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. 2020 Jul 6;10(7):275.
doi: 10.3390/metabo10070275.

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

Priyanka Banerjee  1 , Victor Adriano Okstoft Carmelo  1 , Haja N Kadarmideen  1
Affiliations

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

Priyanka Banerjee et al. Metabolites. .

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.

Keywords: feed efficiency; linear model; metabolomics; pigs; transcriptomics.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Schematic representation of the study design and analysis steps.
Figure 2
Figure 2
Results of IntLIM applied to breed-specific groups. (a) Clustering of 21 identified gene-metabolite pairs (FDR adjusted p-value of interaction coefficient < 0.1, Spearman correlation difference > 0.1 in Duroc and Landrace breeds, (b) Gene-metabolite difference in ENSSSCG00000028124 (SNRPN)—rhodamine B (FDR adjusted p-value = 0.1, Duroc Spearman correlation = 0.7, Landrace Spearman correlation = −0.5), (c) Gene-metabolite difference in ENSSSCG00000000401 (GLS2)—cystathionine ketimine (FDR adjusted p-value = 0.01, Duroc Spearman correlation = −0.9, Landrace Spearman correlation = 0.2).
Figure 3
Figure 3
Results of IntLIM applied to FE-specific groups. (a) Clustering of 12 identified gene-metabolite pairs (FDR adjusted p-value of interaction coefficient < 0.1, Spearman correlation difference > 0.1 in high and low FE groups, (b) Gene-metabolite difference in ENSSSCG00000025106 (THNSL2)—Pyrocatechol (FDR adjusted p-value = 0.06, High-FE Spearman correlation = 0.6, Low-FE Spearman correlation = −0.7), (c) Gene-metabolite difference in ENSSSCG00000036609 (TBXT)—ketoleucine (FDR adjusted p-value = 0.08, High-FE Spearman correlation = 0.5, Low-FE Spearman correlation = −0.3).
Figure 4
Figure 4
Biological mechanism showing the involvement of SGPL1-L-glutamic acid 5-phosphate gene-metabolite pair underlying sphingolipid metabolism identified from Duroc correlated/Landrace anti-correlated cluster.
Figure 5
Figure 5
Biological mechanism showing the involvement of genes identified from Duroc anti-correlated/Landrace correlated clusters underlying feed efficiency.
Figure 6
Figure 6
An overview of gene-metabolite pairs affecting the cGMP-PKG pathway involved with feed efficiency.
See this image and copyright information in PMC

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