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Cao KX, Deng ZC, Li SJ, Yi D, He X, Yang XJ, Guo YM, Sun LH. Poultry Nutrition: Achievement, Challenge, and Strategy. J Nutr 2024; 154:3554-3565. [PMID: 39424066 DOI: 10.1016/j.tjnut.2024.10.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2024] [Revised: 09/25/2024] [Accepted: 10/08/2024] [Indexed: 10/21/2024] Open
Abstract
Poultry, a vital economic animal, provide a high-quality protein source for human nutrition. Over the past decade, the poultry industry has witnessed substantial achievements in breeding, precision feeding, and welfare farming. However, there are still many challenges restricting the sustainable development of the poultry industry. First, overly focused breeding strategies on production performance have been shown to induce metabolic diseases in poultry. Second, a lack of robust methods for assessing the nutritional requirements poses a challenge to the practical implementation of precision feeding. Third, antibiotic alternatives and feed safety management remain pressing concerns within the poultry industry. Lastly, environmental pollution and inadequate welfare management in farming have a negative effect on poultry health. Despite numerous proposed strategies and innovative approaches, each faces its own set of strengths and limitations. In this review, we aim to provide a comprehensive understanding of the poultry industry over the past decade, by examining its achievements, challenges, and strategies, to guide its future direction.
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Affiliation(s)
- Ke-Xin Cao
- State Key Laboratory of Agricultural Microbiology, Hubei Hongshan Laboratory, Frontiers Science Center for Animal Breeding and Sustainable Production, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Zhang-Chao Deng
- State Key Laboratory of Agricultural Microbiology, Hubei Hongshan Laboratory, Frontiers Science Center for Animal Breeding and Sustainable Production, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Shi-Jun Li
- State Key Laboratory of Agricultural Microbiology, Hubei Hongshan Laboratory, Frontiers Science Center for Animal Breeding and Sustainable Production, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Dan Yi
- Hubei Key Laboratory of Animal Nutrition and Feed Science, Wuhan Polytechnic University, Wuhan, Hubei, China
| | - Xi He
- College of Animal Science and Technology, Hunan Agricultural University, Changsha, China
| | - Xiao-Jun Yang
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Yu-Ming Guo
- State Key Laboratory of Animal Nutrition, Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, China Agricultural University, Beijing, China.
| | - Lv-Hui Sun
- State Key Laboratory of Agricultural Microbiology, Hubei Hongshan Laboratory, Frontiers Science Center for Animal Breeding and Sustainable Production, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China.
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Wang J, Xing S, Zhou Y, Liu J, Sun Y, Lei Q, Han H, Liu W, Li D, Li F, Cao D. Research note: A low-density SNP genotyping panel for Chinese native chickens. Poult Sci 2024; 104:104609. [PMID: 39642751 DOI: 10.1016/j.psj.2024.104609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 08/07/2024] [Accepted: 11/26/2024] [Indexed: 12/09/2024] Open
Abstract
The efficiency of molecular breeding largely depends on the cost of genotyping, which plays an important role in breeding and population genetic studies. Compared to pig, cattle, and sheep, poultry has a small size genome, and lower individual value. Developing a relatively low-density genotyping panel is needed to decrease the genotyping cost for poultries. In the present study, we developed a chicken 11 K single nucleotide polymorphism (SNP) genotyping panel for breeding and genetic analysis using genotyping by targeted sequencing (GBTS). The panel design was based on whole genome sequencing data from 7 local breeds and a commercial breed. In total 11,200 SNPs were included in the final chip. Population analyses of the eight breeds showed high SNP call rates and minor allele frequencies (MAF), enabling clear differentiation between populations. The families within population can also be identified. Additionally, the 11K panel was applied to a genomic prediction of Wenshang Barred chickens, the predictive ability and prediction accuracy between the WGS and chip dataset non-significantly differed. Therefore, the newly developed chicken low-density SNP is efficient, cost-effective, and well-suited for application in local Chinese chickens, which can accelerate chicken breeding improvements and enhance conservation efforts.
