scholarly journals TRFBA: an algorithm to integrate genome-scale metabolic and transcriptional regulatory networks with incorporation of expression data

2017 ◽  
pp. btw772 ◽  
Author(s):  
Ehsan Motamedian ◽  
Maryam Mohammadi ◽  
Seyed Abbas Shojaosadati ◽  
Mona Heydari
Keyword(s):  
Regulatory Networks ◽  
Expression Data ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory ◽  
Genome Scale

scholarly journals Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens

2021 ◽  
Vol 7 (27) ◽  
pp. eabf5733
Author(s):  
Rui Lopes ◽  
Kathleen Sprouffske ◽  
Caibin Sheng ◽  
Esther C. H. Uijttewaal ◽  
Adriana Emma Wesdorp ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regulatory Networks ◽  
Essential Role ◽  
Regulatory Elements ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory Elements ◽  
Transcriptional Regulatory ◽  
Functional Relevance ◽  
Genome Scale ◽  
Transcription Program

Millions of putative transcriptional regulatory elements (TREs) have been cataloged in the human genome, yet their functional relevance in specific pathophysiological settings remains to be determined. This is critical to understand how oncogenic transcription factors (TFs) engage specific TREs to impose transcriptional programs underlying malignant phenotypes. Here, we combine cutting edge CRISPR screens and epigenomic profiling to functionally survey ≈15,000 TREs engaged by estrogen receptor (ER). We show that ER exerts its oncogenic role in breast cancer by engaging TREs enriched in GATA3, TFAP2C, and H3K27Ac signal. These TREs control critical downstream TFs, among which TFAP2C plays an essential role in ER-driven cell proliferation. Together, our work reveals novel insights into a critical oncogenic transcription program and provides a framework to map regulatory networks, enabling to dissect the function of the noncoding genome of cancer cells.

INFERRING THE REGULATORY INTERACTION MODELS OF TRANSCRIPTION FACTORS IN TRANSCRIPTIONAL REGULATORY NETWORKS

2012 ◽  
Vol 10 (05) ◽  
pp. 1250012 ◽  
Author(s):  
SHERINE AWAD ◽  
NICHOLAS PANCHY ◽  
SEE-KIONG NG ◽  
JIN CHEN
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Target Genes ◽  
Expression Data ◽  
Regulatory Interaction ◽  
Transcriptional Regulatory Networks ◽  
Interaction Models ◽  
Transcriptional Regulatory

Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the differential expression of a target gene in a TRN is challenging, especially when multiple TFs collaboratively participate in the transcriptional regulation. To unravel the roles of the TFs in the regulatory networks, we model the underlying regulatory interactions in terms of the TF–target interactions' directions (activation or repression) and their corresponding logical roles (necessary and/or sufficient). We design a set of constraints that relate gene expression patterns to regulatory interaction models, and develop TRIM (Transcriptional Regulatory Interaction Model Inference), a new hidden Markov model, to infer the models of TF–target interactions in large-scale TRNs of complex organisms. Besides, by training TRIM with wild-type time-series gene expression data, the activation timepoints of each regulatory module can be obtained. To demonstrate the advantages of TRIM, we applied it on yeast TRN to infer the TF–target interaction models for individual TFs as well as pairs of TFs in collaborative regulatory modules. By comparing with TF knockout and other gene expression data, we were able to show that the performance of TRIM is clearly higher than DREM (the best existing algorithm). In addition, on an individual Arabidopsis binding network, we showed that the target genes' expression correlations can be significantly improved by incorporating the TF–target regulatory interaction models inferred by TRIM into the expression data analysis, which may introduce new knowledge in transcriptional dynamics and bioactivation.

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