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Cai Z, Iso-Touru T, Sanchez MP, Kadri N, Bouwman AC, Chitneedi PK, MacLeod IM, Vander Jagt CJ, Chamberlain AJ, Gredler-Grandl B, Spengeler M, Lund MS, Boichard D, Kühn C, Pausch H, Vilkki J, Sahana G. Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance. Genet Sel Evol 2024; 56:54. [PMID: 39009986 PMCID: PMC11247842 DOI: 10.1186/s12711-024-00920-8] [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: 12/09/2023] [Accepted: 06/26/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Mastitis is a disease that incurs significant costs in the dairy industry. A promising approach to mitigate its negative effects is to genetically improve the resistance of dairy cattle to mastitis. A meta-analysis of genome-wide association studies (GWAS) across multiple breeds for clinical mastitis (CM) and its indicator trait, somatic cell score (SCS), is a powerful method to identify functional genetic variants that impact mastitis resistance. RESULTS We conducted meta-analyses of eight and fourteen GWAS on CM and SCS, respectively, using 30,689 and 119,438 animals from six dairy cattle breeds. Methods for the meta-analyses were selected to properly account for the multi-breed structure of the GWAS data. Our study revealed 58 lead markers that were associated with mastitis incidence, including 16 loci that did not overlap with previously identified quantitative trait loci (QTL), as curated at the Animal QTLdb. Post-GWAS analysis techniques such as gene-based analysis and genomic feature enrichment analysis enabled prioritization of 31 candidate genes and 14 credible candidate causal variants that affect mastitis. CONCLUSIONS Our list of candidate genes can help to elucidate the genetic architecture underlying mastitis resistance and provide better tools for the prevention or treatment of mastitis, ultimately contributing to more sustainable animal production.
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Affiliation(s)
- Zexi Cai
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark.
| | - Terhi Iso-Touru
- Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland
| | - Marie-Pierre Sanchez
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Naveen Kadri
- Animal Genomics, ETH Zurich, 8092, Zurich, Switzerland
| | - Aniek C Bouwman
- Wageningen University and Research, Animal Breeding and Genomics, P.O. Box 338, 6700, AH, Wageningen, The Netherlands
| | - Praveen Krishna Chitneedi
- Institute of Genome Biology, Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany
| | - Iona M MacLeod
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
| | | | - Amanda J Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
| | - Birgit Gredler-Grandl
- Wageningen University and Research, Animal Breeding and Genomics, P.O. Box 338, 6700, AH, Wageningen, The Netherlands
| | | | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark
| | - Didier Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Christa Kühn
- Institute of Genome Biology, Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany
- Agricultural and Environmental Faculty, University Rostock, 18059, Rostock, Germany
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, 8092, Zurich, Switzerland
| | - Johanna Vilkki
- Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark
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2
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Fegraeus K, Rosengren MK, Naboulsi R, Orlando L, Åbrink M, Jouni A, Velie BD, Raine A, Egner B, Mattsson CM, Lång K, Zhigulev A, Björck HM, Franco-Cereceda A, Eriksson P, Andersson G, Sahlén P, Meadows JRS, Lindgren G. An endothelial regulatory module links blood pressure regulation with elite athletic performance. PLoS Genet 2024; 20:e1011285. [PMID: 38885195 PMCID: PMC11182536 DOI: 10.1371/journal.pgen.1011285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 05/02/2024] [Indexed: 06/20/2024] Open
Abstract
The control of transcription is crucial for homeostasis in mammals. A previous selective sweep analysis of horse racing performance revealed a 19.6 kb candidate regulatory region 50 kb downstream of the Endothelin3 (EDN3) gene. Here, the region was narrowed to a 5.5 kb span of 14 SNVs, with elite and sub-elite haplotypes analyzed for association to racing performance, blood pressure and plasma levels of EDN3 in Coldblooded trotters and Standardbreds. Comparative analysis of human HiCap data identified the span as an enhancer cluster active in endothelial cells, interacting with genes relevant to blood pressure regulation. Coldblooded trotters with the sub-elite haplotype had significantly higher blood pressure compared to horses with the elite performing haplotype during exercise. Alleles within the elite haplotype were part of the standing variation in pre-domestication horses, and have risen in frequency during the era of breed development and selection. These results advance our understanding of the molecular genetics of athletic performance and vascular traits in both horses and humans.
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Affiliation(s)
- Kim Fegraeus
- Department of Medical Sciences, Science for life laboratory, Uppsala University, Sweden
| | - Maria K. Rosengren
- Department of Animal Biosciences, Swedish University of Agricultural Sciences Uppsala, Sweden
| | - Rakan Naboulsi
- Department of Animal Biosciences, Swedish University of Agricultural Sciences Uppsala, Sweden
- Childhood Cancer Research Unit, Department of Women’s and Children’s Health, Karolinska Institute, Stockholm
| | - Ludovic Orlando
- Centre d’Anthropobiologie et de Génomique de Toulouse (CNRS UMR 5288), Université Paul Sabatier, Toulouse, France
| | - Magnus Åbrink
- Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Ahmad Jouni
- Department of Animal Biosciences, Swedish University of Agricultural Sciences Uppsala, Sweden
| | - Brandon D. Velie
- School of Life & Environmental Sciences, University of Sydney, Sydney, Australia
| | - Amanda Raine
- Department of Medical Sciences, Science for life laboratory, Uppsala University, Sweden
| | - Beate Egner
- Department of Cardio-Vascular Research, Veterinary Academy of Higher Learning, Babenhausen, Germany
| | - C Mikael Mattsson
- Silicon Valley Exercise Analytics (svexa), MenloPark, CA, United States of America
| | - Karin Lång
- Division of Cardiovascular Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital, Solna, Sweden
| | - Artemy Zhigulev
- KTH Royal Institute of Technology, School of Chemistry, Biotechnology and Health, Science for Life Laboratory, Stockholm, Sweden
| | - Hanna M. Björck
- Division of Cardiovascular Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital, Solna, Sweden
| | - Anders Franco-Cereceda
- Section of Cardiothoracic Surgery, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Per Eriksson
- Division of Cardiovascular Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital, Solna, Sweden
| | - Göran Andersson
- Department of Animal Biosciences, Swedish University of Agricultural Sciences Uppsala, Sweden
| | - Pelin Sahlén
- KTH Royal Institute of Technology, School of Chemistry, Biotechnology and Health, Science for Life Laboratory, Stockholm, Sweden
| | - Jennifer R. S. Meadows
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Gabriella Lindgren
- Department of Animal Biosciences, Swedish University of Agricultural Sciences Uppsala, Sweden
- Center for Animal Breeding and Genetics, Department of Biosystems, KU Leuven, Leuven, Belgium
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3
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Innis SM, Cabot RA. Chromatin profiling and state predictions reveal insights into epigenetic regulation during early porcine development. Epigenetics Chromatin 2024; 17:16. [PMID: 38773546 PMCID: PMC11106951 DOI: 10.1186/s13072-024-00542-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 05/16/2024] [Indexed: 05/24/2024] Open
Abstract
BACKGROUND Given their physiological similarities to humans, pigs are increasingly used as model organisms in human-oriented biomedical studies. Additionally, their value to animal agriculture across the globe has led to the development of numerous studies to investigate how to improve livestock welfare and production efficiency. As such, pigs are uniquely poised as compelling models that can yield findings with potential implications in both human and animal contexts. Despite this, many gaps remain in our knowledge about the foundational mechanisms that govern gene expression in swine across different developmental stages, particularly in early development. To address some of these gaps, we profiled the histone marks H3K4me3, H3K27ac, and H3K27me3 and the SWI/SNF central ATPase BRG1 in two porcine cell lines representing discrete early developmental time points and used the resulting information to construct predicted chromatin state maps for these cells. We combined this approach with analysis of publicly available RNA-seq data to examine the relationship between epigenetic status and gene expression in these cell types. RESULTS In porcine fetal fibroblast (PFF) and trophectoderm cells (PTr2), we saw expected patterns of enrichment for each of the profiled epigenetic features relative to specific genomic regions. H3K4me3 was primarily enriched at and around global gene promoters, H3K27ac was enriched in promoter and intergenic regions, H3K27me3 had broad stretches of enrichment across the genome and narrower enrichment patterns in and around the promoter regions of some genes, and BRG1 primarily had detectable enrichment at and around promoter regions and in intergenic stretches, with many instances of H3K27ac co-enrichment. We used this information to perform genome-wide chromatin state predictions for 10 different states using ChromHMM. Using the predicted chromatin state maps, we identified a subset of genomic regions marked by broad H3K4me3 enrichment, and annotation of these regions revealed that they were highly associated with essential developmental processes and consisted largely of expressed genes. We then compared the identities of the genes marked by these regions to genes identified as cell-type-specific using transcriptome data and saw that a subset of broad H3K4me3-marked genes was also specifically expressed in either PFF or PTr2 cells. CONCLUSIONS These findings enhance our understanding of the epigenetic landscape present in early swine development and provide insight into how variabilities in chromatin state are linked to cell identity. Furthermore, this data captures foundational epigenetic details in two valuable porcine cell lines and contributes to the growing body of knowledge surrounding the epigenetic landscape in this species.
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Affiliation(s)
- Sarah M Innis
- Department of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Ryan A Cabot
- Department of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA.
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4
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Jiang J. MPH: fast REML for large-scale genome partitioning of quantitative genetic variation. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae298. [PMID: 38688661 DOI: 10.1093/bioinformatics/btae298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 05/02/2024]
Abstract
MOTIVATION Genome partitioning of quantitative genetic variation is useful for dissecting the genetic architecture of complex traits. However, existing methods, such as Haseman-Elston regression and linkage disequilibrium score regression, often face limitations when handling extensive farm animal datasets, as demonstrated in this study. RESULTS To overcome this challenge, we present MPH, a novel software tool designed for efficient genome partitioning analyses using restricted maximum likelihood. The computational efficiency of MPH primarily stems from two key factors: the utilization of stochastic trace estimators and the comprehensive implementation of parallel computation. Evaluations with simulated and real datasets demonstrate that MPH achieves comparable accuracy and significantly enhances convergence, speed, and memory efficiency compared to widely used tools like GCTA and LDAK. These advancements facilitate large-scale, comprehensive analyses of complex genetic architectures in farm animals. AVAILABILITY AND IMPLEMENTATION The MPH software is available at https://jiang18.github.io/mph/.
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Affiliation(s)
- Jicai Jiang
- Department of Animal Science, North Carolina State University, Raleigh, NC 27695, United States
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5
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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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6
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Chi K, Liu J, Li X, Wang H, Li Y, Liu Q, Zhou Y, Ge Y. Biomarkers of heart failure: advances in omics studies. Mol Omics 2024; 20:169-183. [PMID: 38224222 DOI: 10.1039/d3mo00173c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Heart failure is a complex syndrome characterized by progressive circulatory dysfunction, manifesting clinically as pulmonary and systemic venous congestion, alongside inadequate tissue perfusion. The early identification of HF, particularly at the mild and moderate stages (stages B and C), presents a clinical challenge due to the overlap of signs, symptoms, and natriuretic peptide levels with other cardiorespiratory pathologies. Nonetheless, early detection coupled with timely pharmacological intervention is imperative for enhancing patient outcomes. Advances in high-throughput omics technologies have enabled researchers to analyze patient-derived biofluids and tissues, discovering biomarkers that are sensitive and specific for HF diagnosis. Due to the diversity of HF etiology, it is insufficient to study the diagnostic data of early HF using a single omics technology. This study reviewed the latest progress in genomics, transcriptomics, proteomics, and metabolomics for the identification of HF biomarkers, offering novel insights into the early clinical diagnosis of HF. However, the validity of biomarkers depends on the disease status, intervention time, genetic diversity and comorbidities of the subjects. Moreover, biomarkers lack generalizability in different clinical settings. Hence, it is imperative to conduct multi-center, large-scale and standardized clinical trials to enhance the diagnostic accuracy and utility of HF biomarkers.
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Affiliation(s)
- Kuo Chi
- Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
| | - Jing Liu
- Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
| | - Xinghua Li
- Changzhi People's Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China.
| | - He Wang
- Department of Cardiovascular Disease II, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
| | - Yanliang Li
- Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
| | - Qingnan Liu
- Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
| | - Yabin Zhou
- Department of Cardiovascular Disease II, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
| | - Yuan Ge
- Department of Cardiovascular Disease II, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
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7
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Hubert JN, Perret M, Riquet J, Demars J. Livestock species as emerging models for genomic imprinting. Front Cell Dev Biol 2024; 12:1348036. [PMID: 38500688 PMCID: PMC10945557 DOI: 10.3389/fcell.2024.1348036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 01/19/2024] [Indexed: 03/20/2024] Open
Abstract
Genomic imprinting is an epigenetically-regulated process of central importance in mammalian development and evolution. It involves multiple levels of regulation, with spatio-temporal heterogeneity, leading to the context-dependent and parent-of-origin specific expression of a small fraction of the genome. Genomic imprinting studies have therefore been essential to increase basic knowledge in functional genomics, evolution biology and developmental biology, as well as with regard to potential clinical and agrigenomic perspectives. Here we offer an overview on the contribution of livestock research, which features attractive resources in several respects, for better understanding genomic imprinting and its functional impacts. Given the related broad implications and complexity, we promote the use of such resources for studying genomic imprinting in a holistic and integrative view. We hope this mini-review will draw attention to the relevance of livestock genomic imprinting studies and stimulate research in this area.
