Evolution Of Transcriptional Regulatory Networks Research Articles

The Elaboration of miRNA Regulation and Gene Regulatory Networks in Plant⁻Microbe Interactions.

Plants are exposed to diverse abiotic and biotic stimuli. These require fast and specific integrated responses. Such responses are coordinated at the protein and transcript levels and are incorporated into larger regulatory networks. Here, we focus on the evolution of transcriptional regulatory networks involved in plant–pathogen interactions. We discuss the evolution of regulatory networks and their role in fine-tuning plant defense responses. Based on the observation that many of the cornerstones of immune signaling in angiosperms are also present in streptophyte algae, it is likely that some regulatory components also predate the origin of land plants. The degree of functional conservation of many of these ancient components has not been elucidated. However, ongoing functional analyses in bryophytes show that some components are conserved. Hence, some of these regulatory components and how they are wired may also trace back to the last common ancestor of land plants or earlier. Of course, an understanding of the similarities and differences during the evolution of plant defense networks cannot ignore the lineage-specific coevolution between plants and their pathogens. In this review, we specifically focus on the small RNA regulatory networks involved in fine-tuning of the strength and timing of defense responses and highlight examples of pathogen exploitation of the host RNA silencing system. These examples illustrate well how pathogens frequently target gene regulation and thereby alter immune responses on a larger scale. That this is effective is demonstrated by the diversity of pathogens from distinct kingdoms capable of manipulating the same gene regulatory networks, such as the RNA silencing machinery.

Plant Lineage-Specific Amplification of Transcription Factor Binding Motifs by Miniature Inverted-Repeat Transposable Elements (MITEs).

Transposable elements are one of the main drivers of plant genome evolution. Transposon insertions can modify the gene coding capacity or the regulation of their expression, the latter being a more subtle effect, and therefore particularly useful for evolution. Transposons have been show to contain transcription factor binding sites that can be mobilized upon transposition with the potential to integrate new genes into transcriptional networks. Miniature inverted-repeat transposable elements (MITEs) are a type of noncoding DNA transposons that could be particularly suited as a vector to mobilize transcription factor binding sites and modify transcriptional networks during evolution. MITEs are small in comparison to other transposons and can be excised, which should make them less mutagenic when inserting into promoters. On the other hand, in spite of their cut-and-paste mechanisms of transposition, they can reach very high copy numbers in genomes. We have previously shown that MITEs have amplified and redistributed the binding motif of the E2F transcription factor in different Brassicas. Here, we show that MITEs have amplified and mobilized the binding motifs of the bZIP60 and PIF3 transcription factors in peach and Prunus mume, and the TCP15/23 binding motif in tomato. Our results suggest that MITEs could have rewired new genes into transcriptional regulatory networks that are responsible for important adaptive responses and breeding traits in plants, such as stress responses, flowering time, or fruit ripening. The results presented here therefore suggest a general impact of MITEs in the evolution of transcriptional regulatory networks in plants.

A Bayesian inference method for the analysis of transcriptional regulatory networks in metagenomic data

BackgroundMetagenomics enables the analysis of bacterial population composition and the study of emergent population features, such as shared metabolic pathways. Recently, we have shown that metagenomics datasets can be leveraged to characterize population-wide transcriptional regulatory networks, or meta-regulons, providing insights into how bacterial populations respond collectively to specific triggers. Here we formalize a Bayesian inference framework to analyze the composition of transcriptional regulatory networks in metagenomes by determining the probability of regulation of orthologous gene sequences. We assess the performance of this approach on synthetic datasets and we validate it by analyzing the copper-homeostasis network of Firmicutes species in the human gut microbiome.ResultsAssessment on synthetic datasets shows that our method provides a robust and interpretable metric for assessing putative regulation by a transcription factor on sets of promoter sequences mapping to an orthologous gene cluster. The inference framework integrates the regulatory contribution of secondary sites and can discern false positives arising from multiple instances of a clonal sequence. Posterior probabilities for orthologous gene clusters decline sharply when less than 20 % of mapped promoters have binding sites, but we introduce a sensitivity adjustment procedure to speed up computation that enhances regulation assessment in heterogeneous ortholog clusters. Analysis of the copper-homeostasis regulon governed by CsoR in the human gut microbiome Firmicutes reveals that CsoR controls itself and copper-translocating P-type ATPases, but not CopZ-type copper chaperones. Our analysis also indicates that CsoR frequently targets promoters with dual CsoR-binding sites, suggesting that it exploits higher-order binding conformations to fine-tune its activity.ConclusionsWe introduce and validate a method for the analysis of transcriptional regulatory networks from metagenomic data that enables inference of meta-regulons in a systematic and interpretable way. Validation of this method on the CsoR meta-regulon of gut microbiome Firmicutes illustrates the usefulness of the approach, revealing novel properties of the copper-homeostasis network in poorly characterized bacterial species and putting forward evidence of new mechanisms of DNA binding for this transcriptional regulator. Our approach will enable the comparative analysis of regulatory networks across metagenomes, yielding novel insights into the evolution of transcriptional regulatory networks.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-016-0082-8) contains supplementary material, which is available to authorized users.

