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

Abstract

<jats:p>Feed efficiency (FE) is an economically important trait. Thus, reliable predictors would help to reduce the production cost and provide sustainability to the pig industry. We carried out metabolome-transcriptome integration analysis on 40 purebred Duroc and Landrace uncastrated male pigs to identify potential gene-metabolite interactions and explore the molecular mechanisms underlying FE. To this end, we applied untargeted metabolomics and RNA-seq approaches to the same animals. After data quality control, we used a linear model approach to integrate the data and find significant differently correlated gene-metabolite pairs separately for the breeds (Duroc and Landrace) and FE groups (low and high FE) followed by a pathway over-representation analysis. We identified 21 and 12 significant gene-metabolite pairs for each group. The valine-leucine-isoleucine biosynthesis/degradation and arginine-proline metabolism pathways were associated with unique metabolites. The unique genes obtained from significant metabolite-gene pairs were associated with sphingolipid catabolism, multicellular organismal process, cGMP, and purine metabolic processes. While some of the genes and metabolites identified were known for their association with FE, others are novel and provide new avenues for further research. Further validation of genes, metabolites, and gene-metabolite interactions in larger cohorts will elucidate the regulatory mechanisms and pathways underlying FE.</jats:p>

Metabolic Profiling and Integration of Metabolomic and Transcriptomic Data From Pectoralis Muscle Reveal Winter-Adaptive Metabolic Responses of Black-Capped Chickadee and American Goldfinch

“…To evaluate whether the relationships between transcript and metabolites differ by species and season, we used a linear model approach (Siddiqui et al, 2018) to integrate the current metabolomics data with the previously published transcriptomics data for the same tissues of black-capped chickadees and American goldfinches (Cheviron and Swanson, 2017). This linear model approach can identify transcript-metabolite associations that are specific to a particular phenotype (Patt et al, 2019;Banerjee et al, 2020). Significant transcript-metabolite pairs were clustered by the direction of association (i.e., Winter correlated and Winter anti-correlated).…”

Overview of Metabolomic Analysis and the Integration with Multi-Omics for Economic Traits in Cattle

“…Generally, a linear regression model is considered to analyze metabolomics data for significant metabolite identification by fitting phenotypes (e.g., RFI) as covariates [9,80]. Sometimes, the elastic net regularization model is also applied to fit microbial communities [86].…”

“…Based on the BioCyc [76], KEGG [77] and Uniprot [78] databases, the genes and proteins can be mapped on the predicted metabolic pathways [73]. R package IntLIM [79] was used to integrate metabolomics and gene expression data for feed efficiency traits in pigs [80], where the interactions of phenotypes and gene expressions are fitted in the model [79]. The linear model that IntLIM [79] used is as follows:…”

Analysis of merged transcriptomic and genomic datasets to identify genes and pathways underlying residual feed intake in growing pigs

“…A number of associations with different types of biological markers have been found in these studies, which overall provide a better understanding of the FE trait complexity. For instance, metagenomics research has shown an association of dozens of bacterial taxonomic units in gut microbiota with FE 8 , 11 , and metabolomics studies in combination with transcriptomic 9 and GWA studies 10 have revealed significant gene-metabolite pairs. Transcriptomic studies have found FE associated differentially expressed genes (DE-genes) in various tissue, including blood 14 , liver 12 , brain 15 and intestine 16 .…”