scholarly journals Dynamics of genetic variation in transcription factors and its implications for the evolution of regulatory networks in Bacteria

2020 ◽  
Vol 48 (8) ◽  
pp. 4100-4114
Author(s):  
Farhan Ali ◽  
Aswin Sai Narain Seshasayee
Keyword(s):  
Genetic Variation ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Large Scale ◽  
Mutation Frequency ◽  
Target Genes ◽  
Point Mutations ◽  
Whole Genome Sequencing Data ◽  
Laboratory Evolution

Abstract The evolution of regulatory networks in Bacteria has largely been explained at macroevolutionary scales through lateral gene transfer and gene duplication. Transcription factors (TF) have been found to be less conserved across species than their target genes (TG). This would be expected if TFs accumulate mutations faster than TGs. This hypothesis is supported by several lab evolution studies which found TFs, especially global regulators, to be frequently mutated. Despite these studies, the contribution of point mutations in TFs to the evolution of regulatory network is poorly understood. We tested if TFs show greater genetic variation than their TGs using whole-genome sequencing data from a large collection of Escherichia coli isolates. TFs were less diverse than their TGs across natural isolates, with TFs of large regulons being more conserved. In contrast, TFs showed higher mutation frequency in adaptive laboratory evolution experiments. However, over long-term laboratory evolution spanning 60 000 generations, mutation frequency in TFs gradually declined after a rapid initial burst. Extrapolating the dynamics of genetic variation from long-term laboratory evolution to natural populations, we propose that point mutations, conferring large-scale gene expression changes, may drive the early stages of adaptation but gene regulation is subjected to stronger purifying selection post adaptation.

scholarly journals Dynamics of genetic variation in Transcription Factors and its implications for the evolution of regulatory networks in Bacteria

2019 ◽  
Author(s):  
Farhan Ali ◽  
Aswin Sai Narain Seshasayee
Keyword(s):  
Genetic Variation ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Large Scale ◽  
Target Genes ◽  
Point Mutations ◽  
Purifying Selection ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Global Regulators

AbstractThe evolution of bacterial regulatory networks has largely been explained at macroevolutionary scales through lateral gene transfer and gene duplication. Transcription factors (TF) have been found to be less conserved across species than their target genes (TG). This would be expected if TFs accumulate mutations faster than TGs. This hypothesis is supported by several lab evolution studies which found TFs, especially global regulators, to be frequently mutated. Despite these studies, the contribution of point mutations in TFs to the evolution of regulatory network is poorly understood. We tested if TFs show greater genetic variation than their TGs using whole-genome sequencing data from a large collection of E coli isolates. We found TFs to be less diverse, across natural isolates, due to their regulatory roles. TFs were enriched in mutations in multiple adaptive lab evolution studies but not in mutation accumulation. However, over long-term evolution, relative frequency of mutations in TFs showed a gradual decay after a rapid initial burst. Our results suggest that point mutations, conferring large-scale expression changes, may drive the early stages of adaptation but gene regulation is subjected to stronger purifying selection post adaptation.

scholarly journals WRKY Transcription Factors in Cassava Contribute to Regulation of Tolerance and Susceptibility to Cassava Mosaic Disease through Stress Responses

Viruses ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1820
Author(s):  
Warren Freeborough ◽  
Nikki Gentle ◽  
Marie E. C. Rey
Keyword(s):  
Transcription Factors ◽  
Stress Responses ◽  
Regulatory Networks ◽  
Large Scale ◽  
Mosaic Disease ◽  
African Cassava Mosaic Virus ◽  
Cassava Mosaic Disease ◽  
Manihot Esculenta Crantz ◽  
Transcriptional Reprogramming ◽  
Negative Role

