1
|
Deng L, Gòdia M, Derks MFL, Harlizius B, Farhangi S, Tang Z, Groenen MAM, Madsen O. Comprehensive expression genome-wide association study of long non-coding RNAs in four porcine tissues. Genomics 2025; 117:111026. [PMID: 40049421 DOI: 10.1016/j.ygeno.2025.111026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2024] [Revised: 02/27/2025] [Accepted: 03/03/2025] [Indexed: 03/10/2025]
Abstract
BACKGROUND Long non-coding RNAs (lncRNAs), a type of non-coding RNA molecules, are known to play critical regulatory roles in various biological processes. However, the functions of the majority of lncRNAs remain largely unknown, and little is understood about the regulation of lncRNA expression. In this study, high-throughput DNA genotyping and RNA sequencing were applied to investigate genomic regions associated with lncRNA expression, commonly referred to as lncRNA expression quantitative trait loci (eQTLs). We analyzed the liver, lung, spleen, and muscle transcriptomes of 100 three-way crossbred sows to identify lncRNA transcripts, explore genomic regions that might influence lncRNA expression, and identify potential regulators interacting with these regions. RESULT We identified 6380 lncRNA transcripts and 3733 lncRNA genes. Correlation tests between the expression of lncRNAs and protein-coding genes were performed. Subsequently, functional enrichment analyses were carried out on protein-coding genes highly correlated with lncRNAs. Our correlation results of these protein-coding genes uncovered terms that are related to tissue specific functions. Additionally, heatmaps of lncRNAs and protein-coding genes at different correlation levels revealed several distinct clusters. An expression genome-wide association study (eGWAS) was conducted using 535,896 genotypes and 1829, 1944, 2089, and 2074 expressed lncRNA genes for liver, spleen, lung, and muscle, respectively. This analysis identified 520,562 significant associations and 6654, 4525, 4842, and 7125 eQTLs for the respective tissues. Only a small portion of these eQTLs were classified as cis-eQTLs. Fifteen regions with the highest eQTL density were selected as eGWAS hotspots and potential mechanisms of lncRNA regulation in these hotspots were explored. However, we did not identify any interactions between the transcription factors or miRNAs in the hotspots and the lncRNAs, nor did we observe a significant enrichment of regulatory elements in these hotspots. While we could not pinpoint the key factors regulating lncRNA expression, our results suggest that the regulation of lncRNAs involves more complex mechanisms. CONCLUSION Our findings provide insights into several features and potential functions of lncRNAs in various tissues. However, the mechanisms by which lncRNA eQTLs regulate lncRNA expression remain unclear. Further research is needed to explore the regulation of lncRNA expression and the mechanisms underlying lncRNA interactions with small molecules and regulatory proteins.
Collapse
Affiliation(s)
- Liyan Deng
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, the Netherlands; Kunpeng Institute of Modern Agriculture at Foshan, Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Foshan 528226, China; Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
| | - Marta Gòdia
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, the Netherlands
| | - Martijn F L Derks
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, the Netherlands; Topigs Norsvin Research Center, 's-Hertogenbosch, the Netherlands
| | | | - Samin Farhangi
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, the Netherlands
| | - Zhonglin Tang
- Kunpeng Institute of Modern Agriculture at Foshan, Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Foshan 528226, China; Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Martien A M Groenen
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, the Netherlands
| | - Ole Madsen
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, the Netherlands.
| |
Collapse
|
2
|
Tao Q, Huang A, Qi J, Yang Z, Guo S, Lu Y, He X, Han X, Jiang S, Xu M, Bai Y, Zhang T, Hu S, Li L, Bai L, Liu H. An mRNA expression atlas for the duck with public RNA-seq datasets. BMC Genomics 2025; 26:268. [PMID: 40102741 PMCID: PMC11916966 DOI: 10.1186/s12864-025-11385-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2024] [Accepted: 02/19/2025] [Indexed: 03/20/2025] Open
Abstract
BACKGROUND Ducks are globally important poultry species and a major source of farm animal products, including meat, eggs, and feathers. A thorough understanding of the functional genomic and transcriptomic sequences is crucial for improving production efficiency. RESULT This study constructed the largest duck mRNA expression atlas among all waterfowl species to date. The atlas encompasses 1,257 tissue samples across 30 tissue types, representing all major organ systems. Using advanced clustering analysis, we established co-expression network clusters to describe the transcriptional features in the duck mRNA expression atlas and, when feasible, assign these features to unique tissue types or pathways. Additionally, we identified 27 low-variance, highly expressed housekeeping genes suitable for gene expression experiments. Furthermore, in-depth analysis revealed potential sex-biased gene expression patterns within tissues and specific gene expression profiles in meat-type and egg-type ducks, providing valuable resources to understand the genetic basis of sex differences and particular phenotypes. This research elucidates the biological processes affecting duck productivity. CONCLUSION This study presents the most extensive gene expression atlas for any waterfowl species to date. These findings are of significant value for advancing duck biological research and industrial applications.
Collapse
Affiliation(s)
- Qiuyu Tao
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - Anqi Huang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - Jingjing Qi
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - Zhao Yang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - Shihao Guo
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - Yinjuan Lu
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - Xinxin He
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - Xu Han
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - Shuaixue Jiang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - Mengru Xu
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - Yuan Bai
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - Tao Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - Shenqiang Hu
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - Liang Li
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - Lili Bai
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China
| | - HeHe Liu
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China.
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, P.R. China.
| |
Collapse
|
3
|
Vu TH, Kim C, Truong AD, Kim JM, Lillehoj HS, Hong YH. Unveiling the role of long non-coding RNAs in chicken immune response to highly pathogenic avian influenza H5N1 infection. Poult Sci 2025; 104:104524. [PMID: 39561559 PMCID: PMC11617284 DOI: 10.1016/j.psj.2024.104524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 10/08/2024] [Accepted: 11/05/2024] [Indexed: 11/21/2024] Open
Abstract
Avian influenza viruses (AIVs) pose a significant threat to global poultry production, necessitating effective control strategies to mitigate economic losses and ensure animal welfare. Long non-coding RNAs (lncRNAs) have emerged as crucial regulators of immune responses, yet their roles in AIV-infected chickens remain poorly understood. This study aimed to investigate the expression profiles of lncRNAs and their targets in Vietnamese Ri chickens infected with the highly pathogenic AIV (HPAIV) H5N1. Through RNA sequencing, we identified novel lncRNAs and analyzed differentially expressed (DE) transcripts at 1 and 3 days post-infection (dpi) in chicken lung tissue. Our results revealed a higher number of DE lncRNAs and mRNAs at 1 dpi and 3 dpi, respectively, compared to control, with resistant chickens exhibiting a notably stronger immune response than susceptible chickens at 3 dpi. Functional analysis implicated these lncRNAs in immune-related pathways crucial for host responses to H5N1 viral infection. Furthermore, we identified lncRNA-mRNA interactions associated with antiviral responses and immune function. Notably, several genes involved in antiviral resistance and immune responses showed higher expression in resistant chickens, confirming their stronger antiviral response. Overall, our study provides insights into the role of lncRNAs in the host's response to HPAIV H5N1 infection in chickens and highlights potential candidates for further investigation into host-pathogen interactions. These findings could drive the development of novel control strategies for AIVs, significantly enhancing poultry health and biosecurity.
Collapse
Affiliation(s)
- Thi Hao Vu
- Department of Animal Science and Technology, Chung-Ang University, Anseong 17546, Republic of Korea; Department of Biochemistry and Immunology, National Institute of Veterinary Research, Hanoi 100000, Vietnam.
| | - Chaeeun Kim
- Department of Animal Science and Technology, Chung-Ang University, Anseong 17546, Republic of Korea.
| | - Anh Duc Truong
- Department of Biochemistry and Immunology, National Institute of Veterinary Research, Hanoi 100000, Vietnam.
| | - Jun-Mo Kim
- Department of Biochemistry and Immunology, National Institute of Veterinary Research, Hanoi 100000, Vietnam.
| | - Hyun S Lillehoj
- Animal Biosciences and Biotechnology Laboratory, Agricultural Research Services, United States Department of Agriculture, Beltsville 20705, MD, USA.
| | - Yeong Ho Hong
- Department of Animal Science and Technology, Chung-Ang University, Anseong 17546, Republic of Korea.
| |
Collapse
|
4
|
Muthusamy M, Ramasamy KT, Peters SO, Palani S, Gowthaman V, Nagarajan M, Karuppusamy S, Thangavelu V, Aranganoor Kannan T. Transcriptomic Profiling Reveals Altered Expression of Genes Involved in Metabolic and Immune Processes in NDV-Infected Chicken Embryos. Metabolites 2024; 14:669. [PMID: 39728450 DOI: 10.3390/metabo14120669] [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: 09/13/2024] [Revised: 10/21/2024] [Accepted: 10/26/2024] [Indexed: 12/28/2024] Open
Abstract
OBJECTIVE The poultry industry is significantly impacted by viral infections, particularly Newcastle Disease Virus (NDV), which leads to substantial economic losses. It is essential to comprehend how the sequence of development affects biological pathways and how early exposure to infections might affect immune responses. METHODS This study employed transcriptome analysis to investigate host-pathogen interactions by analyzing gene expression changes in NDV-infected chicken embryos' lungs. RESULT RNA-Seq reads were aligned with the chicken reference genome (Galgal7), revealing 594 differentially expressed genes: 264 upregulated and 330 downregulated. The most overexpressed genes, with logFC between 8.15 and 8.75, included C8A, FGG, PIT54, FETUB, APOC3, and FGA. Notably, downregulated genes included BPIFB3 (-4.46 logFC) and TRIM39.1 (-4.26 logFC). The analysis also identified 29 novel transcripts and 20 lncRNAs that were upregulated. Gene Ontology and KEGG pathways' analyses revealed significant alterations in gene expression related to immune function, metabolism, cell cycle, nucleic acid processes, and mitochondrial activity due to NDV infection. Key metabolic genes, such as ALDOB (3.27 logFC), PRPS2 (2.66 logFC), and XDH (2.15 logFC), exhibited altered expression patterns, while DCK2 (-1.99 logFC) and TK1 (-2.11 logFC) were also affected. Several immune-related genes showed significant upregulation in infected lung samples, including ALB (6.15 logFC), TLR4 (1.86 logFC), TLR2 (2.79 logFC), and interleukin receptors, such as IL1R2 (3.15 logFC) and IL22RA2 (1.37 logFC). Conversely, genes such as CXCR4 (-1.49 logFC), CXCL14 (-2.57 logFC), GATA3 (-1.51 logFC), and IL17REL (-2.93 logFC) were downregulated. The higher expression of HSP genes underscores their vital role in immune responses. CONCLUSION Comprehension of these genes' interactions is essential for regulating viral replication and immune responses during infections, potentially aiding in the identification of candidate genes for poultry breed improvement amidst NDV challenges.
Collapse
Affiliation(s)
- Malarmathi Muthusamy
- Department of Animal Genetics and Breeding, Veterinary College and Research Institute, Tamil Nadu Veterinary and Animal Sciences University (TANUVAS), Namakkal 637002, India
| | - Kannaki T Ramasamy
- Indian Council of Agricultural Research-Directorate of Poultry Research, Hyderabad 500030, India
| | | | - Srinivasan Palani
- Department of Veterinary Pathology, Veterinary College and Research Institute, Tamil Nadu Veterinary and Animal Sciences University (TANUVAS), Namakkal 637002, India
| | - Vasudevan Gowthaman
- Poultry Disease Diagnosis and Surveillance Laboratory, Tamil Nadu Veterinary and Animal Sciences University (TANUVAS), Namakkal 637002, India
| | - Murali Nagarajan
- Alambadi Cattle Breed Research Centre, Tamil Nadu Veterinary and Animal Sciences University (TANUVAS), Dharmapuri 635111, India
| | - Sivakumar Karuppusamy
- Faculty of Food and Agriculture, The University of the West Indies, St. Augustine 999183, Trinidad and Tobago
| | | | - Thiruvenkadan Aranganoor Kannan
- Department of Animal Genetics and Breeding, Veterinary College and Research Institute, Tamil Nadu Veterinary and Animal Sciences University (TANUVAS), Namakkal 637002, India
| |
Collapse
|
5
|
Semik-Gurgul E, Pawlina-Tyszko K, Gurgul A, Szmatoła T, Rybińska J, Ząbek T. In search of epigenetic hallmarks of different tissues: an integrative omics study of horse liver, lung, and heart. Mamm Genome 2024; 35:600-620. [PMID: 39143382 PMCID: PMC11522055 DOI: 10.1007/s00335-024-10057-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Accepted: 08/01/2024] [Indexed: 08/16/2024]
Abstract
DNA methylation and microRNA (miRNA) expression are epigenetic mechanisms essential for regulating tissue-specific gene expression and metabolic processes. However, high-resolution transcriptome, methylome, or miRNAome data is only available for a few model organisms and selected tissues. Up to date, only a few studies have reported on gene expression, DNA methylation, or miRNA expression in adult equine tissues at the genome-wide level. In the present study, we used RNA-Seq, miRNA-seq, and reduced representation bisulfite sequencing (RRBS) data from the heart, lung, and liver tissues of healthy cold-blooded horses to identify differentially expressed genes (DEGs), differentially expressed miRNA (DE miRNA) and differentially methylated sites (DMSs) between three types of horse tissues. Additionally, based on integrative omics analysis, we described the observed interactions of epigenetic mechanisms with tissue-specific gene expression alterations. The obtained data allowed identification from 4067 to 6143 DMSs, 9733 to 11,263 mRNAs, and 155 to 185 microRNAs, differentially expressed between various tissues. We pointed out specific genes whose expression level displayed a negative correlation with the level of CpG methylation and miRNA expression and revealed biological processes that they enrich. Furthermore, we confirmed and validated the accuracy of the Next-Generation Sequencing (NGS) results with bisulfite sequencing PCR (BSP) and quantitative PCR (qPCR). This comprehensive analysis forms a strong foundation for exploring the epigenetic mechanisms involved in tissue differentiation, especially the growth and development of the equine heart, lungs, and liver.
Collapse
Affiliation(s)
- Ewelina Semik-Gurgul
- Department of Animal Molecular Biology, National Research Institute of Animal Production, Krakowska 1 St, Balice, 32-083, Poland.
| | - Klaudia Pawlina-Tyszko
- Department of Animal Molecular Biology, National Research Institute of Animal Production, Krakowska 1 St, Balice, 32-083, Poland
| | - Artur Gurgul
- Center for Experimental and Innovative Medicine, University of Agriculture in Krakow, Redzina 1c, Krakow, 30-248, Poland
| | - Tomasz Szmatoła
- Department of Animal Molecular Biology, National Research Institute of Animal Production, Krakowska 1 St, Balice, 32-083, Poland
- Center for Experimental and Innovative Medicine, University of Agriculture in Krakow, Redzina 1c, Krakow, 30-248, Poland
| | - Justyna Rybińska
- Department of Animal Molecular Biology, National Research Institute of Animal Production, Krakowska 1 St, Balice, 32-083, Poland
| | - Tomasz Ząbek
- Department of Animal Molecular Biology, National Research Institute of Animal Production, Krakowska 1 St, Balice, 32-083, Poland
| |
Collapse
|
6
|
Zhao X, Wang X, Xue G, Gao Y, Zhang Y, Li Y, Wang Y, Li J. Regulation of cell-mediated immune responses in dairy bulls via long non-coding RNAs from submandibular lymph nodes, peripheral blood, and the spleen. Genomics 2024; 116:110958. [PMID: 39536956 DOI: 10.1016/j.ygeno.2024.110958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 10/18/2024] [Accepted: 11/04/2024] [Indexed: 11/16/2024]
Abstract
Cell-mediated immune responses (CMIRs) are critical to building a robust immune system and reducing disease susceptibility in cattle. Long non-coding RNAs (lncRNAs) regulate various biological processes. However, to the best of our knowledge, the characterization and functions of lncRNAs and their regulations on the bovine CMIR have not been investigated comprehensively. In this study, experimental bulls were immunized with heat-killed preparation of Candida albicans (HKCA) to induce delayed-type hypersensitivity (DTH). Three bulls were classified as high- CMIR responders and three were low-CMIR responders, based on their classical DTH skin reactions. LncRNAs were identified in the submandibular lymph nodes, peripheral blood, and spleen of high- and low-CMIR animals using strand-specific RNA sequencing. A total of 21,003 putative lncRNAs were identified across tissues, and 420, 468, and 599 lncRNAs were differentially expressed between the two groups in the submandibular lymph node, peripheral blood, and spleen tissues, respectively. Functional analysis of the differentially expressed lncRNA (DElncRNA) target genes showed that a number of immune-related Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched, including immune response, cell adhesion, nucleosome, DNA packaging, antigen processing and presentation, and complement and coagulation cascades. Tissue specificity analysis indicated that lncRNA transcripts have stronger tissue specificity than mRNA. Furthermore, an interaction network was constructed based on DElncRNAs and DEGs, and 11, 14, and 11 promising lncRNAs were identified as potential candidate genes influencing immune response regulation in submandibular lymph nodes, peripheral blood, and spleen tissues, respectively. These results provide a foundation for further research into the biological functions of lncRNAs associated with bovine CMIR and identify candidate lncRNA markers for cell-mediated immune responses.