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Affiliation(s)
- Jie Wang
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, 250023, Shandong, China; Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, 250023, Shandong, China; Shandong Provincial Key Laboratory of Livestock and Poultry Breeding (PKL2024B15), Jinan, 250023, Shandong, China
| | - Siyuan Xing
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, 050035, China
| | - Yan Zhou
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, 250023, Shandong, China; Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, 250023, Shandong, China; Shandong Provincial Key Laboratory of Livestock and Poultry Breeding (PKL2024B15), Jinan, 250023, Shandong, China
| | - Jie Liu
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, 250023, Shandong, China; Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, 250023, Shandong, China; Shandong Provincial Key Laboratory of Livestock and Poultry Breeding (PKL2024B15), Jinan, 250023, Shandong, China
| | - Yan Sun
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, 250023, Shandong, China; Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, 250023, Shandong, China; Shandong Provincial Key Laboratory of Livestock and Poultry Breeding (PKL2024B15), Jinan, 250023, Shandong, China
| | - Qiuxia Lei
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, 250023, Shandong, China; Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, 250023, Shandong, China; Shandong Provincial Key Laboratory of Livestock and Poultry Breeding (PKL2024B15), Jinan, 250023, Shandong, China
| | - Haixia Han
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, 250023, Shandong, China; Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, 250023, Shandong, China; Shandong Provincial Key Laboratory of Livestock and Poultry Breeding (PKL2024B15), Jinan, 250023, Shandong, China
| | - Wei Liu
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, 250023, Shandong, China; Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, 250023, Shandong, China; Shandong Provincial Key Laboratory of Livestock and Poultry Breeding (PKL2024B15), Jinan, 250023, Shandong, China
| | - Dapeng Li
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, 250023, Shandong, China; Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, 250023, Shandong, China; Shandong Provincial Key Laboratory of Livestock and Poultry Breeding (PKL2024B15), Jinan, 250023, Shandong, China
| | - Fuwei Li
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, 250023, Shandong, China; Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, 250023, Shandong, China; Shandong Provincial Key Laboratory of Livestock and Poultry Breeding (PKL2024B15), Jinan, 250023, Shandong, China
| | - Dingguo Cao
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, 250023, Shandong, China; Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, 250023, Shandong, China; Shandong Provincial Key Laboratory of Livestock and Poultry Breeding (PKL2024B15), Jinan, 250023, Shandong, China.
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Jin H, Wang H, Wu J, Hu M, Zhou X, Yang S, Zhao A, He K. Asparagine synthetase regulates the proliferation and differentiation of chicken skeletal muscle satellite cells. Anim Biosci 2024; 37:1848-1862. [PMID: 39210809 PMCID: PMC11541025 DOI: 10.5713/ab.24.0271] [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: 04/25/2024] [Revised: 05/16/2024] [Accepted: 07/10/2024] [Indexed: 09/04/2024] Open
Abstract
OBJECTIVE Asparagine synthetase (ASNS) is an aminotransferase responsible for the biosynthesis of aspartate by using aspartic acid and glutamine. ASNS is highly expressed in fast-growing broilers, but few studies have reported the regulatory role of ASNS in muscle development. METHODS To explore the function of ASNS in chicken muscle development, the expression of ASNS in different chicken breeds and tissues were first performed by real-time quantitative reverse transcription polymerase chain reaction (RT-PCR). Then, using real-time quantitative RT-PCR, western blot, EdU assay, cell cycle assay and immunofluorescence, the effects of ASNS on the proliferation and differentiation of chicken skeletal muscle satellite cell (SMSC) were investigated. Finally, potential mechanisms by which ASNS influences chicken muscle fiber differentiation were identified through RNA-Seq. RESULTS The mRNA expression pattern of ASNS in muscles mirrors trends in muscle fiber cross-sectional area, average daily weight gain, and muscle weight across different breeds. ASNS knockdown inhibited SMSC proliferation, while overexpression showed the opposite. Moreover, ASNS attenuated SMSC differentiation by activating the adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) pathway. Additionally, 5-aminoimidazole4-carboxamide1-β-D-ribofuranoside (AICAR) treatment suppressed the cell differentiation induced by siRNA-ASNS. RNA-Seq identified 1,968 differentially expressed genes (DEGs) during chicken SMSC differentiation when overexpression ASNS. Gene ontology (GO) enrichment analysis revealed that these DEGs primarily participated in 8 biological processes, 8 cellular components, and 4 molecular functions. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis identified several significantly enriched signaling pathways, such as the JAK-STAT signaling pathway, tumor necrosis factor signaling pathway, toll-like receptor signaling pathway, and PI3K-Akt signaling pathway. CONCLUSION ASNS promotes proliferation while inhibits the differentiation of chicken SMSCs. This study provides a theoretical basis for studying the role of ASNS in muscle development.
