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. 2021 Jun 2;7(1):39.
doi: 10.1186/s40813-021-00219-w.

Using nontargeted LC-MS metabolomics to identify the Association of Biomarkers in pig feces with feed efficiency

Jie Wu #  1   2 , Yong Ye #  1   2 , Jianping Quan  1   2 , Rongrong Ding  1   2 , Xingwang Wang  1   2 , Zhanwei Zhuang  1   2 , Shenping Zhou  1   2 , Qian Geng  1   2 , Cineng Xu  1   2 , Linjun Hong  1   2 , Zheng Xu  1   2 , Enqin Zheng  1   2 , Gengyuan Cai  1   2 , Zhenfang Wu  3   4   5   6 , Jie Yang  7   8
Affiliations

Using nontargeted LC-MS metabolomics to identify the Association of Biomarkers in pig feces with feed efficiency

Jie Wu et al. Porcine Health Manag. .

Abstract

Background: Improving feed efficiency is economically and environmentally beneficial in the pig industry. A deeper understanding of feed efficiency is essential on many levels for its highly complex nature. The aim of this project is to explore the relationship between fecal metabolites and feed efficiency-related traits, thereby identifying metabolites that may assist in the screening of the feed efficiency of pigs.

Results: We performed fecal metabolomics analysis on 50 individuals selected from 225 Duroc x (Landrace x Yorkshire) (DLY) commercial pigs, 25 with an extremely high feed efficiency and 25 with an extremely low feed efficiency. A total of 6749 and 5644 m/z features were detected in positive and negative ionization modes by liquid chromatography-mass spectrometry (LC/MS). Regrettably, the PCA could not classify the the samples accurately. To improve the classification, OPLS-DA was introduced. However, the predictive ability of the OPLS-DA model did not perform well. Then, through weighted coexpression network analysis (WGCNA), we found that one module in each positive and negative mode was related to residual feed intake (RFI), and six and three metabolites were further identified. The nine metabolites were found to be involved in multiple metabolic pathways, including lipid metabolism (primary bile acid synthesis, linoleic acid metabolism), vitamin D, glucose metabolism, and others. Then, Lasso regression analysis was used to evaluate the importance of nine metabolites obtained by the annotation process.

Conclusions: Altogether, this study provides new insights for the subsequent evaluation of commercial pig feed efficiency through small molecule metabolites, but also provide a reference for the development of new feed additives.

Keywords: Feed efficiency; LC-MS; Pig; WGCNA.

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Conflict of interest statement

The authors have declared that no competing interest exists.

Figures

Fig. 1
Fig. 1
Boxplot of feed efficiency (FE)-related phenotypes. Differences in (A) FCR and (B) RFI trait between high- and low-FE groups. H-FE, high-feed efficiency; L-FE, low-feed efficiency; FCR, feed conversion ratio; RFI, residual feed intake. *** p < 0.0001. **p < 0.01
Fig. 2
Fig. 2
(Orthogonal) Partial Least Squares Discrimination Analysis ((O) PLS-DA) score plots. The analysis was based on LC/MS data of fecal samples from H-FE (green) and L-FE (red) of (A) positive and (B) negative modes
Fig. 3
Fig. 3
Coexpression network analysis of metabolic features. The left panel of the figure shows the correlation between the module and RFI or FCR in (A) negative and (C) positive models. The right panel of the figure shows the scatter plot of module membership and the gene significance in (B) MEgreenyellow or (D) MEtan module. Each row corresponds to ME, and each column corresponds to traits; the number in each module represents the Pearson correlation between the module and RFI or FCR; the number in parentheses represents the p-value of the correlation
Fig. 4
Fig. 4
Assessing the weight of nine metabolites using Lasso regression analysis. A ROC curve of the training set (red) and the test set (green). B Regression coefficients of nine metabolites in the Lasso model. The y-axis of the graph on the right represents metabolites, and the x-axis represents the regression coefficient of metabolites
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References

    1. Carmelo VAO, Banerjee P, da Silva Diniz WJ, Kadarmideen HN. Metabolomic networks and pathways associated with feed efficiency and related-traits in Duroc and landrace pigs. Sci Rep. 2020;10(1):1–14. doi: 10.1038/s41598-019-57182-4. - DOI - PMC - PubMed
    1. Do DN, Strathe AB, Ostersen T, Pant SD, Kadarmideen HN. Genome-wide association and pathway analysis of feed efficiency in pigs reveal candidate genes and pathways for residual feed intake. Front Genet. 2014;5:307. doi: 10.3389/fgene.2014.00307. - DOI - PMC - PubMed
    1. McCormack UM, Curião T, Metzler-Zebeli BU, Magowan E, Berry DP, Reyer H, et al. Porcine feed efficiency-associated intestinal microbiota and physiological traits: Finding consistent cross-locational biomarkers for residual feed intake. Msystems. 2019;4(4):e00324–18. - PMC - PubMed
    1. Saintilan R, Mérour I, Schwob S, Sellier P, Bidanel J, Gilbert H. Genetic parameters and halothane genotype effect for residual feed intake in Piétrain growing pigs. Livest Sci. 2011;142(1–3):203–209. doi: 10.1016/j.livsci.2011.07.013. - DOI
    1. Do DN, Strathe AB, Jensen J, Mark T, Kadarmideen HN. Genetic parameters for different measures of feed efficiency and related traits in boars of three pig breeds. J Anim Sci. 2013;91(9):4069–4079. doi: 10.2527/jas.2012-6197. - DOI - PubMed

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