Collapse
Affiliation(s)
- Xiuxin Zhao
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan 250100, China; Ningxia Key Laboratory of Ruminant Molecular and Cellular Breeding, College of Animal Science and Technology, Ningxia University, Yinchuan 750021, China; Shandong Ox Livestock Breeding Co., Ltd., Jinan 250100, China
| | - Xiao Wang
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan 250100, China
| | - Guanghui Xue
- Shandong Ox Livestock Breeding Co., Ltd., Jinan 250100, China
| | - Yundong Gao
- Shandong Ox Livestock Breeding Co., Ltd., Jinan 250100, China
| | - Yuanpei Zhang
- Shandong Ox Livestock Breeding Co., Ltd., Jinan 250100, China
| | - Yanqin Li
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan 250100, China
| | - Yachun Wang
- Ningxia Key Laboratory of Ruminant Molecular and Cellular Breeding, College of Animal Science and Technology, Ningxia University, Yinchuan 750021, China.
| | - Jianbin Li
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan 250100, China.
| |
Collapse
|
7
|
Hofman B, Szyda J, Frąszczak M, Mielczarek M. Long non-coding RNA variability in porcine skeletal muscle. J Appl Genet 2024; 65:565-573. [PMID: 38539022 DOI: 10.1007/s13353-024-00860-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 01/14/2024] [Accepted: 03/21/2024] [Indexed: 08/09/2024]
Abstract
Recently, numerous studies including various tissues have been carried out on long non-coding RNAs (lncRNAs), but still, its variability has not yet been fully understood. In this study, we characterised the inter-individual variability of lncRNAs in pigs, in the context of number, length and expression. Transcriptomes collected from muscle tissue belonging to six Polish Landrace boars (PL1-PL6), including half-brothers (PL1-PL3), were investigated using bioinformatics (lncRNA identification and functional analysis) and statistical (lncRNA variability) methods. The number of lncRNA ranged from 1289 to 3500 per animal, and the total number of common lncRNAs among all boars was 232. The number, length and expression of lncRNAs significantly varied between individuals, and no consistent pattern has been found between pairs of half-brothers. In detail, PL5 exhibits lower expression than the others, while PL4 has significantly higher expression than PL2-PL3 and PL5-PL6. Noteworthy, comparing the inter-individual variability of lncRNA and mRNA expression, they exhibited concordant patterns. The enrichment analysis for common lncRNA target genes determined a variety of biological processes that play fundamental roles in cell biology, and they were mostly related to whole-body homeostasis maintenance, energy and protein synthesis as well as dynamics of multiple nucleoprotein complexes. The high variability of lncRNA landscape in the porcine genome has been revealed in this study. The inter-individual differences have been found in the context of three aspects: the number, length and expression of lncRNAs, which contribute to a better understanding of its complex nature.
Collapse
Affiliation(s)
- Bartłomiej Hofman
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wroclaw, Poland
| | - Joanna Szyda
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wroclaw, Poland
| | - Magdalena Frąszczak
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wroclaw, Poland
| | - Magda Mielczarek
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wroclaw, Poland.
| |
Collapse
|
8
|
Degalez F, Bardou P, Lagarrigue S. GEGA (Gallus Enriched Gene Annotation): an online tool providing genomics and functional information across 47 tissues for a chicken gene-enriched atlas gathering Ensembl and Refseq genome annotations. NAR Genom Bioinform 2024; 6:lqae101. [PMID: 39157583 PMCID: PMC11327871 DOI: 10.1093/nargab/lqae101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/21/2024] [Accepted: 08/14/2024] [Indexed: 08/20/2024] Open
Abstract
GEGA is a user-friendly tool designed to navigate through various genomic and functional information related to an enriched gene atlas in chicken that integrates the gene catalogues from the two reference databases, NCBI-RefSeq and EMBL-Ensembl/GENCODE, along with four additional rich resources such as FAANG and NONCODE. Using the latest GRCg7b genome assembly, GEGA encompasses a total of 78 323 genes, including 24 102 protein-coding genes (PCGs) and 44 428 long non-coding RNAs (lncRNAs), significantly increasing the number of genes provided by each resource independently. However, GEGA is more than just a gene database. It offers a range of features that allow us to go deeper into the functional aspects of these genes. Users can explore gene expression and co-expression profiles across 47 tissues from 36 datasets and 1400 samples, discover tissue-specific variations and their expression as a function of sex or age and extract orthologous genes or their genomic configuration relative to the closest gene. For the communities interested in a specific gene, a list of genes or a quantitative trait locus region in chicken, GEGA's user-friendly interface facilitates efficient gene analysis, easy downloading of results and a multitude of graphical representations, from genomic information to detailed visualization of expression levels.
Collapse
Affiliation(s)
- Fabien Degalez
- PEGASE, INRAE, Institut Agro, 35590 Saint Gilles, France
| | - Philippe Bardou
- Sigenae, GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326 Castanet Tolosan, France
| | | |
Collapse
|
9
|
Surana P, Dutta P, Davuluri RV. TransTEx: novel tissue-specificity scoring method for grouping human transcriptome into different expression groups. Bioinformatics 2024; 40:btae475. [PMID: 39120880 PMCID: PMC11319638 DOI: 10.1093/bioinformatics/btae475] [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: 03/21/2024] [Revised: 06/12/2024] [Accepted: 08/08/2024] [Indexed: 08/10/2024] Open
Abstract
MOTIVATION Although human tissues carry out common molecular processes, gene expression patterns can distinguish different tissues. Traditional informatics methods, primarily at the gene level, overlook the complexity of alternative transcript variants and protein isoforms produced by most genes, changes in which are linked to disease prognosis and drug resistance. RESULTS We developed TransTEx (Transcript-level Tissue Expression), a novel tissue-specificity scoring method, for grouping transcripts into four expression groups. TransTEx applies sequential cut-offs to tissue-wise transcript probability estimates, subsampling-based P-values and fold-change estimates. Application of TransTEx on GTEx mRNA-seq data divided 199 166 human transcripts into different groups as 17 999 tissue-specific (TSp), 7436 tissue-enhanced, 36 783 widely expressed (Wide), 79 191 lowly expressed (Low), and 57 757 no expression (Null) transcripts. Testis has the most (13 466) TSp isoforms followed by liver (890), brain (701), pituitary (435), and muscle (420). We found that the tissue specificity of alternative transcripts of a gene is predominantly influenced by alternate promoter usage. By overlapping brain-specific transcripts with the cell-type gene-markers in scBrainMap database, we found that 63% of the brain-specific transcripts were enriched in nonneuronal cell types, predominantly astrocytes followed by endothelial cells and oligodendrocytes. In addition, we found 61 brain cell-type marker genes encoding a total of 176 alternative transcripts as brain-specific and 22 alternative transcripts as testis-specific, highlighting the complex TSp and cell-type specific gene regulation and expression at isoform-level. TransTEx can be adopted to the analysis of bulk RNA-seq or scRNA-seq datasets to find tissue- and/or cell-type specific isoform-level gene markers. AVAILABILITY AND IMPLEMENTATION TransTEx database: https://bmi.cewit.stonybrook.edu/transtexdb/ and the R package is available via GitHub: https://github.com/pallavisurana1/TransTEx.
Collapse
Affiliation(s)
- Pallavi Surana
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Pratik Dutta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Ramana V Davuluri
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| |
Collapse
|
10
|
Lee U, Mozeika SM, Zhao L. A Synergistic, Cultivator Model of De Novo Gene Origination. Genome Biol Evol 2024; 16:evae103. [PMID: 38748819 PMCID: PMC11152449 DOI: 10.1093/gbe/evae103] [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] [Accepted: 05/12/2024] [Indexed: 06/07/2024] Open
Abstract
The origin and fixation of evolutionarily young genes is a fundamental question in evolutionary biology. However, understanding the origins of newly evolved genes arising de novo from noncoding genomic sequences is challenging. This is partly due to the low likelihood that several neutral or nearly neutral mutations fix prior to the appearance of an important novel molecular function. This issue is particularly exacerbated in large effective population sizes where the effect of drift is small. To address this problem, we propose a regulation-focused, cultivator model for de novo gene evolution. This cultivator-focused model posits that each step in a novel variant's evolutionary trajectory is driven by well-defined, selectively advantageous functions for the cultivator genes, rather than solely by the de novo genes, emphasizing the critical role of genome organization in the evolution of new genes.
Collapse
Affiliation(s)
- UnJin Lee
- Laboratory of Evolutionary Genetics and Genomics, The Rockefeller University, New York, NY, USA
| | - Shawn M Mozeika
- Laboratory of Evolutionary Genetics and Genomics, The Rockefeller University, New York, NY, USA
| | - Li Zhao
- Laboratory of Evolutionary Genetics and Genomics, The Rockefeller University, New York, NY, USA
| |
Collapse
|
11
|
Piégu B, Lefort G, Douet C, Milhes M, Jacques A, Lareyre JJ, Monget P, Fouchécourt S. A first complete catalog of highly expressed genes in eight chicken tissues reveals uncharacterized gene families specific for the chicken testis. Physiol Genomics 2024; 56:445-456. [PMID: 38497118 DOI: 10.1152/physiolgenomics.00151.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/08/2024] [Accepted: 03/08/2024] [Indexed: 03/19/2024] Open
Abstract
Based on next-generation sequencing, we established a repertoire of differentially overexpressed genes (DoEGs) in eight adult chicken tissues: the testis, brain, lung, liver, kidney, muscle, heart, and intestine. With 4,499 DoEGs, the testis had the highest number and proportion of DoEGs compared with the seven somatic tissues. The testis DoEG set included the highest proportion of long noncoding RNAs (lncRNAs; 1,851, representing 32% of the lncRNA genes in the whole genome) and the highest proportion of protein-coding genes (2,648, representing 14.7% of the protein-coding genes in the whole genome). The main significantly enriched Gene Ontology terms related to the protein-coding genes were "reproductive process," "tubulin binding," and "microtubule cytoskeleton." Using real-time quantitative reverse transcription-polymerase chain reaction, we confirmed the overexpression of genes that encode proteins already described in chicken sperm [such as calcium binding tyrosine phosphorylation regulated (CABYR), spermatogenesis associated 18 (SPATA18), and CDK5 regulatory subunit associated protein (CDK5RAP2)] but whose testis origin had not been previously confirmed. Moreover, we demonstrated the overexpression of vertebrate orthologs of testis genes not yet described in the adult chicken testis [such as NIMA related kinase 2 (NEK2), adenylate kinase 7 (AK7), and CCNE2]. Using clustering according to primary sequence homology, we found that 1,737 of the 2,648 (67%) testis protein-coding genes were unique genes. This proportion was significantly higher than the somatic tissues except muscle. We clustered the other 911 testis protein-coding genes into 495 families, from which 47 had all paralogs overexpressed in the testis. Among these 47 testis-specific families, eight contained uncharacterized duplicated paralogs without orthologs in other metazoans except birds: these families are thus specific for chickens/birds.NEW & NOTEWORTHY Comparative next-generation sequencing analysis of eight chicken tissues showed that the testis has highest proportion of long noncoding RNA and protein-coding genes of the whole genome. We identified new genes in the chicken testis, including orthologs of known mammalian testicular genes. We also identified 47 gene families in which all the members were overexpressed, if not exclusive, in the testis. Eight families, organized in duplication clusters, were unknown, without orthologs in metazoans except birds, and are thus specific for chickens/birds.
Collapse
Affiliation(s)
- Benoît Piégu
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Centre National de la Recherche Scientifique, Université de Tours, PRC, Nouzilly, France
| | - Gaëlle Lefort
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Centre National de la Recherche Scientifique, Université de Tours, PRC, Nouzilly, France
| | - Cécile Douet
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Centre National de la Recherche Scientifique, Université de Tours, PRC, Nouzilly, France
| | - Marine Milhes
- US 1426, GeT-PlaGe, Genotoul, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Castanet-Tolosan, France
| | - Aurore Jacques
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Centre National de la Recherche Scientifique, Université de Tours, PRC, Nouzilly, France
| | - Jean-Jacques Lareyre
- UR1037 LPGP, Fish Physiology and Genomics, Campus de Beaulieu, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Rennes, France
| | - Philippe Monget
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Centre National de la Recherche Scientifique, Université de Tours, PRC, Nouzilly, France
| | - Sophie Fouchécourt
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Centre National de la Recherche Scientifique, Université de Tours, PRC, Nouzilly, France
| |
Collapse
|
12
|
Tian XC, Chen ZY, Nie S, Shi TL, Yan XM, Bao YT, Li ZC, Ma HY, Jia KH, Zhao W, Mao JF. Plant-LncPipe: a computational pipeline providing significant improvement in plant lncRNA identification. HORTICULTURE RESEARCH 2024; 11:uhae041. [PMID: 38638682 PMCID: PMC11024640 DOI: 10.1093/hr/uhae041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 02/02/2024] [Indexed: 04/20/2024]
Abstract
Long non-coding RNAs (lncRNAs) play essential roles in various biological processes, such as chromatin remodeling, post-transcriptional regulation, and epigenetic modifications. Despite their critical functions in regulating plant growth, root development, and seed dormancy, the identification of plant lncRNAs remains a challenge due to the scarcity of specific and extensively tested identification methods. Most mainstream machine learning-based methods used for plant lncRNA identification were initially developed using human or other animal datasets, and their accuracy and effectiveness in predicting plant lncRNAs have not been fully evaluated or exploited. To overcome this limitation, we retrained several models, including CPAT, PLEK, and LncFinder, using plant datasets and compared their performance with mainstream lncRNA prediction tools such as CPC2, CNCI, RNAplonc, and LncADeep. Retraining these models significantly improved their performance, and two of the retrained models, LncFinder-plant and CPAT-plant, alongside their ensemble, emerged as the most suitable tools for plant lncRNA identification. This underscores the importance of model retraining in tackling the challenges associated with plant lncRNA identification. Finally, we developed a pipeline (Plant-LncPipe) that incorporates an ensemble of the two best-performing models and covers the entire data analysis process, including reads mapping, transcript assembly, lncRNA identification, classification, and origin, for the efficient identification of lncRNAs in plants. The pipeline, Plant-LncPipe, is available at: https://github.com/xuechantian/Plant-LncRNA-pipline.
