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Lv J, Yang F, Li Y, Gao N, Zeng Q, Ma H, He J, Zhang Y. Characterization and Function Analysis of miRNA Editing during Fat Deposition in Chinese Indigenous Ningxiang Pigs. Vet Sci 2024; 11:183. [PMID: 38668450 PMCID: PMC11054885 DOI: 10.3390/vetsci11040183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 04/29/2024] Open
Abstract
This study aimed to identify active miRNA editing sites during adipose development in Ningxiang pigs and analyze their characteristics and functions. Based on small RNA-seq data from the subcutaneous adipose tissues of Ningxiang pigs at four stages-30 days (piglet), 90 days (nursery), 150 days (early fattening), and 210 days (late fattening)-we constructed a developmental map of miRNA editing in the adipose tissues of Ningxiang pigs. A total of 505 miRNA editing sites were identified using the revised pipeline, with C-to-U editing types being the most prevalent, followed by U-to-C, A-to-G, and G-to-U. Importantly, these four types of miRNA editing exhibited base preferences. The number of editing sites showed obvious differences among age groups, with the highest occurrence of miRNA editing events observed at 90 days of age and the lowest at 150 days of age. A total of nine miRNA editing sites were identified in the miRNA seed region, with significant differences in editing levels (p < 0.05) located in ssc-miR-23a, ssc-miR-27a, ssc-miR-30b-5p, ssc-miR-15a, ssc-miR-497, ssc-miR-15b, and ssc-miR-425-5p, respectively. Target gene prediction and KEGG enrichment analyses indicated that the editing of miR-497 might potentially regulate fat deposition by inhibiting adipose synthesis via influencing target binding. These results provide new insights into the regulatory mechanism of pig fat deposition.
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Affiliation(s)
- Jiayu Lv
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
| | - Fang Yang
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
| | - Yiyang Li
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
| | - Ning Gao
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
| | - Qinghua Zeng
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
| | - Haiming Ma
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
| | - Jun He
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
| | - Yuebo Zhang
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
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Weng S, Yang X, Yu N, Wang PC, Xiong S, Ruan H. Harnessing ADAR-Mediated Site-Specific RNA Editing in Immune-Related Disease: Prediction and Therapeutic Implications. Int J Mol Sci 2023; 25:351. [PMID: 38203521 PMCID: PMC10779106 DOI: 10.3390/ijms25010351] [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: 10/31/2023] [Revised: 12/15/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024] Open
Abstract
ADAR (Adenosine Deaminases Acting on RNA) proteins are a group of enzymes that play a vital role in RNA editing by converting adenosine to inosine in RNAs. This process is a frequent post-transcriptional event observed in metazoan transcripts. Recent studies indicate widespread dysregulation of ADAR-mediated RNA editing across many immune-related diseases, such as human cancer. We comprehensively review ADARs' function as pattern recognizers and their capability to contribute to mediating immune-related pathways. We also highlight the potential role of site-specific RNA editing in maintaining homeostasis and its relationship to various diseases, such as human cancers. More importantly, we summarize the latest cutting-edge computational approaches and data resources for predicting and analyzing RNA editing sites. Lastly, we cover the recent advancement in site-directed ADAR editing tool development. This review presents an up-to-date overview of ADAR-mediated RNA editing, how site-specific RNA editing could potentially impact disease pathology, and how they could be harnessed for therapeutic applications.
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Affiliation(s)
- Shenghui Weng
- Jiangsu Key Laboratory of Infection and Immunity, Institutes of Biology and Medical Sciences, Soochow University, Suzhou 215123, China; (S.W.); (P.-C.W.)
| | - Xinyi Yang
- Jiangsu Key Laboratory of Infection and Immunity, Institutes of Biology and Medical Sciences, Soochow University, Suzhou 215123, China; (S.W.); (P.-C.W.)
| | - Nannan Yu
- Jiangsu Key Laboratory of Infection and Immunity, Institutes of Biology and Medical Sciences, Soochow University, Suzhou 215123, China; (S.W.); (P.-C.W.)
| | - Peng-Cheng Wang
- Jiangsu Key Laboratory of Infection and Immunity, Institutes of Biology and Medical Sciences, Soochow University, Suzhou 215123, China; (S.W.); (P.-C.W.)
| | - Sidong Xiong
- Jiangsu Key Laboratory of Infection and Immunity, Institutes of Biology and Medical Sciences, Soochow University, Suzhou 215123, China; (S.W.); (P.-C.W.)
| | - Hang Ruan
- Jiangsu Key Laboratory of Infection and Immunity, Institutes of Biology and Medical Sciences, Soochow University, Suzhou 215123, China; (S.W.); (P.-C.W.)
