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Su D, Song Y, Li D, Yang S, Zhan S, Zhong T, Guo J, Cao J, Li L, Zhang H, Wang L. Cold exposure affects glucose metabolism, lipid droplet deposition and mitophagy in skeletal muscle of newborn goats. Domest Anim Endocrinol 2024; 88:106847. [PMID: 38479188 DOI: 10.1016/j.domaniend.2024.106847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 03/07/2024] [Accepted: 03/07/2024] [Indexed: 06/07/2024]
Abstract
Cold exposure is a common stressor for newborn goats. Skeletal muscle plays an important role in maintaining whole-body homeostasis of glucose and lipid metabolism. However, the molecular mechanisms underlying regulation of skeletal muscle of newborn goats by cold exposure remains unclear. In this study, we found a significant increase (P < 0.01) in serum glucagon levels after 24 h of cold exposure (COLD, 6°C), while glucose and insulin concentrations were significantly decreased (P < 0.01) compared to room temperature (RT, 25°C). Additionally, we found that cold exposure reduced glycogen content (P < 0.01) in skeletal muscle. Pathway enrichment analysis revealed that cold exposure activated skeletal muscle glucose metabolism pathways (including insulin resistance and the insulin signaling pathway) and mitophagy-related pathways. Cold exposure up-regulated the expression of genes involved in fatty acid and triglyceride synthesis, promoting skeletal muscle lipid deposition. Notably, cold exposure induced mitophagy in skeletal muscle.
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Affiliation(s)
- Duo Su
- Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Yulong Song
- Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Die Li
- Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Shizhong Yang
- Institute of Liangshan Agricultural Science Research, Xichang 615042, China
| | - Siyuan Zhan
- Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Tao Zhong
- Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Jiazhong Guo
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Jiaxue Cao
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Li Li
- Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Hongping Zhang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Linjie Wang
- Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China.
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Teng J, Gao Y, Yin H, Bai Z, Liu S, Zeng H, Bai L, Cai Z, Zhao B, Li X, Xu Z, Lin Q, Pan Z, Yang W, Yu X, Guan D, Hou Y, Keel BN, Rohrer GA, Lindholm-Perry AK, Oliver WT, Ballester M, Crespo-Piazuelo D, Quintanilla R, Canela-Xandri O, Rawlik K, Xia C, Yao Y, Zhao Q, Yao W, Yang L, Li H, Zhang H, Liao W, Chen T, Karlskov-Mortensen P, Fredholm M, Amills M, Clop A, Giuffra E, Wu J, Cai X, Diao S, Pan X, Wei C, Li J, Cheng H, Wang S, Su G, Sahana G, Lund MS, Dekkers JCM, Kramer L, Tuggle CK, Corbett R, Groenen MAM, Madsen O, Gòdia M, Rocha D, Charles M, Li CJ, Pausch H, Hu X, Frantz L, Luo Y, Lin L, Zhou Z, Zhang Z, Chen Z, Cui L, Xiang R, Shen X, Li P, Huang R, Tang G, Li M, Zhao Y, Yi G, Tang Z, Jiang J, Zhao F, Yuan X, Liu X, Chen Y, Xu X, Zhao S, Zhao P, Haley C, Zhou H, Wang Q, Pan Y, Ding X, Ma L, Li J, Navarro P, Zhang Q, Li B, Tenesa A, Li K, Liu GE, Zhang Z, Fang L. A compendium of genetic regulatory effects across pig tissues. Nat Genet 2024; 56:112-123. [PMID: 38177344 PMCID: PMC10786720 DOI: 10.1038/s41588-023-01585-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 10/13/2023] [Indexed: 01/06/2024]
Abstract
The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.
