scholarly journals Common ancestry of heterodimerizing TALE homeobox transcription factors across Metazoa and Archaeplastida

2018 ◽  
Author(s):  
Sunjoo Joo ◽  
Ming Hsiu Wang ◽  
Gary Lui ◽  
Jenny Lee ◽  
Andrew Barnas ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Bioinformatics Analysis ◽  
Land Plants ◽  
Protein Protein Interaction ◽  
Functional Conservation ◽  
Developmental Mechanisms ◽  
Gene Regulatory ◽  
Homeobox Transcription Factors

AbstractHomeobox transcription factors (TFs) in the TALE superclass are deeply embedded in the gene regulatory networks that orchestrate embryogenesis. Knotted-like homeobox (KNOX) TFs, homologous to animal MEIS, have been found to drive the haploid-to-diploid transition in both unicellular green algae and land plants via heterodimerization with other TALE superclass TFs, representing remarkable functional conservation of a developmental TF across lineages that diverged one billion years ago. To delineate the ancestry of TALE-TALE heterodimerization, we analyzed TALE endowment in the algal radiations of Archaeplastida, ancestral to land plants. Homeodomain phylogeny and bioinformatics analysis partitioned TALEs into two broad groups, KNOX and non-KNOX. Each group shares previously defined heterodimerization domains, plant KNOX-homology in the KNOX group and animal PBC-homology in the non-KNOX group, indicating their deep ancestry. Protein-protein interaction experiments showed that the TALEs in the two groups all participated in heterodimerization. These results indicate that the TF dyads consisting of KNOX/MEIS and PBC-containing TALEs must have evolved early in eukaryotic evolution, a likely function being to accurately execute the haploid-to-diploid transitions during sexual development.Author summaryComplex multicellularity requires elaborate developmental mechanisms, often based on the versatility of heterodimeric transcription factor (TF) interactions. Highly conserved TALE-superclass homeobox TF networks in major eukaryotic lineages suggest deep ancestry of developmental mechanisms. Our results support the hypothesis that in early eukaryotes, the TALE heterodimeric configuration provided transcription-on switches via dimerization-dependent subcellular localization, ensuring execution of the haploid-to-diploid transition only when the gamete fusion is correctly executed between appropriate partner gametes, a system that then diversified in the several lineages that engage in complex multicellular organization.

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 Upregulation and cell specificity of C4 genes are derived from ancestral C3 gene regulatory networks

2020 ◽  
Author(s):  
Pallavi Singh ◽  
Sean R. Stevenson ◽  
Ivan Reyna-Llorens ◽  
Gregory Reeves ◽  
Tina B. Schreier ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Transcript Accumulation ◽  
Specific Expression ◽  
Cell Specificity ◽  
Distinct Cell ◽  
Gene Regulatory

ABSTRACTThe efficient C4 pathway is based on strong up-regulation of genes found in C3 plants, but also compartmentation of their expression into distinct cell-types such as the mesophyll and bundle sheath. Transcription factors associated with these phenomena have not been identified. To address this, we undertook genome-wide analysis of transcript accumulation, chromatin accessibility and transcription factor binding in C4Gynandropsis gynandra. From these data, two models relating to the molecular evolution of C4 photosynthesis are proposed. First, increased expression of C4 genes is associated with increased binding by MYB-related transcription factors. Second, mesophyll specific expression is associated with binding of homeodomain transcription factors. Overall, we conclude that during evolution of the complex C4 trait, C4 cycle genes gain cis-elements that operate in the C3 leaf such that they become integrated into existing gene regulatory networks associated with cell specificity and photosynthesis.

scholarly journals Revealing the Key Regulators of Cell Identity in the Human Adult Pancreas

2020 ◽  
Author(s):  
Lotte Vanheer ◽  
Andrea Alex Schiavo ◽  
Matthias Van Haele ◽  
Tine Haesen ◽  
Adrian Janiszewski ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
List Type ◽  
Cell Identity ◽  
Pancreatic Cell ◽  
Human Adult ◽  
Gene Regulatory ◽  
Cellular Identity

SUMMARYCellular identity during development is under the control of transcription factors that form gene regulatory networks. However, the transcription factors and gene regulatory networks underlying cellular identity in the human adult pancreas remain largely unexplored. Here, we integrate multiple single-cell RNA sequencing datasets of the human adult pancreas, totaling 7393 cells, and comprehensively reconstruct gene regulatory networks. We show that a network of 142 transcription factors forms distinct regulatory modules that characterize pancreatic cell types. We present evidence that our approach identifies key regulators of cell identity in the human adult pancreas. We predict that HEYL and JUND are active in acinar and alpha cells, respectively, and show that these proteins are present in the human adult pancreas as well as in human induced pluripotent stem cell-derived pancreatic cells. The comprehensive gene regulatory network atlas can be explored interactively online. We anticipate our analysis to be the starting point for a more sophisticated dissection of how transcription factors regulate cell identity in the human adult pancreas. Furthermore, given that transcription factors are major regulators of embryo development and are often perturbed in diseases, a comprehensive understanding of how transcription factors work will be relevant in development and disease biology.HIGHLIGHTS-Reconstruction of gene regulatory networks for human adult pancreatic cell types-An interactive resource to explore and visualize gene expression and regulatory states-Predicting putative transcription factors driving pancreatic cell identity-HEYL and JUND as candidate regulators of acinar and alpha cell identity, respectively

scholarly journals Cell-free gene-regulatory network engineering with synthetic transcription factors

