Identifying the key genes and functional enrichment pathways associated with feed efficiency in cattle

Highlights

• The four genes, SHC1, GPX4, ACADL, and IGF1, were identified and validated as the marker genes for cattle RFI.

• Identifying four functional enrichment pathways, fatty acid metabolism, sugar metabolism, energy metabolism, and protein ubiquitination was associated with cattle RFI.

Abstract

Residual feed intake (RFI) is a measurement of feed efficiency, and is inversely correlated with feed efficiency. The differentially expressed genes (DEGs) associated with RFI vary substantially among studies, posing great challenges in finding the RFI-related marker genes. This study attempted to resolve this issue by integrating and comparing the multiple transcriptome sequencing data associated with RFI in the cattle liver, using differential, functional enrichment, protein–protein interaction (PPI) network, weighted co-expression network (WGCNA), and gene set enrichment analyses (GSEA) to identify the candidate genes and functional enrichment pathways that are closely associated with RFI. Four candidate genes namely SHC1, GPX4, ACADL, and IGF1 were identified and validated as the marker genes for RFI. Four functional enrichment pathways, namely the fatty acid metabolism, sugar metabolism, energy metabolism, and protein ubiquitination were also found to be closely related to RFI. This study identified several genes and signaling pathways with shared characteristics, which will provide new insights into the molecular mechanisms related to the regulation of feed efficiency, and provide basis for molecular markers related to feed efficiency in beef cattle.

Introduction

Feed expenditures constitute the vast majority of farming inputs for rearing modern beef cattle, accounting for more than 70% of the expenditure in the beef cattle farms. Improving the efficiency of the animal feed tends to effectively reduce the feed costs as well as save the grain. Residual feed intake (RFI) measures the feed efficiency, belonging to moderate heritability (chicken 0.21–0.50 (Miyumo et al., 2018, Liu et al., 2017); duck 0.24–0.27 (Zeng et al., 2016, Zeng et al., 2018, Basso et al., 2012); pig 0.19–0.63 (Kavlak and Uimari, 2019, E, D.M., , 2018); cattle 0.28–0.40 (Guilherme, 2018, Torres-Vázquez et al., 2018); sheep 0.45 (Tortereau et al., 2020) and is negatively correlated with the feed efficiency. Selecting low RFI (LRFI) herds not only reduces the feeding costs but also decreases the environmental pollution due to livestock manure. Studies have indicated that selecting a herd with LRFI can effectively reduce the methane emissions (Olijhoek et al., 2018, Sharma et al., 2018), and keep the growth rate in line with that of the high RFI (HRFI) (Williams et al., 2019, Zhang et al., 2017). However, it is difficult to measure the RFI for grazing or farms with bad conditions, limiting the large-scale adoption of RFI.

With the widespread availability of the next-generation sequencing technology and the dramatically decreasing sequencing cost, breeding researchers have started to identify biomarkers associated with RFI from QTLs (Li et al., 2021, Lam et al., 2021) or SNPs (M, K.A., , 2018, Higgins et al., 2018). The transcriptome sequencing studies had also been concomitantly applied to the bovine RFI, and several tissues samples (liver (Tizioto, 2017); skeletal muscle (Khansefid, 2017); blood (Xi et al., 2015), adipose tissue (Weber et al., 2016), rumen epithelium (Kong, 2016) have been sequenced for detecting the DEGs. However, the overlap of DEGs was found to be too low in these studies. Many studies have simultaneously focused on the significantly DEGs, despite the transcriptome determining the characteristic of spatial and temporal specificity. Hence, it is difficult to find the marker genes associated with RFI through these studies. Several genes that play a primary role are not significantly DEGs. For instance, several biomarker genes that have been screened and identified using the weighted correlation network analysis (WGCNA) method were related to numerous biological issues such as cancer (Tian, 2020, Chen et al., 2019), feed efficiency (Novais et al., 2019, Salleh et al., 2018), and meat quality (Bordini et al., 2021, Zhao, 2020) although they were not significantly DEGs. Therefore, our focus should not only be limited to significant DEGs alone, but also to the genes that play a major role. The liver constitutes the main location of energy and substance metabolism in animals. Not only is it capable of maintaining the glucose homeostasis in the blood, but also participates in the protein biosynthesis (Koike et al., 2020), immune and detoxification functions (Chen et al., 2009), and synthesis and secretion of the primary bile acids (Grijalva and Vakili, 2013). Based on the physiological functions of the liver, the genes that are closely associated with RFI in the liver need investigation. Considering the large variation among genes in the different experimental subjects and physiological periods, the public database resources were used not only to focus on the DEGs in the used dataset, but also on the genes having greater weight or contribution using the weighted co-expression network analysis (WGCNA) analysis, and then these genes were included them into the key candidate genes (KCGs) pool. The genes of interest within the KCGs pool were then validated for the trends in the expression using three data sets, followed by the single gene set enrichment analysis (GSEA) on the KCGs to confirm the function of each gene set under this expressed condition. The purpose of this study is to explore the landmark genes associated with RFI and the important functional enrichment pathways contributing to RFI variation, providing theoretical support for the breeding efforts to improve feed efficiency.