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Affiliation(s)
- Jinyan Teng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Yahui Gao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA
| | - Hongwei Yin
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhonghao Bai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Shuli Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
- School of Life Sciences, Westlake University, Hangzhou, China
| | - Haonan Zeng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Lijing Bai
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zexi Cai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Bingru Zhao
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xiujin Li
- Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Zhiting Xu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Qing Lin
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Zhangyuan Pan
- Department of Animal Science, University of California, Davis, Davis, CA, USA
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wenjing Yang
- College of Animal Science and Technology, China Agricultural University, Beijing, China
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Xiaoshan Yu
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Dailu Guan
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Yali Hou
- Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Brittney N Keel
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Gary A Rohrer
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | | | - William T Oliver
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Maria Ballester
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Daniel Crespo-Piazuelo
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Raquel Quintanilla
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Konrad Rawlik
- Baillie Gifford Pandemic Science Hub, University of Edinburgh, Edinburgh, UK
| | - Charley Xia
- Lothian Birth Cohort studies, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Yuelin Yao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- School of Informatics, The University of Edinburgh, Edinburgh, UK
| | - Qianyi Zhao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wenye Yao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Liu Yang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Houcheng Li
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Huicong Zhang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Wang Liao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Tianshuo Chen
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Peter Karlskov-Mortensen
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Merete Fredholm
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marcel Amills
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
- Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Alex Clop
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
- Consejo Superior de Investigaciones Científicas, Barcelona, Spain
| | - Elisabetta Giuffra
- Paris-Saclay University, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Jun Wu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiaodian Cai
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Shuqi Diao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiangchun Pan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Chen Wei
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Jinghui Li
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Hao Cheng
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Jack C M Dekkers
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Luke Kramer
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | | | - Ryan Corbett
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Martien A M Groenen
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Ole Madsen
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Marta Gòdia
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Dominique Rocha
- Paris-Saclay University, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Mathieu Charles
- Paris-Saclay University, INRAE, AgroParisTech, GABI, SIGENAE, Jouy-en-Josas, France
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, Zurich, Switzerland
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Laurent Frantz
- Palaeogenomics Group, Department of Veterinary Sciences, Ludwig Maximilian University, Munich, Germany
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Yonglun Luo
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Research, Qingdao, China
| | - Lin Lin
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Zhongyin Zhou
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Zhe Zhang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Zitao Chen
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Leilei Cui
- School of Life Sciences, Nanchang University, Nanchang, China
- Human Aging Research Institute and School of Life Science, Nanchang University, and Jiangxi Key Laboratory of Human Aging, Jiangxi, China
- UCL Genetics Institute, University College London, London, UK
| | - Ruidong Xiang
- Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, Victoria, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, Victoria, Australia
| | - Xia Shen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine, Fudan University, Guangzhou, China
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Pinghua Li
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Ruihua Huang
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Guoqing Tang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Mingzhou Li
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Yunxiang Zhao
- College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Guoqiang Yi
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhonglin Tang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Jicai Jiang
- Department of Animal Science, North Carolina State University, Raleigh, NC, USA
| | - Fuping Zhao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaolong Yuan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiaohong Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Yaosheng Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Xuewen Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education and College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education and College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Pengju Zhao
- Hainan Institute, Zhejiang University, Yongyou Industry Park, Yazhou Bay Sci-Tech City, Sanya, China
| | - Chris Haley
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Qishan Wang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Yuchun Pan
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Xiangdong Ding
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA
| | - Jiaqi Li
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Pau Navarro
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
| | - Qin Zhang
- College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China
| | - Bingjie Li
- Scotland's Rural College (SRUC), Roslin Institute Building, Midlothian, UK
| | - Albert Tenesa
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK.
| | - Kui Li
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA.
| | - Zhe Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China.
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
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Passols M, Llobet-Cabau F, Sebastià C, Castelló A, Valdés-Hernández J, Criado-Mesas L, Sánchez A, Folch JM. Identification of genomic regions, genetic variants and gene networks regulating candidate genes for lipid metabolism in pig muscle. Animal 2023; 17:101033. [PMID: 38064855 DOI: 10.1016/j.animal.2023.101033] [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: 07/06/2023] [Revised: 11/03/2023] [Accepted: 11/06/2023] [Indexed: 12/23/2023] Open
Abstract
The intramuscular fat content and fatty acid composition of porcine meat have a significant impact on its quality and nutritional value. This research aimed to investigate the expression of 45 genes involved in lipid metabolism in the longissimus dorsi muscle of three experimental pig backcrosses, with a 25% of Iberian background. To achieve this objective, we conducted an expression Genome-Wide Association Study (eGWAS) using gene expression levels in muscle measured by high-throughput real-time qPCR for 45 target genes and genotypes from the PorcineSNP60 BeadChip or Axiom Porcine Genotyping Array and 65 single nucleotide polymorphisms (SNPs) located in 20 genes genotyped by a custom-designed Taqman OpenArray in a cohort of 354 animals. The eGWAS analysis identified 301 eSNPs associated with 18 candidate genes (ANK2, APOE, ARNT, CIITA, CPT1A, EGF, ELOVL6, ELOVL7, FADS3, FASN, GPAT3, NR1D2, NR1H2, PLIN1, PPAP2A, RORA, RXRA and UCP3). Three cis-eQTL (expression quantitative trait loci) were identified for GPAT3, RXRA, and UCP3 genes, which indicates that a genetic polymorphism proximal to the same gene is affecting its expression. Furthermore, 24 trans-eQTLs were detected, and eight candidate regulatory genes were located in these genomic regions. Additionally, two trans-regulatory hotspots in Sus scrofa chromosomes 13 and 15 were identified. Moreover, a co-expression analysis performed on 89 candidate genes and the fatty acid composition revealed the regulatory role of four genes (FABP5, PPARG, SCD, and SREBF1). These genes modulate the levels of α-linolenic, arachidonic, and oleic acids, as well as regulating the expression of other candidate genes associated with lipid metabolism. The findings of this study offer novel insights into the functional regulatory mechanism of genes involved in lipid metabolism, thereby enhancing our understanding of this complex biological process.