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou 215123, China
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Ding Y, Hou Y, Ling Z, Chen Q, Xu T, Liu L, Yu N, Ni W, Ding X, Zhang X, Zheng X, Bao W, Yin Z. Identification of Candidate Genes and Regulatory Competitive Endogenous RNA (ceRNA) Networks Underlying Intramuscular Fat Content in Yorkshire Pigs with Extreme Fat Deposition Phenotypes. Int J Mol Sci 2022; 23:ijms232012596. [PMID: 36293455 PMCID: PMC9603960 DOI: 10.3390/ijms232012596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/13/2022] [Accepted: 10/15/2022] [Indexed: 11/27/2022] Open
Abstract
Intramuscular fat (IMF) content is vital for pork quality, serving an important role in economic performance in pig industry. Non-coding RNAs, with mRNAs, are involved in IMF deposition; however, their functions and regulatory mechanisms in porcine IMF remain elusive. This study assessed the whole transcriptome expression profiles of the Longissimus dorsi muscle of pigs with high (H) and low (L) IMF content to identify genes implicated in porcine IMF adipogenesis and their regulatory functions. Hundreds of differentially expressed RNAs were found to be involved in fatty acid metabolic processes, lipid metabolism, and fat cell differentiation. Furthermore, combing co-differential expression analyses, we constructed competing endogenous RNAs (ceRNA) regulatory networks, showing crosstalk among 30 lncRNAs and 61 mRNAs through 20 miRNAs, five circRNAs and 11 mRNAs through four miRNAs, and potential IMF deposition-related ceRNA subnetworks. Functional lncRNAs and circRNAs (such as MSTRG.12440.1, ENSSSCT00000066779, novel_circ_011355, novel_circ_011355) were found to act as ceRNAs of important lipid metabolism-related mRNAs (LEP, IP6K1, FFAR4, CEBPA, etc.) by sponging functional miRNAs (such as ssc-miR-196a, ssc-miR-200b, ssc-miR10391, miR486-y). These findings provide potential regulators and molecular regulatory networks that can be utilized for research on IMF traits in pigs, which would aid in marker-assisted selection to improve pork quality.
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Affiliation(s)
- Yueyun Ding
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
- Anhui Province Key Laboratory of Local Livestock and Poultry Genetic Resource Conservation and Bio-Breeding, Anhui Agricultural University, Hefei 230036, China
| | - Yinhui Hou
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
- Anhui Province Key Laboratory of Local Livestock and Poultry Genetic Resource Conservation and Bio-Breeding, Anhui Agricultural University, Hefei 230036, China
| | - Zijing Ling
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
- Anhui Province Key Laboratory of Local Livestock and Poultry Genetic Resource Conservation and Bio-Breeding, Anhui Agricultural University, Hefei 230036, China
| | - Qiong Chen
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
- Anhui Province Key Laboratory of Local Livestock and Poultry Genetic Resource Conservation and Bio-Breeding, Anhui Agricultural University, Hefei 230036, China
| | - Tao Xu
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
- Anhui Province Key Laboratory of Local Livestock and Poultry Genetic Resource Conservation and Bio-Breeding, Anhui Agricultural University, Hefei 230036, China
| | - Lifei Liu
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
- Anhui Province Key Laboratory of Local Livestock and Poultry Genetic Resource Conservation and Bio-Breeding, Anhui Agricultural University, Hefei 230036, China
| | - Na Yu
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
- Anhui Province Key Laboratory of Local Livestock and Poultry Genetic Resource Conservation and Bio-Breeding, Anhui Agricultural University, Hefei 230036, China
| | - Wenliang Ni
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
- Anhui Province Key Laboratory of Local Livestock and Poultry Genetic Resource Conservation and Bio-Breeding, Anhui Agricultural University, Hefei 230036, China
| | - Xiaoling Ding
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
- Anhui Province Key Laboratory of Local Livestock and Poultry Genetic Resource Conservation and Bio-Breeding, Anhui Agricultural University, Hefei 230036, China
| | - Xiaodong Zhang
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
- Anhui Province Key Laboratory of Local Livestock and Poultry Genetic Resource Conservation and Bio-Breeding, Anhui Agricultural University, Hefei 230036, China
| | - Xianrui Zheng
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
- Anhui Province Key Laboratory of Local Livestock and Poultry Genetic Resource Conservation and Bio-Breeding, Anhui Agricultural University, Hefei 230036, China
| | - Wenbin Bao
- Key Laboratory for Animal Genetics, Breeding, Reproduction and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
- Correspondence: (W.B.); (Z.Y.)
| | - Zongjun Yin
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
- Anhui Province Key Laboratory of Local Livestock and Poultry Genetic Resource Conservation and Bio-Breeding, Anhui Agricultural University, Hefei 230036, China
- Correspondence: (W.B.); (Z.Y.)