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Affiliation(s)
| | | | | | - Julie Demars
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France
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8
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Zhang K, Liang J, Fu Y, Chu J, Fu L, Wang Y, Li W, Zhou Y, Li J, Yin X, Wang H, Liu X, Mou C, Wang C, Wang H, Dong X, Yan D, Yu M, Zhao S, Li X, Ma Y. AGIDB: a versatile database for genotype imputation and variant decoding across species. Nucleic Acids Res 2024; 52:D835-D849. [PMID: 37889051 PMCID: PMC10767904 DOI: 10.1093/nar/gkad913] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/05/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023] Open
Abstract
The high cost of large-scale, high-coverage whole-genome sequencing has limited its application in genomics and genetics research. The common approach has been to impute whole-genome sequence variants obtained from a few individuals for a larger population of interest individually genotyped using SNP chip. An alternative involves low-coverage whole-genome sequencing (lcWGS) of all individuals in the larger population, followed by imputation to sequence resolution. To overcome limitations of processing lcWGS data and meeting specific genotype imputation requirements, we developed AGIDB (https://agidb.pro), a website comprising tools and database with an unprecedented sample size and comprehensive variant decoding for animals. AGIDB integrates whole-genome sequencing and chip data from 17 360 and 174 945 individuals, respectively, across 89 species to identify over one billion variants, totaling a massive 688.57 TB of processed data. AGIDB focuses on integrating multiple genotype imputation scenarios. It also provides user-friendly searching and data analysis modules that enable comprehensive annotation of genetic variants for specific populations. To meet a wide range of research requirements, AGIDB offers downloadable reference panels for each species in addition to its extensive dataset, variant decoding and utility tools. We hope that AGIDB will become a key foundational resource in genetics and breeding, providing robust support to researchers.
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Affiliation(s)
- Kaili Zhang
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Jiete Liang
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuhua Fu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Jinyu Chu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Liangliang Fu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
- The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan 430070, China
| | - Yongfei Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Wangjiao Li
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - You Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Jinhua Li
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaoxiao Yin
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Haiyan Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaolei Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Chunyan Mou
- College of Animal Science and Technology, Southwest University, Chongqing 402460, China
| | - Chonglong Wang
- Key Laboratory of Pig Molecular Quantitative Genetics of Anhui Academy of Agricultural Sciences, Anhui Provincial Key Laboratory of Livestock and Poultry Product Safety Engineering, Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Hefei 230031, China
| | - Heng Wang
- College of Animal Science and Technology, Shandong Agricultural University, Taian 271018, China
| | - Xinxing Dong
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Dawei Yan
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Mei Yu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
- Lingnan Modern Agricultural Science and Technology Guangdong Laboratory, Guangzhou 510642, China
| | - Xinyun Li
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Yunlong Ma
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
- Lingnan Modern Agricultural Science and Technology Guangdong Laboratory, Guangzhou 510642, China
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9
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Kurylo C, Guyomar C, Foissac S, Djebali S. TAGADA: a scalable pipeline to improve genome annotations with RNA-seq data. NAR Genom Bioinform 2023; 5:lqad089. [PMID: 37850035 PMCID: PMC10578202 DOI: 10.1093/nargab/lqad089] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/11/2023] [Accepted: 09/19/2023] [Indexed: 10/19/2023] Open
Abstract
Genome annotation plays a crucial role in providing comprehensive catalog of genes and transcripts for a particular species. As research projects generate new transcriptome data worldwide, integrating this information into existing annotations becomes essential. However, most bioinformatics pipelines are limited in their ability to effectively and consistently update annotations using new RNA-seq data. Here we introduce TAGADA, an RNA-seq pipeline for Transcripts And Genes Assembly, Deconvolution, and Analysis. Given a genomic sequence, a reference annotation and RNA-seq reads, TAGADA enhances existing gene models by generating an improved annotation. It also computes expression values for both the reference and novel annotation, identifies long non-coding transcripts (lncRNAs), and provides a comprehensive quality control report. Developed using Nextflow DSL2, TAGADA offers user-friendly functionalities and ensures reproducibility across different computing platforms through its containerized environment. In this study, we demonstrate the efficacy of TAGADA using RNA-seq data from the GENE-SWiTCH project alongside chicken and pig genome annotations as references. Results indicate that TAGADA can substantially increase the number of annotated transcripts by approximately [Formula: see text] in these species. Furthermore, we illustrate how TAGADA can integrate Illumina NovaSeq short reads with PacBio Iso-Seq long reads, showcasing its versatility. TAGADA is available at github.com/FAANG/analysis-TAGADA.
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Affiliation(s)
- Cyril Kurylo
- GenPhySE, Université de Toulouse, INRAE, INPT, ENVT, Toulouse, France
| | - Cervin Guyomar
- GenPhySE, Université de Toulouse, INRAE, INPT, ENVT, Toulouse, France
| | - Sylvain Foissac
- GenPhySE, Université de Toulouse, INRAE, INPT, ENVT, Toulouse, France
| | - Sarah Djebali
- IRSD, Université de Toulouse, INSERM, INRAE, ENVT, Univ Toulouse III - Paul Sabatier (UPS), Toulouse, France
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10
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Woolley SA, Salavati M, Clark EL. Recent advances in the genomic resources for sheep. Mamm Genome 2023; 34:545-558. [PMID: 37752302 PMCID: PMC10627984 DOI: 10.1007/s00335-023-10018-z] [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: 04/11/2023] [Accepted: 08/30/2023] [Indexed: 09/28/2023]
Abstract
Sheep (Ovis aries) provide a vital source of protein and fibre to human populations. In coming decades, as the pressures associated with rapidly changing climates increase, breeding sheep sustainably as well as producing enough protein to feed a growing human population will pose a considerable challenge for sheep production across the globe. High quality reference genomes and other genomic resources can help to meet these challenges by: (1) informing breeding programmes by adding a priori information about the genome, (2) providing tools such as pangenomes for characterising and conserving global genetic diversity, and (3) improving our understanding of fundamental biology using the power of genomic information to link cell, tissue and whole animal scale knowledge. In this review we describe recent advances in the genomic resources available for sheep, discuss how these might help to meet future challenges for sheep production, and provide some insight into what the future might hold.
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Affiliation(s)
- Shernae A Woolley
- The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
| | - Mazdak Salavati
- The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
- Scotland's Rural College, Parkgate, Barony Campus, Dumfries, DG1 3NE, UK
| | - Emily L Clark
- The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK.
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11
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Zeng Q, Du ZQ. Advances in the discovery of genetic elements underlying longissimus dorsi muscle growth and development in the pig. Anim Genet 2023; 54:709-720. [PMID: 37796678 DOI: 10.1111/age.13365] [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: 07/25/2022] [Revised: 07/08/2023] [Accepted: 07/08/2023] [Indexed: 10/07/2023]
Abstract
As a major source of protein in human diets, pig meat plays a crucial role in ensuring global food security. Key determinants of meat production refer to the chemical and physical compositions or characteristics of muscle fibers, such as the number, hypertrophy potential, fiber-type conversion and intramuscular fat deposition. However, the growth and formation of muscle fibers comprises a complex process under spatio-temporal regulation, that is, the intermingled and concomitant proliferation, differentiation, migration and fusion of myoblasts. Recently, with the fast and continuous development of next-generation sequencing technology, the integration of quantitative trait loci mapping with genome-wide association studies (GWAS) has greatly helped animal geneticists to discover and explore thousands of functional or causal genetic elements underlying muscle growth and development. However, owing to the underlying complex molecular mechanisms, challenges to in-depth understanding and utilization remain, and the cost of large-scale sequencing, which requires integrated analyses of high-throughput omics data, is high. In this review, we mainly elaborate on research advances in integrative analyses (e.g. GWAS, omics) for identifying functional genes or genomic elements for longissimus dorsi muscle growth and development for different pig breeds, describing several successful transcriptome analyses and functional genomics cases, in an attempt to provide some perspective on the future functional annotation of genetic elements for muscle growth and development in pigs.
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Affiliation(s)
- Qingjie Zeng
- College of Animal Science and Technology, Jiangxi Agricultural University, Nanchang, Jiangxi, China
| | - Zhi-Qiang Du
- College of Animal Science, Yangtze University, Jingzhou, Hubei, China
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12
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Wu J, Wu T, Xie X, Niu Q, Zhao Z, Zhu B, Chen Y, Zhang L, Gao X, Niu X, Gao H, Li J, Xu L. Genetic Association Analysis of Copy Number Variations for Meat Quality in Beef Cattle. Foods 2023; 12:3986. [PMID: 37959106 PMCID: PMC10647706 DOI: 10.3390/foods12213986] [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: 09/17/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Meat quality is an economically important trait for global food production. Copy number variations (CNVs) have been previously implicated in elucidating the genetic basis of complex traits. In this article, we detected a total of 112,198 CNVs and 10,102 CNV regions (CNVRs) based on the Bovine HD SNP array. Next, we performed a CNV-based genome-wide association analysis (GWAS) of six meat quality traits and identified 12 significant CNV segments corresponding to eight candidate genes, including PCDH15, CSMD3, etc. Using region-based association analysis, we further identified six CNV segments relevant to meat quality in beef cattle. Among these, TRIM77 and TRIM64 within CNVR4 on BTA29 were detected as candidate genes for backfat thickness (BFT). Notably, we identified a 34 kb duplication for meat color (MC) which was supported by read-depth signals, and this duplication was embedded within the keratin gene family including KRT4, KRT78, and KRT79. Our findings will help to dissect the genetic architecture of meat quality traits from the aspects of CNVs, and subsequently improve the selection process in breeding programs.
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Affiliation(s)
- Jiayuan Wu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Tianyi Wu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Xueyuan Xie
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
- College of Animal Science and Veterinary Medicine, Shanxi Agricultural University, Jinzhong 030801, China
| | - Qunhao Niu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Zhida Zhao
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Bo Zhu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Yan Chen
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Lupei Zhang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Xue Gao
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Xiaoyan Niu
- College of Animal Science and Veterinary Medicine, Shanxi Agricultural University, Jinzhong 030801, China
| | - Huijiang Gao
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Junya Li
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Lingyang Xu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
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13
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Cappelletti E, Piras FM, Sola L, Santagostino M, Petersen JL, Bellone RR, Finno CJ, Peng S, Kalbfleisch TS, Bailey E, Nergadze SG, Giulotto E. The localization of centromere protein A is conserved among tissues. Commun Biol 2023; 6:963. [PMID: 37735603 PMCID: PMC10514049 DOI: 10.1038/s42003-023-05335-7] [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: 04/18/2023] [Accepted: 09/08/2023] [Indexed: 09/23/2023] Open
Abstract
Centromeres are epigenetically specified by the histone H3 variant CENP-A. Although mammalian centromeres are typically associated with satellite DNA, we previously demonstrated that the centromere of horse chromosome 11 (ECA11) is completely devoid of satellite DNA. We also showed that the localization of its CENP-A binding domain is not fixed but slides within an about 500 kb region in different individuals, giving rise to positional alleles. These epialleles are inherited as Mendelian traits but their position can move in one generation. It is still unknown whether centromere sliding occurs during meiosis or during development. Here, we first improve the sequence of the ECA11 centromeric region in the EquCab3.0 assembly. Then, to test whether centromere sliding may occur during development, we map the CENP-A binding domains of ECA11 using ChIP-seq in five tissues of different embryonic origin from the four horses of the equine FAANG (Functional Annotation of ANimal Genomes) consortium. Our results demonstrate that the centromere is localized in the same region in all tissues, suggesting that the position of the centromeric domain is maintained during development.
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Affiliation(s)
| | - Francesca M Piras
- Department of Biology and Biotechnology, University of Pavia, Pavia, Italy
| | - Lorenzo Sola
- Department of Biology and Biotechnology, University of Pavia, Pavia, Italy
| | - Marco Santagostino
- Department of Biology and Biotechnology, University of Pavia, Pavia, Italy
| | - Jessica L Petersen
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Rebecca R Bellone
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, CA, USA
- Veterinary Genetics Laboratory, School of Veterinary Medicine, University of California-Davis, Davis, CA, USA
| | - Carrie J Finno
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, CA, USA
| | - Sichong Peng
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, CA, USA
| | - Ted S Kalbfleisch
- Gluck Equine Research Center, University of Kentucky, Lexington, KY, USA
| | - Ernest Bailey
- Gluck Equine Research Center, University of Kentucky, Lexington, KY, USA
| | - Solomon G Nergadze
- Department of Biology and Biotechnology, University of Pavia, Pavia, Italy
| | - Elena Giulotto
- Department of Biology and Biotechnology, University of Pavia, Pavia, Italy.
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14
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Triant DA, Walsh AT, Hartley GA, Petry B, Stegemiller MR, Nelson BM, McKendrick MM, Fuller EP, Cockett NE, Koltes JE, McKay SD, Green JA, Murdoch BM, Hagen DE, Elsik CG. AgAnimalGenomes: browsers for viewing and manually annotating farm animal genomes. Mamm Genome 2023; 34:418-436. [PMID: 37460664 PMCID: PMC10382368 DOI: 10.1007/s00335-023-10008-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 06/29/2023] [Indexed: 07/30/2023]
Abstract
Current genome sequencing technologies have made it possible to generate highly contiguous genome assemblies for non-model animal species. Despite advances in genome assembly methods, there is still room for improvement in the delineation of specific gene features in the genomes. Here we present genome visualization and annotation tools to support seven livestock species (bovine, chicken, goat, horse, pig, sheep, and water buffalo), available in a new resource called AgAnimalGenomes. In addition to supporting the manual refinement of gene models, these browsers provide visualization tracks for hundreds of RNAseq experiments, as well as data generated by the Functional Annotation of Animal Genomes (FAANG) Consortium. For species with predicted gene sets from both Ensembl and RefSeq, the browsers provide special tracks showing the thousands of protein-coding genes that disagree across the two gene sources, serving as a valuable resource to alert researchers to gene model issues that may affect data interpretation. We describe the data and search methods available in the new genome browsers and how to use the provided tools to edit and create new gene models.
