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利用时间序列转录组数据鉴定胎盘发育的新型调控因子。

Identifying novel regulators of placental development using time-series transcriptome data.

机构信息

Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, USA.

Bioinformatics and Computational Biology, Iowa State University, Ames, IA, USA.

出版信息

Life Sci Alliance. 2022 Dec 13;6(2). doi: 10.26508/lsa.202201788. Print 2023 Feb.

Abstract

The placenta serves as a connection between the mother and the fetus during pregnancy, providing the fetus with oxygen, nutrients, and growth hormones. However, the regulatory mechanisms and dynamic gene interaction networks underlying early placental development are understudied. Here, we generated RNA-sequencing data from mouse fetal placenta at embryonic days 7.5, 8.5, and 9.5 to identify genes with timepoint-specific expression, then inferred gene interaction networks to analyze highly connected network modules. We determined that timepoint-specific gene network modules were associated with distinct developmental processes, and with similar expression profiles to specific human placental cell populations. From each module, we identified hub genes and their direct neighboring genes, which were predicted to govern placental functions. We confirmed that four novel candidate regulators identified through our analyses regulate cell migration in the HTR-8/SVneo cell line. Overall, we predicted several novel regulators of placental development expressed in specific placental cell types using network analysis of bulk RNA-sequencing data. Our findings and analysis approaches will be valuable for future studies investigating the transcriptional landscape of early development.

摘要

胎盘是妊娠期间母体与胎儿之间的连接物,为胎儿提供氧气、营养物质和生长激素。然而,早期胎盘发育的调控机制和动态基因互作网络还研究得不够充分。在这里,我们生成了来自胚胎发育第 7.5、8.5 和 9.5 天的小鼠胎儿胎盘的 RNA-seq 数据,以鉴定具有时间特异性表达的基因,然后推断基因互作网络,以分析高度连接的网络模块。我们确定了时间特异性基因网络模块与不同的发育过程相关,并且与特定的人类胎盘细胞群具有相似的表达谱。从每个模块中,我们鉴定了枢纽基因及其直接相邻基因,这些基因被预测对胎盘功能起调控作用。我们证实,通过我们的分析鉴定的四个新的候选调控因子通过调控 HTR-8/SVneo 细胞系中的细胞迁移来发挥作用。总的来说,我们通过对批量 RNA-seq 数据的网络分析,预测了几种在特定胎盘细胞类型中表达的新型胎盘发育调控因子。我们的研究结果和分析方法将对未来研究早期发育的转录组景观具有重要价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8241/9748866/6b1801635b07/LSA-2022-01788_Fig1.jpg

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