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整合统计学习揭示多能性进展过程中的转录网络动态。

Transcriptional network dynamics during the progression of pluripotency revealed by integrative statistical learning.

机构信息

Charles Perkins Centre, School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia.

Computational Systems Biology Group, Children's Medical Research Institute, University of Sydney, Westmead, NSW 2145, Australia.

出版信息

Nucleic Acids Res. 2020 Feb 28;48(4):1828-1842. doi: 10.1093/nar/gkz1179.

Abstract

The developmental potential of cells, termed pluripotency, is highly dynamic and progresses through a continuum of naive, formative and primed states. Pluripotency progression of mouse embryonic stem cells (ESCs) from naive to formative and primed state is governed by transcription factors (TFs) and their target genes. Genomic techniques have uncovered a multitude of TF binding sites in ESCs, yet a major challenge lies in identifying target genes from functional binding sites and reconstructing dynamic transcriptional networks underlying pluripotency progression. Here, we integrated time-resolved 'trans-omic' datasets together with TF binding profiles and chromatin conformation data to identify target genes of a panel of TFs. Our analyses revealed that naive TF target genes are more likely to be TFs themselves than those of formative TFs, suggesting denser hierarchies among naive TFs. We also discovered that formative TF target genes are marked by permissive epigenomic signatures in the naive state, indicating that they are poised for expression prior to the initiation of pluripotency transition to the formative state. Finally, our reconstructed transcriptional networks pinpointed the precise timing from naive to formative pluripotency progression and enabled the spatiotemporal mapping of differentiating ESCs to their in vivo counterparts in developing embryos.

摘要

细胞的发育潜能,称为多能性,具有高度的动态性,并通过原始、形成和启动状态的连续体进展。小鼠胚胎干细胞 (ESC) 的多能性进展从原始到形成和启动状态受到转录因子 (TF) 和它们的靶基因的控制。基因组技术已经揭示了大量 ESC 中的 TF 结合位点,但主要的挑战在于从功能结合位点中鉴定靶基因,并重建多能性进展背后的动态转录网络。在这里,我们将时间分辨的“转录组”数据集与 TF 结合图谱和染色质构象数据整合在一起,以鉴定一组 TF 的靶基因。我们的分析表明,原始 TF 的靶基因更有可能是 TF 本身,而不是形成 TF 的靶基因,这表明原始 TF 之间存在更密集的层次结构。我们还发现,形成 TF 的靶基因在原始状态下具有允许的表观遗传特征,这表明它们在向形成状态的多能性转变开始之前就已经准备好表达了。最后,我们重建的转录网络确定了从原始到形成多能性进展的精确时间,并能够将分化的 ESC 进行时空映射,以将其与其在发育胚胎中的体内对应物进行比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fbb/7038952/fdf1109f9100/gkz1179fig1.jpg

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