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Affiliation(s)
- Hangfeng Jin
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, Zhejiang, 311300,
China
| | - Han Wang
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, Zhejiang, 311300,
China
| | - Jianqing Wu
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, Zhejiang, 311300,
China
| | - Moran Hu
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, Zhejiang, 311300,
China
| | - Xiaolong Zhou
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, Zhejiang, 311300,
China
| | - Songbai Yang
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, Zhejiang, 311300,
China
| | - Ayong Zhao
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, Zhejiang, 311300,
China
| | - Ke He
- Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, Zhejiang, 311300,
China
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Kour A, Chatterjee RN, Rajaravindra KS, Prince LLL, Haunshi S, Niranjan M, Reddy BLN, Rajkumar U. Delineating maternal influence in regulation of variance in major economic traits of White Leghorns: Bayesian insights. PLoS One 2024; 19:e0307987. [PMID: 39058757 PMCID: PMC11280281 DOI: 10.1371/journal.pone.0307987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 07/14/2024] [Indexed: 07/28/2024] Open
Abstract
Proper variance partitioning and estimation of genetic parameters at appropriate time interval is crucial for understanding the dynamics of trait variance and genetic correlations and for deciding the future breeding strategy of the population. This study was conducted on the same premise to estimate genetic parameters of major economic traits in a White Leghorn strain IWH using Bayesian approach and to identify the role of maternal effects in the regulation of trait variance. Three different models incorporating the direct additive effect (Model 1), direct additive and maternal genetic effect (Model 2) and direct additive, maternal genetic and maternal permanent environmental effects (Model 3) were tried to estimate the genetic parameters for body weight traits (birth weight, body weight at 16, 20, 40 and 52 weeks), Age at sexual maturity (ASM), egg production traits (egg production up to 24, 28, 40, 52, 64 and 72 weeks) and egg weight traits (egg weight at 28, 40 and 52 weeks). Model 2 and Model 3 with maternal effects were found to be the best having the highest accuracy for almost all the traits. The direct additive genetic heritability was moderate for ASM, moderate to high for body weight traits and egg weight traits and low to moderate for egg production traits. Though the maternal heritability (h2mat) and permanent environmental effect (c2mpe) was low (<0.1) for most of the traits, they formed an important component of trait variance. Traits like egg weight at 28 weeks (0.14±0.06) and egg production at 72 weeks (0.13±0.07) reported comparatively higher values for c2mpe and h2mat respectively. Additive genetic correlation was high and positive between body weight traits, between egg weight traits, between consecutive egg production traits and between body weight and egg weight traits. However, a negative genetic correlation existed between egg production and egg weight traits, egg production and body weight traits, ASM and early egg production traits. Overall, a moderate positive genetic correlation was estimated between ASM and body weight traits and ASM and egg weight traits. Based on our findings, we can deduce that maternal effects constitute an important source of variation for all the major economic traits in White Leghorn and should be necessarily considered in genetic evaluation programs.
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Affiliation(s)
- Aneet Kour
- Poultry Genetics and Breeding Division, ICAR-Directorate of Poultry Research, Hyderabad, Telangana, India
| | - R. N. Chatterjee
- Poultry Genetics and Breeding Division, ICAR-Directorate of Poultry Research, Hyderabad, Telangana, India
| | - K. S. Rajaravindra
- Poultry Genetics and Breeding Division, ICAR-Directorate of Poultry Research, Hyderabad, Telangana, India
| | - L. Leslie Leo Prince
- Poultry Genetics and Breeding Division, ICAR-Directorate of Poultry Research, Hyderabad, Telangana, India
| | - Santosh Haunshi
- Poultry Genetics and Breeding Division, ICAR-Directorate of Poultry Research, Hyderabad, Telangana, India
| | - M. Niranjan
- Poultry Genetics and Breeding Division, ICAR-Directorate of Poultry Research, Hyderabad, Telangana, India
| | - B. L. N. Reddy
- Poultry Genetics and Breeding Division, ICAR-Directorate of Poultry Research, Hyderabad, Telangana, India
| | - U. Rajkumar
- Poultry Genetics and Breeding Division, ICAR-Directorate of Poultry Research, Hyderabad, Telangana, India
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Gu S, Huang Q, Jie Y, Sun C, Wen C, Yang N. Transcriptomic and epigenomic landscapes of muscle growth during the postnatal period of broilers. J Anim Sci Biotechnol 2024; 15:91. [PMID: 38961455 PMCID: PMC11223452 DOI: 10.1186/s40104-024-01049-w] [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: 02/04/2024] [Accepted: 05/12/2024] [Indexed: 07/05/2024] Open
Abstract
BACKGROUND Broilers stand out as one of the fastest-growing livestock globally, making a substantial contribution to animal meat production. However, the molecular and epigenetic mechanisms underlying the rapid growth and development of broiler chickens are still unclear. This study aims to explore muscle development patterns and regulatory networks during the postnatal rapid growth phase of fast-growing broilers. We measured the growth performance of Cornish (CC) and White Plymouth Rock (RR) over a 42-d period. Pectoral muscle samples from both CC and RR were randomly collected at day 21 after hatching (D21) and D42 for RNA-seq and ATAC-seq library construction. RESULTS The consistent increase in body weight and pectoral muscle weight across both breeds was observed as they matured, with CC outpacing RR in terms of weight at each stage of development. Differential expression analysis identified 398 and 1,129 genes in the two dimensions of breeds and ages, respectively. A total of 75,149 ATAC-seq peaks were annotated in promoter, exon, intron and intergenic regions, with a higher number of peaks in the promoter and intronic regions. The age-biased genes and breed-biased genes of RNA-seq were combined with the ATAC-seq data for subsequent analysis. The results spotlighted the upregulation of ACTC1 and FDPS at D21, which were primarily associated with muscle structure development by gene cluster enrichment. Additionally, a noteworthy upregulation of MUSTN1, FOS and TGFB3 was spotted in broiler chickens at D42, which were involved in cell differentiation and muscle regeneration after injury, suggesting a regulatory role of muscle growth and repair. CONCLUSIONS This work provided a regulatory network of postnatal broiler chickens and revealed ACTC1 and MUSTN1 as the key responsible for muscle development and regeneration. Our findings highlight that rapid growth in broiler chickens triggers ongoing muscle damage and subsequent regeneration. These findings provide a foundation for future research to investigate the functional aspects of muscle development.
