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Lu J, Yuan H, Liu S, Liu Y, Qin Z, Han W, Zhang R. Gene coexpression network analysis reveals the genes and pathways in pectoralis major muscle and liver associated with wooden breast in broilers. Poult Sci 2024; 103:104056. [PMID: 39094498 DOI: 10.1016/j.psj.2024.104056] [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: 04/08/2024] [Revised: 06/25/2024] [Accepted: 06/27/2024] [Indexed: 08/04/2024] Open
Abstract
Wooden breast (WB) is a myopathy mainly affecting pectoralis major (PM) muscle in modern commercial broiler chickens, causing enormous economic losses in the poultry industry. Recent studies have observed hepatic and PM muscle injury in broilers affected by WB, but the relationships between WB and the 2 tissues are mostly unclear. In the current study, the RNA-seq raw data of PM muscle and liver were downloaded from GSE144000, and we constructed the gene coexpression networks of PM muscle and liver to explore the relationships between WB and the 2 tissues using the weighted gene coexpression network analysis (WGCNA) method. Six and 2 gene coexpression modules were significantly correlated with WB in the PM muscle and liver networks, respectively. TGF-beta signaling, Toll-like receptor signaling and mTOR signaling pathways were significantly enriched in the genes within the 6 gene modules of PM muscle network. Meanwhile, mTOR signaling pathway was significantly enriched in the genes within the 2 gene modules of liver network. In the consensus gene coexpression network across the 2 tissues, salmon module (r = -0.5 and p = 0.05) was significantly negatively correlated with WB, in which Toll-like receptor signaling, apoptosis, and autophagy pathways were significantly enriched. The genes related with the 3 pathways, myeloid differentiation primary response 88 (MYD88), interferon regulatory factor 7 (IRF7), mitogen-activated protein kinase 14 (MAPK14), FBJ murine osteosarcoma viral oncogene homolog (FOS), jun proto-oncogene (JUN), caspase-10, unc-51 like autophagy activating kinase 2 (ULK2) and serine/threonine kinase 11 (LKB1), were identified in salmon module. In this current study, we found that the signaling pathways related with cell inflammation, apoptosis and autophagy might influence WB across 2 tissues in broilers.
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Affiliation(s)
- Jun Lu
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, Heilongjiang, China
| | - Hui Yuan
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, Heilongjiang, China.
| | - Shengnan Liu
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, Heilongjiang, China
| | - Yuan Liu
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, Heilongjiang, China
| | - Ziwen Qin
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, Heilongjiang, China
| | - Wenpeng Han
- Department of Biotechnology, Jieyang Polytechnic, Jieyang City 522000, Guangdong Province, China
| | - Runxiang Zhang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, Heilongjiang, China
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Gao Y, Liu GE, Ma L, Fang L, Li CJ, Baldwin RL. Transcriptomic profiling of gastrointestinal tracts in dairy cattle during lactation reveals molecular adaptations for milk synthesis. J Adv Res 2024:S2090-1232(24)00257-1. [PMID: 38925453 DOI: 10.1016/j.jare.2024.06.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 06/11/2024] [Accepted: 06/21/2024] [Indexed: 06/28/2024] Open
Abstract
During lactation, dairy cattle's digestive tract requires significant adaptations to meet the increased nutrient demands for milk production. As we attempt to improve milk-related traits through selective pressure, it is crucial to understand the biological functions of the epithelia of the rumen, small intestine, and colonic tissues in response to changes in physiological state driven by changes in nutrient demands for milk synthesis. In this study, we obtained a total of 108 transcriptome profiles from three tissues (epithelia of the colon, duodenum, and rumen) of five Holstein cows, spanning eight time points from the early, mid, late lactation periods to the dry period. On average 97.06% of reads were successfully mapped to the reference genome assembly ARS-UCD1.2. We analyzed 27,607 gene expression patterns at multiple periods, enabling direct comparisons within and among tissues during different lactation stages, including early and peak lactation. We identified 1645, 813, and 2187 stage-specific genes in the colon, duodenum, and rumen, respectively, which were enriched for common or specific biological functions among different tissues. Time series analysis categorized the expressed genes within each tissue into four clusters. Furthermore, when the three tissues were analyzed collectively, 36 clusters of similarly expressed genes were identified. By integrating other comprehensive approaches such as gene co-expression analyses, functional enrichment, and cell type deconvolution, we gained profound insights into cattle lactation, revealing tissue-specific characteristics of the gastrointestinal tract and shedding light on the intricate molecular adaptations involved in nutrient absorption, immune regulation, and cellular processes for milk synthesis during lactation.
