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bioRxiv2022

Disruption of the nuclear localization signal in RBM20 is causative in dilated cardiomyopathy

Human patients carrying genetic mutations in RNA binding motif 20 (RBM20) develop a clinically aggressive dilated cardiomyopathy (DCM). RBM20 is a splicing factor with two canonical domains, an RNA recognition motif (RRM) and an arginine-serine rich (RS) domain. RRM loss-of-function disrupts the splicing of RBM20 target transcripts and leads to systolic dysfunction without overt DCM, while mutations in the RS domain precipitate DCM. We show that mice lacking the RS domain (Rbm20{Delta}RS) manifest DCM with mis-splicing of RBM20 target transcripts. We found that RBM20 is mis-localized in Rbm20{Delta}RS mice but not in mice lacking the RRM, which are also deficient in RBM20 splicing. We determine that the RS domain, not other domains including the RRM, is critical for RBM20 nuclear import and define the core nuclear localization signal (NLS) within this domain. Mutation analysis of phosphorylation sites within the RS domain indicate that phosphorylation is dispensable for RBM20 nuclear import. Collectively, our findings establish disruption of the NLS in RBM20 as a causative mechanism in DCM through nucleocytoplasmic transport.

cell biology↗

bioRxiv2020

Extensive genome-wide association analyses identify genotype-by-environment interactions of growth traits in Simmental cattle

BackgroundUnderstanding the genetic basis of genotype-by-environment interactions (GxE) is crucial to understand environmental adaptation in mammals and improve the sustainability of agricultural production. In addition, GxE information could also be useful to predict the vulnerability of populations to climate change. ResultsHere, we present an extensive study investigating the interaction of genome-wide SNP markers with a vast assortment of environmental variables and searching for SNPs controlling phenotypic variance (vQTL) using a large beef cattle dataset. We showed that GxE contribute 10%, 4%, and 3% of the phenotypic variance of birth weight, weaning weight, and yearling weight, respectively. GxE genome-wide association analysis (GWAA) detected a large number of GxE loci affecting growth traits, which the traditional GWAA did not detect, showing that functional loci may have non-additive genetic effects between genotype classes regardless of differences in genotypic means. We also showed that variance-heterogeneity GWAA can detect loci enriched with GxE effects without requiring prior knowledge of the interacting environmental factors. Functional annotation and pathway analysis of GxE genes revealed biological mechanisms by which cattle respond to changes in their environment, such as neural signaling, metabolic, hypoxia-induced, and immune system pathways. Knowledge of these pathways will be important as climate change becomes a burden on animal health and productivity. In addition, ecoregion-specific GxE SNPs detected in this study may play a crucial role in identifying resilient and adapted beef cattle across divergent environments. ConclusionsWe detected novel trait associations with large GxE effects for birth weight, weaning weight, and yearling weight. Functional annotation and pathway analysis uncovered genomic regions involved in response to environmental stimuli. We unraveled the relevance and complexity of the genetic basis of GxE underlying growth traits, providing new insights into how different environmental conditions interact with specific genes influencing adaptation and productivity in beef cattle and potentially across mammals

genomics↗