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Affiliation(s)
- Shuang Gu
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
| | - Qiang Huang
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
| | - Yuchen Jie
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
| | - Congjiao Sun
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
- Sanya Institute of China Agricultural University, Hainan, 572025, China
| | - Chaoliang Wen
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
- Sanya Institute of China Agricultural University, Hainan, 572025, China
| | - Ning Yang
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China.
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China.
- Sanya Institute of China Agricultural University, Hainan, 572025, China.
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Zhang Y, Wang H, Li X, Yang C, Yu C, Cui Z, Liu A, Wang Q, Liu L. Genome-wide characteristics and potential functions of circular RNAs from the embryo muscle development in Chengkou mountain chicken. Front Vet Sci 2024; 11:1375042. [PMID: 38872802 PMCID: PMC11171140 DOI: 10.3389/fvets.2024.1375042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/29/2024] [Indexed: 06/15/2024] Open
Abstract
The Chengkou mountain chicken, a native Chinese poultry breed, holds significant importance in the country's poultry sector due to its delectable meat and robust stress tolerance. Muscle growth and development are pivotal characteristics in poultry breeding, with muscle fiber development during the embryonic period crucial for determining inherent muscle growth potential. Extensive evidence indicates that non-coding RNAs (ncRNAs) play a regulatory role in muscle growth and development. Among ncRNAs, circular RNAs (circRNAs), characterized by a closed-loop structure, have been shown to modulate biological processes through the regulation of microRNAs (miRNAs). This study seeks to identify and characterize the spatiotemporal-specific expression of circRNAs during embryonic muscle development in Chengkou mountain chicken, and to construct the potential regulatory network of circRNAs-miRNA-mRNAs. The muscle fibers of HE-stained sections became more distinct, and their boundaries were more defined over time. Subsequent RNA sequencing of 12 samples from four periods generated 9,904 novel circRNAs, including 917 differentially expressed circRNAs. The weighted gene co-expression network analysis (WGCNA)-identified circRNA source genes significantly enriched pathways related to cell fraction, cell growth, and muscle fiber growth regulation. Furthermore, a competitive endogenous RNA (ceRNA) network constructed using combined data of present and previous differentially expressed circRNAs, miRNA, and mRNA revealed that several circRNA transcripts regulate MYH1D, MYH1B, CAPZA1, and PERM1 proteins. These findings provide insight into the potential pathways and mechanisms through which circRNAs regulate embryonic muscle development in poultry, a theoretical support for trait improvement in domestic chickens.
