Co-expression network analysis to identify metabolites associated with feed efficiency and methane emission in Nelore bulls

• Grant number: 23/01207-0
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: June 01, 2023
• End date: July 31, 2023
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Maria Eugênia Zerlotti Mercadante
• Grantee: Jessica Moraes Malheiros
• Supervisor: Wellison Jarles da Silva Diniz
• Host Institution: Instituto de Zootecnia. Agência Paulista de Tecnologia dos Agronegócios (APTA). Secretaria de Agricultura e Abastecimento (São Paulo - Estado). Nova Odessa , SP, Brazil
• Institution abroad: Auburn University, United States
• Associated to the scholarship: 22/12669-1 - Enteric methane emission, feed efficiency, and digestibility of Nellore cattle, BP.PD

Abstract

The high production and export of beef cattle in Brazil led to a growing number of animals confined in the national territory and allowed the country to rank in a prominent position in the world meat market. Nevertheless, the high cost of food represents the largest amount invested in the composition of diets in feedlots. Feed efficiency is directly related to increased meat production per unit of feed. Additionally, efficient animals are associated with increased livestock system sustainability, as they reduce the demand for food, and consequently, reduce the impact generated by methane emissions. Thus, this project proposes to evaluate the differences and similarities in the correlations of metabolites in fecal sample (untargeted metabolomics) in order to understand the feed efficiency and methane emission phenotypes in Nelore cattle (Bos taurus indicus) belonging to two selection lines previously selected for growth characteristics and feed efficiency. Association analyzes of fecal metabolites with phenotypes of feed efficiency and methane emission will be realized. Furthermore, we will use a modeling network approach based on the Partial Correlation with Information Theory (PCIT) algorithm to identify differentially co-expressed metabolites between the Nelore cattle belonging to different lines selected. These correlations will provide insights on how the metabolites are inter-related and cooperatively working in a complex network of metabolic pathways and regulatory events underling feed efficiency and methane emission. Thus, this project allows to assist in the discovery of potential non-invasive biomarkers for feed efficiency and methane emissions, which can add new insights for selection of Nelore cattle. (AU)