Transcription Factors Exhibit Differential Conservation in Bacteria with Reduced Genomes.

The description of transcriptional regulatory networks has been pivotal in the understanding of operating principles under which organisms respond and adapt to varying conditions. While the study of the topology and dynamics of these networks has been the subject of considerable work, the investigation of the evolution of their topology, as a result of the adaptation of organisms to different environmental conditions, has received little attention. In this work, we study the evolution of transcriptional regulatory networks in bacteria from a genome reduction perspective, which manifests itself as the loss of genes at different degrees. We used the transcriptional regulatory network of Escherichia coli as a reference to compare 113 smaller, phylogenetically-related γ-proteobacteria, including 19 genomes of symbionts. We found that the type of regulatory action exerted by transcription factors, as genomes get progressively smaller, correlates well with their degree of conservation, with dual regulators being more conserved than repressors and activators in conditions of extreme reduction. In addition, we found that the preponderant conservation of dual regulators might be due to their role as both global regulators and nucleoid-associated proteins. We summarize our results in a conceptual model of how each TF type is gradually lost as genomes become smaller and give a rationale for the order in which this phenomenon occurs.

An Arabidopsis Transcriptional Regulatory Map Reveals Distinct Functional and Evolutionary Features of Novel Transcription Factors.

Transcription factors (TFs) play key roles in both development and stress responses. By integrating into and rewiring original systems, novel TFs contribute significantly to the evolution of transcriptional regulatory networks. Here, we report a high-confidence transcriptional regulatory map covering 388 TFs from 47 families in Arabidopsis. Systematic analysis of this map revealed the architectural heterogeneity of developmental and stress response subnetworks and identified three types of novel network motifs that are absent from unicellular organisms and essential for multicellular development. Moreover, TFs of novel families that emerged during plant landing present higher binding specificities and are preferentially wired into developmental processes and these novel network motifs. Further unveiled connection between the binding specificity and wiring preference of TFs explains the wiring preferences of novel-family TFs. These results reveal distinct functional and evolutionary features of novel TFs, suggesting a plausible mechanism for their contribution to the evolution of multicellular organisms.

Transposable elements modulate human RNA abundance and splicing via specific RNA-protein interactions.

BackgroundTransposable elements (TEs) have significantly influenced the evolution of transcriptional regulatory networks in the human genome. Post-transcriptional regulation of human genes by TE-derived sequences has been observed in specific contexts, but has yet to be systematically and comprehensively investigated. Here, we study a collection of 75 CLIP-Seq experiments mapping the RNA binding sites for a diverse set of 51 human proteins to explore the role of TEs in post-transcriptional regulation of human mRNAs and lncRNAs via RNA-protein interactions.ResultsWe detect widespread interactions between RNA binding proteins (RBPs) and many families of TE-derived sequence in the CLIP-Seq data. Further, alignment coverage peaks on specific positions of the TE consensus sequences, illuminating a diversity of TE-specific RBP binding motifs. Evidence of binding and conservation of these motifs in the nonrepetitive transcriptome suggests that TEs have generally appropriated existing sequence preferences of the RBPs. Depletion assays for numerous RBPs show that TE-derived binding sites affect transcript abundance and splicing similarly to nonrepetitive sites. However, in a few cases the effect of RBP binding depends on the specific TE family bound; for example, the ubiquitously expressed RBP HuR confers transcript stability unless bound to an Alu element.ConclusionsOur meta-analysis suggests a widespread role for TEs in shaping RNA-protein regulatory networks in the human genome.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0537-5) contains supplementary material, which is available to authorized users.

DNA-binding specificity changes in the evolution of forkhead transcription factors

The evolution of transcriptional regulatory networks entails the expansion and diversification of transcription factor (TF) families. The forkhead family of TFs, defined by a highly conserved winged helix DNA-binding domain (DBD), has diverged into dozens of subfamilies in animals, fungi, and related protists. We have used a combination of maximum-likelihood phylogenetic inference and independent, comprehensive functional assays of DNA-binding capacity to explore the evolution of DNA-binding specificity within the forkhead family. We present converging evidence that similar alternative sequence preferences have arisen repeatedly and independently in the course of forkhead evolution. The vast majority of DNA-binding specificity changes we observed are not explained by alterations in the known DNA-contacting amino acid residues conferring specificity for canonical forkhead binding sites. Intriguingly, we have found forkhead DBDs that retain the ability to bind very specifically to two completely distinct DNA sequence motifs. We propose an alternate specificity-determining mechanism whereby conformational rearrangements of the DBD broaden the spectrum of sequence motifs that a TF can recognize. DNA-binding bispecificity suggests a previously undescribed source of modularity and flexibility in gene regulation and may play an important role in the evolution of transcriptional regulatory networks.