Among the numerous biological constraints that hinder cassava (Manihot esculenta Crantz) production, foremost is cassava mosaic disease (CMD) caused by virus members of the family Geminiviridae, genus Begomovirus. The mechanisms of CMD tolerance and susceptibility are not fully understood; however, CMD susceptible T200 and tolerant TME3 cassava landraces have been shown to exhibit different large-scale transcriptional reprogramming in response to South African cassava mosaic virus (SACMV). Recent identification of 85 MeWRKY transcription factors in cassava demonstrated high orthology with those in Arabidopsis, however, little is known about their roles in virus responses in this non-model crop. Significant differences in MeWRKY expression and regulatory networks between the T200 and TME3 landraces were demonstrated. Overall, WRKY expression and associated hormone and enriched biological processes in both landraces reflect oxidative and other biotic stress responses to SACMV. Notably, MeWRKY11 and MeWRKY81 were uniquely up and downregulated at 12 and 67 days post infection (dpi) respectively in TME3, implicating a role in tolerance and symptom recovery. AtWRKY28 and AtWRKY40 homologs of MeWRKY81 and MeWRKY11, respectively, have been shown to be involved in regulation of jasmonic and salicylic acid signaling in Arabidopsis. AtWRKY28 is an interactor in the RPW8-NBS resistance (R) protein network and downregulation of its homolog MeWRKY81 at 67 dpi in TME3 suggests a negative role for this WRKY in SACMV tolerance. In contrast, in T200, nine MeWRKYs were differentially expressed from early (12 dpi), middle (32 dpi) to late (67 dpi) infection. MeWRKY27 (homolog AtWRKY33) and MeWRKY55 (homolog AtWRKY53) were uniquely up-regulated at 12, 32 and 67 dpi in T200. AtWRKY33 and AtWRKY53 are positive regulators of leaf senescence and oxidative stress in Arabidopsis, suggesting MeWRKY55 and 27 contribute to susceptibility in T200.

scholarly journals Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

2019 ◽  
Author(s):  
Ning Wang ◽  
Andrew E. Teschendorff
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Large Scale ◽  
Single Cells ◽  
Differential Expression Analysis ◽  
Dropout Rate ◽  
Rna Seq ◽  
Regulatory Activity

AbstractInferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

Identification of transcription factors MYC and C/EBPβ mediated regulatory networks in heart failure based on GEO dataset

2020 ◽  
Author(s):  
Haiwei Wang ◽  
Xinrui Wang ◽  
Liangpu Xu ◽  
Hua Cao
Keyword(s):  
Heart Failure ◽  
Transcription Factors ◽  
Signaling Pathways ◽  
Signaling Pathway ◽  
Insulin Signaling ◽  
Regulatory Networks ◽  
Target Genes ◽  
Transcriptional Profiling ◽  
Insulin Signaling Pathway ◽  
Failing Heart

Abstract Background: Heart failure is one of leading cause of death worldwide. However, the transcriptional profiling of heart failure is unclear. Moreover, the signaling pathways and transcription factors involving the heart failure development also are largely unknown. Using published Gene Expression Omnibus (GEO) datasets, in the present study, we aim to comprehensively analyze the differentially expressed genes in failing heart tissues, and identified the critical signaling pathways and transcription factors involving heart failure development. Methods: The transcriptional profiling of heart failure was identified from previously published gene expression datasets deposited in GSE5406, GSE16499 and GSE68316. The enriched signaling pathways and transcription factors were analyzed using DAVID website and gene set enrichment analysis (GSEA) assay. The transcriptional networks were created by Cytoscape. Results: Compared with the normal heart tissues, 90 genes were particularly differentially expressed in failing heart tissues, and those genes were associated with multiple metabolism signaling pathways and insulin signaling pathway. Metabolism and insulin signaling pathway were both inactivated in failing heart tissues. Transcription factors MYC and C/EBPβ were both negatively associated with the expression profiling of failing heart tissues in GSEA assay. Moreover, compared with normal heart tissues, MYC and C/EBPβ were down regulated in failing heart tissues. Furthermore, MYC and C/EBPβ mediated downstream target genes were also decreased in failing heart tissues. MYC and C/EBPβ were positively correlated with each other. At last, we constructed MYC and C/EBPβ mediated regulatory networks in failing heart tissues, and identified the MYC and C/EBPβ target genes which had been reported involving the heart failure developmental progress. Conclusions: Our results suggested that metabolism pathways and insulin signaling pathway, transcription factors MYC and C/EBPβ played critical roles in heart failure developmental progress.

