Multiomic application to investigate feed efficiency and methane emissions in Nelore bulls

Abstract

Brazil is among the leading countries in meat production and exportation. The continuous growth of the national beef cattle industry relies on a sustainable and efficient production system. Thus, feed efficiency is a key player to address the costs of production and address the current challenges related to sustainability and use of natural resources. To understand the genetic architecture of complex traits of economic importance, biotechnological approaches, such as omics, are been adopted. However, it is essential to acknowledge the limitations of approaches based solely on a single omics discipline. To overcome that, this project proposes to evaluate and integrate the genome information of Nelore bulls to their metagenome and metabolome profiles of the gastrointestinal tract to gain a more in-depth understanding of the biological processes underlying feed efficiency and methane emission. To this end, feed efficiency tests will be conducted with 60 Nelore cattle, divided into three selection lines: Nelore Control (NeC; n = 20), selected based on post-weaning average weight; Nelore Selection (NeS; n = 20), chosen for a higher selection differential in post-weaning weight; and Nelore Traditional (NeT; n = 20), selected for a higher breeding value for post-weaning weight and lower breeding values for residual feed intake (RFI). During the tests, data will be collected to calculate feed efficiency and methane emission traits, along with tail hair samples for genotyping. Additionally, bio-samples including saliva, ruminal fluid, and feces will be collected for metagenomic and metabolomic analyses. The animals will be genotyped using GGP Indicus (50k). The metabolites extracted from the gastrointestinal tract bio-samples will be analyzed using Nuclear Magnetic Resonance (1H NMR), while the microbiota will be analyzed using next-generation sequencing metabarcoding. The combined analysis of phenotypes, genome, metabolome, and metagenome will be conducted through Integrative Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) and Canonical Correlation Analysis (CCA). Functional over-representation analyses will be performed to retrieve information about biological processes, cellular components, and molecular functions. We expect that a multi-omics approach, integrating the microbial community, genomic data, and metabolome, will offer new insights into feed efficiency and methane emission in Nelore cattle, establishing a pioneering database to help in the development of biomarkers for phenotypic prediction in vivo. These biomarkers could improve the accuracy of assessing feed efficiency and methane emission in beef cattle breeding programs. (AU)