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Affiliation(s)
- Yahui Gao
- Animal Genomics and Improvement Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA; Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA; State Key Laboratory of Livestock and Poultry Breeding, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics (QGG), Aarhus University, Aarhus, Denmark
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA
| | - Ransom L Baldwin
- Animal Genomics and Improvement Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA.
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Kuraz Abebe B, Wang J, Guo J, Wang H, Li A, Zan L. A review of the role of epigenetic studies for intramuscular fat deposition in beef cattle. Gene 2024; 908:148295. [PMID: 38387707 DOI: 10.1016/j.gene.2024.148295] [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: 10/26/2023] [Revised: 01/23/2024] [Accepted: 02/15/2024] [Indexed: 02/24/2024]
Abstract
Intramuscular fat (IMF) deposition profoundly influences meat quality and economic value in beef cattle production. Meanwhile, contemporary developments in epigenetics have opened new outlooks for understanding the molecular basics of IMF regulation, and it has become a key area of research for world scholars. Therefore, the aim of this paper was to provide insight and synthesis into the intricate relationship between epigenetic mechanisms and IMF deposition in beef cattle. The methodology involves a thorough analysis of existing literature, including pertinent books, academic journals, and online resources, to provide a comprehensive overview of the role of epigenetic studies in IMF deposition in beef cattle. This review summarizes the contemporary studies in epigenetic mechanisms in IMF regulation, high-resolution epigenomic mapping, single-cell epigenomics, multi-omics integration, epigenome editing approaches, longitudinal studies in cattle growth, environmental epigenetics, machine learning in epigenetics, ethical and regulatory considerations, and translation to industry practices from perspectives of IMF deposition in beef cattle. Moreover, this paper highlights DNA methylation, histone modifications, acetylation, phosphorylation, ubiquitylation, non-coding RNAs, DNA hydroxymethylation, epigenetic readers, writers, and erasers, chromatin immunoprecipitation followed by sequencing, whole genome bisulfite sequencing, epigenome-wide association studies, and their profound impact on the expression of crucial genes governing adipogenesis and lipid metabolism. Nutrition and stress also have significant influences on epigenetic modifications and IMF deposition. The key findings underscore the pivotal role of epigenetic studies in understanding and enhancing IMF deposition in beef cattle, with implications for precision livestock farming and ethical livestock management. In conclusion, this review highlights the crucial significance of epigenetic pathways and environmental factors in affecting IMF deposition in beef cattle, providing insightful information for improving the economics and meat quality of cattle production.
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Affiliation(s)
- Belete Kuraz Abebe
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China; Department of Animal Science, Werabe University, P.O. Box 46, Werabe, Ethiopia
| | - Jianfang Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Juntao Guo
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Hongbao Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Anning Li
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Linsen Zan
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China; National Beef Cattle Improvement Center, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China.
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4
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Wang Y, Huang Y, Zhen Y, Wang J, Wang L, Chen N, Wu F, Zhang L, Shen Y, Bi C, Li S, Pool K, Blache D, Maloney SK, Liu D, Yang Z, Li C, Yu X, Zhang Z, Chen Y, Xue C, Gu Y, Huang W, Yan L, Wei W, Wang Y, Zhang J, Zhang Y, Sun Y, Wang S, Zhao X, Luo C, Wang H, Ding L, Yang QY, Zhou P, Wang M. De novo transcriptome assembly database for 100 tissues from each of seven species of domestic herbivore. Sci Data 2024; 11:488. [PMID: 38734729 PMCID: PMC11088706 DOI: 10.1038/s41597-024-03338-5] [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: 12/13/2023] [Accepted: 05/02/2024] [Indexed: 05/13/2024] Open
Abstract
Domesticated herbivores are an important agricultural resource that play a critical role in global food security, particularly as they can adapt to varied environments, including marginal lands. An understanding of the molecular basis of their biology would contribute to better management and sustainable production. Thus, we conducted transcriptome sequencing of 100 to 105 tissues from two females of each of seven species of herbivore (cattle, sheep, goats, sika deer, horses, donkeys, and rabbits) including two breeds of sheep. The quality of raw and trimmed reads was assessed in terms of base quality, GC content, duplication sequence rate, overrepresented k-mers, and quality score distribution with FastQC. The high-quality filtered RNA-seq raw reads were deposited in a public database which provides approximately 54 billion high-quality paired-end sequencing reads in total, with an average mapping rate of ~93.92%. Transcriptome databases represent valuable resources that can be used to study patterns of gene expression, and pathways that are related to key biological processes, including important economic traits in herbivores.