Collapse
Affiliation(s)
- Xue-Chan Tian
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Zhao-Yang Chen
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Shuai Nie
- Rice Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs & Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China
| | - Tian-Le Shi
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Xue-Mei Yan
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Yu-Tao Bao
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Zhi-Chao Li
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Hai-Yao Ma
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Kai-Hua Jia
- Key Laboratory of Crop Genetic Improvement & Ecology and Physiology, Institute of Crop Germplasm Resources, Shandong Academy of Agricultural Sciences, Jinan 250100, China
| | - Wei Zhao
- Department of Plant Physiology, Umeå Plant Science Centre (UPSC), Umeå University, Umeå 90187, Sweden
| | - Jian-Feng Mao
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- Department of Plant Physiology, Umeå Plant Science Centre (UPSC), Umeå University, Umeå 90187, Sweden
| |
Collapse
|
13
|
Degalez F, Charles M, Foissac S, Zhou H, Guan D, Fang L, Klopp C, Allain C, Lagoutte L, Lecerf F, Acloque H, Giuffra E, Pitel F, Lagarrigue S. Enriched atlas of lncRNA and protein-coding genes for the GRCg7b chicken assembly and its functional annotation across 47 tissues. Sci Rep 2024; 14:6588. [PMID: 38504112 PMCID: PMC10951430 DOI: 10.1038/s41598-024-56705-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 03/09/2024] [Indexed: 03/21/2024] Open
Abstract
Gene atlases for livestock are steadily improving thanks to new genome assemblies and new expression data improving the gene annotation. However, gene content varies across databases due to differences in RNA sequencing data and bioinformatics pipelines, especially for long non-coding RNAs (lncRNAs) which have higher tissue and developmental specificity and are harder to consistently identify compared to protein coding genes (PCGs). As done previously in 2020 for chicken assemblies galgal5 and GRCg6a, we provide a new gene atlas, lncRNA-enriched, for the latest GRCg7b chicken assembly, integrating "NCBI RefSeq", "EMBL-EBI Ensembl/GENCODE" reference annotations and other resources such as FAANG and NONCODE. As a result, the number of PCGs increases from 18,022 (RefSeq) and 17,007 (Ensembl) to 24,102, and that of lncRNAs from 5789 (RefSeq) and 11,944 (Ensembl) to 44,428. Using 1400 public RNA-seq transcriptome representing 47 tissues, we provided expression evidence for 35,257 (79%) lncRNAs and 22,468 (93%) PCGs, supporting the relevance of this atlas. Further characterization including tissue-specificity, sex-differential expression and gene configurations are provided. We also identified conserved miRNA-hosting genes with human counterparts, suggesting common function. The annotated atlas is available at gega.sigenae.org.
Collapse
Affiliation(s)
- Fabien Degalez
- PEGASE, INRAE, Institut Agro, 35590, Saint Gilles, France
| | - Mathieu Charles
- INRAE, BioinfOmics, GenoToul Bioinformatics facility, Sigenae, Université Fédérale de Toulouse, 31326, Castanet-Tolosan, France
- INRAE, AgroParisTech, GABI, Paris-Saclay University, 78350, Jouy-en-Josas, France
| | - Sylvain Foissac
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet-Tolosan, France
| | | | - Dailu Guan
- University of California Davis, Davis, USA
| | | | - Christophe Klopp
- INRAE, BioinfOmics, GenoToul Bioinformatics facility, Sigenae, Université Fédérale de Toulouse, 31326, Castanet-Tolosan, France
| | - Coralie Allain
- PEGASE, INRAE, Institut Agro, 35590, Saint Gilles, France
| | | | | | - Hervé Acloque
- INRAE, AgroParisTech, GABI, Paris-Saclay University, 78350, Jouy-en-Josas, France
| | - Elisabetta Giuffra
- INRAE, AgroParisTech, GABI, Paris-Saclay University, 78350, Jouy-en-Josas, France
| | - Frédérique Pitel
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet-Tolosan, France
| | | |
Collapse
|
14
|
Weghorst F, Torres Marcén M, Faridi G, Lee YCG, Cramer KS. Deep Conservation and Unexpected Evolutionary History of Neighboring lncRNAs MALAT1 and NEAT1. J Mol Evol 2024; 92:30-41. [PMID: 38189925 PMCID: PMC10869381 DOI: 10.1007/s00239-023-10151-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 11/29/2023] [Indexed: 01/09/2024]
Abstract
Long non-coding RNAs (lncRNAs) have begun to receive overdue attention for their regulatory roles in gene expression and other cellular processes. Although most lncRNAs are lowly expressed and tissue-specific, notable exceptions include MALAT1 and its genomic neighbor NEAT1, two highly and ubiquitously expressed oncogenes with roles in transcriptional regulation and RNA splicing. Previous studies have suggested that NEAT1 is found only in mammals, while MALAT1 is present in all gnathostomes (jawed vertebrates) except birds. Here we show that these assertions are incomplete, likely due to the challenges associated with properly identifying these two lncRNAs. Using phylogenetic analysis and structure-aware annotation of publicly available genomic and RNA-seq coverage data, we show that NEAT1 is a common feature of tetrapod genomes except birds and squamates. Conversely, we identify MALAT1 in representative species of all major gnathostome clades, including birds. Our in-depth examination of MALAT1, NEAT1, and their genomic context in a wide range of vertebrate species allows us to reconstruct the series of events that led to the formation of the locus containing these genes in taxa from cartilaginous fish to mammals. This evolutionary history includes the independent loss of NEAT1 in birds and squamates, since NEAT1 is found in the closest living relatives of both clades (crocodilians and tuataras, respectively). These data clarify the origins and relationships of MALAT1 and NEAT1 and highlight an opportunity to study the change and continuity in lncRNA structure and function over deep evolutionary time.
Collapse
Affiliation(s)
- Forrest Weghorst
- Department of Neurobiology and Behavior, University of California, Irvine, USA
| | - Martí Torres Marcén
- Department of Neurobiology and Behavior, University of California, Irvine, USA
| | - Garrison Faridi
- Department of Neurobiology and Behavior, University of California, Irvine, USA
| | - Yuh Chwen G Lee
- Department of Ecology and Evolutionary Biology, University of California, Irvine, USA
| | - Karina S Cramer
- Department of Neurobiology and Behavior, University of California, Irvine, USA.
| |
Collapse
|
15
|
Kang J, Chung A, Suresh S, Bonzi LC, Sourisse JM, Ramirez‐Calero S, Romeo D, Petit‐Marty N, Pegueroles C, Schunter C. Long non-coding RNAs mediate fish gene expression in response to ocean acidification. Evol Appl 2024; 17:e13655. [PMID: 38357358 PMCID: PMC10866067 DOI: 10.1111/eva.13655] [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: 08/24/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 02/16/2024] Open
Abstract
The majority of the transcribed genome does not have coding potential but these non-coding transcripts play crucial roles in transcriptional and post-transcriptional regulation of protein-coding genes. Regulation of gene expression is important in shaping an organism's response to environmental changes, ultimately impacting their survival and persistence as population or species face global change. However, the roles of long non-coding RNAs (lncRNAs), when confronted with environmental changes, remain largely unclear. To explore the potential role of lncRNAs in fish exposed to ocean acidification (OA), we analyzed publicly available brain RNA-seq data from a coral reef fish Acanthochromis polyacanthus. We annotated the lncRNAs in its genome and examined the expression changes of intergenic lncRNAs (lincRNAs) between A. polyacanthus samples from a natural CO2 seep and a nearby control site. We identified 4728 lncRNAs, including 3272 lincRNAs in this species. Remarkably, 93.03% of these lincRNAs were species-specific. Among the 125 highly expressed lincRNAs and 403 differentially expressed lincRNAs in response to elevated CO2, we observed that lincRNAs were either neighboring or potentially trans-regulating differentially expressed coding genes associated with pH regulation, neural signal transduction, and ion transport, which are known to be important in the response to OA in fish. In summary, lncRNAs may facilitate fish acclimation and mediate the responses of fish to OA by modulating the expression of crucial coding genes, which offers insight into the regulatory mechanisms underlying fish responses to environmental changes.
Collapse
Affiliation(s)
- Jingliang Kang
- Swire Institute of Marine Science, School of Biological SciencesThe University of Hong KongPokfulamHong Kong SAR
| | - Arthur Chung
- Swire Institute of Marine Science, School of Biological SciencesThe University of Hong KongPokfulamHong Kong SAR
| | - Sneha Suresh
- Swire Institute of Marine Science, School of Biological SciencesThe University of Hong KongPokfulamHong Kong SAR
| | - Lucrezia C. Bonzi
- Swire Institute of Marine Science, School of Biological SciencesThe University of Hong KongPokfulamHong Kong SAR
| | - Jade M. Sourisse
- Swire Institute of Marine Science, School of Biological SciencesThe University of Hong KongPokfulamHong Kong SAR
| | - Sandra Ramirez‐Calero
- Swire Institute of Marine Science, School of Biological SciencesThe University of Hong KongPokfulamHong Kong SAR
| | - Daniele Romeo
- Swire Institute of Marine Science, School of Biological SciencesThe University of Hong KongPokfulamHong Kong SAR
| | - Natalia Petit‐Marty
- Swire Institute of Marine Science, School of Biological SciencesThe University of Hong KongPokfulamHong Kong SAR
| | - Cinta Pegueroles
- Department of Genetics, Microbiology and Statistics, Institute for Research on Biodiversity (IRBio)University of BarcelonaBarcelonaSpain
| | - Celia Schunter
- Swire Institute of Marine Science, School of Biological SciencesThe University of Hong KongPokfulamHong Kong SAR
- State Key Laboratory of Marine Pollution and Department of ChemistryCity University of Hong KongHong Kong SARChina
| |
Collapse
|
16
|
He T, Li C, Chen Q, Li R, Luo J, Mao J, Yang Z. Combined analysis of lncRNA and mRNA emphasizes the potential role of tryptophan-mediated regulation of muscle development in weaned piglets by lncRNA. J Anim Sci 2024; 102:skae264. [PMID: 39276131 PMCID: PMC11465388 DOI: 10.1093/jas/skae264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Accepted: 09/13/2024] [Indexed: 09/16/2024] Open
Abstract
Pork is an important high-value protein source that fulfills the nutritional requirements for normal growth development, repair, and metabolism. Tryptophan (Trp), a crucial amino acid for piglet growth performance and muscle development, has an essential yet unclear regulatory mechanism. To investigate the biological basis of Trp regulation of piglet muscle development and identify the related regulatory pathways, we studied 20 weaned piglets. The piglets were divided into control (CON, 0.14% Trp) and high Trp (HT, 0.35% Trp) groups. They were fed with different Trp concentrations for 28 d, after which we collected the longissimus dorsi (LD) muscle for histomorphometric analysis and RNA extraction. Our results showed that the HT diet significantly increased the average daily weight gain, myocyte number, and muscle fiber density in weaned piglets. We then analyzed the differentially expressed (DE) genes in the LD muscle through RNA sequencing (RNA-seq). We identified 253 lncRNAs and 1,055 mRNAs mainly involved in myoblast proliferation and myofiber formation, particularly through the FoxO and AMPK signaling pathways and metabolism. Further analysis of the DE lncRNA targeting relationship and construction of a protein-protein interaction network resulted in the discovery of a novel lncRNA, XLOC_021675, or FRPMD, and elucidated its role in regulating piglet muscle development. Finally, we confirmed the RNA-seq results by reverse transcription polymerase chain reaction (RT-PCR). This study provides valuable insights into the regulatory mechanism of lncRNA-mediated Trp regulation of muscle development in weaned piglets offering a theoretical basis for optimizing piglet dietary ratios and enhancing pork production.
Collapse
Affiliation(s)
- Tianle He
- Laboratory for Bio-feed and Molecular Nutrition, College of Animal Science and Technology, Southwest University, Chongqing, China
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing, China
- College of Animal Science and Technology, Ningxia University, Yinchuan, China
| | - Chenlei Li
- College of Animal Science and Technology, Ningxia University, Yinchuan, China
| | - Qingyun Chen
- Laboratory for Bio-feed and Molecular Nutrition, College of Animal Science and Technology, Southwest University, Chongqing, China
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Ruiqian Li
- College of Animal Science and Technology, Ningxia University, Yinchuan, China
| | - Ju Luo
- Laboratory for Bio-feed and Molecular Nutrition, College of Animal Science and Technology, Southwest University, Chongqing, China
| | - Jiani Mao
- Laboratory for Bio-feed and Molecular Nutrition, College of Animal Science and Technology, Southwest University, Chongqing, China
| | - Zhenguo Yang
- Laboratory for Bio-feed and Molecular Nutrition, College of Animal Science and Technology, Southwest University, Chongqing, China
| |
Collapse
|
17
|
Isa AM, Sun Y, Wang Y, Li Y, Yuan J, Ni A, Ma H, Shi L, Tesfay HH, Zong Y, Wang P, Ge P, Chen J. Transcriptome analysis of ovarian tissues highlights genes controlling energy homeostasis and oxidative stress as potential drivers of heterosis for egg number and clutch size in crossbred laying hens. Poult Sci 2024; 103:103163. [PMID: 37980751 PMCID: PMC10684806 DOI: 10.1016/j.psj.2023.103163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/15/2023] [Accepted: 09/29/2023] [Indexed: 11/21/2023] Open
Abstract
Heterosis is the major benefit of crossbreeding and has been exploited in laying hens breeding for a long time. This genetic phenomenon has been linked to various modes of nonadditive gene action. However, the molecular mechanism of heterosis for egg production in laying hens has not been fully elucidated. To fill this research gap, we sequenced mRNAs and lncRNAs of the ovary stroma containing prehierarchical follicles in White Leghorn, Rhode Island Red chickens as well as their reciprocal crossbreds that demonstrated heterosis for egg number and clutch size. We further delineated the modes of mRNAs and lncRNAs expression to identify their potential functions in the observed heterosis. Results showed that dominance was the principal mode of nonadditive expression exhibited by mRNAs and lncRNAs in the prehierarchical follicles of crossbred hens. Specifically, low-parent dominance was the main mode of mRNA expression, while high-parent dominance was the predominant mode of lncRNA expression. Important pathways enriched by genes that showed higher expression in crossbreds compared to either one or both parental lines were cell adhesion molecules, tyrosine and purine metabolism. In contrast, ECM-receptor interaction, focal adhesion, PPAR signaling, and ferroptosis were enriched in genes with lower expression in the crossbred. Protein network interaction identified nonadditively expressed genes including apolipoprotein B (APOB), transferrin, acyl-CoA synthetase medium-chain family member (APOBEC) 3, APOBEC1 complementation factor, and cathepsin S as hub genes. Among these potential hub genes, APOB was the only gene with underdominance expression common to the 2 reciprocal crossbred lines, and has been linked to oxidative stress. LncRNAs with nonadditive expression in the crossbred hens targeted natriuretic peptide receptor 1, epidermal differentiation protein beta, spermatogenesis-associated gene 22, sperm-associated antigen 16, melanocortin 2 receptor, dolichol kinase, glycine amiinotransferase, and prolactin releasing hormone receptor. In conclusion, genes with nonadditive expression in the crossbred may play crucial roles in follicle growth and atresia by improving follicle competence and increasing oxidative stress, respectively. These 2 phenomena could underpin heterosis for egg production in crossbred laying hens.
Collapse
Affiliation(s)
- Adamu Mani Isa
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; Department of Animal Science, Usmanu Danfodiyo University, Sokoto, Nigeria
| | - Yanyan Sun
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yuanmei Wang
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yunlei Li
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Jingwei Yuan
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Aixin Ni
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Hui Ma
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Lei Shi
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Hailai Hagos Tesfay
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yunhe Zong
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Panlin Wang
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Pingzhuang Ge
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Jilan Chen
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| |
Collapse
|
18
|
Marceau A, Wang J, Iqbal V, Jiang J, Liu GE, Ma L. Investigation of lncRNA in Bos taurus Mammary Tissue during Dry and Lactation Periods. Genes (Basel) 2023; 14:1789. [PMID: 37761929 PMCID: PMC10531232 DOI: 10.3390/genes14091789] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/01/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
This study aims to collect RNA-Seq data from Bos taurus samples representing dry and lactating mammary tissue, identify lncRNA transcripts, and analyze findings for their features and functional annotation. This allows for connections to be drawn between lncRNA and the lactation process. RNA-Seq data from 103 samples of Bos taurus mammary tissue were gathered from publicly available databases (60 dry, 43 lactating). The samples were filtered to reveal 214 dry mammary lncRNA transcripts and 517 lactating mammary lncRNA transcripts. The lncRNAs met common lncRNA characteristics such as shorter length, fewer exons, lower expression levels, and less sequence conservation when compared to the genome. Interestingly, several lncRNAs showed sequence similarity to genes associated with strong hair keratin intermediate filaments. Human breast cancer research has associated strong hair keratin filaments with mammary tissue cellular resilience. The lncRNAs were also associated with several genes/proteins that linked to pregnancy using expression correlation and gene ontology. Such findings indicate that there are crucial relationships between the lncRNAs found in mammary tissue and the development of the tissue, to meet both the animal's needs and our own production needs; these relationships should be further investigated to ensure that we continue to breed the most resilient, efficient dairy cattle.