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Affiliation(s)
- Deborah A Triant
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Amy T Walsh
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Gabrielle A Hartley
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, 06269, USA
| | - Bruna Petry
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Morgan R Stegemiller
- Department of Animal, Veterinary and Food Sciences, University of Idaho, Moscow, ID, 83844, USA
| | - Benjamin M Nelson
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Makenna M McKendrick
- Department of Animal and Food Sciences, Oklahoma State University, Stillwater, OK, 74078, USA
| | - Emily P Fuller
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, 06269, USA
| | - Noelle E Cockett
- Department of Animal, Dairy, and Veterinary Sciences, Utah State University, Logan, UT, 84322, USA
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Stephanie D McKay
- Department of Animal and Veterinary Sciences, University of Vermont, Burlington, VT, 05405, USA
| | - Jonathan A Green
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Brenda M Murdoch
- Department of Animal, Veterinary and Food Sciences, University of Idaho, Moscow, ID, 83844, USA
| | - Darren E Hagen
- Department of Animal and Food Sciences, Oklahoma State University, Stillwater, OK, 74078, USA
| | - Christine G Elsik
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA.
- Division of Plant Science & Technology, University of Missouri, Columbia, MO, 65211, USA.
- Institute for Data Science & Informatics, University of Missouri, Columbia, MO, 65211, USA.
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15
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Salavati M, Clark R, Becker D, Kühn C, Plastow G, Dupont S, Moreira GCM, Charlier C, Clark EL. Improving the annotation of the cattle genome by annotating transcription start sites in a diverse set of tissues and populations using Cap Analysis Gene Expression sequencing. G3 (BETHESDA, MD.) 2023; 13:jkad108. [PMID: 37216666 PMCID: PMC10411599 DOI: 10.1093/g3journal/jkad108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 02/27/2023] [Accepted: 05/09/2023] [Indexed: 05/24/2023]
Abstract
Understanding the genomic control of tissue-specific gene expression and regulation can help to inform the application of genomic technologies in farm animal breeding programs. The fine mapping of promoters [transcription start sites (TSS)] and enhancers (divergent amplifying segments of the genome local to TSS) in different populations of cattle across a wide diversity of tissues provides information to locate and understand the genomic drivers of breed- and tissue-specific characteristics. To this aim, we used Cap Analysis Gene Expression (CAGE) sequencing, of 24 different tissues from 3 populations of cattle, to define TSS and their coexpressed short-range enhancers (<1 kb) in the ARS-UCD1.2_Btau5.0.1Y reference genome (1000bulls run9) and analyzed tissue and population specificity of expressed promoters. We identified 51,295 TSS and 2,328 TSS-Enhancer regions shared across the 3 populations (dairy, beef-dairy cross, and Canadian Kinsella composite cattle from 2 individuals, 1 of each sex, per population). Cross-species comparative analysis of CAGE data from 7 other species, including sheep, revealed a set of TSS and TSS-Enhancers that were specific to cattle. The CAGE data set will be combined with other transcriptomic information for the same tissues to create a new high-resolution map of transcript diversity across tissues and populations in cattle for the BovReg project. Here we provide the CAGE data set and annotation tracks for TSS and TSS-Enhancers in the cattle genome. This new annotation information will improve our understanding of the drivers of gene expression and regulation in cattle and help to inform the application of genomic technologies in breeding programs.
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Affiliation(s)
- Mazdak Salavati
- The Roslin Institute, University of Edinburgh, Edinburgh EH25 9RG, UK
| | - Richard Clark
- Edinburgh Clinical Research Facility, Genetics Core, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Doreen Becker
- Institute of Genome Biology, Research Institute for Farm Animal Biology (FBN), Dummerstorf 18196, Germany
| | - Christa Kühn
- Institute of Genome Biology, Research Institute for Farm Animal Biology (FBN), Dummerstorf 18196, Germany
- Faculty of Agricultural and Environmental Sciences, University Rostock, Rostock 18059, Germany
| | - Graham Plastow
- Department of Agricultural, Food and Nutritional Science, Livestock Gentec, University of Alberta, Edmonton T6G 2H1, Canada
| | - Sébastien Dupont
- Unit of Animal Genomics, GIGA Institute, University of Liège, Liège 4000, Belgium
| | | | - Carole Charlier
- Unit of Animal Genomics, GIGA Institute, University of Liège, Liège 4000, Belgium
- Faculty of Veterinary Medicine, University of Liège, Liège 4000, Belgium
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16
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Ma W, Fu Y, Bao Y, Wang Z, Lei B, Zheng W, Wang C, Liu Y. DeepSATA: A Deep Learning-Based Sequence Analyzer Incorporating the Transcription Factor Binding Affinity to Dissect the Effects of Non-Coding Genetic Variants. Int J Mol Sci 2023; 24:12023. [PMID: 37569400 PMCID: PMC10418434 DOI: 10.3390/ijms241512023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/13/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Utilizing large-scale epigenomics data, deep learning tools can predict the regulatory activity of genomic sequences, annotate non-coding genetic variants, and uncover mechanisms behind complex traits. However, these tools primarily rely on human or mouse data for training, limiting their performance when applied to other species. Furthermore, the limited exploration of many species, particularly in the case of livestock, has led to a scarcity of comprehensive and high-quality epigenetic data, posing challenges in developing reliable deep learning models for decoding their non-coding genomes. The cross-species prediction of the regulatory genome can be achieved by leveraging publicly available data from extensively studied organisms and making use of the conserved DNA binding preferences of transcription factors within the same tissue. In this study, we introduced DeepSATA, a novel deep learning-based sequence analyzer that incorporates the transcription factor binding affinity for the cross-species prediction of chromatin accessibility. By applying DeepSATA to analyze the genomes of pigs, chickens, cattle, humans, and mice, we demonstrated its ability to improve the prediction accuracy of chromatin accessibility and achieve reliable cross-species predictions in animals. Additionally, we showcased its effectiveness in analyzing pig genetic variants associated with economic traits and in increasing the accuracy of genomic predictions. Overall, our study presents a valuable tool to explore the epigenomic landscape of various species and pinpoint regulatory deoxyribonucleic acid (DNA) variants associated with complex traits.
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Affiliation(s)
- Wenlong Ma
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China; (W.M.); (Y.F.); (Y.B.); (Z.W.); (B.L.); (W.Z.); (C.W.)
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Yang Fu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China; (W.M.); (Y.F.); (Y.B.); (Z.W.); (B.L.); (W.Z.); (C.W.)
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Yongzhou Bao
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China; (W.M.); (Y.F.); (Y.B.); (Z.W.); (B.L.); (W.Z.); (C.W.)
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
- School of Life Sciences, Henan University, Kaifeng 475004, China
| | - Zhen Wang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China; (W.M.); (Y.F.); (Y.B.); (Z.W.); (B.L.); (W.Z.); (C.W.)
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
- School of Life Sciences, Henan University, Kaifeng 475004, China
| | - Bowen Lei
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China; (W.M.); (Y.F.); (Y.B.); (Z.W.); (B.L.); (W.Z.); (C.W.)
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, China
| | - Weigang Zheng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China; (W.M.); (Y.F.); (Y.B.); (Z.W.); (B.L.); (W.Z.); (C.W.)
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, China
| | - Chao Wang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China; (W.M.); (Y.F.); (Y.B.); (Z.W.); (B.L.); (W.Z.); (C.W.)
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuwen Liu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China; (W.M.); (Y.F.); (Y.B.); (Z.W.); (B.L.); (W.Z.); (C.W.)
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
- Kunpeng Institute of Modern Agriculture at Foshan, Chinese Academy of Agricultural Sciences, Foshan 528226, China
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17
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Patel KK, Venkatesan C, Abdelhalim H, Zeeshan S, Arima Y, Linna-Kuosmanen S, Ahmed Z. Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility. Hum Genomics 2023; 17:47. [PMID: 37270590 DOI: 10.1186/s40246-023-00498-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 05/31/2023] [Indexed: 06/05/2023] Open
Abstract
Atrial fibrillation (AF) and heart failure (HF) contribute to about 45% of all cardiovascular disease (CVD) deaths in the USA and around the globe. Due to the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. To improve the deciphering of CVD mechanisms, we need to deeply investigate well-known and identify novel genes that are responsible for CVD development. With the advancements in sequencing technologies, genomic data have been generated at an unprecedented pace to foster translational research. Correct application of bioinformatics using genomic data holds the potential to reveal the genetic underpinnings of various health conditions. It can help in the identification of causal variants for AF, HF, and other CVDs by moving beyond the one-gene one-disease model through the integration of common and rare variant association, the expressed genome, and characterization of comorbidities and phenotypic traits derived from the clinical information. In this study, we examined and discussed variable genomic approaches investigating genes associated with AF, HF, and other CVDs. We collected, reviewed, and compared high-quality scientific literature published between 2009 and 2022 and accessible through PubMed/NCBI. While selecting relevant literature, we mainly focused on identifying genomic approaches involving the integration of genomic data; analysis of common and rare genetic variants; metadata and phenotypic details; and multi-ethnic studies including individuals from ethnic minorities, and European, Asian, and American ancestries. We found 190 genes associated with AF and 26 genes linked to HF. Seven genes had implications in both AF and HF, which are SYNPO2L, TTN, MTSS1, SCN5A, PITX2, KLHL3, and AGAP5. We listed our conclusion, which include detailed information about genes and SNPs associated with AF and HF.
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Affiliation(s)
- Kush Ketan Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Cynthia Venkatesan
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Saman Zeeshan
- Rutgers Cancer Institute of New Jersey, Rutgers University, 195 Little Albany St, New Brunswick, NJ, USA
| | - Yuichiro Arima
- Developmental Cardiology Laboratory, International Research Center for Medical Sciences, Kumamoto University, 2-2-1 Honjo, Kumamoto City, Kumamoto, Japan
| | - Suvi Linna-Kuosmanen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211, Kuopio, Finland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Zeeshan Ahmed
- Department of Genetics and Genome Sciences, UConn Health, 400 Farmington Ave, Farmington, CT, USA.
- Department of Medicine, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson St, New Brunswick, NJ, USA.
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18
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Herrera-Uribe J, Lim KS, Byrne KA, Daharsh L, Liu H, Corbett RJ, Marco G, Schroyen M, Koltes JE, Loving CL, Tuggle CK. Integrative profiling of gene expression and chromatin accessibility elucidates specific transcriptional networks in porcine neutrophils. Front Genet 2023; 14:1107462. [PMID: 37287538 PMCID: PMC10242145 DOI: 10.3389/fgene.2023.1107462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 04/27/2023] [Indexed: 06/09/2023] Open
Abstract
Neutrophils are vital components of the immune system for limiting the invasion and proliferation of pathogens in the body. Surprisingly, the functional annotation of porcine neutrophils is still limited. The transcriptomic and epigenetic assessment of porcine neutrophils from healthy pigs was performed by bulk RNA sequencing and transposase accessible chromatin sequencing (ATAC-seq). First, we sequenced and compared the transcriptome of porcine neutrophils with eight other immune cell transcriptomes to identify a neutrophil-enriched gene list within a detected neutrophil co-expression module. Second, we used ATAC-seq analysis to report for the first time the genome-wide chromatin accessible regions of porcine neutrophils. A combined analysis using both transcriptomic and chromatin accessibility data further defined the neutrophil co-expression network controlled by transcription factors likely important for neutrophil lineage commitment and function. We identified chromatin accessible regions around promoters of neutrophil-specific genes that were predicted to be bound by neutrophil-specific transcription factors. Additionally, published DNA methylation data from porcine immune cells including neutrophils were used to link low DNA methylation patterns to accessible chromatin regions and genes with highly enriched expression in porcine neutrophils. In summary, our data provides the first integrative analysis of the accessible chromatin regions and transcriptional status of porcine neutrophils, contributing to the Functional Annotation of Animal Genomes (FAANG) project, and demonstrates the utility of chromatin accessible regions to identify and enrich our understanding of transcriptional networks in a cell type such as neutrophils.
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Affiliation(s)
- Juber Herrera-Uribe
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Kyu-Sang Lim
- Department of Animal Science, Iowa State University, Ames, IA, United States
- Department of Animal Resource Science, Kongju National University, Yesan, Republic of Korea
| | - Kristen A. Byrne
- USDA-Agriculture Research Service, National Animal Disease Center, Food Safety and Enteric Pathogens Research Unit, Ames, IA, United States
| | - Lance Daharsh
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Haibo Liu
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Ryan J. Corbett
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Gianna Marco
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Martine Schroyen
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - James E. Koltes
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Crystal L. Loving
- USDA-Agriculture Research Service, National Animal Disease Center, Food Safety and Enteric Pathogens Research Unit, Ames, IA, United States
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19
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Johnsson M. Genomics in animal breeding from the perspectives of matrices and molecules. Hereditas 2023; 160:20. [PMID: 37149663 PMCID: PMC10163706 DOI: 10.1186/s41065-023-00285-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/03/2023] [Indexed: 05/08/2023] Open
Abstract
BACKGROUND This paper describes genomics from two perspectives that are in use in animal breeding and genetics: a statistical perspective concentrating on models for estimating breeding values, and a sequence perspective concentrating on the function of DNA molecules. MAIN BODY This paper reviews the development of genomics in animal breeding and speculates on its future from these two perspectives. From the statistical perspective, genomic data are large sets of markers of ancestry; animal breeding makes use of them while remaining agnostic about their function. From the sequence perspective, genomic data are a source of causative variants; what animal breeding needs is to identify and make use of them. CONCLUSION The statistical perspective, in the form of genomic selection, is the more applicable in contemporary breeding. Animal genomics researchers using from the sequence perspective are still working towards this the isolation of causative variants, equipped with new technologies but continuing a decades-long line of research.
