Jie Wu1,2, Yong Ye1,2, Jianping Quan1,2, Rongrong Ding1,2, Xingwang Wang1,2, Zhanwei Zhuang1,2, Shenping Zhou1,2, Qian Geng1,2, Cineng Xu1,2, Linjun Hong1,2, Zheng Xu1,2, Enqin Zheng1,2, Gengyuan Cai1,2, Zhenfang Wu3,4,5,6, Jie Yang7,8. 1. College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China. 2. Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou, 510642, China. 3. College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China. wzfemail@163.com. 4. Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou, 510642, China. wzfemail@163.com. 5. State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangzhou, 510642l, China. wzfemail@163.com. 6. Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China. wzfemail@163.com. 7. College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China. jieyang2012@hotmail.com. 8. Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou, 510642, China. jieyang2012@hotmail.com.
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.
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.
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