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Affiliation(s)
- M Passols
- Plant and Animal Genomics, Centre for Research in Agrigenomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, España.
| | - F Llobet-Cabau
- Plant and Animal Genomics, Centre for Research in Agrigenomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, España; Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona (UAB), Bellaterra, España
| | - C Sebastià
- Plant and Animal Genomics, Centre for Research in Agrigenomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, España; Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona (UAB), Bellaterra, España
| | - A Castelló
- Plant and Animal Genomics, Centre for Research in Agrigenomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, España; Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona (UAB), Bellaterra, España
| | - J Valdés-Hernández
- Plant and Animal Genomics, Centre for Research in Agrigenomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, España; Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona (UAB), Bellaterra, España
| | - L Criado-Mesas
- Plant and Animal Genomics, Centre for Research in Agrigenomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, España
| | - A Sánchez
- Plant and Animal Genomics, Centre for Research in Agrigenomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, España; Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona (UAB), Bellaterra, España
| | - J M Folch
- Plant and Animal Genomics, Centre for Research in Agrigenomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, España; Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona (UAB), Bellaterra, España
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Li L, Tian Z, Chen J, Tan Z, Zhang Y, Zhao H, Wu X, Yao X, Wen W, Chen W, Guo L. Characterization of novel loci controlling seed oil content in Brassica napus by marker metabolite-based multi-omics analysis. Genome Biol 2023; 24:141. [PMID: 37337206 DOI: 10.1186/s13059-023-02984-z] [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: 12/10/2022] [Accepted: 06/08/2023] [Indexed: 06/21/2023] Open
Abstract
BACKGROUND Seed oil content is an important agronomic trait of Brassica napus (B. napus), and metabolites are considered as the bridge between genotype and phenotype for physical traits. RESULTS Using a widely targeted metabolomics analysis in a natural population of 388 B. napus inbred lines, we quantify 2172 metabolites in mature seeds by liquid chromatography mass spectrometry, in which 131 marker metabolites are identified to be correlated with seed oil content. These metabolites are then selected for further metabolite genome-wide association study and metabolite transcriptome-wide association study. Combined with weighted correlation network analysis, we construct a triple relationship network, which includes 21,000 edges and 4384 nodes among metabolites, metabolite quantitative trait loci, genes, and co-expression modules. We validate the function of BnaA03.TT4, BnaC02.TT4, and BnaC05.UK, three candidate genes predicted by multi-omics analysis, which show significant impacts on seed oil content through regulating flavonoid metabolism in B. napus. CONCLUSIONS This study demonstrates the advantage of utilizing marker metabolites integrated with multi-omics analysis to dissect the genetic basis of agronomic traits in crops.
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Affiliation(s)
- Long Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Zhitao Tian
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Jie Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Zengdong Tan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Yuting Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Hu Zhao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Xiaowei Wu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Xuan Yao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Weiwei Wen
- Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wei Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.
- Hubei Hongshan Laboratory, Wuhan, China.
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China.
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
| | - Liang Guo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.
- Hubei Hongshan Laboratory, Wuhan, China.
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China.
