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      Coordinated Expressional Landscape of the Human Placental miRNome and Transcriptome

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          Abstract

          Placenta is a unique organ that serves its own function, and contributes to maternal gestational adaptation and fetal development. Coordination of its transcriptome to satisfy all the maternal-fetal needs across gestation is not fully understood. MicroRNAs are powerful transcriptome modulators capable to adjust rapidly the expression level and dynamics of large gene sets. This MiR-Seq based study presents a multi-omics investigation of the human placental miRNome and its synergy with the transcriptome. The analysis included 52 placentas representing three trimesters of normal pregnancy, and term cases of late-onset preeclampsia (LO-PE), gestational diabetes and affected fetal growth. Gestational-age dependent differential expression (FDR < 0.05) was detected for 319 of 417 tested miRNAs (76.5%). A shared list of target genes of dynamic miRNAs suggested their coordinated action. The most abundant miR-143-3p revealed as a marker for pregnancy progression. The data suggested critical, but distinct roles of placenta-specific imprinted C19MC and C14MC miRNA clusters. Paternally encoded primate-specific C19MC was highly transcribed during first trimester, potentially fine-tuning the early placental transcriptome in dosage-sensitive manner. Maternally encoded eutherian C14MC showed high expression until term, underlining its key contribution across gestation. A major shift in placental miRNome (16% miRNAs) was observed in LO-PE, but not in other term pregnancy complications. Notably, 13/38 upregulated miRNAs were transcribed from C19MC and only one from C14MC, whereas 11/28 downregulated miRNAs represented C14MC and none C19MC. miR-210-3p, miR-512-5p, miR-32-5p, miR-19a-3p, miR-590-3p, miR-379-5p were differentially expressed in LO-PE and cases of small-for-gestational-age newborns, supporting a shared etiology. Expression correlation analysis with the RNA-Seq data (16,567 genes) of the same samples clustered PE-linked miRNAs into five groups. Large notable clusters of miRNA–gene pairs showing directly and inversely correlated expression dynamics suggested potential functional relationships in both scenarios. The first genome-wide study of placental miR-eQTLs identified 66 placental SNVs associated with the expression of neighboring miRNAs, including PE-linked miRNAs miR-30a-5p, miR-210-3p, miR-490-3p and miR-518-5p. This study provided a rich catalog of miRNAs for further in-depth investigations of their individual and joint effect on placental transcriptome. Several highlighted miRNAs may serve as potential biomarkers for pregnancy monitoring and targets to prevent or treat gestational disorders.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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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              PLINK: a tool set for whole-genome association and population-based linkage analyses.

              Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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                Author and article information

                Contributors
                Journal
                Front Cell Dev Biol
                Front Cell Dev Biol
                Front. Cell Dev. Biol.
                Frontiers in Cell and Developmental Biology
                Frontiers Media S.A.
                2296-634X
                21 July 2021
                2021
                : 9
                : 697947
                Affiliations
                Human Genetics Research Group, Institute of Biomedicine and Translational Medicine, Faculty of Medicine, University of Tartu , Tartu, Estonia
                Author notes

                Edited by: Geetu Tuteja, Iowa State University, United States

                Reviewed by: Simão Teixeira da Rocha, University of Lisbon, Portugal; Gerrit J. Bouma, Colorado State University, United States

                *Correspondence: Maris Laan, maris.laan@ 123456ut.ee

                This article was submitted to Developmental Epigenetics, a section of the journal Frontiers in Cell and Developmental Biology

                Article
                10.3389/fcell.2021.697947
                8334369
                34368147
                b0d68aaf-4edb-4df2-9101-03f7407302e0
                Copyright © 2021 Inno, Kikas, Lillepea and Laan.

                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
                : 20 April 2021
                : 28 June 2021
                Page count
                Figures: 6, Tables: 4, Equations: 0, References: 62, Pages: 20, Words: 0
                Funding
                Funded by: Eesti Teadusagentuur 10.13039/501100002301
                Award ID: IUT34-12
                Award ID: PRG1021
                Categories
                Cell and Developmental Biology
                Original Research

                human placenta,mir-seq,gestational dynamics,pregnancy complications,preeclampsia,transcriptome (rna-seq),mir-eqtl,genetic association study

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