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Affiliation(s)
- Yang Zhang
- College of Animal Science and Technology, Southwest University, Chongqing, China
| | - Haiwei Wang
- Chongqing Academy of Animal Sciences, Chongqing, China
| | - Xingqi Li
- College of Animal Science and Technology, Southwest University, Chongqing, China
| | - Chaowu Yang
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, China
| | - Chunlin Yu
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, China
| | - Zhifu Cui
- College of Animal Science and Technology, Southwest University, Chongqing, China
| | - Anfang Liu
- College of Animal Science and Technology, Southwest University, Chongqing, China
| | - Qigui Wang
- Chongqing Academy of Animal Sciences, Chongqing, China
| | - Lingbin Liu
- College of Animal Science and Technology, Southwest University, Chongqing, China
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7
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Dementieva NV, Shcherbakov YS, Stanishevskaya OI, Vakhrameev AB, Larkina TA, Dysin AP, Nikolaeva OA, Ryabova AE, Azovtseva AI, Mitrofanova OV, Peglivanyan GK, Reinbach NR, Griffin DK, Romanov MN. Large-scale genome-wide SNP analysis reveals the rugged (and ragged) landscape of global ancestry, phylogeny, and demographic history in chicken breeds. J Zhejiang Univ Sci B 2024; 25:324-340. [PMID: 38584094 PMCID: PMC11009443 DOI: 10.1631/jzus.b2300443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/10/2023] [Indexed: 04/09/2024]
Abstract
The worldwide chicken gene pool encompasses a remarkable, but shrinking, number of divergently selected breeds of diverse origin. This study was a large-scale genome-wide analysis of the landscape of the complex molecular architecture, genetic variability, and detailed structure among 49 populations. These populations represent a significant sample of the world's chicken breeds from Europe (Russia, Czech Republic, France, Spain, UK, etc.), Asia (China), North America (USA), and Oceania (Australia). Based on the results of breed genotyping using the Illumina 60K single nucleotide polymorphism (SNP) chip, a bioinformatic analysis was carried out. This included the calculation of heterozygosity/homozygosity statistics, inbreeding coefficients, and effective population size. It also included assessment of linkage disequilibrium and construction of phylogenetic trees. Using multidimensional scaling, principal component analysis, and ADMIXTURE-assisted global ancestry analysis, we explored the genetic structure of populations and subpopulations in each breed. An overall 49-population phylogeny analysis was also performed, and a refined evolutionary model of chicken breed formation was proposed, which included egg, meat, dual-purpose types, and ambiguous breeds. Such a large-scale survey of genetic resources in poultry farming using modern genomic methods is of great interest both from the viewpoint of a general understanding of the genetics of the domestic chicken and for the further development of genomic technologies and approaches in poultry breeding. In general, whole genome SNP genotyping of promising chicken breeds from the worldwide gene pool will promote the further development of modern genomic science as applied to poultry.
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Affiliation(s)
- Natalia V Dementieva
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia.
| | - Yuri S Shcherbakov
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Olga I Stanishevskaya
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Anatoly B Vakhrameev
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Tatiana A Larkina
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Artem P Dysin
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Olga A Nikolaeva
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Anna E Ryabova
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Anastasiia I Azovtseva
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Olga V Mitrofanova
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Grigoriy K Peglivanyan
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Natalia R Reinbach
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Darren K Griffin
- School of Biosciences, University of Kent, Canterbury, CT2 7NJ, UK. ,
| | - Michael N Romanov
- School of Biosciences, University of Kent, Canterbury, CT2 7NJ, UK. ,
- L K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk, Moscow Oblast, 142132, Russia. ,
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Tan X, Zhang J, Dong J, Huang M, Li Q, Wang H, Bai L, Cui M, Zhou Z, Yang S, Wang D. Whole-genome variants dataset of 209 local chickens from China. Sci Data 2024; 11:169. [PMID: 38316816 PMCID: PMC10844214 DOI: 10.1038/s41597-024-02995-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 01/25/2024] [Indexed: 02/07/2024] Open
Abstract
Compared to commercial chickens, local breeds exhibit better in meat quality and flavour, but the productivity (e.g., growth rate, body weight) of local chicken breeds is rather low. Genetic analysis based on whole-genome sequencing contributes to elucidating the genetic markers or putative candidate genes related to some economic traits, facilitating the improvement of production performance, the acceleration of breeding progress, and the conservation of genetic resources. Here, a total of 209 local chickens from 13 breeds were investigated, and the observation of approximately 91.4% high-quality sequences (Q30 > 90%) and a mapping rate over 99% for each individual indicated good results of this study, as confirmed by a genome coverage of 97.6%. Over 19 million single nucleotide polymorphisms (SNPs) and 1.98 million insertion-deletions (InDels) were identified using the reference genome (GRCg7b), further contributing to the public database. This dataset provides valuable resources for studying genetic diversity and adaptation and for the cultivation of new chicken breeds/lines.
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Affiliation(s)
- Xiaodong Tan
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Jiawen Zhang
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Jie Dong
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Minjie Huang
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Qinghai Li
- Animal Husbandry Institute, Hangzhou Academy of Agricultural Sciences, Hangzhou, 310024, China
| | - Huanhuan Wang
- Animal Husbandry Institute, Hangzhou Academy of Agricultural Sciences, Hangzhou, 310024, China
| | - Lijuan Bai
- Zhejiang Animal Husbandry Technology Extension and Breeding Livestock and Poultry Monitoring Station, Hangzhou, 310020, China
| | - Ming Cui
- Zhejiang Animal Husbandry Technology Extension and Breeding Livestock and Poultry Monitoring Station, Hangzhou, 310020, China
| | - Zhenzhen Zhou
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Shuyuan Yang
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Deqian Wang
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China.
