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Integrated genomics-metabolomics approach for feed efficiency and methane emission mitigation in Nelore cattle

Abstract

The increasing production of beef cattle and the exportation of meat in Brazil have positioned the country as one of the leaders in the global market. However, animal feeding remains a challenge due to the high costs for the national livestock industry. Thus, feed efficiency becomes strategic for the sustainability of the sector, as it can assist in mitigating methane emissions. To address this challenge, several strategies related to feed efficiency and methane emission are being explored, including the use of biotechnological tools called omics, which analyze the interactions between biochemical and physiological systems to understand economically relevant traits. However, approaches that focus on a single omics have limitations, as they identify only partial sets of specific molecules that explain biological conditions. Hence, this project aims to investigate and integrate the genome and metabolome of Nellore cattle to gain a deeper understanding of the molecular mechanisms related to feed efficiency and methane emission. To this end, data from performance tests of 275 Nelore cattle belonging to three selection lines will be utilized: 1) Nelore Selection (NeS): selection based on higher post-weaning weight; 2) Traditional Nelore (NeT): selection based on higher genetic value for post-weaning weight and lower genetic value for residual feed intake (RFI); 3) Control Nelore (NeC): selection based on post-weaning weight in the average of contemporary line peers. During the tests, methane emissions will also be collected using the sulfur hexafluoride (SF6) tracer gas technique. During the tests, methane emissions will also be evaluated using the sulfur hexafluoride (SF6) tracer gas technique. Furthermore, animals will be genotyped using GGP Indicus (50k). Bio-samples of blood plasma and feces will also be collected for metabolomic analyses by Nuclear Magnetic Resonance (1H NMR). The 1H NMR spectra will be analyzed for the identification and quantification of metabolites in the bio-samples. The combined analysis of metabolome-genome wide association studies (mGWAS) will be conducted to identify genes with high genetic impact. These candidate genes, along with metabolites and traits of interest, will be integrated using advanced bioinformatics techniques. Functional enrichment will also be performed to gain insights into biological processes, cellular components, and molecular functions. The results of this study will advance the understanding of feed efficiency and methane emissions in Nelore cattle, aiding in the development of biomarkers that can be applied to enhance the accuracy of assessing feed efficiency and methane emissions in selection and crossbreeding programs for beef cattle. Additionally, this will foster the development of new assessment approaches that benefit the entire production chain. (AU)

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