Regulatory Network Structure as a Dominant Determinant of Transcription Factor Evolutionary Rate

The evolution of transcriptional regulatory networks has thus far mostly been studied at the level of cis-regulatory elements. To gain a complete understanding of regulatory network evolution we must also study the evolutionary role of trans-factors, such as transcription factors (TFs). Here, we systematically assess genomic and network-level determinants of TF evolutionary rate in yeast, and how they compare to those of generic proteins, while carefully controlling for differences of the TF protein set, such as expression level. We found significantly distinct trends relating TF evolutionary rate to mRNA expression level, codon adaptation index, the evolutionary rate of physical interaction partners, and, confirming previous reports, to protein-protein interaction degree and regulatory in-degree. We discovered that for TFs, the dominant determinants of evolutionary rate lie in the structure of the regulatory network, such as the median evolutionary rate of target genes and the fraction of species-specific target genes. Decomposing the regulatory network by edge sign, we found that this modular evolution of TFs and their targets is limited to activating regulatory relationships. We show that fast evolving TFs tend to regulate other TFs and niche-specific processes and that their targets show larger evolutionary expression changes than targets of other TFs. We also show that the positive trend relating TF regulatory in-degree and evolutionary rate is likely related to the species-specificity of the transcriptional regulation modules. Finally, we discuss likely causes for TFs' different evolutionary relationship to the physical interaction network, such as the prevalence of transient interactions in the TF subnetwork. This work suggests that positive and negative regulatory networks follow very different evolutionary rules, and that transcription factor evolution is best understood at a network- or systems-level.

Optimality and thermodynamics determine the evolution of transcriptional regulatory networks

Transcriptional motifs are small regulatory interaction patterns that regulate biological functions in highly-interacting cellular networks. Recently, attempts have been made to explain the significance of transcriptional motifs through dynamic function. However, fundamental questions remain unanswered. Why are certain transcriptional motifs with similar dynamic function abundant while others occur rarely? What are the criteria for topological generalization of these motifs into complex networks? Here, we present a novel paradigm that combines non-equilibrium thermodynamics with multiobjective-optimality for network analysis. We found that energetic cost, defined herein as specific dissipation energy, is minimal at the optimal environmental conditions and it correlates inversely with the abundance of the network motifs obtained experimentally for E. coli and S. cerevisiae. This yields evidence that dissipative energetics is the underlying criteria used during evolution for motif selection and that biological systems during transcription tend towards evolutionary selection of subgraphs which produces minimum specific heat dissipation under optimal conditions, thereby explaining the abundance/rare occurrence of some motifs. We show that although certain motifs had similar dynamical functionality, they had significantly different energetic cost, thus explaining the abundance/rare occurrence of these motifs. The presented insights may establish global thermodynamic analysis as a backbone in designing and understanding complex networks systems, such as metabolic and protein interaction networks.

Evolution of transcriptional regulatory networks in yeast populations

Saccharomyces cerevisiae is the most thoroughly studied eukaryote at the cellular, molecular, and genetic level. Recent boost in whole-genome sequencing, array-based allelic variation mapping, and genome-wide transcriptional profiling have unprecedentedly advanced knowledge on cell biology and evolution of this organism. It is now possible to investigate how evolution shapes the functional architecture of yeast genomes and how this architecture relates to the evolution of the regulatory networks controlling the expression of genes that make up an organism. A survey of the information on genetic and whole-genome expression variations in yeast populations shows that a significant score of gene expression variation is dependent on genotype-by-environment interaction. In some cases, large trans effects are the result of mutations in the promoters of key master regulator genes. Yet trans-variation in environmental sensor proteins appears to explain the majority of the expression patterns differentiating strains in natural populations. The challenge is now to use this information to model how individual genetic polymorphisms interact in a condition-dependent fashion to produce phenotypic change. In this study, we show how fruitful application of systems biology to the progress of science and medicine requires the use of evolution as a lens to reconstruct the hierarchical structure of regulation of biological systems. The lessons learned in yeast can be of paramount importance in advancing the application of genomics and systems biology to emerging fields including personalized medicine.

Diversity and complexity in DNA recognition by transcription factors.

