基于基因网络的人类胎盘滋养细胞亚型分析确定了作为治疗药物潜在靶点的关键基因。

IF 1.5 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Integrative Bioinformatics Pub Date : 2023-12-22 eCollection Date: 2023-12-01 DOI:10.1515/jib-2023-0011
Andreas Ian Lackner, Jürgen Pollheimer, Paulina Latos, Martin Knöfler, Sandra Haider
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引用次数: 0

摘要

妊娠早期,胚胎滋养层外细胞(EVT)在改变母体子宫环境方面发挥着至关重要的作用。EVT品系形成和分化的失败可导致妊娠并发症,如子痫前期、胎儿生长受限和妊娠失败。尽管最近取得了一些进展,但我们对控制和影响EVT发育的分子和外部因素的了解仍不全面。最近,我们利用滋养细胞类器官体外模型发现,协调操纵转化生长因子β(TGFβ)信号传导对EVT的发育至关重要。为了进一步研究参与EVT功能和发育的基因网络,我们对RNA-Seq数据进行了加权基因共表达网络分析(WGCNA)。我们发现了10个模块,模块成员中位数超过0.8,规模从1005个(M1)到72个(M27)不等,这些网络基因与TGFβ激活状态或体外培养相关,后者表明EVT表型的形成因素尚未被发现。最后,我们假设某些治疗药物可能会通过影响EVT特异性基因的表达而无意中干扰胎盘的形成。我们利用 STRING 数据库绘制相关性图谱,并利用药物基因相互作用数据库确定药物靶点。我们的药物基因相互作用综合数据集让我们深入了解了某些药物在妊娠早期的潜在风险。
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Gene-network based analysis of human placental trophoblast subtypes identifies critical genes as potential targets of therapeutic drugs.

During early pregnancy, extravillous trophoblasts (EVTs) play a crucial role in modifying the maternal uterine environment. Failures in EVT lineage formation and differentiation can lead to pregnancy complications such as preeclampsia, fetal growth restriction, and pregnancy loss. Despite recent advances, our knowledge on molecular and external factors that control and affect EVT development remains incomplete. Using trophoblast organoid in vitro models, we recently discovered that coordinated manipulation of the transforming growth factor beta (TGFβ) signaling is essential for EVT development. To further investigate gene networks involved in EVT function and development, we performed weighted gene co-expression network analysis (WGCNA) on our RNA-Seq data. We identified 10 modules with a median module membership of over 0.8 and sizes ranging from 1005 (M1) to 72 (M27) network genes associated with TGFβ activation status or in vitro culturing, the latter being indicative for yet undiscovered factors that shape the EVT phenotypes. Lastly, we hypothesized that certain therapeutic drugs might unintentionally interfere with placentation by affecting EVT-specific gene expression. We used the STRING database to map correlations and the Drug-Gene Interaction database to identify drug targets. Our comprehensive dataset of drug-gene interactions provides insights into potential risks associated with certain drugs in early gestation.

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来源期刊
Journal of Integrative Bioinformatics
Journal of Integrative Bioinformatics Medicine-Medicine (all)
CiteScore
3.10
自引率
5.30%
发文量
27
审稿时长
12 weeks
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