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      RNA-Seq explores the functional role of the fibroblast growth factor 10 gene in bovine adipocytes differentiation

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          Abstract

          Objective

          The present study was executed to explore the molecular mechanism of fibroblast growth factor 10 ( FGF10) gene in bovine adipogenesis.

          Methods

          The bovine FGF10 gene was overexpressed through Ad-FGF10 or inhibited through siFGF10 and their negative control (NC) in bovine adipocytes, and the multiplicity of infection, transfection efficiency, interference efficiency were evaluated through quantitative real-time polymerase chain reaction, western blotting and fluorescence microscopy. The lipid droplets, triglycerides (TG) content and the expression levels of adipogenic marker genes were measured during preadipocytes differentiation. The differentially expressed genes were explored through deep RNA sequencing.

          Results

          The highest mRNA level was found in omasum, subcutaneous fat, and intramuscular fat. Moreover, the highest mRNA level was found in adipocytes at day 4 of differentiation. The results of red-oil o staining showed that overexpression (Ad-FGF10) of the FGF10 gene significantly (p<0.05) reduced the lipid droplets and TG content, and their down-regulation (siFGF10) increased the measurement of lipid droplets and TG in differentiated bovine adipocytes. Furthermore, the overexpression of the FGF10 gene down regulated the mRNA levels of adipogenic marker genes such as CCAAT enhancer binding protein alpha ( C/EBPα), fatty acid binding protein ( FABP4), peroxisome proliferator-activated receptor-γ ( PPARγ), lipoprotein lipase ( LPL), and Fas cell surface death receptor ( FAS), similarly, down-regulation of the FGF10 gene enriched the mRNA levels of C/EBPα, PPARγ, FABP4, and LPL genes (p<0.01). Additionally, the protein levels of PPARγ and FABP4 were reduced (p<0.05) in adipocytes infected with Ad- FGF10 gene and enriched in adipocytes transfected with siFGF10. Moreover, a total of 1,774 differentially expressed genes (DEGs) including 157 up regulated and 1,617 down regulated genes were explored in adipocytes infected with Ad-FGF10 or Ad-NC through deep RNA-sequencing. The top Kyoto encyclopedia of genes and genomes pathways regulated through DEGs were the PPAR signaling pathway, cell cycle, base excision repair, DNA replication, apoptosis, and regulation of lipolysis in adipocytes.

          Conclusion

          Therefore, we can conclude that the FGF10 gene is a negative regulator of bovine adipogenesis and could be used as a candidate gene in marker-assisted selection.

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          Most cited references38

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Journal
                Anim Biosci
                Anim Biosci
                Animal Bioscience
                Animal Bioscience
                2765-0189
                2765-0235
                May 2024
                1 November 2023
                : 37
                : 5
                : 929-943
                Affiliations
                [1 ]College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
                [2 ]Faculty of Veterinary and Livestock Technology, S. Seifullin Kazakh Agro Technical University, Astana 010000, Kazakhstan
                [3 ]Department of Livestock Management, Breeding and Genetics, Faculty of Animal Husbandry and Veterinary Sciences, The University of Agriculture Peshawar, 25130, Pakistan
                [4 ]Candidate of Sciences in Agriculture, Researcher of Scientific and Production Centre for Animal Husbandry and Veterinary Limited Liability Partnership, Astana 010000, Kazakhstan
                [5 ]Zhengir Khan West Kazakhstan Agrarian and Technical University, Uralsk 090000, Kazakhstan
                [6 ]Faculty of Agricultural Sciences, Toraighyrov University, Pavlodar 140000, Kazakhstan
                [7 ]College of Veterinary Sciences, Faculty of Animal Husbandry and Veterinary Sciences, The University of Agriculture Peshawar, 25130, Pakistan
                Author notes
                [* ]Corresponding Author: Begenova Ainagul Baibolsynovna, E-mail: begenova_73@ 123456mail.ru
                [a]

                The first two authors contributed equally.

                Author information
                https://orcid.org/0000-0001-8160-9747
                https://orcid.org/0000-0001-5191-3457
                https://orcid.org/0000-0001-9245-3178
                https://orcid.org/0000-0001-5122-9030
                https://orcid.org/0000-0002-6436-6744
                https://orcid.org/0000-0002-2157-9205
                https://orcid.org/0000-0002-5684-7564
                https://orcid.org/0000-0001-7551-5787
                https://orcid.org/0000-0001-9858-9631
                https://orcid.org/0000-0001-9138-3059
                https://orcid.org/0000-0002-5633-972X
                https://orcid.org/0000-0002-1127-1143
                https://orcid.org/0000-0002-1763-3977
                https://orcid.org/0000-0001-6644-0250
                https://orcid.org/0000-0001-6642-5616
                Article
                ab-23-0185
                10.5713/ab.23.0185
                11065710
                37946430
                a62c2e88-a510-408c-aa22-820b37bd69d5
                Copyright © 2024 by Animal Bioscience

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 May 2023
                : 27 July 2023
                : 18 September 2023
                Categories
                Article
                Animal Biotechnology

                bovine adipocytes,fgf10 gene,intramuscular fat,rna sequencing

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