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Affiliation(s)
- Yifan Wang
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P. R. China
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
- College of Life Science, Guizhou University, Guiyang, 550025, P. R. China
| | - Yiming Huang
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P. R. China
- Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China
| | - Yongkang Zhen
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Jiasheng Wang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Limin Wang
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P. R. China
| | - Ning Chen
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P. R. China
| | - Feifan Wu
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Linna Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China
| | - Yizhao Shen
- College of Animal Science and Technology, Hebei Agricultural University, Baoding, 071033, P. R. China
| | - Congliang Bi
- College of Life Science, Linyi University, Linyi, 276005, P. R. China
| | - Song Li
- College of Life Science, Guizhou University, Guiyang, 550025, P. R. China
| | - Kelsey Pool
- UWA Institute of Agriculture, The University of Western Australia, Perth, WA, 6009, Australia
| | - Dominique Blache
- UWA Institute of Agriculture, The University of Western Australia, Perth, WA, 6009, Australia
| | - Shane K Maloney
- UWA Institute of Agriculture, The University of Western Australia, Perth, WA, 6009, Australia
| | - Dongxu Liu
- Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China
| | - Zhiquan Yang
- Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China
| | - Chuang Li
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Xiang Yu
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Zhenbin Zhang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Yifei Chen
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Chun Xue
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Yalan Gu
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Weidong Huang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Lu Yan
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Wenjun Wei
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Yusu Wang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Jinying Zhang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Yifan Zhang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Yiquan Sun
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China
| | - Shengbo Wang
- Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China
| | - Xinle Zhao
- Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China
| | - Chengfang Luo
- Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China
| | - Haodong Wang
- Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China
| | - Luoyang Ding
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
- UWA Institute of Agriculture, The University of Western Australia, Perth, WA, 6009, Australia.
| | - Qing-Yong Yang
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P. R. China.
- Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China.
| | - Ping Zhou
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P. R. China.
| | - Mengzhi Wang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
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Du X, Sun Y, Fu T, Gao T, Zhang T. Research Progress and Applications of Bovine Genome in the Tribe Bovini. Genes (Basel) 2024; 15:509. [PMID: 38674443 PMCID: PMC11050176 DOI: 10.3390/genes15040509] [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: 03/22/2024] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
Various bovine species have been domesticated and bred for thousands of years, and they provide adequate animal-derived products, including meat, milk, and leather, to meet human requirements. Despite the review studies on economic traits in cattle, the genetic basis of traits has only been partially explained by phenotype and pedigree breeding methods, due to the complexity of genomic regulation during animal development and growth. With the advent of next-generation sequencing technology, genomics projects, such as the 1000 Bull Genomes Project, Functional Annotation of Animal Genomes project, and Bovine Pangenome Consortium, have advanced bovine genomic research. These large-scale genomics projects gave us a comprehensive concept, technology, and public resources. In this review, we summarize the genomics research progress of the main bovine species during the past decade, including cattle (Bos taurus), yak (Bos grunniens), water buffalo (Bubalus bubalis), zebu (Bos indicus), and gayal (Bos frontalis). We mainly discuss the development of genome sequencing and functional annotation, focusing on how genomic analysis reveals genetic variation and its impact on phenotypes in several bovine species.
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Affiliation(s)
- Xingjie Du
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China; (X.D.); (Y.S.); (T.F.); (T.G.)
- Henan International Joint Laboratory of Nutrition Regulation and Ecological Raising of Domestic Animal, College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China
| | - Yu Sun
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China; (X.D.); (Y.S.); (T.F.); (T.G.)
- Henan International Joint Laboratory of Nutrition Regulation and Ecological Raising of Domestic Animal, College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China
| | - Tong Fu
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China; (X.D.); (Y.S.); (T.F.); (T.G.)
- Henan International Joint Laboratory of Nutrition Regulation and Ecological Raising of Domestic Animal, College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China
| | - Tengyun Gao
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China; (X.D.); (Y.S.); (T.F.); (T.G.)