scholarly journals Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets

2016 ◽  
Vol 113 (13) ◽  
pp. E1835-E1843 ◽  
Author(s):  
Mina Fazlollahi ◽  
Ivor Muroff ◽  
Eunjee Lee ◽  
Helen C. Causton ◽  
Harmen J. Bussemaker
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Nonsynonymous Mutation ◽  
Gene Regulatory ◽  
Genetic Modulators

Regulation of gene expression by transcription factors (TFs) is highly dependent on genetic background and interactions with cofactors. Identifying specific context factors is a major challenge that requires new approaches. Here we show that exploiting natural variation is a potent strategy for probing functional interactions within gene regulatory networks. We developed an algorithm to identify genetic polymorphisms that modulate the regulatory connectivity between specific transcription factors and their target genes in vivo. As a proof of principle, we mapped connectivity quantitative trait loci (cQTLs) using parallel genotype and gene expression data for segregants from a cross between two strains of the yeast Saccharomyces cerevisiae. We identified a nonsynonymous mutation in the DIG2 gene as a cQTL for the transcription factor Ste12p and confirmed this prediction empirically. We also identified three polymorphisms in TAF13 as putative modulators of regulation by Gcn4p. Our method has potential for revealing how genetic differences among individuals influence gene regulatory networks in any organism for which gene expression and genotype data are available along with information on binding preferences for transcription factors.

scholarly journals The PARP Inhibitor Olaparib Modulates the Transcriptional Regulatory Networks of Long Non-Coding RNAs during Vasculogenic Mimicry

Cells ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2690
Author(s):  
Mónica Fernández-Cortés ◽  
Eduardo Andrés-León ◽  
Francisco Javier Oliver
Keyword(s):  
Transcription Factors ◽  
Binding Sites ◽  
Regulatory Networks ◽  
Target Genes ◽  
Parp Inhibitor ◽  
Vasculogenic Mimicry ◽  
Transcriptome Profiling ◽  
Key Species ◽  
Non Coding Rnas ◽  
The Impact

In highly metastatic tumors, vasculogenic mimicry (VM) involves the acquisition by tumor cells of endothelial-like traits. Poly-(ADP-ribose) polymerase (PARP) inhibitors are currently used against tumors displaying BRCA1/2-dependent deficient homologous recombination, and they may have antimetastatic activity. Long non-coding RNAs (lncRNAs) are emerging as key species-specific regulators of cellular and disease processes. To evaluate the impact of olaparib treatment in the context of non-coding RNA, we have analyzed the expression of lncRNA after performing unbiased whole-transcriptome profiling of human uveal melanoma cells cultured to form VM. RNAseq revealed that the non-coding transcriptomic landscape differed between olaparib-treated and non-treated cells: olaparib significantly modulated the expression of 20 lncRNAs, 11 lncRNAs being upregulated, and 9 downregulated. We subjected the data to different bioinformatics tools and analysis in public databases. We found that copy-number variation alterations in some olaparib-modulated lncRNAs had a statistically significant correlation with alterations in some key tumor suppressor genes. Furthermore, the lncRNAs that were modulated by olaparib appeared to be regulated by common transcription factors: ETS1 had high-score binding sites in the promoters of all olaparib upregulated lncRNAs, while MZF1, RHOXF1 and NR2C2 had high-score binding sites in the promoters of all olaparib downregulated lncRNAs. Finally, we predicted that olaparib-modulated lncRNAs could further regulate several transcription factors and their subsequent target genes in melanoma, suggesting that olaparib may trigger a major shift in gene expression mediated by the regulation lncRNA. Globally, olaparib changed the lncRNA expression landscape during VM affecting angiogenesis-related genes.

scholarly journals Experiment level curation identifies high confidence transcriptional regulatory interactions in neurodevelopment