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Affiliation(s)
- Martin Johnsson
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, Uppsala, 75007, Sweden.
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20
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Cheng J, Cao X, Wang X, Wang J, Yue B, Sun W, Huang Y, Lan X, Ren G, Lei C, Chen H. Dynamic chromatin architectures provide insights into the genetics of cattle myogenesis. J Anim Sci Biotechnol 2023; 14:59. [PMID: 37055796 PMCID: PMC10103417 DOI: 10.1186/s40104-023-00855-y] [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: 09/11/2022] [Accepted: 02/16/2023] [Indexed: 04/15/2023] Open
Abstract
BACKGROUND Sharply increased beef consumption is propelling the genetic improvement projects of beef cattle in China. Three-dimensional genome structure is confirmed to be an important layer of transcription regulation. Although genome-wide interaction data of several livestock species have already been produced, the genome structure states and its regulatory rules in cattle muscle are still limited. RESULTS Here we present the first 3D genome data in Longissimus dorsi muscle of fetal and adult cattle (Bos taurus). We showed that compartments, topologically associating domains (TADs), and loop undergo re-organization and the structure dynamics were consistent with transcriptomic divergence during muscle development. Furthermore, we annotated cis-regulatory elements in cattle genome during myogenesis and demonstrated the enrichments of promoter and enhancer in selection sweeps. We further validated the regulatory function of one HMGA2 intronic enhancer near a strong sweep region on primary bovine myoblast proliferation. CONCLUSIONS Our data provide key insights of the regulatory function of high order chromatin structure and cattle myogenic biology, which will benefit the progress of genetic improvement of beef cattle.
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Affiliation(s)
- Jie Cheng
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
| | - Xiukai Cao
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, 225009, China
| | - Xiaogang Wang
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
| | - Jian Wang
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
| | - Binglin Yue
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Southwest Minzu University, Chengdu, 610225, China
| | - Wei Sun
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, China
| | - Yongzhen Huang
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
| | - Xianyong Lan
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
| | - Gang Ren
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
| | - Chuzhao Lei
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
| | - Hong Chen
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China.
- College of Animal Science, Xinjiang Agricultural University, Urumqi, 830052, China.
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21
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Gu J, Guo J, Zhang Z, Xu Y, Qadri QR, Zhang Z, Wang Z, Wang Q, Pan Y. Molecular Design-Based Breeding: A Kinship Index-Based Selection Method for Complex Traits in Small Livestock Populations. Genes (Basel) 2023; 14:genes14040807. [PMID: 37107565 PMCID: PMC10137344 DOI: 10.3390/genes14040807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 03/29/2023] Open
Abstract
Genomic selection (GS) techniques have improved animal breeding by enhancing the prediction accuracy of breeding values, particularly for traits that are difficult to measure and have low heritability, as well as reducing generation intervals. However, the requirement to establish genetic reference populations can limit the application of GS in pig breeds with small populations, especially when small populations make up most of the pig breeds worldwide. We aimed to propose a kinship index based selection (KIS) method, which defines an ideal individual with information on the beneficial genotypes for the target trait. Herein, the metric for assessing selection decisions is a beneficial genotypic similarity between the candidate and the ideal individual; thus, the KIS method can overcome the need for establishing genetic reference groups and continuous phenotype determination. We also performed a robustness test to make the method more aligned with reality. Simulation results revealed that compared to conventional genomic selection methods, the KIS method is feasible, particularly, when the population size is relatively small.
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Affiliation(s)
- Jiamin Gu
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Jianwei Guo
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Zhenyang Zhang
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
- Zhejiang Key Laboratory of Dairy Cattle Genetic Improvement and Milk Quality Research, Hangzhou 310058, China
| | - Yuejin Xu
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
- Hainan Institute, Zhejiang University, Building 11, Yongyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
- Hainan Yazhou Bay Seed Laboratory, Yongyou Industrial Park, Yazhou Bay Sci-Tech City, Sanya 572025, China
| | - Qamar Raza Qadri
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zhe Zhang
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
- Zhejiang Key Laboratory of Dairy Cattle Genetic Improvement and Milk Quality Research, Hangzhou 310058, China
| | - Zhen Wang
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Qishan Wang
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
- Zhejiang Key Laboratory of Dairy Cattle Genetic Improvement and Milk Quality Research, Hangzhou 310058, China
- Hainan Institute, Zhejiang University, Building 11, Yongyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
- Correspondence: (Q.W.); (Y.P.)
| | - Yuchun Pan
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
- Zhejiang Key Laboratory of Dairy Cattle Genetic Improvement and Milk Quality Research, Hangzhou 310058, China
- Hainan Institute, Zhejiang University, Building 11, Yongyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
- Hainan Yazhou Bay Seed Laboratory, Yongyou Industrial Park, Yazhou Bay Sci-Tech City, Sanya 572025, China
- Correspondence: (Q.W.); (Y.P.)
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22
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Mohamed AR, Ochsenkühn MA, Kazlak AM, Moustafa A, Amin SA. The coral microbiome: towards an understanding of the molecular mechanisms of coral-microbiota interactions. FEMS Microbiol Rev 2023; 47:fuad005. [PMID: 36882224 PMCID: PMC10045912 DOI: 10.1093/femsre/fuad005] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 02/10/2023] [Accepted: 02/15/2023] [Indexed: 03/09/2023] Open
Abstract
Corals live in a complex, multipartite symbiosis with diverse microbes across kingdoms, some of which are implicated in vital functions, such as those related to resilience against climate change. However, knowledge gaps and technical challenges limit our understanding of the nature and functional significance of complex symbiotic relationships within corals. Here, we provide an overview of the complexity of the coral microbiome focusing on taxonomic diversity and functions of well-studied and cryptic microbes. Mining the coral literature indicate that while corals collectively harbour a third of all marine bacterial phyla, known bacterial symbionts and antagonists of corals represent a minute fraction of this diversity and that these taxa cluster into select genera, suggesting selective evolutionary mechanisms enabled these bacteria to gain a niche within the holobiont. Recent advances in coral microbiome research aimed at leveraging microbiome manipulation to increase coral's fitness to help mitigate heat stress-related mortality are discussed. Then, insights into the potential mechanisms through which microbiota can communicate with and modify host responses are examined by describing known recognition patterns, potential microbially derived coral epigenome effector proteins and coral gene regulation. Finally, the power of omics tools used to study corals are highlighted with emphasis on an integrated host-microbiota multiomics framework to understand the underlying mechanisms during symbiosis and climate change-driven dysbiosis.
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Affiliation(s)
- Amin R Mohamed
- Biology Program, New York University Abu Dhabi, Abu Dhabi 129188, United Arab Emirates
| | - Michael A Ochsenkühn
- Biology Program, New York University Abu Dhabi, Abu Dhabi 129188, United Arab Emirates
| | - Ahmed M Kazlak
- Systems Genomics Laboratory, American University in Cairo, New Cairo 11835, Egypt
- Biotechnology Graduate Program, American University in Cairo, New Cairo 11835, Egypt
| | - Ahmed Moustafa
- Systems Genomics Laboratory, American University in Cairo, New Cairo 11835, Egypt
- Biotechnology Graduate Program, American University in Cairo, New Cairo 11835, Egypt
- Department of Biology, American University in Cairo, New Cairo 11835, Egypt
| | - Shady A Amin
- Biology Program, New York University Abu Dhabi, Abu Dhabi 129188, United Arab Emirates
- Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi, Abu Dhabi 129188, United Arab Emirates
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23
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Zheng Y, Young ND, Song J, Chang BC, Gasser RB. An informatic workflow for the enhanced annotation of excretory/secretory proteins of Haemonchus contortus. Comput Struct Biotechnol J 2023; 21:2696-2704. [PMID: 37143762 PMCID: PMC10151223 DOI: 10.1016/j.csbj.2023.03.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/16/2023] [Accepted: 03/16/2023] [Indexed: 03/19/2023] Open
Abstract
Major advances in genomic and associated technologies have demanded reliable bioinformatic tools and workflows for the annotation of genes and their products via comparative analyses using well-curated reference data sets, accessible in public repositories. However, the accurate in silico annotation of molecules (proteins) encoded in organisms (e.g., multicellular parasites) which are evolutionarily distant from those for which these extensive reference data sets are available, including invertebrate model organisms (e.g., Caenorhabditis elegans - free-living nematode, and Drosophila melanogaster - the vinegar fly) and vertebrate species (e.g., Homo sapiens and Mus musculus), remains a major challenge. Here, we constructed an informatic workflow for the enhanced annotation of biologically-important, excretory/secretory (ES) proteins ("secretome") encoded in the genome of a parasitic roundworm, called Haemonchus contortus (commonly known as the barber's pole worm). We critically evaluated the performance of five distinct methods, refined some of them, and then combined the use of all five methods to comprehensively annotate ES proteins, according to gene ontology, biological pathways and/or metabolic (enzymatic) processes. Then, using optimised parameter settings, we applied this workflow to comprehensively annotate 2591 of all 3353 proteins (77.3%) in the secretome of H. contortus. This result is a substantial improvement (10-25%) over previous annotations using individual, "off-the-shelf" algorithms and default settings, indicating the ready applicability of the present, refined workflow to gene/protein sequence data sets from a wide range of organisms in the Tree-of-Life.
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24
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Full-length transcriptome from different life stages of cobia (Rachycentron canadum, Rachycentridae). Sci Data 2023; 10:97. [PMID: 36797271 PMCID: PMC9935508 DOI: 10.1038/s41597-022-01907-0] [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: 10/08/2022] [Accepted: 12/14/2022] [Indexed: 02/18/2023] Open
Abstract
Cobia (Rachycentron canadum, Rachycentridae) is one of the prospective species for mariculture. The transcriptome-based study on cobia was hampered by an inadequate reference genome and a lack of full-length cDNAs. We used a long-read based sequencing technology (PacBio Sequel II Iso-Seq3 SMRT) to obtain complete transcriptome sequences from larvae, juveniles, and various tissues of adult cobia, and a single SMRTcell generated 99 gigabytes of data and 51,205,946,694 bases. A total of 8609435, 7441673 and 9140164 subreads were generated from the larval, juvenile, and adult sample pools, with mean sub-read lengths of 2109.9, 1988.2 and 1996.2 bp, respectively. All samples were combined to increase transcript recovery and clustered into 35661 high-quality reads. This is the first report on a full-length transcriptome from R. canadum. Our results illustrate a significant increase in the identified amount of cobia LncRNAs and alternatively spliced transcripts, which will help improve genome annotation. Furthermore, this information will be beneficial for nutrigenomics and functional studies on cobia and other commercially important mariculture species.
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25
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Li J, Guan D, Halstead MM, Islas-Trejo AD, Goszczynski DE, Ernst CW, Cheng H, Ross P, Zhou H. Transcriptome annotation of 17 porcine tissues using nanopore sequencing technology. Anim Genet 2023; 54:35-44. [PMID: 36385508 DOI: 10.1111/age.13274] [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: 01/28/2022] [Revised: 10/20/2022] [Accepted: 11/01/2022] [Indexed: 11/18/2022]
Abstract
The annotation of animal genomes plays an important role in elucidating molecular mechanisms behind the genetic control of economically important traits. Here, we employed long-read sequencing technology, Oxford Nanopore Technology, to annotate the pig transcriptome across 17 tissues from two Yorkshire littermate pigs. More than 9.8 million reads were obtained from a single flow cell, and 69 781 unique transcripts at 50 108 loci were identified. Of these transcripts, 16 255 were found to be novel isoforms, and 22 344 were found at loci that were novel and unannotated in the Ensembl (release 102) and NCBI (release 106) annotations. Novel transcripts were mostly expressed in cerebellum, followed by lung, liver, spleen, and hypothalamus. By comparing the unannotated transcripts to existing databases, there were 21 285 (95.3%) transcripts matched to the NT database (v5) and 13 676 (61.2%) matched to the NR database (v5). Moreover, there were 4324 (19.4%) transcripts matched to the SwissProt database (v5), corresponding to 11 356 proteins. Tissue-specific gene expression analyses showed that 9749 transcripts were highly tissue-specific, and cerebellum contained the most tissue-specific transcripts. As the same samples were used for the annotation of cis-regulatory elements in the pig genome, the transcriptome annotation generated by this study provides an additional and complementary annotation resource for the Functional Annotation of Animal Genomes effort to comprehensively annotate the pig genome.