Collapse
Affiliation(s)
- Alexis Marceau
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA; (A.M.); (V.I.)
| | - Junjian Wang
- Department of Animal Science, North Carlonina State University, Raleigh, NC 27695, USA; (J.W.); (J.J.)
| | - Victoria Iqbal
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA; (A.M.); (V.I.)
| | - Jicai Jiang
- Department of Animal Science, North Carlonina State University, Raleigh, NC 27695, USA; (J.W.); (J.J.)
| | - George E. Liu
- Animal Genomics and Improvemennt Laboratory, BARC, USDA-ARS, Beltsville, MD 20705, USA;
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA; (A.M.); (V.I.)
| |
Collapse
|
19
|
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.
Collapse
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.
| |
Collapse
|
20
|
Triantaphyllopoulos KA. Long Non-Coding RNAs and Their "Discrete" Contribution to IBD and Johne's Disease-What Stands out in the Current Picture? A Comprehensive Review. Int J Mol Sci 2023; 24:13566. [PMID: 37686376 PMCID: PMC10487966 DOI: 10.3390/ijms241713566] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/23/2023] [Accepted: 08/27/2023] [Indexed: 09/10/2023] Open
Abstract
Non-coding RNAs (ncRNA) have paved the way to new perspectives on the regulation of gene expression, not only in biology and medicine, but also in associated fields and technologies, ensuring advances in diagnostic means and therapeutic modalities. Critical in this multistep approach are the associations of long non-coding RNA (lncRNA) with diseases and their causal genes in their networks of interactions, gene enrichment and expression analysis, associated pathways, the monitoring of the involved genes and their functional roles during disease progression from one stage to another. Studies have shown that Johne's Disease (JD), caused by Mycobacterium avium subspecies partuberculosis (MAP), shares common lncRNAs, clinical findings, and other molecular entities with Crohn's Disease (CD). This has been a subject of vigorous investigation owing to the zoonotic nature of this condition, although results are still inconclusive. In this review, on one hand, the current knowledge of lncRNAs in cells is presented, focusing on the pathogenesis of gastrointestinal-related pathologies and MAP-related infections and, on the other hand, we attempt to dissect the associated genes and pathways involved. Furthermore, the recently characterized and novel lncRNAs share common pathologies with IBD and JD, including the expression, molecular networks, and dataset analysis results. These are also presented in an attempt to identify potential biomarkers pertinent to cattle and human disease phenotypes.
Collapse
Affiliation(s)
- Kostas A Triantaphyllopoulos
- Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 75 Iera Odos St., 11855 Athens, Greece
| |
Collapse
|
21
|
Wang Y, Zhao P, Du H, Cao Y, Peng Q, Fu L. LncDLSM: Identification of Long Non-Coding RNAs With Deep Learning-Based Sequence Model. IEEE J Biomed Health Inform 2023; 27:2117-2127. [PMID: 37027676 DOI: 10.1109/jbhi.2023.3247805] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Long non-coding RNAs (LncRNAs) serve a vital role in regulating gene expressions and other biological processes. Differentiation of lncRNAs from protein-coding transcripts helps researchers dig into the mechanism of lncRNA formation and its downstream regulations related to various diseases. Previous works have been proposed to identify lncRNAs, including traditional bio-sequencing and machine learning approaches. Considering the tedious work of biological characteristic-based feature extraction procedures and inevitable artifacts during bio-sequencing processes, those lncRNA detection methods are not always satisfactory. Hence, in this work, we presented lncDLSM, a deep learning-based framework differentiating lncRNA from other protein-coding transcripts without dependencies on prior biological knowledge. lncDLSM is a helpful tool for identifying lncRNAs compared with other biological feature-based machine learning methods and can be applied to other species by transfer learning achieving satisfactory results. Further experiments showed that different species display distinct boundaries among distributions corresponding to the homology and the specificity among species, respectively.
Collapse
|
22
|
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.
Collapse
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
| |
Collapse
|
23
|
Smith J, Alfieri JM, Anthony N, Arensburger P, Athrey GN, Balacco J, Balic A, Bardou P, Barela P, Bigot Y, Blackmon H, Borodin PM, Carroll R, Casono MC, Charles M, Cheng H, Chiodi M, Cigan L, Coghill LM, Crooijmans R, Das N, Davey S, Davidian A, Degalez F, Dekkers JM, Derks M, Diack AB, Djikeng A, Drechsler Y, Dyomin A, Fedrigo O, Fiddaman SR, Formenti G, Frantz LA, Fulton JE, Gaginskaya E, Galkina S, Gallardo RA, Geibel J, Gheyas AA, Godinez CJP, Goodell A, Graves JA, Griffin DK, Haase B, Han JL, Hanotte O, Henderson LJ, Hou ZC, Howe K, Huynh L, Ilatsia E, Jarvis ED, Johnson SM, Kaufman J, Kelly T, Kemp S, Kern C, Keroack JH, Klopp C, Lagarrigue S, Lamont SJ, Lange M, Lanke A, Larkin DM, Larson G, Layos JKN, Lebrasseur O, Malinovskaya LP, Martin RJ, Martin Cerezo ML, Mason AS, McCarthy FM, McGrew MJ, Mountcastle J, Muhonja CK, Muir W, Muret K, Murphy TD, Ng'ang'a I, Nishibori M, O'Connor RE, Ogugo M, Okimoto R, Ouko O, Patel HR, Perini F, Pigozzi MI, Potter KC, Price PD, Reimer C, Rice ES, Rocos N, Rogers TF, Saelao P, Schauer J, Schnabel RD, Schneider VA, Simianer H, Smith A, et alSmith J, Alfieri JM, Anthony N, Arensburger P, Athrey GN, Balacco J, Balic A, Bardou P, Barela P, Bigot Y, Blackmon H, Borodin PM, Carroll R, Casono MC, Charles M, Cheng H, Chiodi M, Cigan L, Coghill LM, Crooijmans R, Das N, Davey S, Davidian A, Degalez F, Dekkers JM, Derks M, Diack AB, Djikeng A, Drechsler Y, Dyomin A, Fedrigo O, Fiddaman SR, Formenti G, Frantz LA, Fulton JE, Gaginskaya E, Galkina S, Gallardo RA, Geibel J, Gheyas AA, Godinez CJP, Goodell A, Graves JA, Griffin DK, Haase B, Han JL, Hanotte O, Henderson LJ, Hou ZC, Howe K, Huynh L, Ilatsia E, Jarvis ED, Johnson SM, Kaufman J, Kelly T, Kemp S, Kern C, Keroack JH, Klopp C, Lagarrigue S, Lamont SJ, Lange M, Lanke A, Larkin DM, Larson G, Layos JKN, Lebrasseur O, Malinovskaya LP, Martin RJ, Martin Cerezo ML, Mason AS, McCarthy FM, McGrew MJ, Mountcastle J, Muhonja CK, Muir W, Muret K, Murphy TD, Ng'ang'a I, Nishibori M, O'Connor RE, Ogugo M, Okimoto R, Ouko O, Patel HR, Perini F, Pigozzi MI, Potter KC, Price PD, Reimer C, Rice ES, Rocos N, Rogers TF, Saelao P, Schauer J, Schnabel RD, Schneider VA, Simianer H, Smith A, Stevens MP, Stiers K, Tiambo CK, Tixier-Boichard M, Torgasheva AA, Tracey A, Tregaskes CA, Vervelde L, Wang Y, Warren WC, Waters PD, Webb D, Weigend S, Wolc A, Wright AE, Wright D, Wu Z, Yamagata M, Yang C, Yin ZT, Young MC, Zhang G, Zhao B, Zhou H. Fourth Report on Chicken Genes and Chromosomes 2022. Cytogenet Genome Res 2023; 162:405-528. [PMID: 36716736 PMCID: PMC11835228 DOI: 10.1159/000529376] [Show More Authors] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 01/22/2023] [Indexed: 02/01/2023] Open
Affiliation(s)
- Jacqueline Smith
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, UK
| | - James M. Alfieri
- Interdisciplinary Program in Ecology and Evolutionary Biology, Texas A&M University, College Station, Texas, USA
- Department of Biology, Texas A&M University, College Station, Texas, USA
- Department of Poultry Science, Texas A&M University, College Station, Texas, USA
| | | | - Peter Arensburger
- Biological Sciences Department, California State Polytechnic University, Pomona, California, USA
| | - Giridhar N. Athrey
- Interdisciplinary Program in Ecology and Evolutionary Biology, Texas A&M University, College Station, Texas, USA
- Department of Poultry Science, Texas A&M University, College Station, Texas, USA
| | | | - Adam Balic
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, UK
| | - Philippe Bardou
- Université de Toulouse, INRAE, ENVT, GenPhySE, Sigenae, Castanet Tolosan, France
| | | | - Yves Bigot
- PRC, UMR INRAE 0085, CNRS 7247, Centre INRAE Val de Loire, Nouzilly, France
| | - Heath Blackmon
- Interdisciplinary Program in Ecology and Evolutionary Biology, Texas A&M University, College Station, Texas, USA
- Department of Biology, Texas A&M University, College Station, Texas, USA
| | - Pavel M. Borodin
- Department of Molecular Genetics, Cell Biology and Bioinformatics, Institute of Cytology and Genetics of Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russian Federation
| | - Rachel Carroll
- Department of Animal Sciences, Data Science and Informatics Institute, University of Missouri, Columbia, Missouri, USA
| | | | - Mathieu Charles
- University Paris-Saclay, INRAE, AgroParisTech, GABI, Sigenae, Jouy-en-Josas, France
| | - Hans Cheng
- USDA, ARS, USNPRC, Avian Disease and Oncology Laboratory, East Lansing, Michigan, USA
| | | | | | - Lyndon M. Coghill
- Department of Veterinary Pathology, University of Missouri, Columbia, Missouri, USA
| | - Richard Crooijmans
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | | | - Sean Davey
- University of Arizona, Tucson, Arizona, USA
| | - Asya Davidian
- Saint Petersburg State University, Saint Petersburg, Russian Federation
| | - Fabien Degalez
- Centre for Tropical Livestock Genetics and Health (CTLGH) − ILRI, Nairobi, Kenya
| | - Jack M. Dekkers
- Department of Animal Science, University of California, Davis, California, USA
- INRAE, MIAT UR875, Sigenae, Castanet Tolosan, France
| | - Martijn Derks
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Abigail B. Diack
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, UK
| | - Appolinaire Djikeng
- Department of Molecular Microbiology and Immunology, University of Missouri, Columbia, Missouri, USA
| | | | - Alexander Dyomin
- Saint Petersburg State University, Saint Petersburg, Russian Federation
| | | | | | | | - Laurent A.F. Frantz
- Queen Mary University of London, Bethnal Green, London, UK
- Palaeogenomics Group, Department of Veterinary Sciences, LMU Munich, Munich, Germany
| | - Janet E. Fulton
- Hy-Line International, Research and Development, Dallas Center, Iowa, USA
| | - Elena Gaginskaya
- Saint Petersburg State University, Saint Petersburg, Russian Federation
| | - Svetlana Galkina
- Saint Petersburg State University, Saint Petersburg, Russian Federation
| | - Rodrigo A. Gallardo
- School of Veterinary Medicine, University of California, Davis, California, USA
- Department of Animal Science, University of California, Davis, California, USA
| | - Johannes Geibel
- Institute of Farm Animal Genetics, Friedrich-Loeffler-Institut, Neustadt, Germany
- Center for Integrated Breeding Research, University of Göttingen, Göttingen, Germany
| | - Almas A. Gheyas
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, UK
| | - Cyrill John P. Godinez
- Department of Animal Science, College of Agriculture and Food Science, Visayas State University, Baybay City, Philippines
| | | | - Jennifer A.M. Graves
- Department of Environment and Genetics, La Trobe University, Melbourne, Victoria, Australia
- Institute for Applied Ecology, University of Canberra, Canberra, Australian Capital Territory, Australia
| | | | | | - Jian-Lin Han
- CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
- International Livestock Research Institute (ILRI), Addis Ababa, Ethiopia
| | - Olivier Hanotte
- International Livestock Research Institute (ILRI), Addis Ababa, Ethiopia
- Cells, Organisms and Molecular Genetics, School of Life Sciences, University of Nottingham, Nottingham, UK
- Centre for Tropical Livestock Genetics and Health, The Roslin Institute, Edinburgh, UK
| | - Lindsay J. Henderson
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, UK
| | - Zhuo-Cheng Hou
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, MARA, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | | | - Lan Huynh
- Institute for Immunology and Infection Research, University of Edinburgh, Edinburgh, UK
| | - Evans Ilatsia
- Dairy Research Institute, Kenya Agricultural and Livestock Organization, Naivasha, Kenya
| | | | | | - Jim Kaufman
- Institute for Immunology and Infection Research, University of Edinburgh, Edinburgh, UK
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - Terra Kelly
- School of Veterinary Medicine, University of California, Davis, California, USA
- Department of Animal Science, University of California, Davis, California, USA
| | - Steve Kemp
- INRAE, INSTITUT AGRO, PEGASE UMR 1348, Saint-Gilles, France
| | - Colin Kern
- Feed the Future Innovation Lab for Genomics to Improve Poultry, University of California, Davis, California, USA
| | | | - Christophe Klopp
- Department of Animal Science, Iowa State University, Ames, Iowa, USA
| | - Sandrine Lagarrigue
- Centre for Tropical Livestock Genetics and Health (CTLGH) − ILRI, Nairobi, Kenya
| | - Susan J. Lamont
- Department of Animal Science, University of California, Davis, California, USA
- INRAE, MIAT UR875, Sigenae, Castanet Tolosan, France
| | - Margaret Lange
- Centre for Tropical Livestock Genetics and Health (CTLGH) − The Roslin Institute, Edinburgh, UK
| | - Anika Lanke
- College of Veterinary Medicine, Western University of Health Sciences, Pomona, California, USA
| | - Denis M. Larkin
- Department of Comparative Biomedical Sciences, Royal Veterinary College, University of London, London, UK
| | - Greger Larson
- The Palaeogenomics and Bio-Archaeology Research Network, Research Laboratory for Archaeology and History of Art, The University of Oxford, Oxford, UK
| | - John King N. Layos
- College of Agriculture and Forestry, Capiz State University, Mambusao, Philippines
| | - Ophélie Lebrasseur
- Centre d'Anthropobiologie et de Génomique de Toulouse (CAGT), CNRS UMR 5288, Université Toulouse III Paul Sabatier, Toulouse, France
- Instituto Nacional de Antropología y Pensamiento Latinoamericano, Ciudad Autónoma de Buenos Aires, Argentina
| | - Lyubov P. Malinovskaya
- Department of Cytology and Genetics, Novosibirsk State University, Novosibirsk, Russian Federation
| | - Rebecca J. Martin
- Saint Petersburg State University, Saint Petersburg, Russian Federation
| | | | | | | | - Michael J. McGrew
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, UK
- Department of Molecular Microbiology and Immunology, University of Missouri, Columbia, Missouri, USA
| | | | - Christine Kamidi Muhonja
- Department of Veterinary Pathology, University of Missouri, Columbia, Missouri, USA
- Centre for Tropical Livestock Genetics and Health (CTLGH) − ILRI, Nairobi, Kenya
| | - William Muir
- Department of Animal Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Kévin Muret
- Université Paris-Saclay, Commissariat à l'Energie Atomique et aux Energies Alternatives, Centre National de Recherche en Génomique Humaine, Evry, France
| | - Terence D. Murphy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Masahide Nishibori
- Laboratory of Animal Genetics, Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan
| | | | - Moses Ogugo
- Centre for Tropical Livestock Genetics and Health (CTLGH) − ILRI, Nairobi, Kenya
| | - Ron Okimoto
- Cobb-Vantress, Siloam Springs, Arkansas, USA
| | - Ochieng Ouko
- Department of Veterinary Pathology, University of Missouri, Columbia, Missouri, USA
| | - Hardip R. Patel
- The John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Francesco Perini
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, UK
- Department of Agricultural, Food and Environmental Sciences, University of Perugia, Perugia, Italy
| | - María Ines Pigozzi
- INBIOMED (CONICET-UBA), Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires, Argentina
| | | | - Peter D. Price
- Ecology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield, UK
| | - Christian Reimer
- Institute of Farm Animal Genetics, Friedrich-Loeffler-Institut, Neustadt, Germany
| | - Edward S. Rice
- Department of Animal Sciences, Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
