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Yang C, Zhou X, Xue Y, Li D, Wang L, Zhong T, Dai D, Cao J, Guo J, Li L, Zhang H, Zhan S. Transcriptome Analysis Reveals the Profile of Long Non-Coding RNAs during Myogenic Differentiation in Goats. Int J Mol Sci 2023; 24:ijms24076370. [PMID: 37047345 PMCID: PMC10094361 DOI: 10.3390/ijms24076370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/09/2023] [Accepted: 03/24/2023] [Indexed: 03/31/2023] Open
Abstract
The long non-coding RNAs (lncRNAs) are emerging as essential regulators of the growth and development of skeletal muscles. However, little is known about the expression profiles of lncRNAs during the proliferation and differentiation of skeletal muscle satellite cells (MuSCs) in goats. In this study, we investigate potential regulatory lncRNAs that govern muscle development by performing lncRNA expression profiling analysis during the proliferation (cultured in the growth medium, GM) and differentiation (cultured in the differentiation medium, DM1/DM5) of MuSCs. In total, 1001 lncRNAs were identified in MuSC samples, and 314 differentially expressed (DE) (FDR < 0.05, |log2FC| > 1) lncRNAs were screened by pairwise comparisons from three comparison groups (GM-vs-DM1, GM-vs-DM5, DM1-vs-DM5). Moreover, we identified the cis-, trans-, and antisense-regulatory target genes of DE lncRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that these target genes were significantly enriched in muscle development-related GO terms and KEGG pathways. In addition, the network of interactions between DE lncRNAs and their target genes was identified, which included well-known myogenesis regulators such as Myogenic differentiation 1 (MyoD), Myogenin (MyoG), and Myosin heavy chain (MyHC). Meanwhile, competing endogenous RNA (ceRNA) network analysis showed that 237 DE lncRNAs could bind to 329 microRNAs (miRNAs), while miRNAs could target 564 mRNAs. Together, our results provide a genome-wide resource of lncRNAs that may contribute to myogenic differentiation in goats and lay the groundwork for future investigation into their functions during skeletal muscle development.
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Profiling and Functional Analysis of mRNAs during Skeletal Muscle Differentiation in Goats. Animals (Basel) 2022; 12:ani12081048. [PMID: 35454294 PMCID: PMC9024908 DOI: 10.3390/ani12081048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/10/2022] [Accepted: 04/12/2022] [Indexed: 02/01/2023] Open
Abstract
Skeletal myogenesis is a complicated biological event that involves a succession of tightly controlled gene expressions. In order to identify novel regulators of this process, we performed mRNA-Seq studies of goat skeletal muscle satellite cells (MuSCs) cultured under proliferation (GM) and differentiation (DM1/DM5) conditions. A total of 19,871 goat genes were expressed during these stages, 198 of which represented novel transcripts. Notably, in pairwise comparisons at the different stages, 2551 differentially expressed genes (DEGs) were identified (p < 0.05), including 1560 in GM vs. DM1, 1597 in GM vs. DM5, and 959 in DM1 vs. DM5 DEGs. The time-series expression profile analysis clustered the DEGs into eight gene groups, three of which had significantly upregulated and downregulated patterns (p < 0.05). Functional enrichment analysis showed that DEGs were enriched for essential biological processes such as muscle structure development, muscle contraction, muscle cell development, striated muscle cell differentiation, and myofibril assembly, and were involved in pathways such as the MAPK, Wnt and PPAR signaling pathways. Moreover, the expression of eight DEGs (MYL2, DES, MYOG, FAP, PLK2, ADAM, WWC1, and PRDX1) was validated. These findings offer novel insights into the transcriptional regulation of skeletal myogenesis in goats.
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Khan A, Singh K, Jaiswal S, Raza M, Jasrotia RS, Kumar A, Gurjar AKS, Kumari J, Nayan V, Iquebal MA, Angadi UB, Rai A, Datta TK, Kumar D. Whole-Genome-Based Web Genomic Resource for Water Buffalo (Bubalus bubalis). Front Genet 2022; 13:809741. [PMID: 35480326 PMCID: PMC9035531 DOI: 10.3389/fgene.2022.809741] [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: 11/05/2021] [Accepted: 02/14/2022] [Indexed: 11/13/2022] Open
Abstract
Water buffalo (Bubalus bubalis), belonging to the Bovidae family, is an economically important animal as it is the major source of milk, meat, and drought in numerous countries. It is mainly distributed in tropical and subtropical regions with a global population of approximately 202 million. The advent of low cost and rapid sequencing technologies has opened a new vista for global buffalo researchers. In this study, we utilized the genomic data of five commercially important buffalo breeds, distributed globally, namely, Mediterranean, Egyptian, Bangladesh, Jaffrarabadi, and Murrah. Since there is no whole-genome sequence analysis of these five distinct buffalo breeds, which represent a highly diverse ecosystem, we made an attempt for the same. We report the first comprehensive, holistic, and user-friendly web genomic resource of buffalo (BuffGR) accessible at http://backlin.cabgrid.res.in/buffgr/, that catalogues 6028881 SNPs and 613403 InDels extracted from a set of 31 buffalo tissues. We found a total of 7727122 SNPs and 634124 InDels distributed in four breeds of buffalo (Murrah, Bangladesh, Jaffarabadi, and Egyptian) with reference to the Mediterranean breed. It also houses 4504691 SSR markers from all the breeds along with 1458 unique circRNAs, 37712 lncRNAs, and 938 miRNAs. This comprehensive web resource can be widely used by buffalo researchers across the globe for use of markers in marker trait association, genetic diversity among the different breeds of buffalo, use of ncRNAs as regulatory molecules, post-transcriptional regulations, and role in various diseases/stresses. These SNPs and InDelscan also be used as biomarkers to address adulteration and traceability. This resource can also be useful in buffalo improvement programs and disease/breed management.
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Affiliation(s)
- Aamir Khan
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Kalpana Singh
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sarika Jaiswal
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Mustafa Raza
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Rahul Singh Jasrotia
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Animesh Kumar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anoop Kishor Singh Gurjar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Juli Kumari
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Varij Nayan
- ICAR-Central Institute for Research on Buffaloes, Hisar, India
| | - Mir Asif Iquebal
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
- *Correspondence: Mir Asif Iquebal,
| | - U. B. Angadi
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anil Rai
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Dinesh Kumar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
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