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Affiliation(s)
- Jinghui Li
- Department of Animal Science, University of California Davis, Davis, California, USA
| | - Dailu Guan
- Department of Animal Science, University of California Davis, Davis, California, USA
| | - Michelle M Halstead
- Department of Animal Science, University of California Davis, Davis, California, USA
| | - Alma D Islas-Trejo
- Department of Animal Science, University of California Davis, Davis, California, USA
| | - Daniel E Goszczynski
- Department of Animal Science, University of California Davis, Davis, California, USA
| | - Catherine W Ernst
- Department of Animal Science, Michigan State University, East Lansing, Michigan, USA
| | - Hao Cheng
- Department of Animal Science, University of California Davis, Davis, California, USA
| | - Pablo Ross
- Department of Animal Science, University of California Davis, Davis, California, USA
| | - Huaijun Zhou
- Department of Animal Science, University of California Davis, Davis, California, USA
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26
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Kutchy NA, Morenikeji OB, Memili A, Ugur MR. Deciphering sperm functions using biological networks. Biotechnol Genet Eng Rev 2023:1-25. [PMID: 36722689 DOI: 10.1080/02648725.2023.2168912] [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: 05/28/2022] [Indexed: 02/02/2023]
Abstract
The global human population is exponentially increasing, which requires the production of quality food through efficient reproduction as well as sustainable production of livestock. Lack of knowledge and technology for assessing semen quality and predicting bull fertility is hindering advances in animal science and food animal production and causing millions of dollars of economic losses annually. The intent of this systemic review is to summarize methods from computational biology for analysis of gene, metabolite, and protein networks to identify potential markers that can be applied to improve livestock reproduction, with a focus on bull fertility. We provide examples of available gene, metabolic, and protein networks and computational biology methods to show how the interactions between genes, proteins, and metabolites together drive the complex process of spermatogenesis and regulate fertility in animals. We demonstrate the use of the National Center for Biotechnology Information (NCBI) and Ensembl for finding gene sequences, and then use them to create and understand gene, protein and metabolite networks for sperm associated factors to elucidate global cellular processes in sperm. This study highlights the value of mapping complex biological pathways among livestock and potential for conducting studies on promoting livestock improvement for global food security.
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Affiliation(s)
- Naseer A Kutchy
- Department of Anatomy, Physiology and Pharmacology, School of Veterinary Medicine, St. George's University, St. George's, Grenada
- Department of Animal Sciences, School of Environmental and Biological Sciences Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Olanrewaju B Morenikeji
- Division of Biological and Health Sciences, University of Pittsburgh at Bradford, Bradford, PA, USA
| | - Aylin Memili
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
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27
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Wu Z, Bosse M, Rochus CM, Groenen MAM, Crooijmans RPMA. Genomic insight into the influence of selection, crossbreeding, and geography on population structure in poultry. Genet Sel Evol 2023; 55:5. [PMID: 36670351 PMCID: PMC9854048 DOI: 10.1186/s12711-022-00775-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 12/21/2022] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND In poultry, the population structure of local breeds is usually complex mainly due to unrecorded breeding. Local chicken breeds offer an interesting proxy to understand the complexity of population structure in the context of human-mediated development of diverse morphologies and varieties. We studied 37 traditional Dutch chicken breeds to investigate population structure and the corresponding genomic impact using whole-genome sequence data. RESULTS Looking at the genetic differences between breeds, the Dutch chicken breeds demonstrated a complex and admixed subdivided structure. The dissection of this complexity highlighted the influence of selection adhering to management purposes, as well as the role of geographic distance within subdivided breed clusters. Identification of signatures of genetic differentiation revealed genomic regions that are associated with diversifying phenotypic selection between breeds, including dwarf size (bantam) and feather color. In addition, with a case study of a recently developed bantam breed developed by crossbreeding, we provide a genomic perspective on the effect of crossbreeding. CONCLUSIONS This study demonstrates the complex population structure of local traditional Dutch chicken, and provides insight into the genomic basis and the factors involved in the formation of this complexity.
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Affiliation(s)
- Zhou Wu
- grid.4818.50000 0001 0791 5666Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands ,grid.4305.20000 0004 1936 7988Present Address: The Roslin Institute and Royal (Dick) School of Veterinary Studies R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG UK
| | - Mirte Bosse
- grid.4818.50000 0001 0791 5666Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Christina M. Rochus
- grid.4818.50000 0001 0791 5666Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands ,grid.34429.380000 0004 1936 8198Present Address: Centre for Genetic Improvement of Livestock, Animal Biosciences, University of Guelph, Guelph, ON Canada
| | - Martien A. M. Groenen
- grid.4818.50000 0001 0791 5666Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Richard P. M. A. Crooijmans
- grid.4818.50000 0001 0791 5666Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
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28
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Zhu XN, Wang YZ, Li C, Wu HY, Zhang R, Hu XX, Zhang R, Hu XX. Chicken chromatin accessibility atlas accelerates epigenetic annotation of birds and gene fine-mapping associated with growth traits. Zool Res 2023; 44:53-62. [PMID: 36317479 PMCID: PMC9841184 DOI: 10.24272/j.issn.2095-8137.2022.228] [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] [Indexed: 01/04/2023] Open
Abstract
The development of epigenetic maps, such as the ENCODE project in humans, provides resources for gene regulation studies and a reference for research of disease-related regulatory elements. However, epigenetic information, such as a bird-specific chromatin accessibility atlas, is currently lacking for the thousands of bird species currently described. The major genomic difference between birds and mammals is their shorter introns and intergenic distances, which seriously hinders the use of humans and mice as a reference for studying the function of important regulatory regions in birds. In this study, using chicken as a model bird species, we systematically compiled a chicken chromatin accessibility atlas using 53 Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) samples across 11 tissues. An average of 50 796 open chromatin regions were identified per sample, cumulatively accounting for 20.36% of the chicken genome. Tissue specificity was largely reflected by differences in intergenic and intronic peaks, with specific functional regulation achieved by two mechanisms: recruitment of several sequence-specific transcription factors and direct regulation of adjacent functional genes. By integrating data from genome-wide association studies, our results suggest that chicken body weight is driven by different regulatory variants active in growth-relevant tissues. We propose CAB39L (active in the duodenum), RCBTB1 (muscle and liver), and novel long non-coding RNA ENSGALG00000053256 (bone) as candidate genes regulating chicken body weight. Overall, this study demonstrates the value of epigenetic data in fine-mapping functional variants and provides a compendium of resources for further research on the epigenetics and evolution of birds and mammals.
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Affiliation(s)
- Xiao-Ning Zhu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yu-Zhe Wang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China,National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, Beijing 100193, China,E-mail:
| | - Chong Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Han-Yu Wu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China,National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, Beijing 100193, China
| | - Ran Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Xiao-Xiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China,National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, Beijing 100193, China,
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Verardo LL, Brito LF, Carolino N, Magalhães AFB. Editorial: Omics applied to livestock genetics. Front Genet 2023; 14:1155611. [PMID: 36873944 PMCID: PMC9978907 DOI: 10.3389/fgene.2023.1155611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 02/06/2023] [Indexed: 02/18/2023] Open
Affiliation(s)
- Lucas Lima Verardo
- Laboratory of Animal Breeding, Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, MG, Brazil
| | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Nuno Carolino
- Instituto Nacional Investigação Agraria e Veterinaria (INIAV), Oeiras, Portugal
| | - Ana Fabrícia Braga Magalhães
- Laboratory of Animal Breeding, Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, MG, Brazil
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30
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Jones HE, Wilson PB. Progress and opportunities through use of genomics in animal production. Trends Genet 2022; 38:1228-1252. [PMID: 35945076 DOI: 10.1016/j.tig.2022.06.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 06/08/2022] [Accepted: 06/17/2022] [Indexed: 01/24/2023]
Abstract
The rearing of farmed animals is a vital component of global food production systems, but its impact on the environment, human health, animal welfare, and biodiversity is being increasingly challenged. Developments in genetic and genomic technologies have had a key role in improving the productivity of farmed animals for decades. Advances in genome sequencing, annotation, and editing offer a means not only to continue that trend, but also, when combined with advanced data collection, analytics, cloud computing, appropriate infrastructure, and regulation, to take precision livestock farming (PLF) and conservation to an advanced level. Such an approach could generate substantial additional benefits in terms of reducing use of resources, health treatments, and environmental impact, while also improving animal health and welfare.
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Affiliation(s)
- Huw E Jones
- UK Genetics for Livestock and Equines (UKGLE) Committee, Department for Environment, Food and Rural Affairs, Nobel House, 17 Smith Square, London, SW1P 3JR, UK; Nottingham Trent University, Brackenhurst Campus, Brackenhurst Lane, Southwell, NG25 0QF, UK.
| | - Philippe B Wilson
- UK Genetics for Livestock and Equines (UKGLE) Committee, Department for Environment, Food and Rural Affairs, Nobel House, 17 Smith Square, London, SW1P 3JR, UK; Nottingham Trent University, Brackenhurst Campus, Brackenhurst Lane, Southwell, NG25 0QF, UK
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31
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Fu Y, Liu H, Dou J, Wang Y, Liao Y, Huang X, Tang Z, Xu J, Yin D, Zhu S, Liu Y, Shen X, Liu H, Liu J, Yang X, Zhang Y, Xiang Y, Li J, Zheng Z, Zhao Y, Ma Y, Wang H, Du X, Xie S, Xu X, Zhang H, Yin L, Zhu M, Yu M, Li X, Liu X, Zhao S. IAnimal: a cross-species omics knowledgebase for animals. Nucleic Acids Res 2022; 51:D1312-D1324. [PMID: 36300629 PMCID: PMC9825575 DOI: 10.1093/nar/gkac936] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/23/2022] [Accepted: 10/11/2022] [Indexed: 01/30/2023] Open
Abstract
With the exponential growth of multi-omics data, its integration and utilization have brought unprecedented opportunities for the interpretation of gene regulation mechanisms and the comprehensive analyses of biological systems. IAnimal (https://ianimal.pro/), a cross-species, multi-omics knowledgebase, was developed to improve the utilization of massive public data and simplify the integration of multi-omics information to mine the genetic mechanisms of objective traits. Currently, IAnimal provides 61 191 individual omics data of genome (WGS), transcriptome (RNA-Seq), epigenome (ChIP-Seq, ATAC-Seq) and genome annotation information for 21 species, such as mice, pigs, cattle, chickens, and macaques. The scale of its total clean data has reached 846.46 TB. To better understand the biological significance of omics information, a deep learning model for IAnimal was built based on BioBERT and AutoNER to mine 'gene' and 'trait' entities from 2 794 237 abstracts, which has practical significance for comprehending how each omics layer regulates genes to affect traits. By means of user-friendly web interfaces, flexible data application programming interfaces, and abundant functional modules, IAnimal enables users to easily query, mine, and visualize characteristics in various omics, and to infer how genes play biological roles under the influence of various omics layers.
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Affiliation(s)
- Yuhua Fu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Hong Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Jingwen Dou
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Yue Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Yong Liao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Xin Huang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Zhenshuang Tang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - JingYa Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Dong Yin
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Shilin Zhu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Yangfan Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Xiong Shen
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Hengyi Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Jiaqi Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Xin Yang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Yi Zhang
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei 430070, PR China
| | - Yue Xiang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Jingjin Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Zhuqing Zheng
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Yunxia Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Yunlong Ma
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Haiyan Wang
- Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Xiaoyong Du
- Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Shengsong Xie
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Xuewen Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Haohao Zhang
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei 430070, PR China
| | - Lilin Yin
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Mengjin Zhu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Mei Yu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Xinyun Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Xiaolei Liu
- Correspondence may also be addressed to Xiaolei Liu.
| | - Shuhong Zhao
- To whom correspondence should be addressed. Tel: +86 27 87387480;
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Sanchez MP, Tribout T, Fritz S, Guatteo R, Fourichon C, Schibler L, Delafosse A, Boichard D. New insights into the genetic resistance to paratuberculosis in Holstein cattle via single-step genomic evaluation. Genet Sel Evol 2022; 54:67. [PMID: 36243688 PMCID: PMC9569073 DOI: 10.1186/s12711-022-00757-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 09/24/2022] [Indexed: 11/10/2022] Open
Abstract
Background Bovine paratuberculosis, or Johne’s disease (JD), is a contagious and incurable disease caused by Mycobacterium avium subsp. paratuberculosis (MAP). It has adverse effects on animal welfare and is very difficult to control, leading to serious economic consequences. An important line of defense to this disease is host genetic resistance to MAP, which, when it will be more fully understood, could be improved through selective breeding. Using a large dataset of Holstein cows (161,253 animals including 56,766 cows with ELISA serological phenotypes and 12,431 animals with genotypes), we applied a single-step single nucleotide polymorphism (SNP) best linear unbiased prediction approach to investigate the genetic determinism underlying resistance to this disease (heritability estimate and identification of relevant genomic regions) and estimated genetic trends, reliability, and relative risk factors associated with genomic predictions. Results Resistance to JD was moderately heritable (0.14) and 16 genomic regions were detected that accounted for at least 0.05% of the breeding values variance (GV) in resistance to JD, and were located on chromosomes 1, 3, 5, 6, 7, 19, 20, 21, 23, 25, and 27, with the highest percentage of variance explained by regions on chromosomes 23 (0.36% GV), 5 (0.22% GV), 1 (0.14% GV), and 3 (0.13% GV). When estimated for the whole chromosomes, the autosomes with the largest overall contributions were chromosomes 3 (5.3% GV), 10 (4.8%), 23 (4.7%), 1 (3.6%), 7 (3.4%), 5 (2.9%), 12 (2.5%), 11 (2.2%), and 13 (2%). We estimated a slightly favorable genetic trend in resistance to JD over the last two decades, which can be explained by a low positive genetic correlation between resistance to JD and total merit index (+ 0.06). Finally, in a validation population of 907 cows, relatively reliable genomic predictions (reliability = 0.55) were obtained, which allowed the identification of cows at high risk of infection. Conclusions This study provides new insights into the genetic determinism of resistance to JD and shows that this trait can be predicted from SNP genotypes. It has led to the implementation of a single-step genomic evaluation that should rapidly become an effective tool for controlling paratuberculosis on French Holstein farms.