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
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Goto A, Endo Y, Yamashita H. CREG1 stimulates AMPK phosphorylation and glucose uptake in skeletal muscle cells. Biochem Biophys Res Commun 2023; 641:162-167. [PMID: 36528955 DOI: 10.1016/j.bbrc.2022.12.028] [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: 11/30/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022]
Abstract
The cellular repressor of adenovirus early region 1A-stimulated gene 1 (CREG1) is a secreted glycoprotein involved in cell differentiation and energy metabolism. It also binds to insulin-like growth factor 2 receptor (IGF2R), a protein implicated in muscle regeneration. However, whether CREG1 regulates the regeneration and metabolism of skeletal muscles via IGF2R remains unclear. This study investigates the role of CREG1 in skeletal muscle regeneration and glucose uptake in C2C12 myotubes and a cardiotoxin (CTX)-induced mouse skeletal muscle regeneration model. CTX-treated skeletal muscle showed significantly higher levels of IGF2R, CREG1, phospho-AMPKα Thr172, and GLUT4 proteins. Similarly, treatment of myotubes with CREG1 also stimulated AMPKα phosphorylation and GLUT4 expression. CREG1-induced AMPKα phosphorylation and 2DG uptake in myotubes were suppressed by IGF2R knockdown and Compound C, an AMPK inhibitor. These results suggest that CREG1 stimulates glucose uptake in skeletal muscles partially through AMPK activation. Hence, CREG1 plays an essential role in muscle regeneration by affecting glucose metabolism in skeletal muscles.
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Affiliation(s)
- Ayumi Goto
- Department of Biomedical Sciences, College of Life and Health Sciences, Chubu University, Kasugai, Japan.
| | - Yuki Endo
- Department of Biomedical Sciences, College of Life and Health Sciences, Chubu University, Kasugai, Japan
| | - Hitoshi Yamashita
- Department of Biomedical Sciences, College of Life and Health Sciences, Chubu University, Kasugai, Japan.
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6
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Global analysis of the association between pig muscle fatty acid composition and gene expression using RNA-Seq. Sci Rep 2023; 13:535. [PMID: 36631502 PMCID: PMC9834388 DOI: 10.1038/s41598-022-27016-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 12/23/2022] [Indexed: 01/13/2023] Open
Abstract
Fatty acids (FAs) play an essential role as mediators of cell signaling and signal transduction, affecting metabolic homeostasis and determining meat quality in pigs. However, FAs are transformed by the action of several genes, such as those encoding desaturases and elongases of FAs in lipogenic tissues. The aim of the current work was to identify candidate genes, biological processes, and pathways involved in the modulation of intramuscular FA profile from longissimus dorsi muscle. FA profile by gas chromatography of methyl esters and gene expression by RNA-Seq were determined in 129 Iberian × Duroc backcrossed pigs. An association analysis between the muscle transcriptome and its FA profile was performed, followed by a concordance and functional analysis. Overall, a list of well-known (e.g., PLIN1, LEP, ELOVL6, SC5D, NCOA2, ACSL1, MDH1, LPL, LGALS12, TFRC, GOT1, and FBP1) and novel (e.g., TRARG1, TANK, ENSSSCG00000011196, and ENSSSCG00000038429) candidate genes was identified, either in association with specific or several FA traits. Likewise, several of these genes belong to biological processes and pathways linked to energy, lipid, and carbohydrate metabolism, which seem determinants in the modulation of FA compositions. This study can contribute to elucidate the complex relationship between gene expression and FA profile in pig muscle.
