Naval-Sanchez, M. | Search bioRxiv

bioRxiv2018

Systems biology reveals NR2F6 and TGFB1 as key regulators of feed efficiency in beef cattle

Systems biology approaches are used as strategy to uncover tissue-specific perturbations and regulatory genes related to complex phenotypes. We applied this approach to study feed efficiency (FE) in beef cattle, an important trait both economically and environmentally. Poly-A selected RNA of five tissues (adrenal gland, hypothalamus, liver, skeletal muscle and pituitary) of eighteen young bulls, selected for high and low FE, were sequenced (100bp, pared-end). From the 17,354 expressed genes, 1,317 were prioritized by five selection categories (differentially expressed, harbouring SNPs associated with FE, tissue-specific, secreted in plasma and key regulators) and used for network construction. NR2F6 and TGFB were identified and validated by motif discovery as key regulators of hepatic inflammatory response and muscle tissue development, respectively, two biological processes demonstrated to be associated to FE. Moreover, we indicated potential biomarkers of FE which are related to hormonal control of metabolism and sexual maturity. By using robust methodologies and validation strategies, we confirmed main biological processes related to FE in Bos indicus and indicated candidate genes as regulators or biomarkers of superior animals.

systems biology↗

bioRxiv2017

Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics and epigenetics data

Genome sequences for hundreds of mammalian species are available, but an understanding of their genomic regulatory regions, which control gene expression, is only beginning. A comprehensive prediction of potential active regulatory regions is necessary to functionally study the roles of the majority of genomic variants in evolution, domestication, and animal production. We developed a computational method to predict regulatory DNA sequences (promoters, enhancers and transcription factor binding sites) in production animals (cows and pigs) and extended its broad applicability to other mammals. The method utilizes human regulatory features identified from thousands of tissues, cell lines, and experimental assays to find homologous regions that are conserved in sequences and genome organization and are enriched for regulatory elements in the genome sequences of other mammalian species. Importantly, we developed a filtering strategy, including a machine learning classification method, to utilize a very small number of species-specific experimental datasets available to select for the likely active regulatory regions. The method finds the optimal combination of sensitivity and accuracy to unbiasedly predict regulatory regions in mammalian species. Furthermore, we demonstrated the utility of the predicted regulatory datasets in cattle for prioritizing variants associated with multiple production and climate change adaptation traits, and identifying potential genome editing targets.

genomics↗