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Affiliation(s)
- Marie-Pierre Sanchez
- Université Paris Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France.
| | - Thierry Tribout
- Université Paris Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Sébastien Fritz
- Université Paris Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France.,Eliance, 149 Rue de Bercy, 75012, Paris, France
| | | | | | | | | | - Didier Boichard
- Université Paris Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
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Guan D, Halstead MM, Islas-Trejo AD, Goszczynski DE, Cheng HH, Ross PJ, Zhou H. Prediction of transcript isoforms in 19 chicken tissues by Oxford Nanopore long-read sequencing. Front Genet 2022; 13:997460. [PMID: 36246588 PMCID: PMC9561881 DOI: 10.3389/fgene.2022.997460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 08/30/2022] [Indexed: 11/22/2022] Open
Abstract
To identify and annotate transcript isoforms in the chicken genome, we generated Nanopore long-read sequencing data from 68 samples that encompassed 19 diverse tissues collected from experimental adult male and female White Leghorn chickens. More than 23.8 million reads with mean read length of 790 bases and average quality of 18.2 were generated. The annotation and subsequent filtering resulted in the identification of 55,382 transcripts at 40,547 loci with mean length of 1,700 bases. We predicted 30,967 coding transcripts at 19,461 loci, and 16,495 lncRNA transcripts at 15,512 loci. Compared to existing reference annotations, we found ∼52% of annotated transcripts could be partially or fully matched while ∼47% were novel. Seventy percent of novel transcripts were potentially transcribed from lncRNA loci. Based on our annotation, we quantified transcript expression across tissues and found two brain tissues (i.e., cerebellum and cortex) expressed the highest number of transcripts and loci. Furthermore, ∼22% of the transcripts displayed tissue specificity with the reproductive tissues (i.e., testis and ovary) exhibiting the most tissue-specific transcripts. Despite our wide sampling, ∼20% of Ensembl reference loci were not detected. This suggests that deeper sequencing and additional samples that include different breeds, cell types, developmental stages, and physiological conditions, are needed to fully annotate the chicken genome. The application of Nanopore sequencing in this study demonstrates the usefulness of long-read data in discovering additional novel loci (e.g., lncRNA loci) and resolving complex transcripts (e.g., the longest transcript for the TTN locus).
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Affiliation(s)
- Dailu Guan
- Department of Animal Science, University of California Davis, Davis, CA, United States
| | - Michelle M. Halstead
- Department of Animal Science, University of California Davis, Davis, CA, United States
| | - Alma D. Islas-Trejo
- Department of Animal Science, University of California Davis, Davis, CA, United States
| | - Daniel E. Goszczynski
- Department of Animal Science, University of California Davis, Davis, CA, United States
| | - Hans H. Cheng
- USDA, ARS, USNPRC, Avian Disease and Oncology Laboratory, East Lansing, MI, United States
| | - Pablo J. Ross
- Department of Animal Science, University of California Davis, Davis, CA, United States
- *Correspondence: Pablo J. Ross, ; Huaijun Zhou,
| | - Huaijun Zhou
- Department of Animal Science, University of California Davis, Davis, CA, United States
- *Correspondence: Pablo J. Ross, ; Huaijun Zhou,
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Sage SE, Nicholson P, Leeb T, Gerber V, Jagannathan V. Long-Read Transcriptome of Equine Bronchoalveolar Cells. Genes (Basel) 2022; 13:genes13101722. [PMID: 36292607 PMCID: PMC9602388 DOI: 10.3390/genes13101722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/09/2022] [Accepted: 09/21/2022] [Indexed: 11/16/2022] Open
Abstract
We used Pacific Biosciences long-read isoform sequencing to generate full-length transcript sequences in equine bronchoalveolar lavage fluid (BALF) cells. Our dataset consisted of 313,563 HiFi reads comprising 805 Mb of polished sequence information. The resulting equine BALF transcriptome consisted of 14,234 full-length transcript isoforms originating from 7017 unique genes. These genes consisted of 6880 previously annotated genes and 137 novel genes. We identified 3428 novel transcripts in addition to 10,806 previously known transcripts. These included transcripts absent from existing genome annotations, transcripts mapping to putative novel (unannotated) genes and fusion transcripts incorporating exons from multiple genes. We provide transcript-level data for equine BALF cells as a resource to the scientific community.
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Affiliation(s)
- Sophie Elena Sage
- Swiss Institute of Equine Medicine, Department of Clinical Veterinary Medicine, Vetsuisse Faculty, University of Bern, 3001 Bern, Switzerland
| | - Pamela Nicholson
- Next Generation Sequencing Platform, University of Bern, 3001 Bern, Switzerland
| | - Tosso Leeb
- Institute of Genetics, Vetsuisse Faculty, University of Bern, 3001 Bern, Switzerland
| | - Vinzenz Gerber
- Swiss Institute of Equine Medicine, Department of Clinical Veterinary Medicine, Vetsuisse Faculty, University of Bern, 3001 Bern, Switzerland
| | - Vidhya Jagannathan
- Institute of Genetics, Vetsuisse Faculty, University of Bern, 3001 Bern, Switzerland
- Correspondence:
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Keel BN, Lindholm-Perry AK. Recent developments and future directions in meta-analysis of differential gene expression in livestock RNA-Seq. Front Genet 2022; 13:983043. [PMID: 36199583 PMCID: PMC9527320 DOI: 10.3389/fgene.2022.983043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
Decreases in the costs of high-throughput sequencing technologies have led to continually increasing numbers of livestock RNA-Seq studies in the last decade. Although the number of studies has increased dramatically, most livestock RNA-Seq experiments are limited by cost to a small number of biological replicates. Meta-analysis procedures can be used to integrate and jointly analyze data from multiple independent studies. Meta-analyses increase the sample size, which in turn increase both statistical power and robustness of the results. In this work, we discuss cutting edge approaches to combining results from multiple independent RNA-Seq studies to improve livestock transcriptomics research. We review currently published RNA-Seq meta-analyses in livestock, describe many of the key issues specific to RNA-Seq meta-analysis in livestock species, and discuss future perspectives.
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Liu Y, Fu Y, Yang Y, Yi G, Lian J, Xie B, Yao Y, Chen M, Niu Y, Liu L, Wang L, Zhang Y, Fan X, Tang Y, Yuan P, Zhu M, Li Q, Zhang S, Chen Y, Wang B, He J, Lu D, Liachko I, Sullivan ST, Pang B, Chen Y, He X, Li K, Tang Z. Integration of multi-omics data reveals cis-regulatory variants that are associated with phenotypic differentiation of eastern from western pigs. GENETICS SELECTION EVOLUTION 2022; 54:62. [PMID: 36104777 PMCID: PMC9476355 DOI: 10.1186/s12711-022-00754-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 09/02/2022] [Indexed: 11/10/2022]
Abstract
Abstract
Background
The genetic mechanisms that underlie phenotypic differentiation in breeding animals have important implications in evolutionary biology and agriculture. However, the contribution of cis-regulatory variants to pig phenotypes is poorly understood. Therefore, our aim was to elucidate the molecular mechanisms by which non-coding variants cause phenotypic differences in pigs by combining evolutionary biology analyses and functional genomics.
Results
We obtained a high-resolution phased chromosome-scale reference genome with a contig N50 of 18.03 Mb for the Luchuan pig breed (a representative eastern breed) and profiled potential selective sweeps in eastern and western pigs by resequencing the genomes of 234 pigs. Multi-tissue transcriptome and chromatin accessibility analyses of these regions suggest that tissue-specific selection pressure is mediated by promoters and distal cis-regulatory elements. Promoter variants that are associated with increased expression of the lysozyme (LYZ) gene in the small intestine might enhance the immunity of the gastrointestinal tract and roughage tolerance in pigs. In skeletal muscle, an enhancer-modulating single-nucleotide polymorphism that is associated with up-regulation of the expression of the troponin C1, slow skeletal and cardiac type (TNNC1) gene might increase the proportion of slow muscle fibers and affect meat quality.
Conclusions
Our work sheds light on the molecular mechanisms by which non-coding variants shape phenotypic differences in pigs and provides valuable resources and novel perspectives to dissect the role of gene regulatory evolution in animal domestication and breeding.
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Mollandin F, Gilbert H, Croiseau P, Rau A. Accounting for overlapping annotations in genomic prediction models of complex traits. BMC Bioinformatics 2022; 23:365. [PMID: 36068513 PMCID: PMC9446854 DOI: 10.1186/s12859-022-04914-5] [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: 02/16/2022] [Accepted: 08/25/2022] [Indexed: 11/10/2022] Open
Abstract
Background It is now widespread in livestock and plant breeding to use genotyping data to predict phenotypes with genomic prediction models. In parallel, genomic annotations related to a variety of traits are increasing in number and granularity, providing valuable insight into potentially important positions in the genome. The BayesRC model integrates this prior biological information by factorizing the genome according to disjoint annotation categories, in some cases enabling improved prediction of heritable traits. However, BayesRC is not adapted to cases where markers may have multiple annotations. Results We propose two novel Bayesian approaches to account for multi-annotated markers through a cumulative (BayesRC+) or preferential (BayesRC\documentclass[12pt]{minimal}
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\begin{document}$$\pi$$\end{document}π) model of the contribution of multiple annotation categories. We illustrate their performance on simulated data with various genetic architectures and types of annotations. We also explore their use on data from a backcross population of growing pigs in conjunction with annotations constructed using the PigQTLdb. In both simulated and real data, we observed a modest improvement in prediction quality with our models when used with informative annotations. In addition, our results show that BayesRC+ successfully prioritizes multi-annotated markers according to their posterior variance, while BayesRC\documentclass[12pt]{minimal}
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\begin{document}$$\pi$$\end{document}π provides a useful interpretation of informative annotations for multi-annotated markers. Finally, we explore several strategies for constructing annotations from a public database, highlighting the importance of careful consideration of this step. Conclusion When used with annotations that are relevant to the trait under study, BayesRC\documentclass[12pt]{minimal}
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\begin{document}$$\pi$$\end{document}π and BayesRC+ allow for improved prediction and prioritization of multi-annotated markers, and can provide useful biological insight into the genetic architecture of traits. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04914-5.
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Affiliation(s)
- Fanny Mollandin
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, Allée de Vilvert, 78350, Jouy-en-Josas, France.
| | - Hélène Gilbert
- GenPhySE, INRAE, ENVT, Université de Toulouse, 31320, Castanet Tolosan, France
| | - Pascal Croiseau
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, Allée de Vilvert, 78350, Jouy-en-Josas, France
| | - Andrea Rau
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, Allée de Vilvert, 78350, Jouy-en-Josas, France.,BioEcoAgro Joint Research Unit, INRAE, Université de Liège, Université de Lille, Université de Picardie Jules Verne, 50136, Estrée-Mons, France
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Yao Y, Liu S, Xia C, Gao Y, Pan Z, Canela-Xandri O, Khamseh A, Rawlik K, Wang S, Li B, Zhang Y, Pairo-Castineira E, D’Mellow K, Li X, Yan Z, Li CJ, Yu Y, Zhang S, Ma L, Cole JB, Ross PJ, Zhou H, Haley C, Liu GE, Fang L, Tenesa A. Comparative transcriptome in large-scale human and cattle populations. Genome Biol 2022; 23:176. [PMID: 35996157 PMCID: PMC9394047 DOI: 10.1186/s13059-022-02745-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 08/09/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cross-species comparison of transcriptomes is important for elucidating evolutionary molecular mechanisms underpinning phenotypic variation between and within species, yet to date it has been essentially limited to model organisms with relatively small sample sizes. RESULTS Here, we systematically analyze and compare 10,830 and 4866 publicly available RNA-seq samples in humans and cattle, respectively, representing 20 common tissues. Focusing on 17,315 orthologous genes, we demonstrate that mean/median gene expression, inter-individual variation of expression, expression quantitative trait loci, and gene co-expression networks are generally conserved between humans and cattle. By examining large-scale genome-wide association studies for 46 human traits (average n = 327,973) and 45 cattle traits (average n = 24,635), we reveal that the heritability of complex traits in both species is significantly more enriched in transcriptionally conserved than diverged genes across tissues. CONCLUSIONS In summary, our study provides a comprehensive comparison of transcriptomes between humans and cattle, which might help decipher the genetic and evolutionary basis of complex traits in both species.