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7
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Characterization and difference of lipids and metabolites from Jianhe White Xiang and Large White pork by high-performance liquid chromatography–tandem mass spectrometry. Food Res Int 2022; 162:111946. [DOI: 10.1016/j.foodres.2022.111946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/03/2022] [Accepted: 09/13/2022] [Indexed: 11/23/2022]
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8
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Addo S, Jung L. An insight into the runs of homozygosity distribution and breed differentiation in Mangalitsa pigs. Front Genet 2022; 13:909986. [DOI: 10.3389/fgene.2022.909986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 09/30/2022] [Indexed: 11/13/2022] Open
Abstract
Mangalitsa pigs exhibit three distinct coat color patterns based on which they are described as Red, Blond, and Swallow-bellied. The current study investigated genome-wide diversity and selection signatures in the three breeds using fixation index, runs of homozygosity and population structure analyses. The analyses were originally based on quality-controlled data on 77 Mangalitsa animals from Germany, including 23 Blond, 30 Swallow-bellied and 24 Red Mangalitsa genotyped with a customized version of the ProcineSNP60 v2 Genotyping Bead Chip. Also, 20 Hungarian Mangalitsa genotypes were included as outgroup data for comparison. Estimates of observed heterozygosity were 0.27, 0.28, and 0.29, and inbreeding coefficients estimated based on runs of homozygosity were 24.11%, 20.82%, and 16.34% for Blond, Swallow-bellied and Red Mangalitsa, respectively. ROH islands were detected in all breeds, however, none of these were shared amongst them. The KIF16B gene previously reported to play a role in synaptic signaling was found in a ROH island (SSC17: 16–26) in Swallow-bellied Mangalitsa. The same gene was found to harbor a significantly differentiated SNP (MARC0032380) while contrasting either Blond or Red to Swallow-belied Mangalitsa. In the Red Mangalitsa, some ROH islands were associated with genes that play a role in meat quality traits, i.e., ABCA12, VIL1, PLSCR5, and USP37. Our population structure analysis highlighted a separation of the three breeds, but also showed the closest relatedness between Red and Blond Mangalitsa pigs. Findings of this study improve our understanding of the diversity in the three breeds of Mangalitsa pigs.
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9
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Comparative Transcriptomic Analysis of mRNAs, miRNAs and lncRNAs in the Longissimus dorsi Muscles between Fat-Type and Lean-Type Pigs. Biomolecules 2022; 12:biom12091294. [PMID: 36139132 PMCID: PMC9496231 DOI: 10.3390/biom12091294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 11/24/2022] Open
Abstract
In pigs, meat quality and production are two important traits affecting the pig industry and human health. Compared to lean-type pigs, fat-type pigs contain higher intramuscular fat (IMF) contents, better taste and nutritional value. To uncover genetic factors controlling differences related to IMF in pig muscle, we performed RNA-seq analysis on the transcriptomes of the Longissimus dorsi (LD) muscle of Laiwu pigs (LW, fat-type pigs) and commercial Duroc × Landrace × Yorkshire pigs (DLY, lean-type pigs) at 150 d to compare the expression profiles of mRNA, miRNA and lncRNA. A total of 225 mRNAs, 12 miRNAs and 57 lncRNAs were found to be differentially expressed at the criteria of |log2(foldchange)| > 1 and q < 0.05. The mRNA expression of LDHB was significantly higher in the LD muscle of LW compared to DLY pigs with log2(foldchange) being 9.66. Using protein interaction prediction method, we identified more interactions of estrogen-related receptor alpha (ESRRA) associated with upregulated mRNAs, whereas versican (VCAN) and proenkephalin (PENK) were associated with downregulated mRNAs in LW pigs. Integrated analysis on differentially expressed (DE) mRNAs and miRNAs in the LD muscle between LW and DLY pigs revealed two network modules: between five upregulated mRNA genes (GALNT15, FKBP5, PPARGC1A, LOC110258214 and LOC110258215) and six downregulated miRNA genes (ssc-let-7a, ssc-miR190-3p, ssc-miR356-5p, ssc-miR573-5p, ssc-miR204-5p and ssc-miR-10383), and between three downregulated DE mRNA genes (IFRD1, LOC110258600 and LOC102158401) and six upregulated DE miRNA genes (ssc-miR1379-3p, ssc-miR1379-5p, ssc-miR397-5p, ssc-miR1358-5p, ssc-miR299-5p and ssc-miR1156-5p) in LW pigs. Based on the mRNA and ncRNA binding site targeting database, we constructed a regulatory network with miRNA as the center and mRNA and lncRNA as the target genes, including GALNT15/ssc-let-7a/LOC100523888, IFRD1/ssc-miR1379-5p/CD99, etc., forming a ceRNA network in the LD muscles that are differentially expressed between LW and DLY pigs. Collectively, these data may provide resources for further investigation of molecular mechanisms underlying differences in meat traits between lean- and fat-type pigs.
