Transcriptional Control of Parturition: Insights from Gene Regulation Studies in the Myometrium

2021 ◽  
Author(s):  
Nawrah Khader ◽  
Virlana M Shchuka ◽  
Oksana Shynlova ◽  
Jennifer A Mitchell
Keyword(s):  
Transcription Factors ◽  
Regulatory Networks ◽  
Transcriptional Control ◽  
Progesterone Receptors ◽  
Activator Protein 1 ◽  
Transcriptional Regulatory Networks ◽  
Regulatory Pathways ◽  
Transcriptional Regulatory ◽  
Mechanistic Basis ◽  
Labour Onset

Abstract The onset of labour is a culmination of a series of highly coordinated and preparatory physiological events that take place throughout the gestational period. In order to produce the associated contractions needed for fetal delivery, smooth muscle cells in the muscular layer of the uterus (i.e. myometrium) undergo a transition from quiescent to contractile phenotypes. Here, we present the current understanding of the roles transcription factors play in critical labour-associated gene expression changes as part of the molecular mechanistic basis for this transition. Consideration is given to both transcription factors that have been well-studied in a myometrial context, i.e. activator protein 1 (AP-1), progesterone receptors (PRs), estrogen receptors (ERs), and nuclear factor kappa B (NF-κB), as well as additional transcription factors whose gestational event-driving contributions have been demonstrated more recently. These transcription factors may form pregnancy- and labour- associated transcriptional regulatory networks in the myometrium to modulate the timing of labour onset. A more thorough understanding of the transcription factor-mediated, labour-promoting regulatory pathways holds promise for the development of new therapeutic treatments that can be used for the prevention of preterm labour in at-risk women.

scholarly journals Dissecting the Repertoire of DNA-Binding Transcription Factors of the Archaeon Pyrococcus furiosus DSM 3638

Life ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 40 ◽  
Author(s):  
Antonia Denis ◽  
Mario Alberto Martínez-Núñez ◽  
Silvia Tenorio-Salgado ◽  
Ernesto Perez-Rueda
Keyword(s):  
Transcription Factors ◽  
Dna Binding ◽  
Regulatory Networks ◽  
Pyrococcus Furiosus ◽  
Structural Domain ◽  
Transcriptional Regulatory Networks ◽  
Dna Binding Domains ◽  
Binding Domains ◽  
Transcriptional Regulatory ◽  
Cellular Domain

In recent years, there has been a large increase in the amount of experimental evidence for diverse archaeal organisms, and these findings allow for a comprehensive analysis of archaeal genetic organization. However, studies about regulatory mechanisms in this cellular domain are still limited. In this context, we identified a repertoire of 86 DNA-binding transcription factors (TFs) in the archaeon Pyrococcus furiosus DSM 3638, that are clustered into 32 evolutionary families. In structural terms, 45% of these proteins are composed of one structural domain, 41% have two domains, and 14% have three structural domains. The most abundant DNA-binding domain corresponds to the winged helix-turn-helix domain; with few alternative DNA-binding domains. We also identified seven regulons, which represent 13.5% (279 genes) of the total genes in this archaeon. These analyses increase our knowledge about gene regulation in P. furiosus DSM 3638 and provide additional clues for comprehensive modeling of transcriptional regulatory networks in the Archaea cellular domain.

scholarly journals RNA-seq and ChIP-seq as Complementary Approaches for Comprehension of Plant Transcriptional Regulatory Mechanism

2019 ◽  
Vol 21 (1) ◽  
pp. 167 ◽  
Author(s):  
Isiaka Ibrahim Muhammad ◽  
Sze Ling Kong ◽  
Siti Nor Akmar Abdullah ◽  
Umaiyal Munusamy
Keyword(s):  
Transcriptional Regulation ◽  
Regulatory Networks ◽  
Data Sets ◽  
Rna Seq ◽  
Transcriptional Regulatory Networks ◽  
Regulatory Pathways ◽  
Specific Biological Process ◽  
Transcriptional Regulatory ◽  
Chromatin Immunoprecipitation Sequencing ◽  
Sequencing Platforms

The availability of data produced from various sequencing platforms offer the possibility to answer complex questions in plant research. However, drawbacks can arise when there are gaps in the information generated, and complementary platforms are essential to obtain more comprehensive data sets relating to specific biological process, such as responses to environmental perturbations in plant systems. The investigation of transcriptional regulation raises different challenges, particularly in associating differentially expressed transcription factors with their downstream responsive genes. In this paper, we discuss the integration of transcriptional factor studies through RNA sequencing (RNA-seq) and Chromatin Immunoprecipitation sequencing (ChIP-seq). We show how the data from ChIP-seq can strengthen information generated from RNA-seq in elucidating gene regulatory mechanisms. In particular, we discuss how integration of ChIP-seq and RNA-seq data can help to unravel transcriptional regulatory networks. This review discusses recent advances in methods for studying transcriptional regulation using these two methods. It also provides guidelines for making choices in selecting specific protocols in RNA-seq pipelines for genome-wide analysis to achieve more detailed characterization of specific transcription regulatory pathways via ChIP-seq.