| | - Nicolas Rocos
- USDA, ARS, USNPRC, Avian Disease and Oncology Laboratory, East Lansing, Michigan, USA
| | - Thea F. Rogers
- Department of Molecular Evolution and Development, University of Vienna, Vienna, Austria
| | - Perot Saelao
- Department of Animal Science, University of California, Davis, California, USA
- Veterinary Pest Genetics Research Unit, USDA, Kerrville, Texas, USA
| | - Jens Schauer
- Institute of Farm Animal Genetics, Friedrich-Loeffler-Institut, Neustadt, Germany
| | - Robert D. Schnabel
- Department of Animal Sciences, University of Missouri, Columbia, Missouri, USA
| | - Valerie A. Schneider
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Henner Simianer
- Center for Integrated Breeding Research, University of Göttingen, Göttingen, Germany
| | - Adrian Smith
- Department of Zoology, University of Oxford, Oxford, UK
| | - Mark P. Stevens
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, UK
| | - Kyle Stiers
- Department of Veterinary Pathology, University of Missouri, Columbia, Missouri, USA
| | | | | | - Anna A. Torgasheva
- Department of Molecular Genetics, Cell Biology and Bioinformatics, Institute of Cytology and Genetics of Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russian Federation
| | - Alan Tracey
- University Paris-Saclay, INRAE, AgroParisTech, GABI, Sigenae, Jouy-en-Josas, France
| | - Clive A. Tregaskes
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
- Saint Petersburg State University, Saint Petersburg, Russian Federation
| | - Lonneke Vervelde
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, UK
| | - Ying Wang
- Department of Animal Science, University of California, Davis, California, USA
| | - Wesley C. Warren
- Department of Animal Sciences, Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Department of Animal Sciences, University of Missouri, Columbia, Missouri, USA
| | - Paul D. Waters
- School of Biotechnology and Biomolecular Science, Faculty of Science, UNSW Sydney, Sydney, New South Wales, Australia
| | - David Webb
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Steffen Weigend
- Institute of Farm Animal Genetics, Friedrich-Loeffler-Institut, Neustadt, Germany
- Center for Integrated Breeding Research, University of Göttingen, Göttingen, Germany
| | - Anna Wolc
- INRAE, MIAT UR875, Sigenae, Castanet Tolosan, France
- Hy-Line International, Research and Development, Dallas Center, Iowa, USA
| | - Alison E. Wright
- Ecology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield, UK
| | - Dominic Wright
- AVIAN Behavioural Genomics and Physiology, IFM Biology, Linköping University, Linköping, Sweden
| | - Zhou Wu
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, UK
| | - Masahito Yamagata
- Center for Brain Science, Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA
| | | | - Zhong-Tao Yin
- Department of Animal Sciences, Data Science and Informatics Institute, University of Missouri, Columbia, Missouri, USA
| | | | - Guojie Zhang
- Center for Evolutionary and Organismal Biology, Zhejiang University School of Medicine, Hangzhou, China
| | - Bingru Zhao
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, California, USA
| |
Collapse
|
24
|
Zhou Z, Leng C, Wang Z, Long L, Lv Y, Gao Z, Wang Y, Wang S, Li P. The potential regulatory role of the lncRNA-miRNA-mRNA axis in teleost fish. Front Immunol 2023; 14:1065357. [PMID: 36895573 PMCID: PMC9988957 DOI: 10.3389/fimmu.2023.1065357] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 02/06/2023] [Indexed: 02/23/2023] Open
Abstract
Research over the past two decades has confirmed that noncoding RNAs (ncRNAs), which are abundant in cells from yeast to vertebrates, are no longer "junk" transcripts but functional regulators that can mediate various cellular and physiological processes. The dysregulation of ncRNAs is closely related to the imbalance of cellular homeostasis and the occurrence and development of various diseases. In mammals, ncRNAs, such as long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), have been shown to serve as biomarkers and intervention targets in growth, development, immunity, and disease progression. The regulatory functions of lncRNAs on gene expression are usually mediated by crosstalk with miRNAs. The most predominant mode of lncRNA-miRNA crosstalk is the lncRNA-miRNA-mRNA axis, in which lncRNAs act as competing endogenous RNAs (ceRNAs). Compared to mammals, little attention has been given to the role and mechanism of the lncRNA-miRNA-mRNA axis in teleost species. In this review, we provide current knowledge about the teleost lncRNA-miRNA-mRNA axis, focusing on its physiological and pathological regulation in growth and development, reproduction, skeletal muscle, immunity to bacterial and viral infections, and other stress-related immune responses. Herein, we also explored the potential application of the lncRNA-miRNA-mRNA axis in the aquaculture industry. These findings contribute to an enhanced understanding of ncRNA and ncRNA-ncRNA crosstalk in fish biology to improve aquaculture productivity, fish health and quality.
Collapse
Affiliation(s)
- Zhixia Zhou
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, China
| | - Cuibo Leng
- The Affiliated Qingdao Central Hospital of Qingdao University, The Second Affiliated Hospital of Medical College of Qingdao University, Qingdao, China
| | - Zhan Wang
- The Affiliated Qingdao Central Hospital of Qingdao University, The Second Affiliated Hospital of Medical College of Qingdao University, Qingdao, China
| | - Linhai Long
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, China
| | - Yiju Lv
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, China
| | - Ziru Gao
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, China
| | - Yin Wang
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, China
| | - Shoushi Wang
- The Affiliated Qingdao Central Hospital of Qingdao University, The Second Affiliated Hospital of Medical College of Qingdao University, Qingdao, China
| | - Peifeng Li
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, China
| |
Collapse
|
25
|
Zhang BB, Li MX, Wang HN, Liu C, Sun YY, Ma TH. An integrative analysis of lncRNAs and mRNAs highlights the potential roles of lncRNAs in the process of follicle selection in Taihang chickens. Theriogenology 2023; 195:122-130. [DOI: 10.1016/j.theriogenology.2022.10.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/17/2022] [Accepted: 10/20/2022] [Indexed: 11/07/2022]
|
26
|
Marceau A, Gao Y, Baldwin RL, Li CJ, Jiang J, Liu GE, Ma L. Investigation of rumen long noncoding RNA before and after weaning in cattle. BMC Genomics 2022; 23:531. [PMID: 35869425 PMCID: PMC9308236 DOI: 10.1186/s12864-022-08758-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/12/2022] [Indexed: 11/10/2022] Open
Abstract
Background This study aimed to identify long non-coding RNA (lncRNA) from the rumen tissue in dairy cattle, explore their features including expression and conservation levels, and reveal potential links between lncRNA and complex traits that may indicate important functional impacts of rumen lncRNA during the transition to the weaning period. Results A total of six cattle rumen samples were taken with three replicates from before and after weaning periods, respectively. Total RNAs were extracted and sequenced with lncRNA discovered based on size, coding potential, sequence homology, and known protein domains. As a result, 404 and 234 rumen lncRNAs were identified before and after weaning, respectively. However, only nine of them were shared under two conditions, with 395 lncRNAs found only in pre-weaning tissues and 225 only in post-weaning samples. Interestingly, none of the nine common lncRNAs were differentially expressed between the two weaning conditions. LncRNA averaged shorter length, lower expression, and lower conservation scores than the genome overall, which is consistent with general lncRNA characteristics. By integrating rumen lncRNA before and after weaning with large-scale GWAS results in cattle, we reported significant enrichment of both pre- and after-weaning lncRNA with traits of economic importance including production, reproduction, health, and body conformation phenotypes. Conclusions The majority of rumen lncRNAs are uniquely expressed in one of the two weaning conditions, indicating a functional role of lncRNA in rumen development and transition of weaning. Notably, both pre- and post-weaning lncRNA showed significant enrichment with a variety of complex traits in dairy cattle, suggesting the importance of rumen lncRNA for cattle performance in the adult stage. These relationships should be further investigated to better understand the specific roles lncRNAs are playing in rumen development and cow performance. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08758-4.
Collapse
|
27
|
Bilbao-Arribas M, Jugo BM. Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep. Front Genet 2022; 13:1067350. [PMID: 36482891 PMCID: PMC9725098 DOI: 10.3389/fgene.2022.1067350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 11/08/2022] [Indexed: 11/23/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) are involved in several biological processes, including the immune system response to pathogens and vaccines. The annotation and functional characterization of lncRNAs is more advanced in humans than in livestock species. Here, we take advantage of the increasing number of high-throughput functional experiments deposited in public databases in order to uniformly analyse, profile unannotated lncRNAs and integrate 422 ovine RNA-seq samples from the ovine immune system. We identified 12302 unannotated lncRNA genes with support from independent CAGE-seq and histone modification ChIP-seq assays. Unannotated lncRNAs showed low expression levels and sequence conservation across other mammal species. There were differences in expression levels depending on the genomic location-based lncRNA classification. Differential expression analyses between unstimulated and samples stimulated with pathogen infection or vaccination resulted in hundreds of lncRNAs with changed expression. Gene co-expression analyses revealed immune gene-enriched clusters associated with immune system activation and related to interferon signalling, antiviral response or endoplasmic reticulum stress. Besides, differential co-expression networks were constructed in order to find condition-specific relationships between coding genes and lncRNAs. Overall, using a diverse set of immune system samples and bioinformatic approaches we identify several ovine lncRNAs associated with the response to an external stimulus. These findings help in the improvement of the ovine lncRNA catalogue and provide sheep-specific evidence for the implication in the general immune response for several lncRNAs.
Collapse
|
28
|
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: 5] [Impact Index Per Article: 1.7] [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).
Collapse
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,
| |
Collapse
|
29
|
Zhang D, Zhou Y, Huang R, Zhai Y, Wu D, An X, Zhang S, Shi L, Li Q, Kong X, Yu H, Li Z. LncRNA affects epigenetic reprogramming of porcine embryo development by regulating global epigenetic modification and the downstream gene SIN3A. Front Physiol 2022; 13:971965. [PMID: 36187791 PMCID: PMC9523245 DOI: 10.3389/fphys.2022.971965] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
The study of preimplantation development is of great significance to reproductive biology and regenerative medicine. With the development of high-throughput deep sequencing technology, it has been found that lncRNAs play a very important role in the regulation of embryonic development. In this study, key lncRNAs that regulate embryonic development were screened by analyzing the expression pattern of lncRNAs in porcine in vivo fertilization (IVV) embryos. By knocking down lncRNA expression in in vitro fertilization (IVF) embryos, we investigated its function and mechanism of regulating embryonic development. The results showed that the expression pattern of lncRNA was consistent with the time of gene activation. The lncRNAs were highly expressed in the 4-cell to blastocyst stage but barely expressed in the oocytes and 2-cell stage. So we speculated this part of lncRNAs may regulate gene expression. The lncRNA LOC102165808 (named lncT because the gene near this lncRNA is TFAP2C) was one of them. The knockdown (KD) of lncT inhibited embryonic development, resulting in decreased H3K4me3, H3K4me2, and H3K9me3, and increased DNA methylation. Meanwhile, RNAseq showed SIN3A was the top decreased gene in lncT-KD embryos. There was a severe blastocyst formation defect in SIN3A-KD embryos. Both lncT and SIN3A could affect NANOG and induce more cell apoptosis. In conclusion, the knockdown of lncT inhibits embryonic development by regulating H3K4me3, H3K4me2, DNA methylation, pluripotency gene, and apoptosis, and SIN3A is one of the downstream genes of lncT in regulating embryonic development.
Collapse
Affiliation(s)
- Daoyu Zhang
- Key Laboratory of Organ Regeneration and Transplantation of Ministry of Education, First Hospital, Jilin University, Changchun, China
| | - Yongfeng Zhou
- Key Laboratory of Organ Regeneration and Transplantation of Ministry of Education, First Hospital, Jilin University, Changchun, China
| | - Rong Huang
- Key Laboratory of Organ Regeneration and Transplantation of Ministry of Education, First Hospital, Jilin University, Changchun, China
| | - Yanhui Zhai
- Key Laboratory of Organ Regeneration and Transplantation of Ministry of Education, First Hospital, Jilin University, Changchun, China
| | - Di Wu
- Department of Emergency Medicine, First Hospital, Jilin University, Changchun, China
| | - Xinglan An
- Key Laboratory of Organ Regeneration and Transplantation of Ministry of Education, First Hospital, Jilin University, Changchun, China
| | - Sheng Zhang
- Key Laboratory of Organ Regeneration and Transplantation of Ministry of Education, First Hospital, Jilin University, Changchun, China
| | - Lijing Shi
- College of Animal Science, Jilin University, Changchun, China
| | - Qi Li
- Key Laboratory of Organ Regeneration and Transplantation of Ministry of Education, First Hospital, Jilin University, Changchun, China
| | - Xiangjie Kong
- Key Laboratory of Organ Regeneration and Transplantation of Ministry of Education, First Hospital, Jilin University, Changchun, China
| | - Hao Yu
- College of Animal Science, Jilin University, Changchun, China
| | - Ziyi Li
- Key Laboratory of Organ Regeneration and Transplantation of Ministry of Education, First Hospital, Jilin University, Changchun, China
- *Correspondence: Ziyi Li,
| |
Collapse
|
30
|
Corona-Gomez JA, Coss-Navarrete EL, Garcia-Lopez IJ, Klapproth C, Pérez-Patiño JA, Fernandez-Valverde SL. Transcriptome-guided annotation and functional classification of long non-coding RNAs in Arabidopsis thaliana. Sci Rep 2022; 12:14063. [PMID: 35982083 PMCID: PMC9388643 DOI: 10.1038/s41598-022-18254-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/08/2022] [Indexed: 11/16/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) are a prominent class of eukaryotic regulatory genes. Despite the numerous available transcriptomic datasets, the annotation of plant lncRNAs remains based on dated annotations that have been historically carried over. We present a substantially improved annotation of Arabidopsis thaliana lncRNAs, generated by integrating 224 transcriptomes in multiple tissues, conditions, and developmental stages. We annotate 6764 lncRNA genes, including 3772 that are novel. We characterize their tissue expression patterns and find 1425 lncRNAs are co-expressed with coding genes, with enriched functional categories such as chloroplast organization, photosynthesis, RNA regulation, transcription, and root development. This improved transcription-guided annotation constitutes a valuable resource for studying lncRNAs and the biological processes they may regulate.