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10
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Chen B, Yue Y, Li J, Liu J, Yuan C, Guo T, Zhang D, Yang B, Lu Z. Transcriptome-metabolome analysis reveals how sires affect meat quality in hybrid sheep populations. Front Nutr 2022; 9:967985. [PMID: 36034900 PMCID: PMC9403842 DOI: 10.3389/fnut.2022.967985] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 07/28/2022] [Indexed: 12/03/2022] Open
Abstract
Crossbreeding improves and enhances meat quality and is widely used in sheep production; however, the molecular mechanisms underlying the meat quality of various crossbred sheep remain unknown. In this study, male Southdown, Suffolk and Hu sheep were crossbred with female Hu sheep, and the transcriptomes and metabolomes of the longissimus dorsi muscle of the F1 generation were sequenced to explore how different sire breeds affect meat quality. The results showed that 631 differentially expressed genes and 119 significantly altered metabolites contributed to muscle development characteristics and meat quality-related diversity (P < 0.05). These genes and metabolites were significantly enriched in lipid metabolism pathways, including arachidonic acid metabolism and PPAR signaling. Several candidate genes were associated with muscle growth, such as MYLK3, MYL10, FIGN, MYH8, MYOM3, LMCD1, and FLRT1. Among these, MYH8 and MYL10 participated in regulating muscle growth and development and were correlated with meat quality-related fatty acid levels (|r| > 0.5 and p < 0.05). We selected mRNA from four of these genes to verify the accuracy of the sequencing data via qRT-PCR. Our findings provide further insight into the key genes and metabolites involved in muscle growth and meat quality in hybrid sheep populations.
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Affiliation(s)
- Bowen Chen
- Key Laboratory of Animal Genetics and Breeding on the Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.,Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Yaojing Yue
- Key Laboratory of Animal Genetics and Breeding on the Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.,Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Jianye Li
- Key Laboratory of Animal Genetics and Breeding on the Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.,Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Jianbin Liu
- Key Laboratory of Animal Genetics and Breeding on the Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.,Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Chao Yuan
- Key Laboratory of Animal Genetics and Breeding on the Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.,Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Tingting Guo
- Key Laboratory of Animal Genetics and Breeding on the Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.,Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Dan Zhang
- Key Laboratory of Animal Genetics and Breeding on the Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.,Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Bohui Yang
- Key Laboratory of Animal Genetics and Breeding on the Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.,Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou, China
| | - Zengkui Lu
- Key Laboratory of Animal Genetics and Breeding on the Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.,Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou, China
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11
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Johnsson M, Jungnickel MK. Evidence for and localization of proposed causative variants in cattle and pig genomes. Genet Sel Evol 2021; 53:67. [PMID: 34461824 PMCID: PMC8404348 DOI: 10.1186/s12711-021-00662-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 08/20/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND This paper reviews the localization of published potential causative variants in contemporary pig and cattle reference genomes, and the evidence for their causality. In spite of the difficulties inherent to the identification of causative variants from genetic mapping and genome-wide association studies, researchers in animal genetics have proposed putative causative variants for several traits relevant to livestock breeding. RESULTS For this review, we read the literature that supports potential causative variants in 13 genes (ABCG2, DGAT1, GHR, IGF2, MC4R, MSTN, NR6A1, PHGK1, PRKAG3, PLRL, RYR1, SYNGR2 and VRTN) in cattle and pigs, and localized them in contemporary reference genomes. We review the evidence for their causality, by aiming to separate the evidence for the locus, the proposed causative gene and the proposed causative variant, and report the bioinformatic searches and tactics needed to localize the sequence variants in the cattle or pig genome. CONCLUSIONS Taken together, there is usually good evidence for the association at the locus level, some evidence for a specific causative gene at eight of the loci, and some experimental evidence for a specific causative variant at six of the loci. We recommend that researchers who report new potential causative variants use referenced coordinate systems, show local sequence context, and submit variants to repositories.