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Affiliation(s)
- Yuelin Yao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- School of Informatics, The University of Edinburgh, Edinburgh, EH8 9AB UK
| | - Shuli Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Charley Xia
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
- Department of Psychology, 7 George Square, The University of Edinburgh, Edinburgh, EH8 9JZ UK
| | - Yahui Gao
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, MA 20742 USA
| | - Zhangyuan Pan
- Department of Animal Science, University of California, Davis, CA 95616 USA
- Present address: Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
| | - Ava Khamseh
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- School of Informatics, The University of Edinburgh, Edinburgh, EH8 9AB UK
| | - Konrad Rawlik
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223 Yunnan China
| | - Bingjie Li
- Scotland’s Rural College (SRUC), Roslin Institute Building, Midlothian, EH25 9RG UK
| | - Yi Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Erola Pairo-Castineira
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
| | - Kenton D’Mellow
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
| | - Xiujin Li
- Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225 Guangdong China
| | - Ze Yan
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Cong-jun Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
| | - Ying Yu
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Shengli Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MA 20742 USA
| | - John B. Cole
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
| | - Pablo J. Ross
- Department of Animal Science, University of California, Davis, CA 95616 USA
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, CA 95616 USA
| | - Chris Haley
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
| | - George E. Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- Present address: Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Albert Tenesa
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
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Corbett RJ, Luttman AM, Herrera-Uribe J, Liu H, Raney NE, Grabowski JM, Loving CL, Tuggle CK, Ernst CW. Assessment of DNA methylation in porcine immune cells reveals novel regulatory elements associated with cell-specific gene expression and immune capacity traits. BMC Genomics 2022; 23:575. [PMID: 35953767 PMCID: PMC9367135 DOI: 10.1186/s12864-022-08773-5] [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: 11/29/2021] [Accepted: 07/18/2022] [Indexed: 11/15/2022] Open
Abstract
Background Genetics studies in the porcine immune system have enhanced selection practices for disease resistance phenotypes and increased the efficacy of porcine models in biomedical research; however limited functional annotation of the porcine immunome has hindered progress on both fronts. Among epigenetic mechanisms that regulate gene expression, DNA methylation is the most ubiquitous modification made to the DNA molecule and influences transcription factor binding as well as gene and phenotype expression. Human and mouse DNA methylation studies have improved mapping of regulatory elements in these species, but comparable studies in the pig have been limited in scope. Results We performed whole-genome bisulfite sequencing to assess DNA methylation patterns in nine pig immune cell populations: CD21+ and CD21− B cells, four T cell fractions (CD4+, CD8+, CD8+CD4+, and SWC6γδ+), natural killer and myeloid cells, and neutrophils. We identified 54,391 cell differentially methylated regions (cDMRs), and clustering by cDMR methylation rate grouped samples by cell lineage. 32,737 cDMRs were classified as cell lowly methylated regions (cLMRs) in at least one cell type, and cLMRs were broadly enriched in genes and regions of intermediate CpG density. We observed strong correlations between differential methylation and expression across immune cell populations, with cell-specific low methylation disproportionately impacting genes exhibiting enriched gene expression in the same cell type. Motif analysis of cLMRs revealed cell type-specific enrichment of transcription factor binding motifs, indicating that cell-specific methylation patterns may influence accessibility by trans-acting factors. Lastly, cDMRs were enriched for immune capacity GWAS SNPs, and many such overlaps occurred within genes known to influence immune cell development and function (CD8B, NDRG1). Conclusion Our DNA methylation data improve functional annotation of the porcine genome through characterization of epigenomic regulatory patterns that contribute to immune cell identity and function, and increase the potential for identifying mechanistic links between genotype and phenotype. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08773-5.
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Affiliation(s)
- Ryan J Corbett
- Genetics & Genome Sciences Graduate Program, Michigan State University, East Lansing, MI, USA
| | - Andrea M Luttman
- Genetics & Genome Sciences Graduate Program, Michigan State University, East Lansing, MI, USA
| | | | - Haibo Liu
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Nancy E Raney
- Department of Animal Science, Michigan State University, East Lansing, MI, USA
| | - Jenna M Grabowski
- Department of Animal Science, Michigan State University, East Lansing, MI, USA
| | | | | | - Catherine W Ernst
- Department of Animal Science, Michigan State University, East Lansing, MI, USA.
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40
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Nash T, Vervelde L. Advances, challenges and future applications of avian intestinal in vitro models. Avian Pathol 2022; 51:317-329. [PMID: 35638458 DOI: 10.1080/03079457.2022.2084363] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
There is a rapidly growing interest in how the avian intestine is affected by dietary components and probiotic microorganisms, as well as its role in the spread of infectious diseases in both the developing and developed world. A paucity of physiologically relevant models has limited research in this essential field of poultry gut health and led to an over-reliance on the use of live birds for experiments. The intestine is characterized by a complex cellular composition with numerous functions, unique dynamic locations and interdependencies making this organ challenging to recreate in vitro. This review illustrates the in vitro tools that aim to recapitulate this intestinal environment; from the simplest cell lines, which mimic select features of the intestine but lack anatomical and physiological complexity, to the more recently developed complex 3D enteroids, which recreate more of the intestine's intricate microanatomy, heterogeneous cell populations and signalling gradients. We highlight the benefits and limitations of in vitro intestinal models and describe their current applications and future prospective utilizations in intestinal biology and pathology research. We also describe the scope to improve on the current systems to include, for example, microbiota and a dynamic mechanical environment, vital components which enable the intestine to develop and maintain homeostasis in vivo. As this review explains, no one model is perfect, but the key to choosing a model or combination of models is to carefully consider the purpose or scientific question.
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Affiliation(s)
- Tessa Nash
- The Roslin Institute & R(D)SVS, University of Edinburgh, Edinburgh, UK
| | - Lonneke Vervelde
- The Roslin Institute & R(D)SVS, University of Edinburgh, Edinburgh, UK
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41
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Kaplow IM, Schäffer DE, Wirthlin ME, Lawler AJ, Brown AR, Kleyman M, Pfenning AR. Inferring mammalian tissue-specific regulatory conservation by predicting tissue-specific differences in open chromatin. BMC Genomics 2022; 23:291. [PMID: 35410163 PMCID: PMC8996547 DOI: 10.1186/s12864-022-08450-7] [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: 08/17/2021] [Accepted: 03/07/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Evolutionary conservation is an invaluable tool for inferring functional significance in the genome, including regions that are crucial across many species and those that have undergone convergent evolution. Computational methods to test for sequence conservation are dominated by algorithms that examine the ability of one or more nucleotides to align across large evolutionary distances. While these nucleotide alignment-based approaches have proven powerful for protein-coding genes and some non-coding elements, they fail to capture conservation of many enhancers, distal regulatory elements that control spatial and temporal patterns of gene expression. The function of enhancers is governed by a complex, often tissue- and cell type-specific code that links combinations of transcription factor binding sites and other regulation-related sequence patterns to regulatory activity. Thus, function of orthologous enhancer regions can be conserved across large evolutionary distances, even when nucleotide turnover is high. RESULTS We present a new machine learning-based approach for evaluating enhancer conservation that leverages the combinatorial sequence code of enhancer activity rather than relying on the alignment of individual nucleotides. We first train a convolutional neural network model that can predict tissue-specific open chromatin, a proxy for enhancer activity, across mammals. Next, we apply that model to distinguish instances where the genome sequence would predict conserved function versus a loss of regulatory activity in that tissue. We present criteria for systematically evaluating model performance for this task and use them to demonstrate that our models accurately predict tissue-specific conservation and divergence in open chromatin between primate and rodent species, vastly out-performing leading nucleotide alignment-based approaches. We then apply our models to predict open chromatin at orthologs of brain and liver open chromatin regions across hundreds of mammals and find that brain enhancers associated with neuron activity have a stronger tendency than the general population to have predicted lineage-specific open chromatin. CONCLUSION The framework presented here provides a mechanism to annotate tissue-specific regulatory function across hundreds of genomes and to study enhancer evolution using predicted regulatory differences rather than nucleotide-level conservation measurements.
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Affiliation(s)
- Irene M Kaplow
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA. .,Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Daniel E Schäffer
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Morgan E Wirthlin
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.,Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Alyssa J Lawler
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ashley R Brown
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.,Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Michael Kleyman
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.,Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Andreas R Pfenning
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA. .,Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA. .,Department of Biology, Carnegie Mellon University, Pittsburgh, PA, USA.
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42
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Zhang T, Wang T, Niu Q, Xu L, Chen Y, Gao X, Gao H, Zhang L, Liu GE, Li J, Xu L. Transcriptional atlas analysis from multiple tissues reveals the expression specificity patterns in beef cattle. BMC Biol 2022; 20:79. [PMID: 35351103 PMCID: PMC8966188 DOI: 10.1186/s12915-022-01269-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 03/03/2022] [Indexed: 11/12/2022] Open
Abstract
Background A comprehensive analysis of gene expression profiling across tissues can provide necessary information for an in-depth understanding of their biological functions. We performed a large-scale gene expression analysis and generated a high-resolution atlas of the transcriptome in beef cattle. Results Our transcriptome atlas was generated from 135 bovine tissues in adult beef cattle, covering 51 tissue types of major organ systems (e.g., muscular system, digestive system, immune system, reproductive system). Approximately 94.76% of sequencing reads were successfully mapped to the reference genome assembly ARS-UCD1.2. We detected a total of 60,488 transcripts, and 32% of them were not reported before. We identified 2654 housekeeping genes (HKGs) and 477 tissue-specific genes (TSGs) across tissues. Using weighted gene co-expression network analysis, we obtained 24 modules with 237 hub genes (HUBGs). Functional enrichment analysis showed that HKGs mainly maintain the basic biological activities of cells, while TSGs were involved in tissue differentiation and specific physiological processes. HKGs in bovine tissues were more conserved in terms of expression pattern as compared to TSGs and HUBGs among multiple species. Finally, we obtained a subset of tissue-specific differentially expressed genes (DEGs) between beef and dairy cattle and several functional pathways, which may be involved in production and health traits. Conclusions We generated a large-scale gene expression atlas across the major tissues in beef cattle, providing valuable information for enhancing genome assembly and annotation. HKGs, TSGs, and HUBGs further contribute to better understanding the biology and evolution of multiple tissues in cattle. DEGs between beef and dairy cattle also fill in the knowledge gaps about differential transcriptome regulation of bovine tissues underlying economically important traits. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01269-4.
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Affiliation(s)
- Tianliu Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, People's Republic of China
| | - Tianzhen Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, People's Republic of China
| | - Qunhao Niu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, People's Republic of China
| | - Lei Xu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, People's Republic of China
| | - Yan Chen
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, People's Republic of China
| | - Xue Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, People's Republic of China
| | - Huijiang Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, People's Republic of China
| | - Lupei Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, People's Republic of China
| | - George E Liu
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, Maryland, 20705, USA
| | - Junya Li
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, People's Republic of China.
| | - Lingyang Xu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, People's Republic of China.
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43
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Prediction of histone post-translational modification patterns based on nascent transcription data. Nat Genet 2022; 54:295-305. [PMID: 35273399 PMCID: PMC9444190 DOI: 10.1038/s41588-022-01026-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 01/24/2022] [Indexed: 01/01/2023]
Abstract
The role of histone modifications in transcription remains incompletely understood. Here, we examine the relationship between histone modifications and transcription using experimental perturbations combined with sensitive machine-learning tools. Transcription predicted the variation in active histone marks and complex chromatin states, like bivalent promoters, down to single-nucleosome resolution and at an accuracy that rivaled the correspondence between independent ChIP-seq experiments. Blocking transcription rapidly removed two punctate marks, H3K4me3 and H3K27ac, from chromatin indicating that transcription is required for active histone modifications. Transcription was also required for maintenance of H3K27me3, consistent with a role for RNA in recruiting PRC2. A subset of DNase-I-hypersensitive sites were refractory to prediction, precluding models where transcription initiates pervasively at any open chromatin. Our results, in combination with past literature, support a model in which active histone modifications serve a supportive, rather than an essential regulatory, role in transcription.
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44
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Salavati M, Woolley SA, Cortés Araya Y, Halstead MM, Stenhouse C, Johnsson M, Ashworth CJ, Archibald AL, Donadeu FX, Hassan MA, Clark EL. Profiling of open chromatin in developing pig (Sus scrofa) muscle to identify regulatory regions. G3 (BETHESDA, MD.) 2022; 12:6460335. [PMID: 34897420 PMCID: PMC9210303 DOI: 10.1093/g3journal/jkab424] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 12/03/2021] [Indexed: 06/14/2023]
Abstract
There is very little information about how the genome is regulated in domestic pigs (Sus scrofa). This lack of knowledge hinders efforts to define and predict the effects of genetic variants in pig breeding programs. To address this knowledge gap, we need to identify regulatory sequences in the pig genome starting with regions of open chromatin. We used the "Improved Protocol for the Assay for Transposase-Accessible Chromatin (Omni-ATAC-Seq)" to identify putative regulatory regions in flash-frozen semitendinosus muscle from 24 male piglets. We collected samples from the smallest-, average-, and largest-sized male piglets from each litter through five developmental time points. Of the 4661 ATAC-Seq peaks identified that represent regions of open chromatin, >50% were within 1 kb of known transcription start sites. Differential read count analysis revealed 377 ATAC-Seq defined genomic regions where chromatin accessibility differed significantly across developmental time points. We found regions of open chromatin associated with downregulation of genes involved in muscle development that were present in small-sized fetal piglets but absent in large-sized fetal piglets at day 90 of gestation. The dataset that we have generated provides a resource for studies of genome regulation in pigs and contributes valuable functional annotation information to filter genetic variants for use in genomic selection in pig breeding programs.