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Cai W, Hu J, Fan W, Xu Y, Tang J, Xie M, Zhang Y, Guo Z, Zhou Z, Hou S. Genetic parameters and genomic prediction of growth and breast morphological traits in a crossbreed duck population. Evol Appl 2024; 17:e13638. [PMID: 38333555 PMCID: PMC10848588 DOI: 10.1111/eva.13638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 09/02/2023] [Accepted: 12/07/2023] [Indexed: 02/10/2024] Open
Abstract
Genomic selection (GS) has great potential to increase genetic gain in poultry breeding. However, the performance of genomic prediction in duck growth and breast morphological (BM) traits remains largely unknown. The objective of this study was to evaluate the benefits of genomic prediction for duck growth and BM traits using methods such as GBLUP, single-step GBLUP, Bayesian models, and different marker densities. This study collected phenotypic data for 14 growth and BM traits in a crossbreed population of 1893 Pekin duck × mallard, which included 941 genotyped ducks. The estimation of genetic parameters indicated high heritabilities for body weight (0.54-0.72), whereas moderate-to-high heritabilities for average daily gain (0.21-0.57) traits. The heritabilities of BM traits ranged from low to moderate (0.18-0.39). The prediction ability of GS on growth and BM traits increased by 7.6% on average compared to the pedigree-based BLUP method. The single-step GBLUP outperformed GBLUP in most traits with an average of 0.3% higher reliability in our study. Most of the Bayesian models had better performance on predictive reliability, except for BayesR. BayesN emerged as the top-performing model for genomic prediction of both growth and BM traits, exhibiting an average increase in reliability of 3.0% compared to GBLUP. The permutation studies revealed that 50 K markers had achieved ideal prediction reliability, while 3 K markers still achieved 90.8% predictive capability would further reduce the cost for duck growth and BM traits. This study provides promising evidence for the application of GS in improving duck growth and BM traits. Our findings offer some useful strategies for optimizing the predictive ability of GS in growth and BM traits and provide theoretical foundations for designing a low-density panel in ducks.
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Affiliation(s)
- Wentao Cai
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Jian Hu
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Wenlei Fan
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
- College of Animal Science and TechnologyQingdao Agricultural UniversityQingdaoChina
| | - Yaxi Xu
- College of Animal Science and TechnologyBeijing University of AgricultureBeijingChina
| | - Jing Tang
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Ming Xie
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Yunsheng Zhang
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Zhanbao Guo
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Zhengkui Zhou
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Shuisheng Hou
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
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Tan X, Liu R, Zhao D, He Z, Li W, Zheng M, Li Q, Wang Q, Liu D, Feng F, Zhu D, Zhao G, Wen J. Large-scale genomic and transcriptomic analyses elucidate the genetic basis of high meat yield in chickens. J Adv Res 2024; 55:1-16. [PMID: 36871617 PMCID: PMC10770282 DOI: 10.1016/j.jare.2023.02.016] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/16/2023] [Accepted: 02/26/2023] [Indexed: 03/07/2023] Open
Abstract
INTRODUCTION Investigating the genetic markers and genomic signatures related to chicken meat production by combing multi-omics methods could provide new insights into modern chicken breeding technology systems. OBJECT Chicken is one of the most efficient and environmentally friendly livestock, especially the fast-growing white-feathered chicken (broiler), which is well known for high meat yield, but the underlying genetic basis is poorly understood. METHOD We generated whole-genome resequencing of three purebred broilers (n = 748) and six local breeds/lines (n = 114), and sequencing data of twelve chicken breeds (n = 199) were obtained from the NCBI database. Additionally, transcriptome sequencing of six tissues from two chicken breeds (n = 129) at two developmental stages was performed. A genome-wide association study combined with cis-eQTL mapping and the Mendelian randomization was applied. RESULT We identified > 17 million high-quality SNPs, of which 21.74% were newly identified, based on 21 chicken breeds/lines. A total of 163 protein-coding genes underwent positive selection in purebred broilers, and 83 genes were differentially expressed between purebred broilers and local chickens. Notably, muscle development was proven to be the major difference between purebred broilers and local chickens, or ancestors, based on genomic and transcriptomic evidence from multiple tissues and stages. The MYH1 gene family showed the top selection signatures and muscle-specific expression in purebred broilers. Furthermore, we found that the causal gene SOX6 influenced breast muscle yield and also related to myopathy occurrences. A refined haplotype was provided, which had a significant effect on SOX6 expression and phenotypic changes. CONCLUSION Our study provides a comprehensive atlas comprising the typical genomic variants and transcriptional characteristics for muscle development and suggests a new regulatory target (SOX6-MYH1s axis) for breast muscle yield and myopathy, which could aid in the development of genome-scale selective breeding aimed at high meat yield in broiler chickens.