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Affiliation(s)
- Martin Johnsson
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07 Uppsala, Sweden
| | - Melissa K. Jungnickel
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG Scotland, UK
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12
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Identification of Distant Regulatory Elements Using Expression Quantitative Trait Loci Mapping for Heat-Responsive Genes in Oysters. Genes (Basel) 2021; 12:genes12071040. [PMID: 34356056 PMCID: PMC8303352 DOI: 10.3390/genes12071040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 06/27/2021] [Accepted: 06/28/2021] [Indexed: 11/17/2022] Open
Abstract
Many marine ectotherms, especially those inhabiting highly variable intertidal zones, develop high phenotypic plasticity in response to rapid climate change by modulating gene expression levels. Herein, we examined the regulatory architecture of heat-responsive gene expression plasticity in oysters using expression quantitative trait loci (eQTL) analysis. Using a backcross family of Crassostrea gigas and its sister species Crassostrea angulata under acute stress, 56 distant regulatory regions accounting for 6–26.6% of the gene expression variation were identified for 19 heat-responsive genes. In total, 831 genes and 164 single nucleotide polymorphisms (SNPs) that could potentially regulate expression of the target genes were screened in the eQTL region. The association between three SNPs and the corresponding target genes was verified in an independent family. Specifically, Marker13973 was identified for heat shock protein (HSP) family A member 9 (HspA9). Ribosomal protein L10a (RPL10A) was detected approximately 2 kb downstream of the distant regulatory SNP. Further, Marker14346-48 and Marker14346-85 were in complete linkage disequilibrium and identified for autophagy-related gene 7 (ATG7). Nuclear respiratory factor 1 (NRF1) was detected approximately 3 kb upstream of the two SNPs. These results suggested regulatory relationships between RPL10A and HSPA9 and between NRF1 and ATG7. Our findings indicate that distant regulatory mutations play an important role in the regulation of gene expression plasticity by altering upstream regulatory factors in response to heat stress. The identified eQTLs provide candidate biomarkers for predicting the persistence of oysters under future climate change scenarios.
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Wang L, Zhang Y, Zhang B, Zhong H, Lu Y, Zhang H. Candidate gene screening for lipid deposition using combined transcriptomic and proteomic data from Nanyang black pigs. BMC Genomics 2021; 22:441. [PMID: 34118873 PMCID: PMC8201413 DOI: 10.1186/s12864-021-07764-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/02/2021] [Indexed: 11/21/2022] Open
Abstract
Background Lower selection intensities in indigenous breeds of Chinese pig have resulted in obvious genetic and phenotypic divergence. One such breed, the Nanyang black pig, is renowned for its high lipid deposition and high genetic divergence, making it an ideal model in which to investigate lipid position trait mechanisms in pigs. An understanding of lipid deposition in pigs might improve pig meat traits in future breeding and promote the selection progress of pigs through modern molecular breeding techniques. Here, transcriptome and tandem mass tag-based quantitative proteome (TMT)-based proteome analyses were carried out using longissimus dorsi (LD) tissues from individual Nanyang black pigs that showed high levels of genetic variation. Results A large population of Nanyang black pigs was phenotyped using multi-production trait indexes, and six pigs were selected and divided into relatively high and low lipid deposition groups. The combined transcriptomic and proteomic data identified 15 candidate genes that determine lipid deposition genetic divergence. Among them, FASN, CAT, and SLC25A20 were the main causal candidate genes. The other genes could be divided into lipid deposition-related genes (BDH2, FASN, CAT, DHCR24, ACACA, GK, SQLE, ACSL4, and SCD), PPARA-centered fat metabolism regulatory factors (PPARA, UCP3), transcription or translation regulators (SLC25A20, PDK4, CEBPA), as well as integrin, structural proteins, and signal transduction-related genes (EGFR). Conclusions This multi-omics data set has provided a valuable resource for future analysis of lipid deposition traits, which might improve pig meat traits in future breeding and promote the selection progress in pigs, especially in Nanyang black pigs. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07764-2.
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Affiliation(s)
- Liyuan Wang
- College of Life Science and Agricultural Engineering, Nanyang Normal University, Nanyang, China.,National Engineering Laboratory for Animal Breeding/Beijing Key Laboratory for Animal Genetic Improvement, China Agricultural University, Beijing, China.,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yawen Zhang
- National Engineering Laboratory for Animal Breeding/Beijing Key Laboratory for Animal Genetic Improvement, China Agricultural University, Beijing, China
| | - Bo Zhang
- National Engineering Laboratory for Animal Breeding/Beijing Key Laboratory for Animal Genetic Improvement, China Agricultural University, Beijing, China
| | - Haian Zhong
- National Engineering Laboratory for Animal Breeding/Beijing Key Laboratory for Animal Genetic Improvement, China Agricultural University, Beijing, China
| | - Yunfeng Lu
- College of Life Science and Agricultural Engineering, Nanyang Normal University, Nanyang, China.
| | - Hao Zhang
- National Engineering Laboratory for Animal Breeding/Beijing Key Laboratory for Animal Genetic Improvement, China Agricultural University, Beijing, China.