2021 ◽  
Author(s):  
Eric Ching-Pan Chu ◽  
Alexander Morin ◽  
Tak Hou Calvin Chang ◽  
Tue Nguyen ◽  
Yi-Cheng Tsai ◽  
...  
Keyword(s):  
Nervous System ◽  
Experimental Evidence ◽  
Regulatory Networks ◽  
Large Scale ◽  
Target Genes ◽  
High Confidence ◽  
Transcriptional Regulatory Networks ◽  
Regulatory Interactions ◽  
Transcriptional Regulatory ◽  
Significant Enrichment

To facilitate the development of large-scale transcriptional regulatory networks (TRNs) that may enable in-silico analyses of disease mechanisms, a reliable catalogue of experimentally verified direct transcriptional regulatory interactions (DTRIs) is needed for training and validation. There has been a long history of using low-throughput experiments to validate single DTRIs. Therefore, we hypothesize that a reliable set of DTRIs could be produced by curating the published literature for such evidence. In our survey of previous curation efforts, we identified the lack of details about the quantity and the types of experimental evidence to be a major gap, despite the importance of such details for the identification of bona fide DTRIs. We developed a curation protocol to inspect the published literature for support of DTRIs at the experiment level, focusing on genes important to the development of the mammalian nervous system. We sought to record three types of low-throughput experiments: Transcription factor (TF) perturbation, TF-DNA binding, and TF-reporter assays. Using this protocol, we examined a total of 1,310 papers to assemble a collection of 1,499 unique DTRIs, involving 251 TFs and 825 target genes, many of which were not reported in any other DTRI resource. The majority of DTRIs (965, 64%) were supported by two or more types of experimental evidence and 27% were supported by all three. Of the DTRIs with all three types of evidence, 170 had been tested using primary tissues or cells and 44 had been tested directly in the central nervous system. We used our resource to document research biases among reports towards a small number of well-studied TFs. To demonstrate a use case for this resource, we compared our curation to a previously published high-throughput perturbation screen and found significant enrichment of the curated targets among genes differentially expressed in the developing brain in response to Pax6 deletion. This study demonstrates a proof-of-concept for the assembly of a high confidence DTRI resource in order to support the development of large-scale TRNs.

scholarly journals FLI1 and FRA1 transcription factors drive the transcriptional regulatory networks characterizing muscle invasive bladder cancer

2021 ◽  
Author(s):  
Perihan Yagmur Guneri Sozeri ◽  
Gulden Ozden Yilmaz ◽  
Asli Kisim ◽  
Aleyna Eray ◽  
Hamdiye Uzuner ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Target Genes ◽  
Cell Junction ◽  
Transcriptional Regulatory Networks ◽  
Histopathological Classification ◽  
Knock Down ◽  
Muscle Invasive ◽  
Regulatory Landscape

Bladder cancer is mostly present in the form of urothelium carcinoma, causing over 150.000 deaths each year. Its histopathological classification as muscle invasive (MIBC) and non-muscle invasive (NMIBC) is the most prominent aspect, affecting the prognosis and progression of this disease. In this study, we defined the active regulatory landscape of MIBC and NMIBC cell lines using H3K27ac-seq and used an integrative data approach to combine our findings with existing data. Our analysis revealed FRA1 and FLI1 as the two critical transcription factors differentially regulating MIBC regulatory landscape. Importantly, we show that FRA1 and FLI1 regulate the genes involved in epithelial cell migration and cell junction organization. Knock-down of FRA1 and FLI1 in MIBC revealed the downregulation of several EMT-related genes such as MAP4K4 and FLOT1. Further, ChIP-SICAP performed for FRA1 and FLI1 enabled us to infer chromatin binding partners of these two transcription factors and link this information with their target genes, providing a comprehensive regulatory circuit for the genes implicated in invasive ability of the bladder cancer cells. Finally, for the first time we show that knock-down of FRA1 and FRA1-FLI1 double knock-down results in significant reduction of invasion capacity of MIBC cells towards muscle microenvironment using IC-CHIP assays. Our results collectively highlight the role of these two transcription factors in invasive characteristics of bladder cancer in selection and design of targeted options for treatment of MIBC.

scholarly journals The Wheat GENIE3 Network Provides Biologically-Relevant Information in Polyploid Wheat