scholarly journals An Integrated Database of Small RNAs and Their Interplay With Transcriptional Gene Regulatory Networks in Corynebacteria

2021 ◽  
Vol 12 ◽  
Author(s):  
Mariana Teixeira Dornelles Parise ◽  
Doglas Parise ◽  
Flavia Figueira Aburjaile ◽  
Anne Cybelle Pinto Gomide ◽  
Rodrigo Bentes Kato ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Small Rnas ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Networks ◽  
Bacterial Gene ◽  
Bacterial Gene Expression ◽  
Transcriptional Regulatory ◽  
Animal Pathogens ◽  
Gene Regulatory

Small RNAs (sRNAs) are one of the key players in the post-transcriptional regulation of bacterial gene expression. These molecules, together with transcription factors, form regulatory networks and greatly influence the bacterial regulatory landscape. Little is known concerning sRNAs and their influence on the regulatory machinery in the genus Corynebacterium, despite its medical, veterinary and biotechnological importance. Here, we expand corynebacterial regulatory knowledge by integrating sRNAs and their regulatory interactions into the transcriptional regulatory networks of six corynebacterial species, covering four human and animal pathogens, and integrate this data into the CoryneRegNet database. To this end, we predicted sRNAs to regulate 754 genes, including 206 transcription factors, in corynebacterial gene regulatory networks. Amongst them, the sRNA Cd-NCTC13129-sRNA-2 is predicted to directly regulate ydfH, which indirectly regulates 66 genes, including the global regulator glxR in C. diphtheriae. All of the sRNA-enriched regulatory networks of the genus Corynebacterium have been made publicly available in the newest release of CoryneRegNet(www.exbio.wzw.tum.de/coryneregnet/) to aid in providing valuable insights and to guide future experiments.

scholarly journals Genome-wide screening of upstream transcription factors using an expression library

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 51
Author(s):  
Naoya Yahagi ◽  
Yoshinori Takeuchi
Keyword(s):  
Transcription Factors ◽  
Regulatory Networks ◽  
Dependent Manner ◽  
Expression Library ◽  
Transcriptional Regulatory Networks ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
Powerful Approach ◽  
Transcription Factor Expression ◽  
Expression Cdna Library

The identification of upstream transcription factors regulating the expression of a gene is generally not an easy process.  To facilitate this task, we constructed an expression cDNA library named Transcription Factor Expression Library (TFEL), which is composed of nearly all the transcription factors in the mouse genome. Genome-wide screening using this library (TFEL scan method) enables us to easily identify transcription factors controlling any given promoter or enhancer of interest in a chromosomal context-dependent manner. Thus, TFEL scan method is a powerful approach to explore transcriptional regulatory networks.

scholarly journals Transcriptional integration of plant responses to iron availability

2020 ◽  
Author(s):  
Fei Gao ◽  
Christian Dubos
Keyword(s):  
Transcription Factors ◽  
Regulatory Networks ◽  
Iron Homeostasis ◽  
Future Research ◽  
Plant Responses ◽  
Transcriptional Regulatory Networks ◽  
Helix Loop Helix ◽  
Regulatory Cascade ◽  
Transcriptional Regulatory ◽  
Electron Transport Chains

Abstract Iron is one of the most important micronutrients for plant growth and development. It functions as the enzyme cofactor or component of electron transport chains in various vital metabolic processes, including photosynthesis, respiration, and amino acid biosynthesis. To maintain iron homeostasis, and therefore prevent any deficiency or excess that could be detrimental, plants have evolved complex transcriptional regulatory networks to tightly control iron uptake, translocation, assimilation, and storage. These regulatory networks are composed of various transcription factors; among them, members of the basic helix-loop-helix (bHLH) family play an essential role. Here, we first review recent advances in understanding the roles of bHLH transcription factors involved in the regulatory cascade controlling iron homeostasis in the model plant Arabidopsis, and extend this understanding to rice and other plant species. The importance of other classes of transcription factors will also be discussed. Second, we elaborate on the post-translational mechanisms involved in the regulation of these regulatory networks. Finally, we provide some perspectives on future research that should be conducted in order to precisely understand how plants control the homeostasis of this micronutrient.

scholarly journals ANGI-01. IDENTIFICATION OF INVASION-DETERMINISTIC TRANSCRIPTION FACTORS IN GLIOBLASTOMA USING INTEGRATIVE TRANSCRIPTOME ANALYSIS

