Whole genome sequencing of mouse lines divergently selected for fatness (FLI) and leanness (FHI) revealed several genetic variants as candidates for novel obesity genes.

BACKGROUND: Analysing genomes of animal model organisms is widely used for understanding the genetic basis of complex traits and diseases, such as obesity, for which only a few mouse models exist, however, without their lean counterparts. OBJECTIVE: To analyse genetic differences in the unique mouse models of polygenic obesity (Fat line) and leanness (Lean line) originating from the same base population and established by divergent selection over more than 60 generations. METHODS: Genetic variability was analysed using WGS. Variants were identified with GATK and annotated with Ensembl VEP. g.Profiler, WebGestalt, and KEGG were used for GO and pathway enrichment analysis. miRNA seed regions were obtained with miRPathDB 2.0, LncRRIsearch was used to predict targets of identified lncRNAs, and genes influencing adipose tissue amount were searched using the IMPC database. RESULTS: WGS analysis revealed 6.3 million SNPs, 1.3 million were new. Thousands of potentially impactful SNPs were identified, including within 24 genes related to adipose tissue amount. SNP density was highest in pseudogenes and regulatory RNAs. The Lean line carries SNP rs248726381 in the seed region of mmu-miR-3086-3p, which may affect fatty acid metabolism. KEGG analysis showed deleterious missense variants in immune response and diabetes genes, with food perception pathways being most enriched. Gene prioritisation considering SNP GERP scores, variant consequences, and allele comparison with other mouse lines identified seven novel obesity candidate genes: 4930441H08Rik, Aff3, Fam237b, Gm36633, Pced1a, Tecrl, and Zfp536. CONCLUSION: WGS revealed many genetic differences between the lines that accumulated over the selection period, including variants with potential negative impacts on gene function. Given the increasing availability of mouse strains and genetic polymorphism catalogues, the study is a valuable resource for researchers to study obesity.

Differential alternative polyadenylation response to high-fat diet between polygenic obese and healthy lean mice.

Obesity's complex etiology due to the interplay of environment and genetics makes it a more challenging research and health problem. Some of the contributing genetic factors that have not yet been examined in detail entail mRNA polyadenylation (PA). Genes with multiple PA sites express mRNA isoforms differing in coding sequence or 3'UTR through alternative polyadenylation (APA). Alterations in PA have been associated with various diseases; however, its contribution to obesity is not well-researched. Following an 11-week high-fat diet, the APA sites in the hypothalamus of two unique mouse models for polygenic obesity (Fat line) and healthy leanness (Lean line) were determined using whole transcriptome termini site sequencing (WTTS-seq). We found 17 genes of interest with differentially expressed APA (DE-APA) isoforms, among which seven were previously associated with obesity or obesity-related traits (Pdxdc1, Smyd3, Rpl14, Copg1, Pcna, Ric3, Stx3) but have not yet been studied in the context of APA. The remaining ten genes (Ccdc25, Dtd2, Gm14403, Hlf, Lyrm7, Mrpl3, Pisd-ps3, Sbsn, Slx1b, Spon1) represent novel candidates associated with obesity/adiposity due to variability brought about by differential usage of APA sites. Our results provide insights into the relationship between PA and the hypothalamus in the context of obesity, by being the first study of DE-APA sites and DE-APA isoforms in these mouse models. Future studies are needed further to explore the role of APA isoforms in polygenic obesity by expanding the scope of research to other metabolically important tissues (such as liver and adipose tissues) and investigating the potential for targeting PA as a therapeutic strategy for obesity management.

Genome-wide screening for genetic variants in polyadenylation signal (PAS) sites in mouse selection lines for fatness and leanness.

Alternative polyadenylation (APA) determines mRNA stability, localisation, translation and protein function. Several diseases, including obesity, have been linked to APA. Studies have shown that single nucleotide polymorphisms in polyadenylation signals (PAS-SNPs) can influence APA and affect phenotype and disease susceptibility. However, these studies focussed on associations between single PAS-SNP alleles with very large effects and phenotype. Therefore, we performed a genome-wide screening for PAS-SNPs in the polygenic mouse selection lines for fatness and leanness by whole-genome sequencing. The genetic variants identified in the two lines were overlapped with locations of PAS sites obtained from the PolyASite 2.0 database. Expression data for selected genes were extracted from the microarray expression experiment performed on multiple tissue samples. In total, 682 PAS-SNPs were identified within 583 genes involved in various biological processes, including transport, protein modifications and degradation, cell adhesion and immune response. Moreover, 63 of the 583 orthologous genes in human have been previously associated with human diseases, such as nervous system and physical disorders, and immune, endocrine, and metabolic diseases. In both lines, PAS-SNPs have also been identified in genes broadly involved in APA, such as Polr2c, Eif3e and Ints11. Five PAS-SNPs within 5 genes (Car, Col4a1, Itga7, Lat, Nmnat1) were prioritised as potential functional variants and could contribute to the phenotypic disparity between the two selection lines. The developed PAS-SNPs catalogue presents a key resource for planning functional studies to uncover the role of PAS-SNPs in APA, disease susceptibility and fat deposition.