2020 ◽  
Vol 10 (10) ◽  
pp. 3675-3686 ◽  
Author(s):  
Sophie A. Harrington ◽  
Anna E. Backhaus ◽  
Ajit Singh ◽  
Keywan Hassani-Pak ◽  
Cristobal Uauy
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Candidate Gene ◽  
Web Application ◽  
Regulatory Networks ◽  
Target Genes ◽  
Gene Prediction ◽  
Relevant Information ◽  
Rna Seq ◽  
Biologically Relevant

Gene regulatory networks are powerful tools which facilitate hypothesis generation and candidate gene discovery. However, the extent to which the network predictions are biologically relevant is often unclear. Recently a GENIE3 network which predicted targets of wheat transcription factors was produced. Here we used an independent RNA-Seq dataset to test the predictions of the wheat GENIE3 network for the senescence-regulating transcription factor NAM-A1 (TraesCS6A02G108300). We re-analyzed the RNA-Seq data against the RefSeqv1.0 genome and identified a set of differentially expressed genes (DEGs) between the wild-type and nam-a1 mutant which recapitulated the known role of NAM-A1 in senescence and nutrient remobilisation. We found that the GENIE3-predicted target genes of NAM-A1 overlap significantly with the DEGs, more than would be expected by chance. Based on high levels of overlap between GENIE3-predicted target genes and the DEGs, we identified candidate senescence regulators. We then explored genome-wide trends in the network related to polyploidy and found that only homeologous transcription factors are likely to share predicted targets in common. However, homeologs which vary in expression levels across tissues are less likely to share predicted targets than those that do not, suggesting that they may be more likely to act in distinct pathways. This work demonstrates that the wheat GENIE3 network can provide biologically-relevant predictions of transcription factor targets, which can be used for candidate gene prediction and for global analyses of transcription factor function. The GENIE3 network has now been integrated into the KnetMiner web application, facilitating its use in future studies.

scholarly journals Identification of the Regulatory Elements and Target Genes of Megakaryopoietic Transcription Factor MEF2C

2019 ◽  
Vol 119 (05) ◽  
pp. 716-725 ◽  
Author(s):  
Xianguo Kong ◽  
Lin Ma ◽  
Edward Chen ◽  
Chad Shaw ◽  
Leonard Edelstein
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Genetic Variation ◽  
Transcription Factors ◽  
Cell Fate ◽  
Target Genes ◽  
Regulatory Elements ◽  
Haematopoietic Stem Cells ◽  
Platelet Production ◽  
Induced Pluripotent

AbstractMegakaryopoiesis produces specialized haematopoietic stem cells in the bone marrow that give rise to megakaryocytes which ultimately produce platelets. Defects in megakaryopoiesis can result in altered platelet counts and physiology, leading to dysfunctional haemostasis and thrombosis. Additionally, dysregulated megakaryopoiesis is also associated with myeloid pathologies. Transcription factors play critical roles in cell differentiation by regulating the temporal and spatial patterns of gene expression which ultimately decide cell fate. Several transcription factors have been described as regulating megakaryopoiesis including myocyte enhancer factor 2C (MEF2C); however, the genes regulated by MEF2C that influence megakaryopoiesis have not been reported. Using chromatin immunoprecipitation-sequencing and Gene Ontology data we identified five candidate genes that are bound by MEF2C and regulate megakaryopoiesis: MOV10, AGO3, HDAC1, RBBP5 and WASF2. To study expression of these genes, we silenced MEF2C gene expression in the Meg01 megakaryocytic cell line and in induced pluripotent stem cells by CRISPR/Cas9 editing. We also knocked down MEF2C expression in cord blood-derived haematopoietic stem cells by siRNA. We found that absent or reduced MEF2C expression resulted in defects in megakaryocytic differentiation and reduced levels of the candidate target genes. Luciferase assays confirmed that genomic sequences within the target genes are regulated by MEF2C levels. Finally, we demonstrate that small deletions linked to a platelet count-associated single nucleotide polymorphism alter transcriptional activity, suggesting a mechanism by which genetic variation in MEF2C alters platelet production. These data help elucidate the mechanism behind MEF2C regulation of megakaryopoiesis and genetic variation driving platelet production.

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