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi30-vi30
Author(s):  
Junseong Park ◽  
Jin-Kyoung Shim ◽  
Jae Eun Lee ◽  
Seon Jin Yoon ◽  
Jihwan Yoo ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Transcriptome Analysis ◽  
Poor Prognosis ◽  
Regulatory Networks ◽  
Xenograft Model ◽  
Mr Images ◽  
Transcriptional Regulatory Networks ◽  
Orthotopic Xenograft ◽  
Orthotopic Xenograft Model ◽  
Transcriptional Regulatory

Abstract Glioblastoma (GBM) is one of the most lethal human tumors with a highly infiltrative phenotype. Although invasiveness is related to poor prognosis, no therapeutic intervention targeting invasion is available for treatment of GBM patient, partially due to the diversity and redundancy of invasion machinery genes. In this regard, additional identification of deterministic and causative targets for invasion is required. Invasiveness of GBM patients and matched tumorspheres (TSs) was quantified using MR images and collagen-based 3D invasion assays, respectively. Transcriptome of GBM samples were obtained using microarrays. The knockdown effects of invasion-deterministic transcription factors (TFs) were evaluated using western blot and mouse orthotopic xenograft model. This study is aimed to identify novel transcriptional regulatory networks, which can collectively modulate invasion-involved genes in GBM. After classification of 23 GBM patient-derived TSs into low and high invasion groups, we applied single sample gene set enrichment analysis using TF target gene sets. According to enrichment scores, TFs responsible for low (PCBP1) and high (STAT3 and SRF) invasiveness were identified. Consistently with computational prediction, knockdown of PCBP1 significantly increased invasion area, whereas knockdown of STAT3 or SRF significantly suppressed invasive properties in all tested TSs. Notably, MR images showed coherent patterns with invasion of originated TS, and high invasiveness was associated with poor prognosis. In addition, mouse orthotopic xenograft model using TSs with down-regulated STAT3 or SRF showed significantly prolonged survival time compared to control. We identified invasion-deterministic TFs in glioblastoma using integrative transcriptome analysis. Owing to relationship among these transcriptional regulatory networks, invasive phenotype, and prognosis, we suggest that these TFs as novel drug targets for GBM.

scholarly journals Unraveling the functions of uncharacterized transcription factors in Escherichia coli using ChIP-exo

2021 ◽  
Author(s):  
Ye Gao ◽  
Hyun Gyu Lim ◽  
Hans Verkler ◽  
Richard Szubin ◽  
Daniel Quach ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Transcription Factors ◽  
Dna Binding ◽  
Binding Sites ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Networks ◽  
Cellular Processes ◽  
Transcriptional Regulatory ◽  
Mutant Phenotypes ◽  
K 12

Bacteria regulate gene expression to adapt to changing environments through transcriptional regulatory networks (TRNs). Although extensively studied, no TRN is fully characterized since the identity and activity of all the transcriptional regulators that comprise a TRN are not known. Here, we experimentally evaluate 40 uncharacterized proteins in Escherichia coli K-12 MG1655, which were computationally predicted to be transcription factors (TFs). First, we used a multiplexed ChIP-exo assay to characterize genome-wide binding sites for these candidate TFs; 34 of them were found to be DNA-binding protein. We then compared the relative location between binding sites and RNA polymerase (RNAP). We found 48% (283/588) overlap between the TFs and RNAP. Finally, we used these data to infer potential functions for 10 of the 34 TFs with validated DNA binding sites and consensus binding motifs. These TFs were found to have various roles in regulating primary cellular processes in E. coli. Taken together, this study: (1) significantly expands the number of confirmed TFs, close to the estimated total of about 280 TFs; (2) predicts the putative functions of the newly discovered TFs, and (3) confirms the functions of representative TFs through mutant phenotypes.

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.

scholarly journals Genome-wide screening of upstream transcription factors using an expression library

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 51
Author(s):  
Naoya Yahagi ◽  
Yoshinori Takeuchi
Keyword(s):  
Transcription Factors ◽  
Regulatory Networks ◽  
Dependent Manner ◽  
Expression Library ◽  
Transcriptional Regulatory Networks ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
Powerful Approach ◽  
Transcription Factor Expression ◽  
Expression Cdna Library

The identification of upstream transcription factors regulating the expression of a gene is generally not an easy process.  To facilitate this task, we constructed an expression cDNA library named Transcription Factor Expression Library (TFEL), which is composed of nearly all the transcription factors in the mouse genome. Genome-wide screening using this library (TFEL scan method) enables us to easily identify transcription factors controlling any given promoter or enhancer of interest in a chromosomal context-dependent manner. Thus, TFEL scan method is a powerful approach to explore transcriptional regulatory networks.

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