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Affiliation(s)
- Xiaodong Tan
- State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Ranran Liu
- State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Di Zhao
- State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Zhengxiao He
- State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Wei Li
- State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Maiqing Zheng
- State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Qinghe Li
- State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Qiao Wang
- State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Dawei Liu
- Foshan Gaoming Xinguang Agricultural and Animal Industrials Corporation, Foshan 528515, China
| | - Furong Feng
- Foshan Gaoming Xinguang Agricultural and Animal Industrials Corporation, Foshan 528515, China
| | - Dan Zhu
- Foshan Gaoming Xinguang Agricultural and Animal Industrials Corporation, Foshan 528515, China
| | - Guiping Zhao
- State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| | - Jie Wen
- State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
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Wang H, Zhao X, Wen J, Wang C, Zhang X, Ren X, Zhang J, Li H, Muhatai G, Qu L. Comparative population genomics analysis uncovers genomic footprints and genes influencing body weight trait in Chinese indigenous chicken. Poult Sci 2023; 102:103031. [PMID: 37716235 PMCID: PMC10511812 DOI: 10.1016/j.psj.2023.103031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/27/2023] [Accepted: 08/11/2023] [Indexed: 09/18/2023] Open
Abstract
Body weight of chicken is a typical quantitative trait, which shows phenotypic variations due to selective breeding. Despite some QTL loci have been obtained, the body weight of native chicken breeds in different geographic regions varies greatly, its genetic basis remains unresolved questions. To address this issue, we analyzed 117 Chinese indigenous chickens from 10 breeds (Huiyang Bearded, Xinhua, Hotan Black, Baicheng You, Liyang, Yunyang Da, Jining Bairi, Lindian, Beijing You, Tibetan). We applied fixation index (FST) analysis to find selected genomic regions and genes associated with body weight traits. Our study suggests that NELL1, XYLT1, and NCAPG/LCORL genes are strongly selected in the body weight trait of Chinese indigenous chicken breeds. In addition, the IL1RAPL1 gene was strongly selected in large body weight chickens, while the PCDH17 and CADM2 genes were strongly selected in small body weight chickens. This result suggests that the patterns of genetic variation of native chicken and commercial chicken, and/or distinct local chicken breeds may follow different evolutionary mechanisms.
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Affiliation(s)
- Huie Wang
- Xinjiang Production & Construction Corps Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin, College of Life Science and Technology, College of Animal Science and Technology, Tarim University, Alar 843300, China
| | - Xiurong Zhao
- State Key Laboratory of Animal Nutrition, Department of Animal Genetics and Breeding, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Junhui Wen
- State Key Laboratory of Animal Nutrition, Department of Animal Genetics and Breeding, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Chengqian Wang
- Xinjiang Production & Construction Corps Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin, College of Life Science and Technology, College of Animal Science and Technology, Tarim University, Alar 843300, China
| | - Xinye Zhang
- State Key Laboratory of Animal Nutrition, Department of Animal Genetics and Breeding, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Xufang Ren
- State Key Laboratory of Animal Nutrition, Department of Animal Genetics and Breeding, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jinxin Zhang
- State Key Laboratory of Animal Nutrition, Department of Animal Genetics and Breeding, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Haiying Li
- College of Animal Science, Xinjiang Agricultural University, Urumqi 830000, China
| | - Gemingguli Muhatai
- Xinjiang Production & Construction Corps Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin, College of Life Science and Technology, College of Animal Science and Technology, Tarim University, Alar 843300, China
| | - Lujiang Qu
- Xinjiang Production & Construction Corps Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin, College of Life Science and Technology, College of Animal Science and Technology, Tarim University, Alar 843300, China; State Key Laboratory of Animal Nutrition, Department of Animal Genetics and Breeding, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
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12
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Wu F, Chen Z, Zhang Z, Wang Z, Zhang Z, Wang Q, Pan Y. The Role of SOCS3 in Regulating Meat Quality in Jinhua Pigs. Int J Mol Sci 2023; 24:10593. [PMID: 37445769 DOI: 10.3390/ijms241310593] [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: 05/24/2023] [Revised: 06/15/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
Meat quality is an important economic trait that influences the development of the pig industry. Skeletal muscle development and glycolytic potential (GP) are two crucial aspects that significantly impact meat quality. It has been reported that abnormal skeletal muscle development and high glycogen content results in low meat quality. However, the genetic mechanisms underlying these factors are still unclear. Compared with intensive pig breeds, Chinese indigenous pig breeds, such as the Jinhua pig, express superior meat quality characteristics. The differences in the meat quality traits between Jinhua and intensive pig breeds make them suitable for uncovering the genetic mechanisms that regulate meat quality traits. In this study, the Jinhua pig breed and five intensive pig breeds, including Duroc, Landrace, Yorkshire, Berkshire, and Pietrain pig breeds, were selected as experimental materials. First, the FST and XP-EHH methods were used to screen the selective signatures on the genome in the Jinhua population. Then, combined with RNA-Seq data, the study further confirmed that SOCS3 could be a key candidate gene that influences meat quality by mediating myoblast proliferation and glycometabolism because of the down-regulated expression of SOCS3 in Jinhua pigs compared with Landrace pigs. Finally, through SOCS3 knockout (KO) and overexpression (OE) experiments in mouse C2C12 cells, the results showed that SOCS3 regulated the cell proliferation of myoblasts. Moreover, SOCS3 is involved in regulating glucose uptake by the IRS1/PI3K/AKT signaling pathway. Overall, these findings provide a basis for the genetic improvement of meat quality traits in the pig industry.