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Affiliation(s)
- Mazdak Salavati
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh EH25 9RG, UK
- Centre for Tropical Livestock Genetics and Health (CTLGH), Roslin Institute, University of Edinburgh, Edinburgh EH25 9RG, UK
| | - Shernae A Woolley
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh EH25 9RG, UK
| | - Yennifer Cortés Araya
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh EH25 9RG, UK
| | - Michelle M Halstead
- Department of Animal Science, University of California Davis, Davis, CA 95616, USA
| | - Claire Stenhouse
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh EH25 9RG, UK
- Department of Animal Science, Texas A&M University, College Station, TX 77843, USA
| | - Martin Johnsson
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh EH25 9RG, UK
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala 750 07, Sweden
| | - Cheryl J Ashworth
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh EH25 9RG, UK
| | - Alan L Archibald
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh EH25 9RG, UK
| | - Francesc X Donadeu
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh EH25 9RG, UK
| | - Musa A Hassan
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh EH25 9RG, UK
- Centre for Tropical Livestock Genetics and Health (CTLGH), Roslin Institute, University of Edinburgh, Edinburgh EH25 9RG, UK
| | - Emily L Clark
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh EH25 9RG, UK
- Centre for Tropical Livestock Genetics and Health (CTLGH), Roslin Institute, University of Edinburgh, Edinburgh EH25 9RG, UK
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45
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Mondal P, Bailey KL, Cartwright SB, Band V, Carlson MA. Large Animal Models of Breast Cancer. Front Oncol 2022; 12:788038. [PMID: 35186735 PMCID: PMC8855936 DOI: 10.3389/fonc.2022.788038] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/18/2022] [Indexed: 01/29/2023] Open
Abstract
In this mini review the status, advantages, and disadvantages of large animal modeling of breast cancer (BC) will be discussed. While most older studies of large animal BC models utilized canine and feline subjects, more recently there has been interest in development of porcine BC models, with some early promising results for modeling human disease. Widely used rodent models of BC were briefly reviewed to give context to the work on the large animal BC models. Availability of large animal BC models could provide additional tools for BC research, including availability of human-sized subjects and BC models with greater biologic relevance.
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Affiliation(s)
- Pinaki Mondal
- Department of Surgery, University of Nebraska Medical Center, Omaha, NE, United States,Department of Surgery, VA Medical Center, Omaha, NE, United States
| | - Katie L. Bailey
- Department of Surgery, University of Nebraska Medical Center, Omaha, NE, United States
| | - Sara B. Cartwright
- Department of Surgery, University of Nebraska Medical Center, Omaha, NE, United States
| | - Vimla Band
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, United States,Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, United States
| | - Mark A. Carlson
- Department of Surgery, University of Nebraska Medical Center, Omaha, NE, United States,Department of Surgery, VA Medical Center, Omaha, NE, United States,Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, United States,Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, United States,Center for Advanced Surgical Technology, University of Nebraska Medical Center, Omaha, NE, United States,*Correspondence: Mark A. Carlson,
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46
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Stephan T, Burgess SM, Cheng H, Danko CG, Gill CA, Jarvis ED, Koepfli KP, Koltes JE, Lyons E, Ronald P, Ryder OA, Schriml LM, Soltis P, VandeWoude S, Zhou H, Ostrander EA, Karlsson EK. Darwinian genomics and diversity in the tree of life. Proc Natl Acad Sci U S A 2022; 119:e2115644119. [PMID: 35042807 PMCID: PMC8795533 DOI: 10.1073/pnas.2115644119] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Genomics encompasses the entire tree of life, both extinct and extant, and the evolutionary processes that shape this diversity. To date, genomic research has focused on humans, a small number of agricultural species, and established laboratory models. Fewer than 18,000 of ∼2,000,000 eukaryotic species (<1%) have a representative genome sequence in GenBank, and only a fraction of these have ancillary information on genome structure, genetic variation, gene expression, epigenetic modifications, and population diversity. This imbalance reflects a perception that human studies are paramount in disease research. Yet understanding how genomes work, and how genetic variation shapes phenotypes, requires a broad view that embraces the vast diversity of life. We have the technology to collect massive and exquisitely detailed datasets about the world, but expertise is siloed into distinct fields. A new approach, integrating comparative genomics with cell and evolutionary biology, ecology, archaeology, anthropology, and conservation biology, is essential for understanding and protecting ourselves and our world. Here, we describe potential for scientific discovery when comparative genomics works in close collaboration with a broad range of fields as well as the technical, scientific, and social constraints that must be addressed.
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Affiliation(s)
- Taylorlyn Stephan
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20817
| | - Shawn M Burgess
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20817
| | - Hans Cheng
- Avian Disease and Oncology Laboratory, Agricultural Research Service, US Department of Agriculture, East Lansing, MI 48823
| | - Charles G Danko
- Department of Biomedical Sciences, Baker Institute for Animal Health, Cornell University, Ithaca, NY 14850
| | - Clare A Gill
- Department of Animal Science, Texas A&M University, College Station, TX 77843
| | - Erich D Jarvis
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY 10065
- HHMI, Chevy Chase, MD 20815
| | - Klaus-Peter Koepfli
- Smithsonian-Mason School of Conservation, George Mason University, Front Royal, VA 22630
- Smithsonian Conservation Biology Institute, National Zoological Park, Washington, DC 20008
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50011
| | - Eric Lyons
- School of Plant Sciences, BIO5 Institute, University of Arizona, Tucson, AZ 85721
| | - Pamela Ronald
- Department of Plant Pathology, University of California, Davis, CA 95616
- The Genome Center, University of California, Davis, CA 95616
- The Innovative Genomics Institute, University of California, Berkeley, CA 94720
- Grass Genetics, Joint Bioenergy Institute, Emeryville, CA 94608
| | - Oliver A Ryder
- San Diego Zoo Wildlife Alliance, Escondido, CA 92027
- Department of Evolution, Behavior, and Ecology, University of California San Diego, La Jolla, CA 92093
| | - Lynn M Schriml
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Pamela Soltis
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32611
| | - Sue VandeWoude
- Department of Micro-, Immuno-, and Pathology, Colorado State University, Fort Collins, CO 80532
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, CA 95616
| | - Elaine A Ostrander
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20817
| | - Elinor K Karlsson
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01655;
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01655
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
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Tellam RL, Vuocolo T, Denman S, Ingham A, Wijffels G, James PJ, Colditz IG. Dermatophilosis (lumpy wool) in sheep: a review of pathogenesis, aetiology, resistance and vaccines. ANIMAL PRODUCTION SCIENCE 2022. [DOI: 10.1071/an21119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Lumpy wool (dermatophilosis) develops following prolonged wetting of sheep when bacterial proliferation in wool and on skin induce an exudative dermatitis, causing a superficial skin lesion and damage to wool follicles and fibres. The incidence of dermatophilosis is strongly dependent on wet and warm weather and, hence, infection is sporadic. While older animals are less at risk than are lambs, it is unclear whether this reflects naturally acquired immune resistance or the maturation of skin and wool fibres. Dermatophilosis directly causes wool production losses and it also is a risk factor for blowfly strike, which has a substantial economic impact and increasing challenges associated with current control procedures. This review assessed research on the bacterial causes of lumpy wool, the characteristics of the resulting immune defence reactions in sheep, current control strategies, and limitations of previous attempts to control lumpy wool by sheep vaccination.
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48
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Lai E, Danner AL, Famula TR, Oberbauer AM. Pleiotropic Loci Associated With Foot Disorders and Common Periparturient Diseases in Holstein Cattle. Front Genet 2021; 12:742934. [PMID: 34938311 PMCID: PMC8685441 DOI: 10.3389/fgene.2021.742934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 11/01/2021] [Indexed: 11/13/2022] Open
Abstract
Lameness is an animal welfare issue that incurs substantial financial and environmental costs. This condition is commonly caused by digital dermatitis (DD), sole ulcers (SU), and white line disease (WLD). Susceptibility to these three foot disorders is due in part to genetics, indicating that genomic selection against these foot lesions can be used to reduce lameness prevalence. It is unclear whether selection against foot lesions will lead to increased susceptibility to other common diseases such as mastitis and metritis. Thus, the aim of this study was to determine the genetic correlation between causes of lameness and other common health disorders to identify loci contributing to the correlation. Genetic correlation estimates between SU and DD and between SU and WLD were significantly different from zero (p < 0.05), whereas estimates between DD and mastitis, DD and milk fever, and SU and metritis were suggestive (p < 0.1). All five of these genetic correlation estimates were positive. Two-trait genome-wide association studies (GWAS) for each of these five pairs of traits revealed common regions of association on BTA1 and BTA8 for pairs that included DD or SU as one of the traits, respectively. Other regions of association were unique to the pair of traits and not observed in GWAS for other pairs of traits. The positive genetic correlation estimates between foot disorders and other health disorders imply that selection against foot disorders may also decrease susceptibility to other health disorders. Linkage disequilibrium blocks defined around significant and suggestive SNPs from the two-trait GWAS included genes and QTL that were functionally relevant, supporting that these regions included pleiotropic loci.
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Affiliation(s)
| | | | | | - Anita M. Oberbauer
- Animal Science Department, University of California, Davis, Davis, CA, United States
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Marti-Marimon M, Vialaneix N, Lahbib-Mansais Y, Zytnicki M, Camut S, Robelin D, Yerle-Bouissou M, Foissac S. Major Reorganization of Chromosome Conformation During Muscle Development in Pig. Front Genet 2021; 12:748239. [PMID: 34675966 PMCID: PMC8523936 DOI: 10.3389/fgene.2021.748239] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/14/2021] [Indexed: 12/12/2022] Open
Abstract
The spatial organization of the genome in the nucleus plays a crucial role in eukaryotic cell functions, yet little is known about chromatin structure variations during late fetal development in mammals. We performed in situ high-throughput chromosome conformation capture (Hi-C) sequencing of DNA from muscle samples of pig fetuses at two late stages of gestation. Comparative analysis of the resulting Hi-C interaction matrices between both groups showed widespread differences of different types. First, we discovered a complex landscape of stable and group-specific Topologically Associating Domains (TADs). Investigating the nuclear partition of the chromatin into transcriptionally active and inactive compartments, we observed a genome-wide fragmentation of these compartments between 90 and 110 days of gestation. Also, we identified and characterized the distribution of differential cis- and trans-pairwise interactions. In particular, trans-interactions at chromosome extremities revealed a mechanism of telomere clustering further confirmed by 3D Fluorescence in situ Hybridization (FISH). Altogether, we report major variations of the three-dimensional genome conformation during muscle development in pig, involving several levels of chromatin remodeling and structural regulation.
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Affiliation(s)
| | | | | | | | - Sylvie Camut
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France
| | - David Robelin
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France
| | | | - Sylvain Foissac
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France
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50
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Passamonti MM, Somenzi E, Barbato M, Chillemi G, Colli L, Joost S, Milanesi M, Negrini R, Santini M, Vajana E, Williams JL, Ajmone-Marsan P. The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock. Animals (Basel) 2021; 11:2833. [PMID: 34679854 PMCID: PMC8532622 DOI: 10.3390/ani11102833] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/21/2021] [Accepted: 09/23/2021] [Indexed: 12/14/2022] Open
Abstract
Livestock radiated out from domestication centres to most regions of the world, gradually adapting to diverse environments, from very hot to sub-zero temperatures and from wet and humid conditions to deserts. The climate is changing; generally global temperature is increasing, although there are also more extreme cold periods, storms, and higher solar radiation. These changes impact livestock welfare and productivity. This review describes advances in the methodology for studying livestock genomes and the impact of the environment on animal production, giving examples of discoveries made. Sequencing livestock genomes has facilitated genome-wide association studies to localize genes controlling many traits, and population genetics has identified genomic regions under selection or introgressed from one breed into another to improve production or facilitate adaptation. Landscape genomics, which combines global positioning and genomics, has identified genomic features that enable animals to adapt to local environments. Combining the advances in genomics and methods for predicting changes in climate is generating an explosion of data which calls for innovations in the way big data sets are treated. Artificial intelligence and machine learning are now being used to study the interactions between the genome and the environment to identify historic effects on the genome and to model future scenarios.
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Affiliation(s)
- Matilde Maria Passamonti
- Department of Animal Science, Food and Nutrition—DIANA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.M.P.); (E.S.); (M.B.); (L.C.); (R.N.); (J.L.W.)
| | - Elisa Somenzi
- Department of Animal Science, Food and Nutrition—DIANA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.M.P.); (E.S.); (M.B.); (L.C.); (R.N.); (J.L.W.)
| | - Mario Barbato
- Department of Animal Science, Food and Nutrition—DIANA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.M.P.); (E.S.); (M.B.); (L.C.); (R.N.); (J.L.W.)
| | - Giovanni Chillemi
- Department for Innovation in Biological, Agro-Food and Forest Systems–DIBAF, Università Della Tuscia, Via S. Camillo de Lellis snc, 01100 Viterbo, Italy; (G.C.); (M.M.)
| | - Licia Colli
- Department of Animal Science, Food and Nutrition—DIANA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.M.P.); (E.S.); (M.B.); (L.C.); (R.N.); (J.L.W.)
- Research Center on Biodiversity and Ancient DNA—BioDNA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy
| | - Stéphane Joost
- Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; (S.J.); (E.V.)
| | - Marco Milanesi
- Department for Innovation in Biological, Agro-Food and Forest Systems–DIBAF, Università Della Tuscia, Via S. Camillo de Lellis snc, 01100 Viterbo, Italy; (G.C.); (M.M.)
| | - Riccardo Negrini
- Department of Animal Science, Food and Nutrition—DIANA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.M.P.); (E.S.); (M.B.); (L.C.); (R.N.); (J.L.W.)
| | - Monia Santini
- Impacts on Agriculture, Forests and Ecosystem Services (IAFES) Division, Fondazione Centro Euro-Mediterraneo Sui Cambiamenti Climatici (CMCC), Viale Trieste 127, 01100 Viterbo, Italy;
| | - Elia Vajana
- Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; (S.J.); (E.V.)
| | - John Lewis Williams
- Department of Animal Science, Food and Nutrition—DIANA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.M.P.); (E.S.); (M.B.); (L.C.); (R.N.); (J.L.W.)
| | - Paolo Ajmone-Marsan
- Department of Animal Science, Food and Nutrition—DIANA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.M.P.); (E.S.); (M.B.); (L.C.); (R.N.); (J.L.W.)
- Nutrigenomics and Proteomics Research Center—PRONUTRIGEN, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy
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