- Henan International Joint Laboratory of Nutrition Regulation and Ecological Raising of Domestic Animal, College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China
| | - Tianliu Zhang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China; (X.D.); (Y.S.); (T.F.); (T.G.)
- Henan International Joint Laboratory of Nutrition Regulation and Ecological Raising of Domestic Animal, College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China
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Wang Y, Jin W, Pan X, Liao W, Shen Q, Cai J, Gong W, Tian Y, Xu D, Li Y, Li J, Gong J, Zhang Z, Yuan X. Pig-eRNAdb: a comprehensive enhancer and eRNA dataset of pigs. Sci Data 2024; 11:157. [PMID: 38302497 PMCID: PMC10834423 DOI: 10.1038/s41597-024-02960-7] [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: 05/31/2023] [Accepted: 01/11/2024] [Indexed: 02/03/2024] Open
Abstract
Enhancers and the enhancer RNAs (eRNAs) have been strongly implicated in regulations of transcriptions. Based the multi-omics data (ATAC-seq, ChIP-seq and RNA-seq) from public databases, Pig-eRNAdb is a dataset that comprehensively integrates enhancers and eRNAs for pigs using the machine learning strategy, which incorporates 82,399 enhancers and 37,803 eRNAs from 607 samples across 15 tissues of pigs. This user-friendly dataset covers a comprehensive depth of enhancers and eRNAs annotation for pigs. The coordinates of enhancers and the expression patterns of eRNAs are downloadable. Besides, thousands of regulators on eRNAs, the target genes of eRNAs, the tissue-specific eRNAs, and the housekeeping eRNAs are also accessible as well as the sequence similarity of eRNAs with humans. Moreover, the tissue-specific eRNA-trait associations encompass 652 traits are also provided. It will crucially facilitate investigations on enhancers and eRNAs with Pig-eRNAdb as a reference dataset in pigs.
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Affiliation(s)
- Yifei Wang
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Weiwei Jin
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xiangchun Pan
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Weili Liao
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Qingpeng Shen
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Jiali Cai
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Wentao Gong
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Yuhan Tian
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Dantong Xu
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Yipeng Li
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Jiaqi Li
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Jing Gong
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zhe Zhang
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
| | - Xiaolong Yuan
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
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7
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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: 0] [Impact Index Per Article: 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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8
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Liu M, Zhang C, Chen J, Xu Q, Liu S, Chao X, Yang H, Wang T, Muhammad A, Schinckel AP, Zhou B. Characterization and analysis of transcriptomes of multiple tissues from estrus and diestrus in pigs. Int J Biol Macromol 2024; 256:128324. [PMID: 38007026 DOI: 10.1016/j.ijbiomac.2023.128324] [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: 08/07/2023] [Revised: 11/01/2023] [Accepted: 11/12/2023] [Indexed: 11/27/2023]
Abstract
A comprehensive understanding of the complex regulatory mechanisms governing estrus and ovulation across multiple tissues in mammals is imperative to improve the reproductive performance of livestock and mitigate ovulation-related disorders in humans. To comprehensively elucidate the regulatory landscape, we analyzed the transcriptome of protein-coding genes and long intergenic non-coding RNAs (lincRNAs) in 58 samples (including the hypothalamus, pituitary, ovary, vagina, and vulva) derived from European Large White gilts and Chinese Mi gilts during estrus and diestrus. We constructed an intricate regulatory network encompassing 358 hub genes across the five examined tissues. Furthermore, our investigation identified 85 differentially expressed lincRNAs that are predicted to target 230 genes associated with critical functions including behavior, receptors, and apoptosis. Importantly, we found that vital components of estrus and ovulation events involve "Apoptosis" pathway in the hypothalamus, "Autophagy" in the ovary, as well as "Hypoxia" and "Angiogenesis" in the vagina and vulva. We have identified several differentially expressed transcription factors (TFs), such as SPI1 and HES2, which regulate these pathways. SPI1 may suppress transcription in the autophagy pathway, promoting apoptosis and inhibiting the proliferation of ovarian granulosa cells. Our study provides the most comprehensive transcriptional profiling information related to estrus and ovulation events.