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Affiliation(s)
- Fen Wu
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
| | - Zitao Chen
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
| | - Zhenyang Zhang
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
| | - Zhen Wang
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
| | - Zhe Zhang
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
| | - Qishan Wang
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yuchun Pan
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
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Zhang X, Li Y, Zhu C, Li F, Liu Z, Li X, Shen X, Wu Z, Fu M, Xu D, Tian Y, Huang Y. DNA Demethylation of Myogenic Genes May Contribute to Embryonic Leg Muscle Development Differences between Wuzong and Shitou Geese. Int J Mol Sci 2023; 24:ijms24087188. [PMID: 37108353 PMCID: PMC10138404 DOI: 10.3390/ijms24087188] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/05/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023] Open
Abstract
Skeletal muscle development from embryonic stages to hatching is critical for poultry muscle growth, during which DNA methylation plays a vital role. However, it is not yet clear how DNA methylation affects early embryonic muscle development between goose breeds of different body size. In this study, whole genome bisulfite sequencing (WGBS) was conducted on leg muscle tissue from Wuzong (WZE) and Shitou (STE) geese on embryonic day 15 (E15), E23, and post-hatch day 1. It was found that at E23, the embryonic leg muscle development of STE was more intense than that of WZE. A negative correlation was found between gene expression and DNA methylation around transcription start sites (TSSs), while a positive correlation was observed in the gene body near TTSs. It was also possible that earlier demethylation of myogenic genes around TSSs contributes to their earlier expression in WZE. Using pyrosequencing to analyze DNA methylation patterns of promoter regions, we also found that earlier demethylation of the MyoD1 promoter in WZE contributed to its earlier expression. This study reveals that DNA demethylation of myogenic genes may contribute to embryonic leg muscle development differences between Wuzong and Shitou geese.
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Affiliation(s)
- Xumeng Zhang
- College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Yong Li
- College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Chenyu Zhu
- College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Fada Li
- College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Zhiyuan Liu
- College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Xiujin Li
- College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Xu Shen
- College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Zhongping Wu
- College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Mengsi Fu
- College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Danning Xu
- College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Yunbo Tian
- College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Yunmao Huang
- College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
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Trait Analysis in Domestic Rabbits (Oryctolagus cuniculus f. domesticus) Using SNP Markers from Genotyping-by-Sequencing Data. Animals (Basel) 2022; 12:ani12162052. [PMID: 36009642 PMCID: PMC9404428 DOI: 10.3390/ani12162052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Rabbit breeding is an important branch of agricultural animal breeding; their fur color and weight are desirable traits for artificial breeding. Polymorphism can provide potential molecular markers for studying rabbit traits and improve rabbit breeds with such markers in the future. In this study, single-nucleotide polymorphism markers in genotyping-by-sequencing data were used to analyze rabbit traits. In total, three genes were identified to be associated with fur color and four with weight. The results of this study provide a data base for the research and improvement of rabbit breeding program. Abstract The domestic rabbit (Oryctolagus cuniculus f. domesticus) is a very important variety in biomedical research and agricultural animal breeding. Due to the different geographical areas in which rabbit breeds originated, and the long history of domestication/artificial breeding, rabbits have experienced strong selection pressure, which has shaped many traits of most rabbit varieties, such as color and weight. An efficient genome-wide single-nucleotide polymorphism (SNP) detection strategy is genotyping-by-sequencing (GBS), which has been widely used in many organisms. This study attempted to explore bi-allelic SNPs associated with fur color and weight-related traits using GBS in five rabbit breeds. The data consisted of a total 831,035 SNPs in 150 individuals from Californian rabbits (CF), German Zika rabbits (ZK), Qixing rabbits (QX), Sichuan grey rabbits (SG), and Sichuan white rabbits (SW). In addition, these five breeds of rabbits were obviously independent populations, with high genetic differentiation among breeds and low genetic diversity within breeds. A total of 32,144 SNP sites were identified by selective sweep among the different varieties. The genes that carried SNP loci in these selected regions were related to important traits (fur color and weight) and signal pathways, such as the MAPK/ERK signaling pathway and the Hippo signaling pathway. In addition, genes related to fur color and weight were identified, such as ASIPs, MITFs and KITs, ADCY3s, YAPs, FASs, and ACSL5s, and they had more SNP sites. The research offers the foundation for further exploration of molecular genetic markers of SNPs that are related to traits.
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