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从蛋白质-蛋白质相互作用网络中系统地识别转录调控模块。

Systematic identification of transcriptional regulatory modules from protein-protein interaction networks.

机构信息

World Premier International (WPI) Immunology Frontier Research Center (IFReC), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Osaka, Japan, South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, 190 Kaiyuan Ave, Guangzhou 510663, China and Fibrosis Laboratories, Institute of Cellular Medicine, Newcastle University Medical School, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom.

出版信息

Nucleic Acids Res. 2014 Jan;42(1):e6. doi: 10.1093/nar/gkt913. Epub 2013 Oct 16.

DOI:10.1093/nar/gkt913
PMID:24137002
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3874207/
Abstract

Transcription factors (TFs) combine with co-factors to form transcriptional regulatory modules (TRMs) that regulate gene expression programs with spatiotemporal specificity. Here we present a novel and generic method (rTRM) for the reconstruction of TRMs that integrates genomic information from TF binding, cell type-specific gene expression and protein-protein interactions. rTRM was applied to reconstruct the TRMs specific for embryonic stem cells (ESC) and hematopoietic stem cells (HSC), neural progenitor cells, trophoblast stem cells and distinct types of terminally differentiated CD4(+) T cells. The ESC and HSC TRM predictions were highly precise, yielding 77 and 96 proteins, of which ∼75% have been independently shown to be involved in the regulation of these cell types. Furthermore, rTRM successfully identified a large number of bridging proteins with known roles in ESCs and HSCs, which could not have been identified using genomic approaches alone, as they lack the ability to bind specific DNA sequences. This highlights the advantage of rTRM over other methods that ignore PPI information, as proteins need to interact with other proteins to form complexes and perform specific functions. The prediction and experimental validation of the co-factors that endow master regulatory TFs with the capacity to select specific genomic sites, modulate the local epigenetic profile and integrate multiple signals will provide important mechanistic insights not only into how such TFs operate, but also into abnormal transcriptional states leading to disease.

摘要

转录因子 (TFs) 与共因子结合形成转录调控模块 (TRMs),以时空特异性调节基因表达程序。在这里,我们提出了一种新颖的通用方法 (rTRM),用于重建 TRMs,该方法整合了 TF 结合、细胞类型特异性基因表达和蛋白质-蛋白质相互作用的基因组信息。rTRM 被应用于重建胚胎干细胞 (ESC) 和造血干细胞 (HSC)、神经祖细胞、滋养层干细胞和不同类型的终末分化 CD4(+) T 细胞的 TRM。ESC 和 HSC TRM 的预测具有很高的准确性,产生了 77 种和 96 种蛋白质,其中约 75% 的蛋白质已被独立证明参与这些细胞类型的调控。此外,rTRM 成功地鉴定了大量在 ESC 和 HSC 中具有已知作用的桥接蛋白,这些蛋白不能仅通过基因组方法来鉴定,因为它们缺乏结合特定 DNA 序列的能力。这突出了 rTRM 相对于其他忽略 PPI 信息的方法的优势,因为蛋白质需要与其他蛋白质相互作用才能形成复合物并执行特定的功能。预测和实验验证赋予主调控 TF 选择特定基因组位点、调节局部表观遗传特征和整合多个信号的共因子,不仅将为这些 TF 如何运作提供重要的机制见解,还将为导致疾病的异常转录状态提供重要的机制见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4376/3874207/91e951f471f1/gkt913f4p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4376/3874207/3c40497c7a13/gkt913f1p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4376/3874207/00281b3f2545/gkt913f2p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4376/3874207/741e6ce0f198/gkt913f3p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4376/3874207/91e951f471f1/gkt913f4p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4376/3874207/3c40497c7a13/gkt913f1p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4376/3874207/00281b3f2545/gkt913f2p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4376/3874207/741e6ce0f198/gkt913f3p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4376/3874207/91e951f471f1/gkt913f4p.jpg

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