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Affiliation(s)
- Mingzheng Liu
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
| | - Chunlei Zhang
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
| | - Jiahao Chen
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
| | - Qinglei Xu
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
| | - Shuhan Liu
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
| | - Xiaohuan Chao
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
| | - Huan Yang
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
| | - Tianshuo Wang
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
| | - Asim Muhammad
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
| | - Allan P Schinckel
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907-2054, USA.
| | - Bo Zhou
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
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9
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Wang W, Zhang T, Du L, Li K, Zhang L, Li H, Gao X, Xu L, Li J, Gao H. Transcriptomic analysis reveals diverse expression patterns underlying the fiber diameter of oxidative and glycolytic skeletal muscles in steers. Meat Sci 2024; 207:109350. [PMID: 37844514 DOI: 10.1016/j.meatsci.2023.109350] [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: 12/21/2022] [Revised: 08/18/2023] [Accepted: 10/02/2023] [Indexed: 10/18/2023]
Abstract
Skeletal muscles consist of heterogeneous fibers with various contractile and metabolic properties that affect meat quality. The size of muscle fibers contributes to muscle mass and myopathy. Thus, improved understanding of the expression patterns underlying fiber size might open possibilities to change them using genetic methods. The aim of this study was to reveal transcriptomic landscapes of one oxidative (Psoas major) and three glycolytic (Longissimus lumborum, Triceps brachii, and Semimembranosus) muscles. Principal component analysis (PCA) showed significant differences in gene expression among the four muscles. Specifically, 2777 differentially expressed genes (DEGs) were detected between six pairwise comparisons of the four muscles. Weighted gene co-expression network analysis (WGCNA) identified six modules, which were significantly associated with muscle fiber diameter. We also identified 23 candidate genes, and enrichment analysis showed that biosynthesis of amino acids (bta01230), sarcomere (GO:0030017), and regulation of actin cytoskeleton (bta04810) overlapped in DEGs and WGCNA. Nineteen of these genes (e.g., EEF1A2, FARSB, and PINK1) have been reported to promote or inhibit muscle growth and development. Our findings contribute to the understanding of fiber size differences among oxidative and glycolytic muscles, which may provide a basis for breeding to improve meat yield.
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Affiliation(s)
- Wenxiang Wang
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| | - Tianliu Zhang
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| | - Lili Du
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| | - Keanning Li
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| | - Lupei Zhang
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| | - Haipeng Li
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| | - Xue Gao
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| | - Lingyang Xu
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| | - Junya Li
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| | - Huijiang Gao
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
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10
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Shen Q, Gong W, Pan X, Cai J, Jiang Y, He M, Zhao S, Li Y, Yuan X, Li J. Comprehensive Analysis of CircRNA Expression Profiles in Multiple Tissues of Pigs. Int J Mol Sci 2023; 24:16205. [PMID: 38003395 PMCID: PMC10671760 DOI: 10.3390/ijms242216205] [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: 09/28/2023] [Revised: 11/01/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023] Open
Abstract
Circular RNAs (circRNAs) are a class of non-coding RNAs with diverse functions, and previous studies have reported that circRNAs are involved in the growth and development of pigs. However, studies about porcine circRNAs over the past few years have focused on a limited number of tissues. Based on 215 publicly available RNA sequencing (RNA-seq) samples, we conducted a comprehensive analysis of circRNAs in nine pig tissues, namely, the gallbladder, heart, liver, longissimus dorsi, lung, ovary, pituitary, skeletal muscle, and spleen. Here, we identified a total of 82,528 circRNAs and discovered 3818 novel circRNAs that were not reported in the CircAtlas database. Moreover, we obtained 492 housekeeping circRNAs and 3489 tissue-specific circRNAs. The housekeeping circRNAs were enriched in signaling pathways regulating basic biological tissue activities, such as chromatin remodeling, nuclear-transcribed mRNA catabolic process, and protein methylation. The tissue-specific circRNAs were enriched in signaling pathways related to tissue-specific functions, such as muscle system process in skeletal muscle, cilium organization in pituitary, and cortical cytoskeleton in ovary. Through weighted gene co-expression network analysis, we identified 14 modules comprising 1377 hub circRNAs. Additionally, we explored circRNA-miRNA-mRNA networks to elucidate the interaction relationships between tissue-specific circRNAs and tissue-specific genes. Furthermore, our conservation analysis revealed that 19.29% of circRNAs in pigs shared homologous positions with their counterparts in humans. In summary, this extensive profiling of housekeeping, tissue-specific, and co-expressed circRNAs provides valuable insights into understanding the molecular mechanisms of pig transcriptional expression, ultimately deepening our understanding of genetic and biological processes.