Sequence preferences of DNA binding proteins are a primary mechanism by which cells interpret the genome. Despite the central importance of these proteins in physiology, development, and evolution, comprehensive DNA binding specificities have been determined experimentally for only a few proteins. Here, we used microarrays containing all 10-base pair sequences to examine the binding specificities of 104 distinct mouse DNA binding proteins representing 22 structural classes. Our results reveal a complex landscape of binding, with virtually every protein analyzed possessing unique preferences. Roughly half of the proteins each recognized multiple distinctly different sequence motifs, challenging our molecular understanding of how proteins interact with their DNA binding sites. This complexity in DNA recognition may be important in gene regulation and in the evolution of transcriptional regulatory networks.

Regulation by transcription factors in bacteria: beyond description

Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. We review recent concepts and developments: cis elements and trans regulatory factors, chromosome organization and structure, transcriptional regulatory networks (TRNs) and transcriptomics. We also summarize new important discoveries that will probably affect the direction of research in gene regulation: epigenetics and stochasticity in transcriptional regulation, synthetic circuits and plasticity and evolution of TRNs. Many of the new discoveries in gene regulation are not extensively tested with wetlab approaches. Consequently, we review this broad area in Inference of TRNs and Dynamical Models of TRNs. Finally, we have stepped backwards to trace the origins of these modern concepts, synthesizing their history in a timeline schema.

Impact of Transcription Units rearrangement on the evolution of the regulatory network of gamma-proteobacteria

BackgroundIn the past years, several studies begun to unravel the structure, dynamical properties, and evolution of transcriptional regulatory networks. However, even those comparative studies that focus on a group of closely related organisms are limited by the rather scarce knowledge on regulatory interactions outside a few model organisms, such as E. coli among the prokaryotes.ResultsIn this paper we used the information annotated in Tractor_DB (a database of regulatory networks in gamma-proteobacteria) to calculate a normalized Site Orthology Score (SOS) that quantifies the conservation of a regulatory link across thirty genomes of this subclass. Then we used this SOS to assess how regulatory connections have evolved in this group, and how the variation of basic regulatory connection is reflected on the structure of the chromosome. We found that individual regulatory interactions shift between different organisms, a process that may be described as rewiring the network. At this evolutionary scale (the gamma-proteobacteria subclass) this rewiring process may be an important source of variation of regulatory incoming interactions for individual networks. We also noticed that the regulatory links that form feed forward motifs are conserved in a better correlated manner than triads of random regulatory interactions or pairs of co-regulated genes. Furthermore, the rewiring process that takes place at the most basic level of the regulatory network may be linked to rearrangements of genetic material within bacterial chromosomes, which change the structure of Transcription Units and therefore the regulatory connections between Transcription Factors and structural genes.ConclusionThe rearrangements that occur in bacterial chromosomes-mostly inversion or horizontal gene transfer events – are important sources of variation of gene regulation at this evolutionary scale.

Genetic and epigenetic dissection of cis regulatory variation

Divergence in gene expression is of interest because it generates molecular markers for phenotypic variation, potentially including the causes underlying this variation. Alteration of gene expression patterns can have a direct genetic (or epigenetic) basis in cis regulatory polymorphism or can be indirectly regulated by trans-acting factors. Expression mapping studies have begun to reveal the local (suggesting cis) and distant (usually trans) patterns of inheritance of genetic variation that underlies transcriptional polymorphism. The molecular basis that contributes to transcriptional divergence is, however, largely unknown especially for genes under selection that might influence phenotype. Additional genome-wide empirical data from many related organisms are required to dissect cis-, trans-, and cis x trans- dependent sources of variation in gene expression to provide a better understanding of the evolution of transcriptional regulatory networks.

Stability and evolution of transcriptional regulatory networks

Stability and evolution of transcriptional regulatory networks

Structure and evolution of transcriptional regulatory networks

The regulatory interactions between transcription factors and their target genes can be conceptualised as a directed graph. At a global level, these regulatory networks display a scale-free topology, indicating the presence of regulatory hubs. At a local level, substructures such as motifs and modules can be discerned in these networks. Despite the general organisational similarity of networks across the phylogenetic spectrum, there are interesting qualitative differences among the network components, such as the transcription factors. Although the DNA-binding domains of the transcription factors encoded by a given organism are drawn from a small set of ancient conserved superfamilies, their relative abundance often shows dramatic variation among different phylogenetic groups. Large portions of these networks appear to have evolved through extensive duplication of transcription factors and targets, often with inheritance of regulatory interactions from the ancestral gene. Interactions are conserved to varying degrees among genomes. Insights from the structure and evolution of these networks can be translated into predictions and used for engineering of the regulatory networks of different organisms.