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For: Zhou G, Liang X, He X, Li J, Tian G, Liu Y, Wang X, Chen Y, Yang Y. Compound enzyme preparation supplementation improves the production performance of goats by regulating rumen microbiota. Appl Microbiol Biotechnol 2023;107:7287-7299. [PMID: 37750915 DOI: 10.1007/s00253-023-12804-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 09/05/2023] [Accepted: 09/18/2023] [Indexed: 09/27/2023]

For:	Zhou G, Liang X, He X, Li J, Tian G, Liu Y, Wang X, Chen Y, Yang Y. Compound enzyme preparation supplementation improves the production performance of goats by regulating rumen microbiota. Appl Microbiol Biotechnol 2023;107:7287-7299. [PMID: 37750915 DOI: 10.1007/s00253-023-12804-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 09/05/2023] [Accepted: 09/18/2023] [Indexed: 09/27/2023]

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Cited by Other Article(s)

Jiang S, Si J, Mo J, Zhang S, Chen K, Gao J, Xu D, Bai L, Lan G, Liang J. Integrated Microbiome and Serum Metabolome Analysis Reveals Molecular Regulatory Mechanisms of the Average Daily Weight Gain of Yorkshire Pigs. Animals (Basel) 2024;14:278. [PMID: 38254447 PMCID: PMC10812420 DOI: 10.3390/ani14020278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/31/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open

Abstract

The average daily weight gain (ADG) is considered a crucial indicator for assessing growth rates in the swine industry. Therefore, investigating the gastrointestinal microbiota and serum metabolites influencing the ADG in pigs is pivotal for swine breed selection. This study involved the inclusion of 350 purebred Yorkshire pigs (age: 90 ± 2 days; body weight: 41.20 ± 4.60 kg). Concurrently, serum and fecal samples were collected during initial measurements of blood and serum indices. The pigs were categorized based on their ADG, with 27 male pigs divided into high-ADG (HADG) and low-ADG (LADG) groups based on their phenotype values. There were 12 pigs in LADG and 15 pigs in HADG. Feces and serum samples were collected on the 90th day. Microbiome and non-targeted metabolomics analyses were conducted using 16S rRNA sequencing and liquid chromatography-mass spectrometry (LC-MS). Pearson correlation, with Benjamini-Hochberg (BH) adjustment, was employed to assess the associations between these variables. The abundance of Lactobacillus and Prevotella in LADG was significantly higher than in HADG, while Erysipelothrix, Streptomyces, Dubosiella, Parolsenella, and Adlercreutzia in LADG were significantly lower than in HADG. The concentration of glutamine, etiocholanolone glucuronide, and retinoyl beta-glucuronide in LADG was significantly higher than in HADG, while arachidonic acid, allocholic acid, oleic acid, phenylalanine, and methyltestosterone in LADG were significantly lower than in HADG. The Lactobacillus-Streptomyces networks (Lactobacillus, Streptomyces, methyltestosterone, phenylalanine, oleic acid, arachidonic acid, glutamine, 3-ketosphingosine, L-octanoylcarnitine, camylofin, 4-guanidinobutyrate 3-methylcyclopentadecanone) were identified as the most influential at regulating swine weight gain. These findings suggest that the gastrointestinal tract regulates the daily weight gain of pigs through the network of Lactobacillus and Streptomyces. However, this study was limited to fecal and serum samples from growing and fattening boars. A comprehensive consideration of factors affecting the daily weight gain in pig production, including gender, parity, season, and breed, is warranted.

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Affiliation(s)

Shan Jiang College of Animal Science & Technology, Guangxi University, Nanning 530004, China; (S.J.); (J.S.); (J.M.); (S.Z.); (K.C.); (J.G.); (D.X.); (G.L.) Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
Jinglei Si College of Animal Science & Technology, Guangxi University, Nanning 530004, China; (S.J.); (J.S.); (J.M.); (S.Z.); (K.C.); (J.G.); (D.X.); (G.L.) Guangxi State Farms Yongxin Animal Husbandary Group Co., Ltd., Nanning 530022, China
Jiayuan Mo College of Animal Science & Technology, Guangxi University, Nanning 530004, China; (S.J.); (J.S.); (J.M.); (S.Z.); (K.C.); (J.G.); (D.X.); (G.L.)
Shuai Zhang College of Animal Science & Technology, Guangxi University, Nanning 530004, China; (S.J.); (J.S.); (J.M.); (S.Z.); (K.C.); (J.G.); (D.X.); (G.L.)
Kuirong Chen College of Animal Science & Technology, Guangxi University, Nanning 530004, China; (S.J.); (J.S.); (J.M.); (S.Z.); (K.C.); (J.G.); (D.X.); (G.L.)
Jiuyu Gao College of Animal Science & Technology, Guangxi University, Nanning 530004, China; (S.J.); (J.S.); (J.M.); (S.Z.); (K.C.); (J.G.); (D.X.); (G.L.)
Di Xu College of Animal Science & Technology, Guangxi University, Nanning 530004, China; (S.J.); (J.S.); (J.M.); (S.Z.); (K.C.); (J.G.); (D.X.); (G.L.)
Lijing Bai Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
Ganqiu Lan College of Animal Science & Technology, Guangxi University, Nanning 530004, China; (S.J.); (J.S.); (J.M.); (S.Z.); (K.C.); (J.G.); (D.X.); (G.L.)
Jing Liang College of Animal Science & Technology, Guangxi University, Nanning 530004, China; (S.J.); (J.S.); (J.M.); (S.Z.); (K.C.); (J.G.); (D.X.); (G.L.)

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