Collapse
Affiliation(s)
| | | | | | - Christopher Klapproth
- Bioinformatics Group, Department of Computer Science and Interdisciplinary Center of Bioinformatics, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.,ScaDS.AI Leipzig (Center for Scalable Data Analytics and Artificial Intelligence), Humboldstrasse 25, 04105, Leipzig, Germany
| | | | | |
Collapse
|
31
|
Jara E, Peñagaricano F, Armstrong E, Menezes C, Tardiz L, Rodons G, Iriarte A. Identification of Long Noncoding RNAs Involved in Eyelid Pigmentation of Hereford Cattle. Front Genet 2022; 13:864567. [PMID: 35601493 PMCID: PMC9114348 DOI: 10.3389/fgene.2022.864567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/20/2022] [Indexed: 12/05/2022] Open
Abstract
Several ocular pathologies in cattle, such as ocular squamous cell carcinoma and infectious keratoconjunctivitis, have been associated with low pigmentation of the eyelids. The main objective of this study was to analyze the transcriptome of eyelid skin in Hereford cattle using strand-specific RNA sequencing technology to characterize and identify long noncoding RNAs (lncRNAs). We compared the expression of lncRNAs between pigmented and unpigmented eyelids and analyzed the interaction of lncRNAs and putative target genes to reveal the genetic basis underlying eyelid pigmentation in cattle. We predicted 4,937 putative lncRNAs mapped to the bovine reference genome, enriching the catalog of lncRNAs in Bos taurus. We found 27 differentially expressed lncRNAs between pigmented and unpigmented eyelids, suggesting their involvement in eyelid pigmentation. In addition, we revealed potential links between some significant differentially expressed lncRNAs and target mRNAs involved in the immune response and pigmentation. Overall, this study expands the catalog of lncRNAs in cattle and contributes to a better understanding of the biology of eyelid pigmentation.
Collapse
Affiliation(s)
- Eugenio Jara
- Unidad de Genética y Mejora Animal, Departamento de Producción Animal, Facultad de Veterinaria, Universidad de La República, Montevideo, Uruguay
| | - Francisco Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States
| | - Eileen Armstrong
- Unidad de Genética y Mejora Animal, Departamento de Producción Animal, Facultad de Veterinaria, Universidad de La República, Montevideo, Uruguay
| | - Claudia Menezes
- Laboratorio de Endocrinología y Metabolismo Animal, Facultad de Veterinaria, Universidad de La República, Montevideo, Uruguay
| | - Lucía Tardiz
- Unidad de Genética y Mejora Animal, Departamento de Producción Animal, Facultad de Veterinaria, Universidad de La República, Montevideo, Uruguay
| | - Gastón Rodons
- Unidad de Genética y Mejora Animal, Departamento de Producción Animal, Facultad de Veterinaria, Universidad de La República, Montevideo, Uruguay
| | - Andrés Iriarte
- Laboratorio de Biología Computacional, Departamento de Desarrollo Biotecnológico, Instituto de Higiene, Facultad de Medicina, Universidad de La República, Montevideo, Uruguay
- *Correspondence: Andrés Iriarte,
| |
Collapse
|
32
|
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: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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.
Collapse
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
| |
Collapse
|
33
|
Mumtaz PT, Bhat B, Ibeagha-Awemu EM, Taban Q, Wang M, Dar MA, Bhat SA, Shabir N, Shah RA, Ganie NA, Velayutham D, Haq ZU, Ahmad SM. Mammary epithelial cell transcriptome reveals potential roles of lncRNAs in regulating milk synthesis pathways in Jersey and Kashmiri cattle. BMC Genomics 2022; 23:176. [PMID: 35246027 PMCID: PMC8896326 DOI: 10.1186/s12864-022-08406-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 02/15/2022] [Indexed: 11/10/2022] Open
Abstract
Background Long noncoding RNAs (lncRNAs) are now proven as essential regulatory elements, playing diverse roles in many biological processes including mammary gland development. However, little is known about their roles in the bovine lactation process. Results To identify and characterize the roles of lncRNAs in bovine lactation, high throughput RNA sequencing data from Jersey (high milk yield producer), and Kashmiri cattle (low milk yield producer) were utilized. Transcriptome data from three Kashmiri and three Jersey cattle throughout their lactation stages were utilized for differential expression analysis. At each stage (early, mid and late) three samples were taken from each breed. A total of 45 differentially expressed lncRNAs were identified between the three stages of lactation. The differentially expressed lncRNAs were found co-expressed with genes involved in the milk synthesis processes such as GPAM, LPL, and ABCG2 indicating their potential regulatory effects on milk quality genes. KEGG pathways analysis of potential cis and trans target genes of differentially expressed lncRNAs indicated that 27 and 48 pathways were significantly enriched between the three stages of lactation in Kashmiri and Jersey respectively, including mTOR signaling, PI3K-Akt signaling, and RAP1 signaling pathways. These pathways are known to play key roles in lactation biology and mammary gland development. Conclusions Expression profiles of lncRNAs across different lactation stages in Jersey and Kashmiri cattle provide a valuable resource for the study of the regulatory mechanisms involved in the lactation process as well as facilitate understanding of the role of lncRNAs in bovine lactation biology. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08406-x.
Collapse
Affiliation(s)
- Peerzada Tajamul Mumtaz
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e- Kashmir University of Agricultural Sciences and Technology - Kashmir, SKUAST-K, Shuhama, Jammu, 190006, India.,Department of Biochemistry, School of Life Sciences Jaipur National University, Jaipur, India
| | - Basharat Bhat
- Division of Animal Breeding and Genetics, Faculty of Veterinary Sciences and Animal Husbandry, SKUAST-K, Shuhama, Jammu, India
| | - Eveline M Ibeagha-Awemu
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, Quebec, Canada
| | - Qamar Taban
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e- Kashmir University of Agricultural Sciences and Technology - Kashmir, SKUAST-K, Shuhama, Jammu, 190006, India
| | - Mengqi Wang
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, Quebec, Canada
| | - Mashooq Ahmad Dar
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e- Kashmir University of Agricultural Sciences and Technology - Kashmir, SKUAST-K, Shuhama, Jammu, 190006, India
| | - Shakil Ahmad Bhat
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e- Kashmir University of Agricultural Sciences and Technology - Kashmir, SKUAST-K, Shuhama, Jammu, 190006, India
| | - Nadeem Shabir
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e- Kashmir University of Agricultural Sciences and Technology - Kashmir, SKUAST-K, Shuhama, Jammu, 190006, India
| | - Riaz Ahmad Shah
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e- Kashmir University of Agricultural Sciences and Technology - Kashmir, SKUAST-K, Shuhama, Jammu, 190006, India
| | - Nazir A Ganie
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e- Kashmir University of Agricultural Sciences and Technology - Kashmir, SKUAST-K, Shuhama, Jammu, 190006, India
| | | | - Zulfqar Ul Haq
- Division of Livestock Production and Management, SKUAST-K, Srinagar, India
| | - Syed Mudasir Ahmad
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e- Kashmir University of Agricultural Sciences and Technology - Kashmir, SKUAST-K, Shuhama, Jammu, 190006, India.
| |
Collapse
|
34
|
Nukala SB, Jousma J, Cho Y, Lee WH, Ong SG. Long non-coding RNAs and microRNAs as crucial regulators in cardio-oncology. Cell Biosci 2022; 12:24. [PMID: 35246252 PMCID: PMC8895873 DOI: 10.1186/s13578-022-00757-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 02/10/2022] [Indexed: 12/23/2022] Open
Abstract
Cancer is one of the leading causes of morbidity and mortality worldwide. Significant improvements in the modern era of anticancer therapeutic strategies have increased the survival rate of cancer patients. Unfortunately, cancer survivors have an increased risk of cardiovascular diseases, which is believed to result from anticancer therapies. The emergence of cardiovascular diseases among cancer survivors has served as the basis for establishing a novel field termed cardio-oncology. Cardio-oncology primarily focuses on investigating the underlying molecular mechanisms by which anticancer treatments lead to cardiovascular dysfunction and the development of novel cardioprotective strategies to counteract cardiotoxic effects of cancer therapies. Advances in genome biology have revealed that most of the genome is transcribed into non-coding RNAs (ncRNAs), which are recognized as being instrumental in cancer, cardiovascular health, and disease. Emerging studies have demonstrated that alterations of these ncRNAs have pathophysiological roles in multiple diseases in humans. As it relates to cardio-oncology, though, there is limited knowledge of the role of ncRNAs. In the present review, we summarize the up-to-date knowledge regarding the roles of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in cancer therapy-induced cardiotoxicities. Moreover, we also discuss prospective therapeutic strategies and the translational relevance of these ncRNAs.
Collapse
Affiliation(s)
- Sarath Babu Nukala
- Department of Pharmacology & Regenerative Medicine, The University of Illinois College of Medicine, 909 S Wolcott Ave, COMRB 4100, Chicago, IL, 60612, USA
| | - Jordan Jousma
- Department of Pharmacology & Regenerative Medicine, The University of Illinois College of Medicine, 909 S Wolcott Ave, COMRB 4100, Chicago, IL, 60612, USA
| | - Yoonje Cho
- Department of Pharmacology & Regenerative Medicine, The University of Illinois College of Medicine, 909 S Wolcott Ave, COMRB 4100, Chicago, IL, 60612, USA
| | - Won Hee Lee
- Department of Basic Medical Sciences, University of Arizona College of Medicine, ABC-1 Building, 425 North 5th Street, Phoenix, AZ, 85004, USA.
| | - Sang-Ging Ong
- Department of Pharmacology & Regenerative Medicine, The University of Illinois College of Medicine, 909 S Wolcott Ave, COMRB 4100, Chicago, IL, 60612, USA.
- Division of Cardiology, Department of Medicine, The University of Illinois College of Medicine, 909 S Wolcott Ave, COMRB 4100, Chicago, IL, 60612, USA.
| |
Collapse
|
35
|
Karimi P, Bakhtiarizadeh MR, Salehi A, Izadnia HR. Transcriptome analysis reveals the potential roles of long non-coding RNAs in feed efficiency of chicken. Sci Rep 2022; 12:2558. [PMID: 35169237 PMCID: PMC8847365 DOI: 10.1038/s41598-022-06528-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 02/02/2022] [Indexed: 12/13/2022] Open
Abstract
Feed efficiency is an important economic trait and reduces the production costs per unit of animal product. Up to now, few studies have conducted transcriptome profiling of liver tissue in feed efficiency-divergent chickens (Ross vs native breeds). Also, molecular mechanisms contributing to differences in feed efficiency are not fully understood, especially in terms of long non-coding RNAs (lncRNAs). Hence, transcriptome profiles of liver tissue in commercial and native chicken breeds were analyzed. RNA-Seq data along with bioinformatics approaches were applied and a series of lncRNAs and target genes were identified. Furthermore, protein-protein interaction network construction, co-expression analysis, co-localization analysis of QTLs and functional enrichment analysis were used to functionally annotate the identified lncRNAs. In total, 2,290 lncRNAs were found (including 1,110 annotated, 593 known and 587 novel), of which 53 (including 39 known and 14 novel), were identified as differentially expressed genes between two breeds. The expression profile of lncRNAs was validated by RT-qPCR. The identified novel lncRNAs showed a number of characteristics similar to those of known lncRNAs. Target prediction analysis showed that these lncRNAs have the potential to act in cis or trans mode. Functional enrichment analysis of the predicted target genes revealed that they might affect the differences in feed efficiency of chicken by modulating genes associated with lipid metabolism, carbohydrate metabolism, growth, energy homeostasis and glucose metabolism. Some gene members of significant modules in the constructed co-expression networks were reported as important genes related to feed efficiency. Co-localization analysis of QTLs related to feed efficiency and the identified lncRNAs suggested several candidates to be involved in residual feed intake. The findings of this study provided valuable resources to further clarify the genetic basis of regulation of feed efficiency in chicken from the perspective of lncRNAs.
Collapse
Affiliation(s)
- Parastoo Karimi
- Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, Iran
| | | | - Abdolreza Salehi
- Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, Iran
| | - Hamid Reza Izadnia
- Animal Science Improvement Research Department, Agricultural and Natural Resources Research and Education Center, Safiabad AREEO, Dezful, Iran
| |
Collapse
|
36
|
Mumtaz PT, Taban Q, Bhat B, Ahmad SM, Dar MA, Kashoo ZA, Ganie NA, Shah RA. Expression of lncRNAs in response to bacterial infections of goat mammary epithelial cells reveals insights into mammary gland diseases. Microb Pathog 2021; 162:105367. [PMID: 34963641 DOI: 10.1016/j.micpath.2021.105367] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/16/2021] [Accepted: 12/17/2021] [Indexed: 10/19/2022]
Abstract
Mastitis or inflammation of the mammary gland is a highly economic and deadly alarming disease for the dairy sector as well as policymakers caused by microbial infection. Transcriptomic and proteomic approaches have been widely employed to identify the underlying molecular mechanisms of bacterial infections in the mammary gland. Numerous differentially expressed mRNAs, miRNAs, and proteins together with their associated signaling pathways have been identified during bacterial infection, paving the way for analysis of their biological functions. Long noncoding RNAs (lncRNAs) are important regulators of multiple biological processes. However, little is known regarding their role in bacterial infection in mammary epithelial cells. Hence, RNA-sequencing was performed by infecting primary mammary epithelial cells (pMECs) with both gram-negative (E. coli) and gram-positive bacteria (S. aureus). Using stringent pipeline, a set of 1957 known and 1175 novel lncRNAs were identified, among which, 112 lncRNAs were found differentially expressed in bacteria challenged PMECs compared with the control. Additionally, potential targets of the lncRNAs were predicted in cis- and trans-configuration. KEGG analysis revealed that DE lncRNAs were associated with at least 15 immune-related pathways. Therefore, our study revealed that bacterial challenge triggers the expression of lncRNAs associated with immune response and defense mechanisms in goat mammary epithelial cells.
Collapse
Affiliation(s)
- Peerzada Tajamul Mumtaz
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Shuhama, SKUAST-K, India; Department of Biochemistry, School of Life Sciences Jaipur National University, India
| | - Qamar Taban
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Shuhama, SKUAST-K, India
| | - Basharat Bhat
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Shuhama, SKUAST-K, India
| | - Syed Mudasir Ahmad
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Shuhama, SKUAST-K, India.
| | - Mashooq Ahmad Dar
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Shuhama, SKUAST-K, India
| | - Zahid Amin Kashoo
- Division of Veterinary Microbiology, Faculty of Veterinary Sciences and Animal Husbandry, Shuhama, SKUAST-K, India
| | - Nazir A Ganie
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Shuhama, SKUAST-K, India
| | - Riaz Ahmad Shah
- Division of Animal Biotechnology, Faculty of Veterinary Sciences and Animal Husbandry, Shuhama, SKUAST-K, India
| |
Collapse
|
37
|
Lagarrigue S, Lorthiois M, Degalez F, Gilot D, Derrien T. LncRNAs in domesticated animals: from dog to livestock species. Mamm Genome 2021; 33:248-270. [PMID: 34773482 PMCID: PMC9114084 DOI: 10.1007/s00335-021-09928-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 10/19/2021] [Indexed: 11/29/2022]
Abstract
Animal genomes are pervasively transcribed into multiple RNA molecules, of which many will not be translated into proteins. One major component of this transcribed non-coding genome is the long non-coding RNAs (lncRNAs), which are defined as transcripts longer than 200 nucleotides with low coding-potential capabilities. Domestic animals constitute a unique resource for studying the genetic and epigenetic basis of phenotypic variations involving protein-coding and non-coding RNAs, such as lncRNAs. This review presents the current knowledge regarding transcriptome-based catalogues of lncRNAs in major domesticated animals (pets and livestock species), covering a broad phylogenetic scale (from dogs to chicken), and in comparison with human and mouse lncRNA catalogues. Furthermore, we describe different methods to extract known or discover novel lncRNAs and explore comparative genomics approaches to strengthen the annotation of lncRNAs. We then detail different strategies contributing to a better understanding of lncRNA functions, from genetic studies such as GWAS to molecular biology experiments and give some case examples in domestic animals. Finally, we discuss the limitations of current lncRNA annotations and suggest research directions to improve them and their functional characterisation.
