scholarly journals ModEx: A text mining system for extracting mode of regulation of transcription factor-gene regulatory interaction

2020 ◽  
Vol 102 ◽  
pp. 103353 ◽  
Author(s):  
Saman Farahmand ◽  
Todd Riley ◽  
Kourosh Zarringhalam
Keyword(s):  
Transcription Factor ◽  
Text Mining ◽  
Regulation Of Transcription ◽  
Transcription Factor Gene ◽  
Regulatory Interaction ◽  
Mining System ◽  
Factor Gene ◽  
Gene Regulatory ◽  
Text Mining System ◽  
Mode Of Regulation

scholarly journals ModEx: A text mining system for extracting mode of regulation of Transcription Factor-gene regulatory interaction

2019 ◽  
Author(s):  
Saman Farahmand ◽  
Todd Riley ◽  
Kourosh Zarringhalam
Keyword(s):  
Gene Expression ◽  
Text Mining ◽  
Gene Interactions ◽  
Mining System ◽  
Link Type ◽  
Gene Regulatory ◽  
Rest Api ◽  
Tf Gene ◽  
Text Mining System ◽  
Mode Of Regulation

ABSTRACTBackgroundTranscription factors (TFs) are proteins that are fundamental to transcription and regulation of gene expression. Each TF may regulate multiple genes and each gene may be regulated by multiple TFs. TFs can act as either activator or repressor of gene expression. This complex network of interactions between TFs and genes underlies many developmental and biological processes and is implicated in several human diseases such as cancer. Hence deciphering the network of TF-gene interactions with information on mode of regulation (activation vs. repression) is an important step toward understanding the regulatory pathways that underlie complex traits. There are many experimental, computational, and manually curated databases of TF-gene interactions. In particular, high-throughput ChIP-Seq datasets provide a large-scale map or transcriptional regulatory interactions. However, these interactions are not annotated with information on context and mode of regulation. Such information is crucial to gain a global picture of gene regulatory mechanisms and can aid in developing machine learning models for applications such as biomarker discovery, prediction of response to therapy, and precision medicine.MethodsIn this work, we introduce a text-mining system to annotate ChIP-Seq derived interaction with such meta data through mining PubMed articles. We evaluate the performance of our system using gold standard small scale manually curated databases.ResultsOur results show that the method is able to accurately extract mode of regulation with F-score 0.77 on TRRUST curated interaction and F-score 0.96 on intersection of TRUSST and ChIP-network. We provide a HTTP REST API for our code to facilitate usage.AvailibilitySource code and datasets are available for download on GitHub: https://github.com/samanfrm/modex HTTP REST API: https://watson.math.umb.edu/modex/[type query]

scholarly journals Weighted gene co-expression network-based approach to identify key genes associated with anthracycline-induced cardiotoxicity and construction of miRNA-transcription factor-gene regulatory network

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Guoxing Wan ◽  
Peinan Chen ◽  
Xue Sun ◽  
Xiaojun Cai ◽  
Xiongjie Yu ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Signaling Pathway ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Transcription Factor Gene ◽  
Transcription Profiling ◽  
Factor Gene ◽  
Gene Regulatory ◽  
Key Genes

Abstract Background Cardiotoxicity is a common complication following anthracycline chemotherapy and represents one of the serious adverse reactions affecting life, which severely limits the effective use of anthracyclines in cancer therapy. Although some genes have been investigated by individual studies, the comprehensive analysis of key genes and molecular regulatory network in anthracyclines-induced cardiotoxicity (AIC) is lacking but urgently needed. Methods The present study integrating several transcription profiling datasets aimed to identify key genes associated with AIC by weighted correlation network analysis (WGCNA) and differentially expressed analysis (DEA) and also constructed miRNA-transcription factor-gene regulatory network. A total of three transcription profiling datasets involving 47 samples comprising 41 rat heart tissues and 6 human induced pluripotent stem cell-derived cardiomyocytes (hiPSCMs) samples were enrolled. Results The WGCNA and DEA with E-MTAB-1168 identified 14 common genes affected by doxorubicin administrated by 4 weeks or 6 weeks. Functional and signal enrichment analyses revealed that these genes were mainly enriched in the regulation of heart contraction, muscle contraction, heart process, and oxytocin signaling pathway. Ten (Ryr2, Casq1, Fcgr2b, Postn, Tceal5, Ccn2, Tnfrsf12a, Mybpc2, Ankrd23, Scn3b) of the 14 genes were verified by another gene expression profile GSE154603. Importantly, three key genes (Ryr2, Tnfrsf12a, Scn3b) were further validated in a hiPSCMs-based in-vitro model. Additionally, the miRNA-transcription factor-gene regulatory revealed several top-ranked transcription factors including Tcf12, Ctcf, Spdef, Ebf1, Sp1, Rcor1 and miRNAs including miR-124-3p, miR-195-5p, miR-146a-5p, miR-17-5p, miR-15b-5p, miR-424-5p which may be involved in the regulation of genes associated with AIC. Conclusions Collectively, the current study suggested the important role of the key genes, oxytocin signaling pathway, and the miRNA-transcription factor-gene regulatory network in elucidating the molecular mechanism of AIC.

scholarly journals Differential regulation of transcription factor gene expression and phenotypic markers in developing sympathetic neurons

Development ◽  
1995 ◽  
Vol 121 (3) ◽  
pp. 887-901 ◽  
Author(s):  
A.K. Groves ◽  
K.M. George ◽  
J.P. Tissier-Seta ◽  
J.D. Engel ◽  
J.F. Brunet ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Zinc Finger Protein ◽  
Sympathetic Neurons ◽  
Floor Plate ◽  
Neuronal Membrane ◽  
Regulation Of Transcription ◽  
Transcription Factor Gene ◽  
Phenotypic Markers ◽  
Factor Gene

We have examined the regulation of transcription factor gene expression and phenotypic markers in developing chick sympathetic neurons. Sympathetic progenitor cells first express the bHLH transcriptional regulator Cash-1 (a chicken achaete-scute homologue), followed by coordinate expression of Phox2, a paired homeodomain protein, and GATA-2, a zinc finger protein. SCG10, a pan-neuronal membrane protein, is first detected one stage later, followed by the catecholaminergic neurotransmitter enzyme tyrosine hydroxylase (TH). We have used these markers to ask two questions: (1) is their expression dependent upon inductive signals derived from the notochord or floor plate?; (2) does their sequential expression reflect a single linear pathway or multiple parallel pathways? Notochord ablation experiments indicate that the floor plate is essential for induction of GATA-2, Phox2 and TH, but not for that of Cash-1 and SCG10. Taken together these data suggest that the development of sympathetic neurons involves multiple transcriptional regulatory cascades: one, dependent upon notochord or floor plate-derived signals and involving Phox2 and GATA-2, is assigned to the expression of the neurotransmitter phenotype; the other, independent of such signals and involving Cash-1, is assigned to the expression of pan-neuronal properties. The parallel specification of different components of the terminal neuronal phenotype is likely to be a general feature of neuronal development.

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