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Affiliation(s)
- Qingpeng Shen
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Wentao Gong
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Xiangchun Pan
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Jiali Cai
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Yao Jiang
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
- School of Medical, Molecular and Forensic Sciences, Murdoch University, Murdoch, WA 6149, Australia
| | - Mingran He
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Shanghui Zhao
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Yipeng Li
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Xiaolong Yuan
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Jiaqi Li
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
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11
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Wang Z, Tian W, Wang D, Guo Y, Cheng Z, Zhang Y, Li X, Zhi Y, Li D, Li Z, Jiang R, Li G, Tian Y, Kang X, Li H, Dunn IC, Liu X. Comparative analyses of dynamic transcriptome profiles highlight key response genes and dominant isoforms for muscle development and growth in chicken. Genet Sel Evol 2023; 55:73. [PMID: 37872550 PMCID: PMC10591418 DOI: 10.1186/s12711-023-00849-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 10/06/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND Modern breeding strategies have resulted in significant differences in muscle mass between indigenous chicken and specialized broiler. However, the molecular regulatory mechanisms that underlie these differences remain elusive. The aim of this study was to identify key genes and regulatory mechanisms underlying differences in breast muscle development between indigenous chicken and specialized broiler. RESULTS Two time-series RNA-sequencing profiles of breast muscles were generated from commercial Arbor Acres (AA) broiler (fast-growing) and Chinese indigenous Lushi blue-shelled-egg (LS) chicken (slow-growing) at embryonic days 10, 14, and 18, and post-hatching day 1 and weeks 1, 3, and 5. Principal component analysis of the transcriptome profiles showed that the top four principal components accounted for more than 80% of the total variance in each breed. The developmental axes between the AA and LS chicken overlapped at the embryonic stages but gradually separated at the adult stages. Integrative investigation of differentially-expressed transcripts contained in the top four principal components identified 44 genes that formed a molecular network associated with differences in breast muscle mass between the two breeds. In addition, alternative splicing analysis revealed that genes with multiple isoforms always had one dominant transcript that exhibited a significantly higher expression level than the others. Among the 44 genes, the TNFRSF6B gene, a mediator of signal transduction pathways and cell proliferation, harbored two alternative splicing isoforms, TNFRSF6B-X1 and TNFRSF6B-X2. TNFRSF6B-X1 was the dominant isoform in both breeds before the age of one week. A switching event of the dominant isoform occurred at one week of age, resulting in TNFRSF6B-X2 being the dominant isoform in AA broiler, whereas TNFRSF6B-X1 remained the dominant isoform in LS chicken. Gain-of-function assays demonstrated that both isoforms promoted the proliferation of chicken primary myoblasts, but only TNFRSF6B-X2 augmented the differentiation and intracellular protein content of chicken primary myoblasts. CONCLUSIONS For the first time, we identified several key genes and dominant isoforms that may be responsible for differences in muscle mass between slow-growing indigenous chicken and fast-growing commercial broiler. These findings provide new insights into the regulatory mechanisms underlying breast muscle development in chicken.
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Affiliation(s)
- Zhang Wang
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China
| | - Weihua Tian
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China
| | - Dandan Wang
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China
| | - Yulong Guo
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China
| | - Zhimin Cheng
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China
| | - Yanyan Zhang
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China
| | - Xinyan Li
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China
| | - Yihao Zhi
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China
| | - Donghua Li
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, 450002, China
- International Joint Research Laboratory for Poultry Breeding of Henan, Zhengzhou, 450002, China
| | - Zhuanjian Li
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, 450002, China
- International Joint Research Laboratory for Poultry Breeding of Henan, Zhengzhou, 450002, China
| | - Ruirui Jiang
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, 450002, China
- International Joint Research Laboratory for Poultry Breeding of Henan, Zhengzhou, 450002, China
| | - Guoxi Li
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, 450002, China
- International Joint Research Laboratory for Poultry Breeding of Henan, Zhengzhou, 450002, China
| | - Yadong Tian
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, 450002, China
- International Joint Research Laboratory for Poultry Breeding of Henan, Zhengzhou, 450002, China
| | - Xiangtao Kang
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, 450002, China
- International Joint Research Laboratory for Poultry Breeding of Henan, Zhengzhou, 450002, China
| | - Hong Li
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China.
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, 450002, China.
