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      Transcriptome Analysis of Bovine Rumen Tissue in Three Developmental Stages

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          Abstract

          Rumen development is a crucial physiological challenge for ruminants. However, the molecular mechanism regulating rumen development has not been clearly elucidated. In this study, we investigated genes involved in rumen development in 13 rumen tissues from three developmental stages (birth, youth, and adult) using RNA sequencing. We identified that 6,048 genes were differentially expressed among three developmental stages. Using weighted correlation network analysis, we found that 12 modules were significantly associated with developmental stages. Functional annotation and protein–protein interaction (PPI) network analysis revealed that CCNB1, CCNB2, IGF1, IGF2, HMGCL, BDH1, ACAT1, HMGCS2, and CREBBP involved in rumen development. Integrated transcriptome with GWAS information of carcass weight (CW), stomach weight (SW), marbling score (MS), backfat thickness (BFT), ribeye area (REA), and lean meat weight (LMW), we found that upregulated DEGs (fold change 0∼1) in birth–youth comparison were significantly enriched with GWAS signals of MS, downregulated DEGs (fold change >3) were significantly enriched with GWAS signals of SW, and fold change 0∼1 up/downregulated DEGs in birth–adult comparison were significantly enriched with GWAS signals of CW, LMW, REA, and BFT. Furthermore, we found that GWAS signals for CW, LMW, and REA were enriched in turquoise module, and GWAS signals for CW was enriched in lightgreen module. Our study provides novel insights into the molecular mechanism underlying rumen development in cattle and highlights an integrative analysis for illustrating the genetic architecture of beef complex traits.

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          Most cited references97

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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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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                03 March 2022
                2022
                : 13
                : 821406
                Affiliations
                [1] 1 Institute of Animal Science , Chinese Academy of Agricultural Sciences , Beijing, China
                [2] 2 Institute of Animal Husbandry and Veterinary Research , Anhui Academy of Agricultural Sciences , Hefei, China
                Author notes

                Edited by: Jiuzhou Song, University of Maryland, College Park, United States

                Reviewed by: Xiaomei Sun, Yangzhou University, China

                Yi Ding, Allen Institute for Brain Science, United States

                *Correspondence: Junya Li, lijunya@ 123456caas.cn ; Lupei Zhang, zhanglupei@ 123456cass.cn

                This article was submitted to Livestock Genomics, a section of the journal Frontiers in Genetics

                Article
                821406
                10.3389/fgene.2022.821406
                8928727
                35309117
                791d6cb3-7613-49d2-9695-ff4b4dd8cda3
                Copyright © 2022 Zhang, Cai, Li, Wang, Wang, Zhang, Xu, Xu, Hu, Zhu, Gao, Chen, Gao, Li and Zhang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 24 November 2021
                : 21 January 2022
                Funding
                Funded by: Agricultural Science and Technology Innovation Program , doi 10.13039/501100012421;
                Award ID: ASTIP-IAS03 CAAS-ZDRW202102
                Funded by: Agriculture Research System of China , doi 10.13039/501100010203;
                Award ID: CARS-37
                Categories
                Genetics
                Original Research

                Genetics
                rumen development,carcass traits,beef cattle,transcriptome,gwas enrichment analysis
                Genetics
                rumen development, carcass traits, beef cattle, transcriptome, gwas enrichment analysis

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