2019 ◽  
Vol 116 (13) ◽  
pp. 5892-5901 ◽  
Author(s):  
Zoe Swank ◽  
Nadanai Laohakunakorn ◽  
Sebastian J. Maerkl
Keyword(s):  
Transcription Factors ◽  
High Throughput ◽  
Gene Regulatory Networks ◽  
Protein Interactions ◽  
Transcriptional Repression ◽  
Regulatory Networks ◽  
De Novo ◽  
Mechanistic Modeling ◽  
Free System ◽  
Gene Regulatory

Gene-regulatory networks are ubiquitous in nature and critical for bottom-up engineering of synthetic networks. Transcriptional repression is a fundamental function that can be tuned at the level of DNA, protein, and cooperative protein–protein interactions, necessitating high-throughput experimental approaches for in-depth characterization. Here, we used a cell-free system in combination with a high-throughput microfluidic device to comprehensively study the different tuning mechanisms of a synthetic zinc-finger repressor library, whose affinity and cooperativity can be rationally engineered. The device is integrated into a comprehensive workflow that includes determination of transcription-factor binding-energy landscapes and mechanistic modeling, enabling us to generate a library of well-characterized synthetic transcription factors and corresponding promoters, which we then used to build gene-regulatory networks de novo. The well-characterized synthetic parts and insights gained should be useful for rationally engineering gene-regulatory networks and for studying the biophysics of transcriptional regulation.

A Linear Programming Framework for Inferring Gene Regulatory Networks by Integrating Heterogeneous Data

2010 ◽  
pp. 450-475 ◽  
Author(s):  
Yong Wang ◽  
Rui-Sheng Wang ◽  
Trupti Joshi ◽  
Dong Xu ◽  
Xiang-Sun Zhang ◽  
...  
Keyword(s):  
Linear Programming ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Protein Interaction ◽  
Programming Framework ◽  
Gene Regulatory

There exist many heterogeneous data sources that are closely related to gene regulatory networks. These data sources provide rich information for depicting complex biological processes at different levels and from different aspects. Here, we introduce a linear programming framework to infer the gene regulatory networks. Within this framework, we extensively integrate the available information derived from multiple time-course expression datasets, ChIP-chip data, regulatory motif-binding patterns, protein-protein interaction data, protein-small molecule interaction data, and documented regulatory relationships in literature and databases. Results on synthetic and real experimental data both demonstrate that the linear programming framework allows us to recover gene regulations in a more robust and reliable manner.

Introduction: What This Book Aims to Do and What It Is Not

2014 ◽  
Author(s):  
Günter P. Wagner
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Common Ancestor ◽  
Morphological Characters ◽  
Evolutionary Origin ◽  
Developmental Mechanisms ◽  
Character Identity ◽  
Gene Regulatory

This book examines homology, the correspondence of characters from different species or even within the same organism, from a mechanistic perspective. Homology is explained by derivation from a common ancestor that had the same character or trait. This explanation applies at least to characters from different species. Accordingly, this concept has applications in many fields of biology by referring to morphological characters, behaviors, proteins and genes, gene regulatory networks, and developmental mechanisms and processes. The book considers one class of homology relationships, that between morphological characters, and describes the so-called character identity networks. It argues that the evolutionary origin of characters and body plans is the origin of those gene regulatory networks that underlie character identity.

scholarly journals Randomization Strategies Affect Motif Significance Analysis in TF-miRNA-Gene Regulatory Networks

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Sepideh Sadegh ◽  
Maryam Nazarieh ◽  
Christian Spaniol ◽  
Volkhard Helms
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Degree Distribution ◽  
Regulatory Networks ◽  
Target Genes ◽  
Mirna Gene ◽  
Biological Cells ◽  
Significance Analysis ◽  
Gene Regulatory ◽  
Randomized Networks

AbstractGene-regulatory networks are an abstract way of capturing the regulatory connectivity between transcription factors, microRNAs, and target genes in biological cells. Here, we address the problem of identifying enriched co-regulatory three-node motifs that are found significantly more often in real network than in randomized networks. First, we compare two randomization strategies, that either only conserve the degree distribution of the nodes’ in- and out-links, or that also conserve the degree distributions of different regulatory edge types. Then, we address the issue how convergence of randomization can be measured. We show that after at most 10 × |E| edge swappings, converged motif counts are obtained and the memory of initial edge identities is lost.

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.

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