Collapse
Affiliation(s)
| | - Matthias Lorthiois
- Univ Rennes, CNRS, IGDR (Institut de Génétique et Développement de Rennes) - UMR 6290, 2 av Prof Leon Bernard, F-35000, Rennes, France
| | - Fabien Degalez
- INRAE, INSTITUT AGRO, PEGASE UMR 1348, 35590, Saint-Gilles, France
| | - David Gilot
- CLCC Eugène Marquis, INSERM, Université Rennes, UMR_S 1242, 35000, Rennes, France
| | - Thomas Derrien
- Univ Rennes, CNRS, IGDR (Institut de Génétique et Développement de Rennes) - UMR 6290, 2 av Prof Leon Bernard, F-35000, Rennes, France.
| |
Collapse
|
38
|
Natarelli L, Virgili F, Weber C. SARS-CoV-2, Cardiovascular Diseases, and Noncoding RNAs: A Connected Triad. Int J Mol Sci 2021; 22:12243. [PMID: 34830125 PMCID: PMC8620514 DOI: 10.3390/ijms222212243] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/10/2021] [Accepted: 11/11/2021] [Indexed: 12/23/2022] Open
Abstract
Coronavirus Disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is characterized by important respiratory impairments frequently associated with severe cardiovascular damages. Moreover, patients with pre-existing comorbidity for cardiovascular diseases (CVD) often present a dramatic increase in inflammatory cytokines release, which increases the severity and adverse outcomes of the infection and, finally, mortality risk. Despite this evident association at the clinical level, the mechanisms linking CVD and COVID-19 are still blurry and unresolved. Noncoding RNAs (ncRNAs) are functional RNA molecules transcribed from DNA but usually not translated into proteins. They play an important role in the regulation of gene expression, either in relatively stable conditions or as a response to different stimuli, including viral infection, and are therefore considered a possible important target in the design of specific drugs. In this review, we introduce known associations and interactions between COVID-19 and CVD, discussing the role of ncRNAs within SARS-CoV-2 infection from the perspective of the development of efficient pharmacological tools to treat COVID-19 patients and taking into account the equally dramatic associated consequences, such as those affecting the cardiovascular system.
Collapse
Affiliation(s)
- Lucia Natarelli
- Institute for Cardiovascular Prevention (IPEK), Ludwig-Maximilians-Universität (LMU), 800336 Munich, Germany;
| | - Fabio Virgili
- Research Center for Food and Nutrition, Council for Agricultural Research and Economics, 00178 Rome, Italy;
| | - Christian Weber
- Institute for Cardiovascular Prevention (IPEK), Ludwig-Maximilians-Universität (LMU), 800336 Munich, Germany;
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, 80336 Munich, Germany
- Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, 6229 HX Maastricht, The Netherlands
- Munich Cluster for Systems Neurology (SyNergy), Institute for Stroke and Dementia Research, 81377 Munich, Germany
| |
Collapse
|
39
|
Yuan J, Ni A, Li Y, Bian S, Liu Y, Wang P, Shi L, Isa AM, Ge P, Sun Y, Ma H, Chen J. Transcriptome Analysis Revealed Potential Mechanisms of Resistance to Trichomoniasis gallinae Infection in Pigeon ( Columba livia). Front Vet Sci 2021; 8:672270. [PMID: 34595226 PMCID: PMC8477972 DOI: 10.3389/fvets.2021.672270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 06/03/2021] [Indexed: 12/14/2022] Open
Abstract
Trichomoniasis gallinae (T. gallinae) is one of the most pathogenic parasites in pigeon, particularly in squabs. Oral cavity is the main site for the host-parasite interaction. Herein, we used RNA-sequencing technology to characterize lncRNA and mRNA profiles and compared transcriptomic dynamics of squabs, including four susceptible birds (S) from infected group, four tolerant birds (T) without parasites after T. gallinae infection, and three birds from uninfected group (N), to understand molecular mechanisms underlying host resistance to this parasite. We identified 29,809 putative lncRNAs and characterized their genomic features subsequently. Differentially expressed (DE) genes, DE-lncRNAs and cis/trans target genes of DE-lncRNAs were further compared among the three groups. The KEGG analysis indicated that specific intergroup DEGs were involved in carbon metabolism (S vs. T), metabolic pathways (N vs. T) and focal adhesion pathway (N vs. S), respectively. Whereas, the cis/trans genes of DE-lncRNAs were enriched in cytokine-cytokine receptor interaction, toll-like receptor signaling pathway, p53 signaling pathway and insulin signaling pathway, which play crucial roles in immune system of the host animal. This suggests T. gallinae invasion in pigeon mouth may modulate lncRNAs expression and their target genes. Moreover, co-expression analysis identified crucial lncRNA-mRNA interaction networks. Several DE-lncRNAs including MSTRG.82272.3, MSTRG.114849.42, MSTRG.39405.36, MSTRG.3338.5, and MSTRG.105872.2 targeted methylation and immune-related genes, such as JCHAIN, IL18BP, ANGPT1, TMRT10C, SAMD9L, and SOCS3. This implied that DE-lncRNAs exert critical influence on T. gallinae infections. The quantitative exploration of host transcriptome changes induced by T. gallinae infection broaden both transcriptomic and epigenetic insights into T. gallinae resistance and its pathological mechanism.
Collapse
Affiliation(s)
- Jingwei Yuan
- Institute of Animal Science, China Academy of Agricultural Science, Beijing, China
| | - Aixin Ni
- Institute of Animal Science, China Academy of Agricultural Science, Beijing, China
| | - Yunlei Li
- Institute of Animal Science, China Academy of Agricultural Science, Beijing, China
| | - Shixiong Bian
- Institute of Animal Science, China Academy of Agricultural Science, Beijing, China
| | - Yunjie Liu
- Institute of Animal Science, China Academy of Agricultural Science, Beijing, China
| | - Panlin Wang
- Institute of Animal Science, China Academy of Agricultural Science, Beijing, China
| | - Lei Shi
- Institute of Animal Science, China Academy of Agricultural Science, Beijing, China
| | - Adamu Mani Isa
- Institute of Animal Science, China Academy of Agricultural Science, Beijing, China.,Department of Animal Science, Usmanu Danfodiyo University, Sokoto, Nigeria
| | - Pingzhuang Ge
- Institute of Animal Science, China Academy of Agricultural Science, Beijing, China
| | - Yanyan Sun
- Institute of Animal Science, China Academy of Agricultural Science, Beijing, China
| | - Hui Ma
- Institute of Animal Science, China Academy of Agricultural Science, Beijing, China
| | - Jilan Chen
- Institute of Animal Science, China Academy of Agricultural Science, Beijing, China
| |
Collapse
|
40
|
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: 20] [Impact Index Per Article: 5.0] [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.
Collapse
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
| |
Collapse
|
41
|
Li X, Han X, Sun C, Li G, Wang K, Li X, Qiao R. Analysis of mRNA and Long Non-Coding RNA Expression Profiles in Developing Yorkshire Pig Spleens. Animals (Basel) 2021; 11:ani11102768. [PMID: 34679790 PMCID: PMC8532824 DOI: 10.3390/ani11102768] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/16/2021] [Accepted: 09/18/2021] [Indexed: 12/19/2022] Open
Abstract
Simple Summary Epidemic disease is a prominent problem in intensive pig production. The spleen is a blood bank and the largest immune organ, and most of the diseases in pig farms will be reflected as spleen abnormality. The results showed how the mRNA–lncRNA expression profiles in Yorkshire spleens varied with age (seven, 90, and 180 days after birth). Our study shows that 90 days after birth the gene-expression profile of pig spleen no longer changes significantly. The results are helpful for a better understanding of the transcriptome and functional genomics of spleen tissue in farm animals and could provide reference for precision pig disease research and prevention and control in pig farms. Abstract Epidemic diseases cause great economic loss in pig farms each year; some of these diseases are characterized mainly in the spleen, but mRNA and lncRNA (long non-coding RNA) expression networks in developing Yorkshire pig spleens remain obscure. Here, we profiled the systematic characters of mRNA and lncRNA repertoires in three groups of spleens from nine Yorkshire pigs, each three aged at seven days, 90 days, and 180 days. By using a precise mRNA and lncRNA identification pipeline, we identified 19,647 genes and 219 known and 3219 putative lncRNA transcripts; 1729 genes and 64 lncRNAs therein were found to express differentially. The gene expression characteristics of genes and lncRNAs were found to be basically fixed before 90 days after birth. Three large gene expression modules were detected. The enrichment analyses of differentially expressed genes and the potential target genes of differentially expressed lncRNAs both displayed the crucial roles of up-regulation in immune activation and hematopoiesis, and down-regulation in cell replication and division in 90 days and 180 days compared to seven days. ENSSSCT00000001325 was the only lncRNA transcript that existed in the three groups. CDK1, PCNA, and PLK were detected to be node genes that varied with age. This study contributes to a further understanding of mRNA and lncRNA expression in different developmental pig spleens.
Collapse
|
42
|
Chang T, An B, Liang M, Duan X, Du L, Cai W, Zhu B, Gao X, Chen Y, Xu L, Zhang L, Gao H, Li J. PacBio Single-Molecule Long-Read Sequencing Provides New Light on the Complexity of Full-Length Transcripts in Cattle. Front Genet 2021; 12:664974. [PMID: 34527015 PMCID: PMC8437344 DOI: 10.3389/fgene.2021.664974] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 08/06/2021] [Indexed: 12/02/2022] Open
Abstract
Cattle (Bos taurus) is one of the most widely distributed livestock species in the world, and provides us with high-quality milk and meat which have a huge impact on the quality of human life. Therefore, accurate and complete transcriptome and genome annotation are of great value to the research of cattle breeding. In this study, we used error-corrected PacBio single-molecule real-time (SMRT) data to perform whole-transcriptome profiling in cattle. Then, 22.5 Gb of subreads was generated, including 381,423 circular consensus sequences (CCSs), among which 276,295 full-length non-chimeric (FLNC) sequences were identified. After correction by Illumina short reads, we obtained 22,353 error-corrected isoforms. A total of 305 alternative splicing (AS) events and 3,795 alternative polyadenylation (APA) sites were detected by transcriptome structural analysis. Furthermore, we identified 457 novel genes, 120 putative transcription factors (TFs), and 569 novel long non-coding RNAs (lncRNAs). Taken together, this research improves our understanding and provides new insights into the complexity of full-length transcripts in cattle.
Collapse
Affiliation(s)
- Tianpeng Chang
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Bingxing An
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mang Liang
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xinghai Duan
- College of Animal Science and Technology, Southwest University, Chongqing, China
| | - Lili Du
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wentao Cai
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Bo Zhu
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xue Gao
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yan Chen
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lingyang Xu
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lupei Zhang
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Huijiang Gao
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Junya Li
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| |
Collapse
|
43
|
Integrated Analysis of lncRNA and mRNA in Subcutaneous Adipose Tissue of Ningxiang Pig. BIOLOGY 2021; 10:biology10080726. [PMID: 34439958 PMCID: PMC8389317 DOI: 10.3390/biology10080726] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/24/2021] [Accepted: 07/27/2021] [Indexed: 12/16/2022]
Abstract
Simple Summary This study shows the transcription profiles and the functional network in lncRNA and mRNA in the subcutaneous adipose tissue of Ningxiang piglets in four stages of development (piglets, nursery pigs, early fattening, and late fattening). A total of 2872 novel lncRNAs have now been determined. A total of 10,084 DEmRNAs and 931 DElncRNAs were determined. Interestingly, most DEmRNAs were up-regulated in the piglet stage and, in contrast, DElncRNAs were up-regulated in the late fattening stage. A complicated interaction between mRNAs and lncRNAs was determined via STEM and WGCNA, demonstrating that lncRNAs are an essential regulatory component in mRNAs. Modules 2 and 5 shows a similar mode of transcriptions for both mRNA and lncRNA, which are mainly involved in steroid biosynthesis, glycosphingolipid biosynthesis, metabolic pathways, and glycerolipid metabolism. The transcription levels of mRNAs and lncRNAs for both modules were higher in the early and late fattening stage. This may be explained by the active fatty acids, sterols, steroids, and lipid-related metabolic activity in the subcutaneous adipose tissue during the early and late fattening stage. Abstract Ningxiang pigs, a Chinese bred pig known for its tender meat and high quality unsaturated fatty acids. This study discovers the transcription profiles and functional networks in long non-coding RNA (lncRNA) and messenger RNA (mRNA) in subcutaneous adipose tissue. Subcutaneous adipose tissue was collected from piglet, nursery pig, early fattening, and late fattening stage of Ningxiang piglets, and lncRNA and mRNA transcription of each stage was profiled. A total of 339,204,926 (piglet), 315,609,246 (nursery), 266,798,202 (early fattening), and 343,740,308 (late fattening) clean reads were generated, and 2872 novel lncRNAs were identified. Additionally, 10,084 differential mRNAs (DEmRNAs) and 931 differential lncRNAs were determined. Most DEmRNAs were up-regulated in the piglet stage, while they were down-regulated in late fattening stage. A complicated interaction between mRNAs and lncRNAs was identified via STEM and WGCNA, demonstrated that lncRNAs are a significant regulatory component in mRNAs. The findings showed that modules 2 and 5 have a similar mode of transcription for both mRNA and lncRNA, and were mainly participated in steroid biosynthesis, glycosphingolipid biosynthesis, metabolic pathways, and glycerolipid metabolism. The mRNAs and lncRNAs transcription levels of both modules was higher in the early and late fattening stage, which may be due to the active activity of the metabolism in relation to fatty acids, sterols, steroids, and lipids in the subcutaneous adipose tissue during the early and late fattening stage. These findings could be expected to result in further research of the functional properties of lncRNA from subcutaneous adipose tissue at different stages of development in Ningxiang pigs.
Collapse
|
44
|
Identification and expression analysis of lncRNA in seven organs of Rhinopithecus roxellana. Funct Integr Genomics 2021; 21:543-555. [PMID: 34291340 DOI: 10.1007/s10142-021-00797-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 06/05/2021] [Accepted: 07/08/2021] [Indexed: 12/13/2022]
Abstract
Long non-coding RNA (lncRNA) represents a new direction to identify expression profiles and regulatory mechanisms in various organisms. Here, we report the first dataset of lncRNAs of the golden snub-nosed monkey (GSM), including 12,557 putative lncRNAs identified from seven organs. Compared with mRNA, GSM lncRNA had fewer exons and isoforms, and longer length. LncRNA showed more obvious tissue-specific expression than mRNA. However, for the top ten most abundant genes in each organ, mRNAs expression was more tissue-specific than lncRNAs. By identification of specifically expressed lncRNAs and mRNAs in each organ, it indicates that the expression of SEG-lncRNA (specifically expressed lncRNA) and SEG-mRNA (specifically expressed mRNA) had high correlation. In particular, combined our lncRNA and mRNA data, we identified 92 heart SEG-lncRNAs targeted ten mRNA genes in the oxidative phosphorylation pathway and upregulated the expression of these target genes such as ND4, ATP6, and ATP8. These may contribute to GSM adaption to its high-elevation environment. We also identified 171 liver SEG-lncRNAs, which targeted 27 genes associated with the metabolism of xenobiotics and leaded to high expression of these target genes in liver. These lncRNAs may play important roles in GSM adaptation to a folivory diet.