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14
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Zhou J, Zhang Y, Wu J, Qiao M, Xu Z, Peng X, Mei S. Proteomic and lipidomic analyses reveal saturated fatty acids, phosphatidylinositol, phosphatidylserine, and associated proteins contributing to intramuscular fat deposition. J Proteomics 2021; 241:104235. [PMID: 33894376 DOI: 10.1016/j.jprot.2021.104235] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 04/05/2021] [Accepted: 04/09/2021] [Indexed: 12/15/2022]
Abstract
Intramuscular fat (IMF) content is an important factor in porcine meat quality. Previous studies have screened multiple candidate genes related to IMF deposition, but the lipids that affect IMF deposition and their lipid-protein network remain unknown. In this study, we performed proteomic and lipidomic analyses of the longissimus dorsi (LD) muscle from high-IMF (IMFH) and low-IMF (IMF-L) groups of Xidu black pigs. Eighty-eight proteins and 143 lipids were differentially abundant between the groups. The differentially abundant proteins were found to be involved in cholesterol metabolism, the PPAR signaling pathway, and ferroptosis. The triacylglycerols (TAGs) upregulated in the IMF-H group were mainly shown to be synthesized by saturated fatty acids (SFAs), while the downregulated TAGs were mainly synthesized by polyunsaturated fatty acids (PUFAs). All differentially abundant phosphatidylinositols (PIs) and phosphatidylserines (PSs) were found to be upregulated in the IMF-H group. A correlation analysis of the proteomic and lipidomic revealed candidate proteins (APOA4, VDAC3, PRNP, CTSB, GSPT1) related to TAG, PI, and PS lipids. These results revealed differences in proteins and lipids between the IMF-H and IMF-L groups, which represent new candidate proteins and lipids that should be investigated to determine the molecular mechanisms controlling IMF deposition in pigs. SIGNIFICANCE: Intramuscular fat (IMF) is a key factor affecting meat quality, and meat with a higher IMF content can have a better flavor. In this study, proteomic results show that the ferroptosis pathway, including the PRNP, VDAC3 and CP proteins, affects IMF deposition. Lipid composition is the key factor affecting IMF deposition, but there are few reports on this. In this study, through lipidomic analysis, we suggest that saturated fatty acid (SFA), phosphatidylinositol (PI), and phosphatidylserine (PS) may contribute to IMF deposition. A correlation analysis reveals the potential regulatory network between lipids and proteins. This study clarifies the difference in protein and lipid compositions in longissimus dorsi (LD) muscle with high and low IMF contents. This information suggests that it would be beneficial to increase the intramuscular fat content of pork not only from a genetic perspective but also from a nutritional perspective.
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Affiliation(s)
- Jiawei Zhou
- Institute of Animal Science and Veterinary Medicine, Hubei Academy of Agricultural Sciences, Wuhan 430064, China; Hubei Key Lab for Animal Embryo Engineering and Molecular Breeding, Wuhan 430064, China
| | - Yu Zhang
- Institute of Animal Science and Veterinary Medicine, Hubei Academy of Agricultural Sciences, Wuhan 430064, China; Hubei Key Lab for Animal Embryo Engineering and Molecular Breeding, Wuhan 430064, China
| | - Junjing Wu
- Institute of Animal Science and Veterinary Medicine, Hubei Academy of Agricultural Sciences, Wuhan 430064, China; Hubei Key Lab for Animal Embryo Engineering and Molecular Breeding, Wuhan 430064, China
| | - Mu Qiao
- Institute of Animal Science and Veterinary Medicine, Hubei Academy of Agricultural Sciences, Wuhan 430064, China; Hubei Key Lab for Animal Embryo Engineering and Molecular Breeding, Wuhan 430064, China
| | - Zhong Xu
- Institute of Animal Science and Veterinary Medicine, Hubei Academy of Agricultural Sciences, Wuhan 430064, China; Hubei Key Lab for Animal Embryo Engineering and Molecular Breeding, Wuhan 430064, China
| | - Xianwen Peng
- Institute of Animal Science and Veterinary Medicine, Hubei Academy of Agricultural Sciences, Wuhan 430064, China; Hubei Key Lab for Animal Embryo Engineering and Molecular Breeding, Wuhan 430064, China
| | - Shuqi Mei
- Institute of Animal Science and Veterinary Medicine, Hubei Academy of Agricultural Sciences, Wuhan 430064, China; Hubei Key Lab for Animal Embryo Engineering and Molecular Breeding, Wuhan 430064, China.
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