- International Joint Research Laboratory for Poultry Breeding of Henan, Zhengzhou, 450002, China.
| | - Ian C Dunn
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, EH25 9RG, UK.
| | - Xiaojun Liu
- College of Animal Science and Technology, Henan Agricultural University, No. 63, Nongye Road, Zhengzhou, 450002, China.
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, 450002, China.
- International Joint Research Laboratory for Poultry Breeding of Henan, Zhengzhou, 450002, China.
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12
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Pallotti S, Picciolini M, Antonini M, Renieri C, Napolioni V. Genome-wide scan for runs of homozygosity in South American Camelids. BMC Genomics 2023; 24:470. [PMID: 37605116 PMCID: PMC10440933 DOI: 10.1186/s12864-023-09547-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/31/2023] [Indexed: 08/23/2023] Open
Abstract
BACKGROUND Alpaca (Vicugna pacos), llama (Lama glama), vicugna (Vicugna vicugna) and guanaco (Lama guanicoe), are the camelid species distributed over the Andean high-altitude grasslands, the Altiplano, and the Patagonian arid steppes. Despite the wide interest on these animals, most of the loci under selection are still unknown. Using whole-genome sequencing (WGS) data we investigated the occurrence and the distribution of Runs Of Homozygosity (ROHs) across the South American Camelids (SACs) genome to identify the genetic relationship between the four species and the potential signatures of selection. RESULTS A total of 37 WGS samples covering the four species was included in the final analysis. The multi-dimensional scaling approach showed a clear separation between the four species; however, admixture analysis suggested a strong genetic introgression from vicugna and llama to alpaca. Conversely, very low genetic admixture of the guanaco with the other SACs was found. The four species did not show significant differences in the number, length of ROHs (100-500 kb) and genomic inbreeding values. Longer ROHs (> 500 kb) were found almost exclusively in alpaca. Seven overlapping ROHs were shared by alpacas, encompassing nine loci (FGF5, LOC107034918, PRDM8, ANTXR2, LOC102534792, BSN, LOC116284892, DAG1 and RIC8B) while nine overlapping ROHs were found in llama with twenty-five loci annotated (ERC2, FZD9, BAZ1B, BCL7B, LOC116284208, TBL2, MLXIPL, PHF20, TRNAD-AUC, LOC116284365, RBM39, ARFGEF2, DCAF5, EXD2, HSPB11, LRRC42, LDLRAD1, TMEM59, LOC107033213, TCEANC2, LOC102545169, LOC116278408, SMIM15, NDUFAF2 and RCOR1). Four overlapping ROHs, with three annotated loci (DLG1, KAT6B and PDE4D) and three overlapping ROHs, with seven annotated genes (ATP6V1E1, BCL2L13, LOC116276952, BID, KAT6B, LOC116282667 and LOC107034552), were detected for vicugna and guanaco, respectively. CONCLUSIONS The signatures of selection revealed genomic areas potentially selected for production traits as well as for natural adaptation to harsh environment. Alpaca and llama hint a selection driven by environment as well as by farming purpose while vicugna and guanaco showed selection signals for adaptation to harsh environment. Interesting, signatures of selection on KAT6B gene were identified for both vicugna and guanaco, suggesting a positive effect on wild populations fitness. Such information may be of interest to further ecological and animal production studies.
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Affiliation(s)
- Stefano Pallotti
- Genomic And Molecular Epidemiology (GAME) Lab, School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy.
| | | | - Marco Antonini
- Italian National Agency for New Technologies, Energy and Sustainable Development (ENEA), Roma, Italy
| | - Carlo Renieri
- School of Pharmacy and Health Products, University of Camerino, Camerino, Italy
| | - Valerio Napolioni
- Genomic And Molecular Epidemiology (GAME) Lab, School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
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13
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Cai W, Zhang Y, Chang T, Wang Z, Zhu B, Chen Y, Gao X, Xu L, Zhang L, Gao H, Song J, Li J. The eQTL colocalization and transcriptome-wide association study identify potentially causal genes responsible for economic traits in Simmental beef cattle. J Anim Sci Biotechnol 2023; 14:78. [PMID: 37165455 PMCID: PMC10173583 DOI: 10.1186/s40104-023-00876-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 04/05/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND A detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle. To prioritize the putative variants and genes, we ran a comprehensive genome-wide association studies (GWAS) analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle. Then, we applied expression quantitative trait loci (eQTL) mapping between the genotype variants and transcriptome of three tissues (longissimus dorsi muscle, backfat, and liver) in 120 cattle. RESULTS We identified 1,580 association signals for 21 beef agronomic traits using GWAS. We then illuminated 854,498 cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits. These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions. We observed an average of 43.5% improvement in cis-eQTL discovery using multi-tissue eQTL mapping. Fine-mapping analysis revealed that 111, 192, and 194 variants were most likely to be causative to regulate gene expression in backfat, liver, and muscle, respectively. The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits. Via the colocalization and Mendelian randomization analyses, we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues, which included genes, such as NADSYN1, NDUFS3, LTF and KIFC2 in liver, GRAMD1C, TMTC2 and ZNF613 in backfat, as well as TIGAR, NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits. CONCLUSIONS The extensive atlas of GWAS, eQTL, fine-mapping, and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle.
