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      Screening and Conjoint Analysis of Key lncRNAs for Milk Fat Metabolism in Dairy Cows

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          Abstract

          Long noncoding RNAs (lncRNAs) play an important regulatory role in various biological processes as a key regulatory factor. However, the complete expression profile of lncRNAs in dairy cows and its function in milk fat synthesis are unknown. In this study, RNA sequencing (RNA-seq) was used to research the whole genome expression of lncRNAs and mRNA transcripts in high and low milk fat percentage (MFP) bovine mammary epithelial cells (BMECs), and joint analysis was carried out. We identified a total of 47 differentially expressed genes (DEGs) and 38 differentially expressed lncRNAs (DELs, Padj <0.05), enrichment analysis screened out 11 candidate DEGs that may regulate milk fat metabolism. Downregulated differential gene ENPP2 (The expression level in BMECs of high milk fat dairy cows was lower than that of low milk fat cows) and upregulated differential gene BCAT1 are more likely to participate in the milk fat metabolism, and its function needs further experiments verification. The enrichment analysis of target genes predicted by DELs identified 7 cis (co-localization) and 10 trans (co-expression) candidate target genes related to milk lipid metabolism, corresponding to a total of 18 DELs. Among them, the targeting relationship between long intervening/intergenic noncoding RNA (lincRNA) TCONS_00082721 and FABP4 is worthy of attention. One hundred and fifty-six competing endogenous RNAs (ceRNAs) interaction regulation networks related to milk fat metabolism were constructed based on the expression information of DELs, differential microRNAs (miRNAs), and lipid metabolism-related target genes. The regulatory network centered on miR-145 will be the focus of subsequent experimental research. The ceRNAs regulatory network related to TCONS_00082721 and TCONS_00172817 are more likely to be involved in milk fat synthesis. These results will provide new ways to understand the complex biology of dairy cow milk fat synthesis and provide valuable information for breed improvement of Chinese Holstein cow.

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          Most cited references53

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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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            The landscape of long noncoding RNAs in the human transcriptome.

            Long noncoding RNAs (lncRNAs) are emerging as important regulators of tissue physiology and disease processes including cancer. To delineate genome-wide lncRNA expression, we curated 7,256 RNA sequencing (RNA-seq) libraries from tumors, normal tissues and cell lines comprising over 43 Tb of sequence from 25 independent studies. We applied ab initio assembly methodology to this data set, yielding a consensus human transcriptome of 91,013 expressed genes. Over 68% (58,648) of genes were classified as lncRNAs, of which 79% were previously unannotated. About 1% (597) of the lncRNAs harbored ultraconserved elements, and 7% (3,900) overlapped disease-associated SNPs. To prioritize lineage-specific, disease-associated lncRNA expression, we employed non-parametric differential expression testing and nominated 7,942 lineage- or cancer-associated lncRNA genes. The lncRNA landscape characterized here may shed light on normal biology and cancer pathogenesis and may be valuable for future biomarker development.
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              Long non-coding RNAs: new players in cell differentiation and development.

              Genomes of multicellular organisms are characterized by the pervasive expression of different types of non-coding RNAs (ncRNAs). Long ncRNAs (lncRNAs) belong to a novel heterogeneous class of ncRNAs that includes thousands of different species. lncRNAs have crucial roles in gene expression control during both developmental and differentiation processes, and the number of lncRNA species increases in genomes of developmentally complex organisms, which highlights the importance of RNA-based levels of control in the evolution of multicellular organisms. In this Review, we describe the function of lncRNAs in developmental processes, such as in dosage compensation, genomic imprinting, cell differentiation and organogenesis, with a particular emphasis on mammalian development.
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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 February 2022
                2022
                : 13
                : 772115
                Affiliations
                [1] 1 School of Agriculture , Ningxia University , Yinchuan, China
                [2] 2 Key Laboratory of Ruminant Molecular and Cellular Breeding , Ningxia Hui Autonomous Region , Ningxia University , Yinchuan, China
                [3] 3 Animal Husbandry Extension Station , Yinchuan, China
                Author notes

                Edited by: Ying Yu, China Agricultural University, China

                Reviewed by: Juliana Afonso, Embrapa Pecuária Sudeste, Brazil

                Runjun Yang, Jilin University, China

                Shaobin Li, Gansu Agricultural University, China

                *Correspondence: Yaling Gu, guyl@ 123456nxu.edu.cn
                [ † ]

                These authors have contributed equally to this work and share first authorship

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

                Article
                772115
                10.3389/fgene.2022.772115
                8850724
                35186023
                f80969b7-e54c-4742-be34-4e0dac98ebcd
                Copyright © 2022 Mu, Hu, Feng, Ma, Wang, Liu, Yu, Wen, Zhang and Gu.

                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
                : 07 September 2021
                : 05 January 2022
                Categories
                Genetics
                Original Research

                Genetics
                holstein cattle,lncrnas,gene,conjoint analysis,milk fat percentage
                Genetics
                holstein cattle, lncrnas, gene, conjoint analysis, milk fat percentage

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