Collapse
|
45
|
Tixier-Boichard M, Fabre S, Dhorne-Pollet S, Goubil A, Acloque H, Vincent-Naulleau S, Ross P, Wang Y, Chanthavixay G, Cheng H, Ernst C, Leesburg V, Giuffra E, Zhou H. Tissue Resources for the Functional Annotation of Animal Genomes. Front Genet 2021; 12:666265. [PMID: 34234809 PMCID: PMC8256271 DOI: 10.3389/fgene.2021.666265] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/29/2021] [Indexed: 11/25/2022] Open
Abstract
In order to generate an atlas of the functional elements driving genome expression in domestic animals, the Functional Annotation of Animal Genome (FAANG) strategy was to sample many tissues from a few animals of different species, sexes, ages, and production stages. This article presents the collection of tissue samples for four species produced by two pilot projects, at INRAE (National Research Institute for Agriculture, Food and Environment) and the University of California, Davis. There were three mammals (cattle, goat, and pig) and one bird (chicken). It describes the metadata characterizing these reference sets (1) for animals with origin and selection history, physiological status, and environmental conditions; (2) for samples with collection site and tissue/cell processing; (3) for quality control; and (4) for storage and further distribution. Three sets are identified: set 1 comprises tissues for which collection can be standardized and for which representative aliquots can be easily distributed (liver, spleen, lung, heart, fat depot, skin, muscle, and peripheral blood mononuclear cells); set 2 comprises tissues requiring special protocols because of their cellular heterogeneity (brain, digestive tract, secretory organs, gonads and gametes, reproductive tract, immune tissues, cartilage); set 3 comprises specific cell preparations (immune cells, tracheal epithelial cells). Dedicated sampling protocols were established and uploaded in https://data.faang.org/protocol/samples. Specificities between mammals and chicken are described when relevant. A total of 73 different tissues or tissue sections were collected, and 21 are common to the four species. Having a common set of tissues will facilitate the transfer of knowledge within and between species and will contribute to decrease animal experimentation. Combining data on the same samples will facilitate data integration. Quality control was performed on some tissues with RNA extraction and RNA quality control. More than 5,000 samples have been stored with unique identifiers, and more than 4,000 were uploaded onto the Biosamples database, provided that standard ontologies were available to describe the sample. Many tissues have already been used to implement FAANG assays, with published results. All samples are available without restriction for further assays. The requesting procedure is described. Members of FAANG are encouraged to apply a range of molecular assays to characterize the functional status of collected samples and share their results, in line with the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles.
Collapse
Affiliation(s)
| | | | | | - Adeline Goubil
- University Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Hervé Acloque
- University Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | | | - Pablo Ross
- UC Davis Department of Animal Science, University of California, Davis, Davis, CA, United States
| | - Ying Wang
- UC Davis Department of Animal Science, University of California, Davis, Davis, CA, United States
| | - Ganrea Chanthavixay
- UC Davis Department of Animal Science, University of California, Davis, Davis, CA, United States
| | - Hans Cheng
- USDA-ARS Avian Disease and Oncology Laboratory, East Lansing, MI, United States
| | - Catherine Ernst
- Department of Animal Science, Michigan State University, East Lansing, MI, United States
| | - Vicki Leesburg
- USDA-ARS, Fort Keogh Livestock and Range Research Laboratory, Miles City, MT, United States
| | - Elisabetta Giuffra
- University Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Huaijun Zhou
- UC Davis Department of Animal Science, University of California, Davis, Davis, CA, United States
| | | |
Collapse
|
46
|
Murugesan KD, Gupta ID, Onteru SK, Dash A, Sukhija N, Sivalingam J, Mohanty AK. Profiling and integrated analysis of whole-transcriptome changes in uterine caruncles of pregnant and non-pregnant buffaloes. Genomics 2021; 113:2338-2349. [PMID: 34022349 DOI: 10.1016/j.ygeno.2021.05.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 03/04/2021] [Accepted: 05/17/2021] [Indexed: 11/18/2022]
Abstract
Improved reproductive performance in buffaloes can be achieved by understanding the basic mechanism governing the embryonic attachment and feto-maternal communication. Considering this, trascriptomic profiling and integrative analysis of long intergenic non-coding RNAs were carried out in the uterine caruncles of pregnant and non-pregnant buffaloes. Transcriptome data of pregnant and non-pregnant uterine caruncles after quality control was used to perform the analysis. Total of 86 novel lincRNAs expressed in uterine caruncular tissues were identified and characterized. Differential expression analysis revealed that 447 mRNAs and 185 mRNAs were up- and down- regulated, respectively. The number of up- and down- regulated lincRNAs were 114 and 13, respectively. Of the identified 86 novel lincRNAs, six novel lincRNAs were up-regulated in the pregnant uterine caruncles. GO terms (biological process) and PANTHER pathways associated with reproduction and embryogenesis were over-represented in differentially expressed genes. Through miRNA interaction analysis, interactions of 16 differentially expressed lincRNAs with mi-RNAs involved in reproduction were identified. This study has provided a catalogue of differentially expressed genes and novel regions previously unknown to play a significant role in buffalo reproduction. The results from the current study extends the buffalo uterine lncRNAs database and provides candidate regulators for future molecular genetic studies on buffalo uterine physiology to improve the embryo implantation and successful completion of pregnancy.
Collapse
Affiliation(s)
- Kousalya Devi Murugesan
- Animal Genetics and Breeding Division, National Dairy Research Institute, Karnal 132001, Haryana, India.
| | - I D Gupta
- Animal Genetics and Breeding Division, National Dairy Research Institute, Karnal 132001, Haryana, India.
| | - Suneel Kumar Onteru
- Animal Biochemistry Division, National Dairy Research Institute, Karnal 132001, Haryana, India
| | - Aishwarya Dash
- Animal Genetics and Breeding Division, National Dairy Research Institute, Karnal 132001, Haryana, India
| | - Nidhi Sukhija
- Animal Genetics and Breeding Division, National Dairy Research Institute, Karnal 132001, Haryana, India
| | - Jayakumar Sivalingam
- Animal Genetics and Breeding Division, National Bureau of Animal Genetic Resources, Karnal 132001, Haryana, India.
| | - Ashok Kumar Mohanty
- Proteomics and Cell Biology Lab, Animal Biotechnology Center, National Dairy Research Institute, Karnal 132001, Haryana, India
| |
Collapse
|
47
|
Liu J, Zhou Y, Hu X, Yang J, Lei Q, Liu W, Han H, Li F, Cao D. Transcriptome Analysis Reveals the Profile of Long Non-coding RNAs During Chicken Muscle Development. Front Physiol 2021; 12:660370. [PMID: 34040544 PMCID: PMC8141850 DOI: 10.3389/fphys.2021.660370] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/26/2021] [Indexed: 12/19/2022] Open
Abstract
The developmental complexity of muscle arises from elaborate gene regulation. Long non-coding RNAs (lncRNAs) play critical roles in muscle development through the regulation of transcription and post-transcriptional gene expression. In chickens, previous studies have focused on the lncRNA profile during the embryonic periods, but there are no studies that explore the profile from the embryonic to post-hatching period. Here, we reconstructed 14,793 lncRNA transcripts and identified 2,858 differentially expressed lncRNA transcripts and 4,282 mRNAs from 12-day embryos (E12), 17-day embryos (E17), 1-day post-hatch chicks (D1), 14-day post-hatch chicks (D14), 56-day post-hatch chicks (D56), and 98-day post-hatch chicks (D98), based on our published RNA-seq datasets. We performed co-expression analysis for the differentially expressed lncRNAs and mRNAs, using STEM, and identified two profiles with opposite expression trends: profile 4 with a downregulated pattern and profile 21 with an upregulated pattern. The cis- and trans-regulatory interactions between the lncRNAs and mRNAs were predicted within each profile. Functional analysis of the lncRNA targets showed that lncRNAs in profile 4 contributed to the cell proliferation process, while lncRNAs in profile 21 were mainly involved in metabolism. Our work highlights the lncRNA profiles involved in the development of chicken breast muscle and provides a foundation for further experiments on the role of lncRNAs in the regulation of muscle development.
Collapse
Affiliation(s)
- Jie Liu
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, China.,Poultry Breeding Engineering Technology Center of Shandong Province, Jinan, China
| | - Yan Zhou
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Xin Hu
- Molecular and Cellular Biology, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Jingchao Yang
- Shandong Animal Husbandry General Station, Jinan, China
| | - Qiuxia Lei
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, China.,Poultry Breeding Engineering Technology Center of Shandong Province, Jinan, China
| | - Wei Liu
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, China.,Poultry Breeding Engineering Technology Center of Shandong Province, Jinan, China
| | - Haixia Han
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Fuwei Li
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Dingguo Cao
- Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, China.,Poultry Breeding Engineering Technology Center of Shandong Province, Jinan, China
| |
Collapse
|
48
|
Marete A, Ariel O, Ibeagha-Awemu E, Bissonnette N. Identification of Long Non-coding RNA Isolated From Naturally Infected Macrophages and Associated With Bovine Johne's Disease in Canadian Holstein Using a Combination of Neural Networks and Logistic Regression. Front Vet Sci 2021; 8:639053. [PMID: 33969037 PMCID: PMC8100051 DOI: 10.3389/fvets.2021.639053] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 02/15/2021] [Indexed: 01/15/2023] Open
Abstract
Mycobacterium avium ssp. paratuberculosis (MAP) causes chronic enteritis in most ruminants. The pathogen MAP causes Johne's disease (JD), a chronic, incurable, wasting disease. Weight loss, diarrhea, and a gradual drop in milk production characterize the disease's clinical phase, culminating in death. Several studies have characterized long non-coding RNA (lncRNA) in bovine tissues, and a previous study characterizes (lncRNA) in macrophages infected with MAP in vitro. In this study, we aim to characterize the lncRNA in macrophages from cows naturally infected with MAP. From 15 herds, feces and blood samples were collected for each cow older than 24 months, twice yearly over 3–5 years. Paired samples were analyzed by fecal PCR and blood ELISA. We used RNA-seq data to study lncRNA in macrophages from 33 JD(+) and 33 JD(–) dairy cows. We performed RNA-seq analysis using the “new Tuxedo” suite. We characterized lncRNA using logistic regression and multilayered neural networks and used DESeq2 for differential expression analysis and Panther and Reactome classification systems for gene ontology (GO) analysis. The study identified 13,301 lncRNA, 605 of which were novel lncRNA. We found seven genes close to differentially expressed lncRNA, including CCDC174, ERI1, FZD1, TWSG1, ZBTB38, ZNF814, and ZSCAN4. None of the genes associated with susceptibility to JD have been cited in the literature. LncRNA target genes were significantly enriched for biological process GO terms involved in immunity and nucleic acid regulation. These include the MyD88 pathway (TLR5), GO:0043312 (neutrophil degranulation), GO:0002446 (neutrophil-mediated immunity), and GO:0042119 (neutrophil activation). These results identified lncRNA with potential roles in host immunity and potential candidate genes and pathways through which lncRNA might function in response to MAP infection.
Collapse
Affiliation(s)
- Andrew Marete
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada
| | - Olivier Ariel
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada.,Faculty of Science, Sherbrooke University, Sherbrooke, QC, Canada
| | - Eveline Ibeagha-Awemu
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada
| | - Nathalie Bissonnette
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada
| |
Collapse
|
49
|
Jiang A, Yin D, Zhang L, Li B, Li R, Zhang X, Zhang Z, Liu H, Kim K, Wu W. Parsing the microRNA genetics basis regulating skeletal muscle fiber types and meat quality traits in pigs. Anim Genet 2021; 52:292-303. [PMID: 33840112 DOI: 10.1111/age.13064] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/23/2021] [Indexed: 12/29/2022]
Abstract
Muscle fibers are closely related to human diseases and livestock meat quality. However, the genetics basis of microRNAs (miRNAs) in regulating muscle fibers is not completely understood. In this study, we constructed the whole genome-wide miRNA expression profiles of porcine fast-twitch muscle [biceps femoris (Bf)] and slow-twitch muscle [soleus (Sol)], and identified hundreds of miRNAs, including four skeletal muscle-highly expressed miRNAs, ssc-miR-378, ssc-let-7f, ssc-miR-26a, and ssc-miR-27b-3p. Moreover, we identified 63 differentially expressed (DE) miRNAs between biceps femoris vs. soleus, which are the key candidate miRNAs regulating the skeletal muscle fiber types. In addition, we found that the expression of DE ssc-miR-499-5p was significantly correlated to the expression of Myoglobin (r = 0.6872, P < 0.0001) and Myosin heavy chain 7 (MYH7; r = 0.5408, P = 0.0020), and pH45 min (r = 0.3806, P = 0.0380) and glucose content (r = -0.4382, P = 0.0154); while the expression of DE ssc-miR-499-3p was significantly correlated to the expression of Myoglobin (r = 0.5340, P = 0.0024) and pH45 min (r = 0.4857, P = 0.0065). Taken together, our data established a sound foundation for further studies on the regulatory mechanisms of miRNAs in skeletal muscle fiber conversion and meat quality traits in livestock, and could provide a genetic explanation of the role of miRNAs in human muscular diseases.
Collapse
Affiliation(s)
- A Jiang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China
| | - D Yin
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China
| | - L Zhang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China
| | - B Li
- College of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang, 110866, China
| | - R Li
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China
| | - X Zhang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China
| | - Z Zhang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China
| | - H Liu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China
| | - K Kim
- Department of Food Science, Purdue University, West Lafayette, IN, 47897, USA
| | - W Wu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China
| |
Collapse
|
50
|
Gong Y, Zhang Y, Li B, Xiao Y, Zeng Q, Xu K, Duan Y, He J, Ma H. Insight into Liver lncRNA and mRNA Profiling at Four Developmental Stages in Ningxiang Pig. BIOLOGY 2021; 10:310. [PMID: 33917834 PMCID: PMC8068270 DOI: 10.3390/biology10040310] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/01/2021] [Accepted: 04/07/2021] [Indexed: 12/23/2022]
Abstract
Ningxiang pigs, a fat-type pig, are native to Ningxiang County in Hunan Province, with thousands of years of breeding history. This study aims to explore the expression profiles and functional networks on messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) in the liver. Liver tissue of Ningxiang piglets was collected at 30, 90, 150, and 210 days after birth (four development stages), and the mRNA and lncRNA expression was profiled. Compared to mRNA and lncRNA expression profiles, most differentially expressed mRNAs (DEmRNAs) were upregulated at 30 days; however, most DElncRNAs were downregulated at 210 days. Via Short Time-series Expression Miner (STEM) analysis and weighted gene co-expression network analysis (WGCNA), a complex interaction between mRNAs and lncRNAs was identified, indicating that lncRNAs may be a critical regulatory element for mRNAs. One module of genes in particular (module profile 4) was related to fibril organization, vasculogenesis, GTPase activator activity, and regulation of kinase activity. The mRNAs and lncRNAs in module profile 4 had a similar pattern of expression, indicating that they have functional and regulatory relationships. Only CAV1, PACSIN2, and CDC42 in the particular mRNA profile 4 were the target genes of lncRNAs in that profile, which shows the possible regulatory relationship between lncRNAs and mRNAs. The expression of these genes and lncRNAs in profile 4 was the highest at 30 days, and it is believed that these RNAs may play a critical role during the suckling period in order to meet the dietary requirements of piglets. In the lncRNA-mRNA co-expression network, the identified gene hubs and associated lncRNAs were shown to be involved in saccharide, lipid, and glucose metabolism, which may play an important role in the development and health of the liver. This result will lead to further investigation of liver lncRNA functions at various stages of development in Ningxiang pigs.
Collapse
Affiliation(s)
- Yan Gong
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (Y.G.); (Y.Z.); (B.L.); (Y.X.); (Q.Z.); (J.H.)
| | - Yuebo Zhang
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (Y.G.); (Y.Z.); (B.L.); (Y.X.); (Q.Z.); (J.H.)
| | - Biao Li
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (Y.G.); (Y.Z.); (B.L.); (Y.X.); (Q.Z.); (J.H.)
| | - Yu Xiao
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (Y.G.); (Y.Z.); (B.L.); (Y.X.); (Q.Z.); (J.H.)
| | - Qinghua Zeng
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (Y.G.); (Y.Z.); (B.L.); (Y.X.); (Q.Z.); (J.H.)
- Ningxiang Pig Farm of Dalong Livestock Technology Co. Ltd., Ningxiang 410600, China
| | - Kang Xu
- Laboratory of Animal Nutritional Physiology and Metabolic Process, Key Laboratory of Agroecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Science, Changsha 410125, China; (K.X.); (Y.D.)
| | - Yehui Duan
- Laboratory of Animal Nutritional Physiology and Metabolic Process, Key Laboratory of Agroecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Science, Changsha 410125, China; (K.X.); (Y.D.)
| | - Jianhua He
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (Y.G.); (Y.Z.); (B.L.); (Y.X.); (Q.Z.); (J.H.)
| | - Haiming Ma
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (Y.G.); (Y.Z.); (B.L.); (Y.X.); (Q.Z.); (J.H.)
| |
Collapse
|