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      A genome-wide landscape of mRNAs, lncRNAs, circRNAs and miRNAs during intramuscular adipogenesis in cattle

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          Abstract

          Background

          Intramuscular preadipocyte differentiation plays a critical role in bovine intramuscular fat (IMF) deposition. However, the roles of different RNAs, including mRNAs, circRNAs, lncRNAs and miRNAs, in regulating the adipogenic differentiation of intramuscular preadipocytes remain largely unclear.

          Results

          In the present study, a whole transcriptome sequencing and analysis, including the analysis of mRNAs, circRNAs, lncRNAs and miRNAs, during different differentiation stages (0, 3, 6, and 9 d) of intramuscular preadipocytes from Qinchuan cattle was performed. All samples were prepared with 3 biological replicates. Here, a total of 27,153 mRNAs, 14,070 circRNAs, 7035 lncRNAs, and 427 miRNAs were annotated. Among them, we identified 4848 differentially expressed mRNAs (DEMs), 181 DE circRNAs (DECs), 501 DE lncRNAs (DELs) and 77 DE miRNAs (DEmiRs) between 0 d and other differentiation days (3, 6, and 9 d). GO and KEGG functional enrichment analyses showed that these differentially expressed genes were mainly enriched in cell differentiation, fat metabolism and adipogenesis-related pathways. Furthermore, weighted gene coexpression network analysis (WGCNA) and co-expression network analysis screened out multiple important mRNAs, circRNAs and lncRNAs related to intramuscular adipogenesis. Based on the competing endogenous RNA (ceRNA) regulatory mechanism, we finally identified 24 potential ceRNA networks and 31 potential key genes, including FOXO1/miR-330/circRNA2018/MSTRG.20301, GPAM/miR-27b/ciRNA489 and SESN3/miR-433/circRNA2627MSTRG.20342.

          Conclusions

          This study provides new insights into the differential expression patterns of different transcript types (i.e., mRNAs, circRNAs, lncRNAs and miRNAs) in intramuscular preadipocyte differentiation. Our findings provide data support for studying the molecular mechanism of key mRNAs and noncoding RNAs in IMF deposition, and provide new candidate markers for the molecular breeding of beef cattle.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-022-08911-z.

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          Most cited references67

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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              Cytoscape: a software environment for integrated models of biomolecular interaction networks.

              Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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                Author and article information

                Contributors
                meichugang@163.com
                zanlinsen@163.com
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                6 October 2022
                6 October 2022
                2022
                : 23
                : 691
                Affiliations
                [1 ]GRID grid.144022.1, ISNI 0000 0004 1760 4150, College of Animal Science and Technology, , Northwest A&F University, ; Yangling, 712100 Shaanxi China
                [2 ]GRID grid.144022.1, ISNI 0000 0004 1760 4150, College of Grassland Agriculture, , Northwest A&F University, ; Yangling, 712100 Shaanxi China
                [3 ]GRID grid.144022.1, ISNI 0000 0004 1760 4150, National Beef Cattle Improvement Center, , Northwest A&F University, ; Yangling, 712100 Shaanxi China
                Article
                8911
                10.1186/s12864-022-08911-z
                9535873
                59755982-9d3b-4aa6-aa75-ac819f7e9ae9
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 11 June 2022
                : 26 September 2022
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Genetics
                cattle,imf,preadipocyte differentiation,circrna,lncrna,mirna,rna-seq,wgcna,cerna
                Genetics
                cattle, imf, preadipocyte differentiation, circrna, lncrna, mirna, rna-seq, wgcna, cerna

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