ArticleMetabolites2020
Integrative Analysis of Metabolomic and Transcriptomic Profiles Uncovers Biological Pathways of Feed Efficiency in Pigs.
Article in Metabolites, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- Comparative Transcriptomic and Metabolomic Analysis Reveals the Regulatory Mechanism of Feed Conversion Efficiency in Spotted Knifejaw (Oplegnathus punctatus).Marine biotechnology (New York, N.Y.) · 2026Article
- Candidate blood biomarkers linked with feed intake efficiency and weight gain in sheep.Scientific reports · 2026Article
- Integrating Longitudinal Metabolite Profiles Improves Trait Prediction in Pigs in a Trait- and Timepoint-Dependent Manner.bioRxiv : the preprint server for biology · 2025Article
- Expression quantitative trait loci associated with performance traits, blood biochemical parameters, and cytokine profile in pigs.Frontiers in genetics · 2025Article
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- Integrative transcriptomic and metabolomic analysis reveals alterations in energy metabolism and mitochondrial functionality in broiler chickens with wooden breast.Scientific reports · 2023Article
- Integrated analysis of transcriptome and metabolome revealed biological basis of sows from estrus to lactation.iScience · 2023Article
- Analysis of merged transcriptomic and genomic datasets to identify genes and pathways underlying residual feed intake in growing pigs.Scientific reports · 2022Article
- Effect of chronic and acute enterotoxigenic E. coli challenge on growth performance, intestinal inflammation, microbiome, and metabolome of weaned piglets.Scientific reports · 2022Article
- Overview of Metabolomic Analysis and the Integration with Multi-Omics for Economic Traits in Cattle.Metabolites · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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.
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