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Affiliation(s)
- Wentao Cai
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yapeng Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Tianpeng Chang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Zezhao Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Bo Zhu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yan Chen
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Xue Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Lingyang Xu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Lupei Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Huijiang Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jiuzhou Song
- Department of Animal and Avian Science, University of Maryland, College Park, MD, 20742, USA.
| | - Junya Li
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
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14
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Si J, Dai D, Li K, Fang L, Zhang Y. A Multi-Tissue Gene Expression Atlas of Water Buffalo ( Bubalus bubalis) Reveals Transcriptome Conservation between Buffalo and Cattle. Genes (Basel) 2023; 14:genes14040890. [PMID: 37107649 PMCID: PMC10137413 DOI: 10.3390/genes14040890] [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: 03/22/2023] [Revised: 04/04/2023] [Accepted: 04/07/2023] [Indexed: 04/29/2023] Open
Abstract
We generated 73 transcriptomic data of water buffalo, which were integrated with publicly available data in this species, yielding a large dataset of 355 samples representing 20 major tissue categories. We established a multi-tissue gene expression atlas of water buffalo. Furthermore, by comparing them with 4866 cattle transcriptomic data from the cattle genotype-tissue expression atlas (CattleGTEx), we found that the transcriptomes of the two species exhibited conservation in their overall gene expression patterns, tissue-specific gene expression and house-keeping gene expression. We further identified conserved and divergent expression genes between the two species, with the largest number of differentially expressed genes found in the skin, which may be related to structural and functional differences in the skin of the two species. This work provides a source of functional annotation of the buffalo genome and lays the foundations for future genetic and evolutionary studies in water buffalo.
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Affiliation(s)
- Jingfang Si
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Dongmei Dai
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Kun Li
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Lingzhao Fang
- The Center for Quantitative Genetics and Genomics (QGG), Aarhus University, 11, 8000 Aarhus, Denmark
| | - Yi Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
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Expression Profile of Housekeeping Genes and Tissue-Specific Genes in Multiple Tissues of Pigs. Animals (Basel) 2022; 12:ani12243539. [PMID: 36552460 PMCID: PMC9774903 DOI: 10.3390/ani12243539] [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: 11/07/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
Pigs have become an ideal model system for human disease research and development and an important farm animal that provides a valuable source of nutrition. To profile the all-sided gene expression and their biological functions across multiple tissues, we conducted a comprehensive analysis of gene expression on a large scale around the side of housekeeping genes (HKGs), tissue specific genes (TSGs), and the co-expressed genes in 14 various tissues. In this study, we identified 2351 HKGs and 3018 TSGs across tissues, among which 4 HKGs (COX1, UBB, OAZ1/NPFF) exhibited low variation and high expression levels, and 31 particular TSGs (e.g., PDC, FKBP6, STAT2, and COL1A1) were exclusively expressed in several tissues, including endocrine brain, ovaries, livers, backfat, jejunum, kidneys, lungs, and longissimus dorsi muscles. We also obtained 17 modules with 230 hub genes (HUBGs) by weighted gene co-expression network analysis. On the other hand, HKGs functions were enriched in the signaling pathways of the ribosome, spliceosome, thermogenesis, oxidative phosphorylation, and nucleocytoplasmic transport, which have been highly suggested to involve in the basic biological tissue activities. While TSGs were highly enriched in the signaling pathways that were involved in specific physiological processes, such as the ovarian steroidogenesis pathway in ovaries and the renin-angiotensin system pathway in kidneys. Collectively, these stable, specifical, and co-expressed genes provided useful information for the investigation of the molecular mechanism for an understanding of the genetic and biological processes of complex traits in pigs.
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