How Can We Advance Integrative Biology Research in Animal Science in 21st Century? Experience at University of Ljubljana from 2002 to 2022.

In this perspective analysis, we strive to answer the following question: how can we advance integrative biology research in the 21st century with lessons from animal science? At the University of Ljubljana, Biotechnical Faculty, Department of Animal Science, we share here our three lessons learned in the two decades from 2002 to 2022 that we believe could inform integrative biology, systems science, and animal science scholarship in other countries and geographies. Cultivating multiomics knowledge through a conceptual lens of integrative biology is crucial for life sciences research that can stand the test of diverse biological, clinical, and ecological contexts. Moreover, in an era of the current COVID-19 pandemic, animal nutrition and animal science, and the study of their interactions with human health (and vice versa) through integrative biology approaches hold enormous prospects and significance for systems medicine and ecosystem health.

Genetic variants of the hypoxia-inducible factor 3 alpha subunit (Hif3a) gene in the Fat and Lean mouse selection lines.

BACKGROUND: Adipose tissue hypoxia and members of the hypoxia-inducible factor alpha (HIFA) are involved in development of obesity. However, the mechanism and functions of HIF3A, one of three HIFA paralogs, in fat deposition have not been sufficiently studied. METHODS AND RESULTS: In the present study, we investigated whether Hif3a sequence variants are associated with divergent fat deposition in mouse selection lines for fatness and leanness. Sequencing and RFLP were used to analyse sequence variants within Hif3a. To identify candidate regulatory variants, we performed literature screening and used databases and bioinformatics tools like Ensembl, MethPrimer, TargetScanMouse, miRDB, PolyAsite, RISE, LncRRIsearch, RNAfold, PredictProtein, CAIcal, and switches.ELM Resource. There are 90 sequence variants in Hif3a between the two mouse lines. While most Fat line variants locate within intronic regions, Lean line variants are mainly in 3' UTR. We constructed a map of Hif3a potential regulatory regions and identified 39 regulatory variants by integrating data on constrained and regulatory elements, CpGs, and miRNAs and lncRNAs binding sites. Moreover, 3' UTR and two exonic variants may influence mRNA stability, translation rate and protein functionality. We propose as priority candidates for further functional studies a missense (rs37398126) and synonymous (rs37739792) variants, and intronic (rs47471302) variant that overlap conserved element in promoter region and predicted lncRNAs binding site. CONCLUSION: The results indicate a potential involvement of Hif3a in fat deposition. Additionally, approach used in the present study may serve as a general guideline for constructing an integrative gene map for prioritizing candidate gene variants with phenotypic effects.

Syndromic male subfertility: A network view of genome-phenome associations.

BACKGROUND: Male infertility is a disorder of the reproductive system with a highly complex genetic landscape. In most cases, the reason for male infertility remains unknown; however, the importance of genetic abnormalities in the diagnosis of subfertility/infertility is becoming increasingly recognized. Several syndromes include impaired male fertility in the clinical picture, although a comprehensive analysis of genetic causes of the syndromology perspective of male reproduction is not yet available. OBJECTIVES: (1) To develop a catalog of syndromes and corresponding genes associated with impaired male fertility and (2) to visualize an up-to-date genome-phenome network of syndromic male subfertility. MATERIALS AND METHODS: Published literature was retrieved from the Online Mendelian Inheritance in Man, Orphanet, Human Phenotype Ontology and PubMed databases using keywords "male infertility," "syndrome," "gene," and "case report"; time period from 1980 to September, 2021. Retrieved data were organized as a catalog and complemented with identification numbers of syndromes (MIM ID) and genes (Gene ID). The genome-phenome network and the phenome network were visualized using Cytoscape and Gephi software platforms. Protein-protein interaction analysis was performed using STRING tool. RESULTS: Retrieved syndromes were presented as (1) a catalog containing 63 syndromes and 93 associated genes, (2) a genome-phenome network including CHD7 and WT1 genes and Noonan and Kartagener syndromes, and (3) a phenome network including 63 syndromes, and 25 categories of clinical features. DISCUSSION: The developed catalog will contribute to the advances and translational impact toward understanding the factors of syndromic male infertility. Visualized networks provide simple, flexible tools for clinicians and researchers to quickly generate hypotheses and gain a deeper understanding of underlying mechanisms affecting male reproduction. CONCLUSION: Recognition of the significance of genome-phenome visualization as part of network medicine can help expedite efforts toward unravelling molecular mechanisms and enable advances personal/precision medicine of male reproduction and other complex traits.