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Zhao L, Zhang D, Li X, Zhang Y, Zhao Y, Xu D, Cheng J, Wang J, Li W, Lin C, Yang X, Ma Z, Cui P, Zhang X, Wang W. Comparative proteomics reveals genetic mechanisms of body weight in Hu sheep and Dorper sheep. J Proteomics 2022; 267:104699. [PMID: 35995385 DOI: 10.1016/j.jprot.2022.104699] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/06/2022] [Accepted: 08/08/2022] [Indexed: 10/15/2022]
Abstract
Body weight (BW) is a critical economic trait for meat production in sheep, and it is a complex trait affected by numerous elements. The aim of this study was to investigate the genetic mechanisms of sheep BW by a label-free proteomics approach. The result showed, a total of 27, 14, 61, and 65 differentially abundant proteins (DAPs) were identified in the Hu_HBW vs. Hu_LBW, DP_HBW vs. DP_LBW, Hu_HBW vs. DP_HBW, and Hu_LBW vs. DP_LBW comparisons, respectively. Five proteins (including ILK, AHCYL2, MLIP, CYB5A, and SMTNL1) related to fat synthesis and muscle development were detected in the Hu sheep group. In the Dorper sheep group, the screened DAPs strictly related to muscle development and fat synthesis were significantly enriched in MAP kinase activity (MAPK12), Arachidonic acid metabolism, and Steroid hormone biosynthesis (PGFS, LOC101107119) pathways. Several DAPs related to immune responses (SERPINA1, FGG, SERPINC1, and LOC101108131), fat deposition (APOH, GC, AHSG, SKP1, ACSL1, ACAT1, and ACADS), and muscle development (LMOD3 and LRRC39) were detected in the Hu vs. Dorper sheep comparison. These analyses indicated that the BW of sheep is regulated via a variety of pathways, and these DAPs can be further investigated as candidate markers for predicting the BW of sheep. SIGNIFICANCE: Body weight is one of the key traits in sheep and involves multiple coordinated regulatory mechanisms, but the genetic mechanism of BW is still unclear in sheep. In the current study, the label-free method was used to identify the proteins and pathways related to BW using LT muscle of Hu sheep and Dorper sheep with different BW. These findings will provide new candidate proteins and vital pathways into the molecular mechanisms involved growth traits in sheep.
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Affiliation(s)
- Liming Zhao
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Deyin Zhang
- The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu 730020, China
| | - Xiaolong Li
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Yukun Zhang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Yuan Zhao
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Dan Xu
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Jiangbo Cheng
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Jianghui Wang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Wenxin Li
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Changchun Lin
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Xiaobin Yang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Zongwu Ma
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Panpan Cui
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Xiaoxue Zhang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.
| | - Weimin Wang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China; The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu 730020, China.
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Yu M, Zheng H, Xu D, Shuai Y, Tian S, Cao T, Zhou M, Zhu Y, Zhao S, Li X. Non-contact detection method of pregnant sows backfat thickness based on two-dimensional images. Anim Genet 2022; 53:769-781. [PMID: 35989407 DOI: 10.1111/age.13248] [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: 06/19/2022] [Revised: 07/16/2022] [Accepted: 07/27/2022] [Indexed: 11/27/2022]
Abstract
Since sow backfat thickness (BFT) is highly correlated with its service life and reproductive effectiveness, dynamic monitoring of BFT is a critical component of large-scale sow farm productivity. Existing contact measures of sow BFT have their problems including, high measurement intensity and sows' stress reaction, low biological safety, and difficulty in meeting the requirements for multiple measurements. This article presents a two-dimensional (2D) image-based approach for determining the BFT of pregnant sows when combined with the backfat growth rate (BGR). The 2D image features of sows extracted by convolutional neural networks (CNN) and the artificially defined phenotypic features of sows such as hip width, hip height, body length, hip height-width ratio, length-width ratio, and waist-hip ratio, were used respectively, combined with BGR, to construct a prediction model for sow BFT using support vector regression (SVR). Following testing and comparison, it was shown that using CNN to extract features from images could effectively replace artificially defined features, BGR contributed to the model's accuracy improvement. The CNN-BGR-SVR model performed the best, with R2 of 0.72 and mean absolute error of 1.21 mm, and root mean square error of 1.50 mm, and mean absolute percentage error of 7.57%. The results demonstrated that the CNN-BGR-SVR model based on 2D images was capable of detecting sow BFT, establishing a new reference for non-contact sow BFT detection technology.
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Affiliation(s)
- Mengyuan Yu
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Hongya Zheng
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Dihong Xu
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yonghui Shuai
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Shanfeng Tian
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Tingjin Cao
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Mingyan Zhou
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yuhua Zhu
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Shuhong Zhao
- College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Xuan Li
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China.,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
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