence bringing a significant benefit to this field of study. Autophagy 2018; 14(3):419-436

Genome annotation for clinical genomic diagnostics: strengths and weaknesses

The Human Genome Project and advances in DNA sequencing technologies have revolutionized the identification of genetic disorders through the use of clinical exome sequencing. However, in a considerable number of patients, the genetic basis remains unclear. As clinicians begin to consider whole-genome sequencing, an understanding of the processes and tools involved and the factors to consider in the annotation of the structure and function of genomic elements that might influence variant identification is crucial. Here, we discuss and illustrate the strengths and weaknesses of approaches for the annotation and classification of important elements of protein-coding genes, other genomic elements such as pseudogenes and the non-coding genome, comparative-genomic approaches for inferring gene function, and new technologies for aiding genome annotation, as a practical guide for clinicians when considering pathogenic sequence variation. Complete and accurate annotation of structure and function of genome features has the potential to reduce both false-negative (from missing annotation) and false-positive (from incorrect annotation) errors in causal variant identification in exome and genome sequences. Re-analysis of unsolved cases will be necessary as newer technology improves genome annotation, potentially improving the rate of diagnosis