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Revealing the action mechanisms of dexamethasone on the birth weight of infant using RNA-sequencing data of trophoblast cells

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      Abstract

      Dexamethasone (DEX) could induce low birth weight of infant, and low birth weight has close associations with glucocorticoid levels, insulin resistance, hypertension, and metabolic syndrome in adulthood. This study was designed to reveal the action mechanisms of DEX on the birth weight of infant.Using quantitative real-time polymerase chain reaction (qRT-PCR), trophoblast cells of human placenta were identified and the optimum treatment time of DEX were determined. Trophoblast cells were treated by DEX (DEX group) or ethanol (control group) (each group had 3 samples), and then were performed with RNA-sequencing. Afterward, the differentially expressed genes (DEGs) were identified by R package, and their potential functions were successively enriched using DAVID database and Enrichr method. Followed by protein–protein interaction (PPI) network was constructed using Cytoscape software. Using Enrichr method and TargetScan software, the transcription factors (TFs) and micorRNAs (miRNAs) targeted the DEGs separately were predicted. Based on MsigDB database, gene set enrichment analysis (GSEA) was performed.There were 391 DEGs screened from the DEX group. Upregulated SRR and potassium voltage-gated channel subfamily J member 4 (KCNJ4) and downregulated GALNT1 separately were enriched in PDZ (an acronym of PSD-95, Dlg, and ZO-1) domain binding and Mucin type O-glycan biosynthesis. In the PPI network, CDK2 and CDK4 had higher degrees. TFs ATF2 and E2F4 and miRNA miR-16 were predicted for the DEGs. Moreover, qRT-PCR analysis confirmed that SRR and KCNJ4 were significantly upregulated.These genes might affect the roles of DEX in the birth weight of infant, and might be promising therapeutic targets for reducing the side effects of DEX.

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      Most cited references 48

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      Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

      Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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        Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms

        High-throughput mRNA sequencing (RNA-Seq) holds the promise of simultaneous transcript discovery and abundance estimation 1-3 . We introduce an algorithm for transcript assembly coupled with a statistical model for RNA-Seq experiments that produces estimates of abundances. Our algorithms are implemented in an open source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed more than 430 million paired 75bp RNA-Seq reads from a mouse myoblast cell line representing a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Analysis of transcript expression over the time series revealed complete switches in the dominant transcription start site (TSS) or splice-isoform in 330 genes, along with more subtle shifts in a further 1,304 genes. These dynamics suggest substantial regulatory flexibility and complexity in this well-studied model of muscle development.
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          TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions

          TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.
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            Author and article information

            Affiliations
            [a ]Department of Obstetrics and Gynecology, Hangzhou First People's Hospital, Nanjing Medical University, Hangzhou, Zhejiang Province, China
            [b ]Department of Obstetrics and Gynecology, Charite Medical University, Berlin, Germany.
            Author notes
            []Correspondence: Jinyi Tong, Department of Obstetrics and Gynecology, Hangzhou First People's Hospital, Nanjing Medical University, No. 261 Huansha Road, Hangzhou 310006, Zhejiang Province, China (e-mail: JinyiTong45@ 123456126.com ).
            Journal
            Medicine (Baltimore)
            Medicine (Baltimore)
            MEDI
            Medicine
            Wolters Kluwer Health
            0025-7974
            1536-5964
            January 2018
            26 January 2018
            : 97
            : 4
            29369181
            5794365
            MD-D-17-03006
            10.1097/MD.0000000000009653
            09653
            (Editor)
            Copyright © 2018 the Author(s). Published by Wolters Kluwer Health, Inc.

            This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0

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