Genome regulation and gene interaction networks inferred from muscle transcriptome underlying feed efficiency in Pigs

Abstract

11Improvement of feed efficiency (FE) is key for sustainability and cost reduction in pig 12 production. Our aim was to characterize the muscle transcriptomic profiles in Danbred 13Duroc (Duroc) and Danbred Landrace (Landrace), in relation to FE for identifying 14 potential biomarkers. RNA-seq data was analyzed employing differential gene expression 15 methods, gene-gene interaction and network analysis, including pathway and functional 16 analysis. We compared the results with genome regulation in human exercise data. In the 17 differential expression analysis, 13 genes were differentially expressed, including: 18 MRPS11, MTRF1, TRIM63, MGAT4A, KLH30. Based on a novel gene selection method, 19the divergent count, we performed pathway enrichment analysis. We found 5 significantly 20 enriched pathways related to feed conversion ratio (FCR). These pathways were mainly 21 mitochondrial, and summarized in the mitochondrial translation elongation (MTR) 22pathway. In the gene interaction analysis, highlights include the mitochondrial genes: 23 PPIF, MRPL35, NDUFS4and the fat metabolism and obesity genes: AACS, SMPDL3B, 24 CTNNBL1, NDUFS4 and LIMD2. In the network analysis, we identified two modules 25 significantly correlated with FCR. Pathway enrichment of modules identified MTR, 26 electron transport chain and DNA repair as enriched pathways. In the network analysis, 27 the mitochondrial gene group NDUF was a key hub group, showing potential as 28 biomarkers. Comparing with human transcriptomic exercise studies, genes related to 29 exercise displayed enrichment in our FCR related genes. We conclude that mitochondrial 30 activity is a driver for FCR in muscle tissue, and mitochondrial genes could be potential 31 biomarkers for FCR in pigs. We hypothesize that increased FE mimics processes 32 triggered in exercised muscle. 33 34 35 calculation is dependent on individual population and production factors (Hoque, Kadowaki et al. 56 2009, Do, Strathe et al. 2013. However, in pig production, the side effects of FCR selection and 57 simplicity are desired traits, thus perhaps explaining why the pig population in Denmark and in 58 general pig production, FCR has been the main efficiency phenotype used for selection (Gilbert, 59 Billon et al. 2017). One can also hypothesize that FCR is more easily translatable between 60 breeds/populations, as it is a simple dimensionless ratio, which has a simple and generally 61 comparable interpretation. In contrast, it is more difficult to easily compare RFI values across 62 different populations or breeds. In regards to the biological and/or genetic background of FCR in 63 pigs, the results remain somewhat elusive (Ding, Yang et al. 2018), thus inviting for further analysis 64 on the topic. 65

Genetic variations (eQTLs) in muscle transcriptome and mitochondrial genes, and trans-eQTL molecular pathways in feed efficiency from Danish breeding pigs

“…We therefore based the set of genes on the methods in Carmelo et. al [32]. In brief, Differential Expression analysis (DEA) was performed using three different DE methods (Limma, EdgeR, Deseq2) [37][38][39] with FCR as the phenotype of interest.…”

“…Given that the samples were collected in slaughterhouse setting, it was necessary to include RIN in the model, but this should not be an issue if appropriately corrected for [42]. Breed and age have an effect on expression, as seen in our previous study [32] and thus must be accounted for. While the samples come from a selection of 28 different breeders in Denmark, there still was some relationship between pigs, especially if they came from the same breeder.…”

Genetic variations (eQTLs) in muscle transcriptome and mitochondrial genes, and trans-eQTL molecular pathways in feed efficiency from Danish breeding pigs

“…As samples were collected on different days, it was necessary to correct for this using the batch effect. Breed and age have an effect on expression, as seen in our previous study (30) and thus must be corrected for. While the samples come from a selection of 28 different breeders in Denmark, there is still some relationship between some pigs, especially if they came from the same breeder.…”

Integrative Analysis of Metabolomic and Transcriptomic Profiles Uncovers Biological Pathways of Feed Efficiency in Pigs

“…Paired-end sequencing (100 bp) was performed on the BGISEQ-500 platform after Oligo dT library preparation. Read quality control, mapping, and gene counts were reported elsewhere [ 14 ]. Lowly expressed genes were filtered out, and the gene counts normalization was carried out by applying the variance stabilizing transformation ( VST ) function from DeSeq2 [ 56 ].…”

“…Recently, we have investigated RNA-seq data on the 41 Danish production pigs that underwent feed efficiency and performance testing trials to identify differentially expressed genes and gene networks and reported 13 genes as potential biomarkers for feed efficiency [ 14 ]. Despite the new insights into key genes and molecular mechanisms reported in these studies, these approaches rely solely on data from a single biological layer.…”