Dynamic Transcriptional Regulatory Networks Research Articles

Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators.

The transcriptional regulatory network (TRN) is a graph framework that helps understand the complex transcriptional regulation mechanisms in the transcription process. Identifying the phenotype-specific transcription regulators is vital to reveal the functional roles of transcription elements in associating the specific phenotypes. Although many methods have been developed towards detecting the phenotype-specific transcription elements based on the static TRN in the past decade, most of them are not satisfactory for elucidating the phenotype-related functional roles of transcription regulators in multiple levels, as the dynamic characteristics of transcription regulators are usually ignored in static models. In this study, we introduce a novel framework called DTGN to identify the phenotype-specific transcription factors (TFs) and pathways by constructing dynamic TRNs. We first design a graph autoencoder model to integrate the phenotype-oriented time-series gene expression data and static TRN to learn the temporal representations of genes. Then, based on the learned temporal representations of genes, we develop a statistical method to construct a series of dynamic TRNs associated with the development of specific phenotypes. Finally, we identify the phenotype-specific TFs and pathways from the constructed dynamic TRNs. Results from multiple phenotypic datasets show that the proposed DTGN framework outperforms most existing methods in identifying phenotype-specific TFs and pathways. Our framework offers a new approach to exploring the functional roles of transcription regulators that associate with specific phenotypes in a dynamic model.

The transcriptome reveals the molecular regulatory network of primordial follicle depletion in obese mice

To explore the dynamic transcriptional regulatory network of primordial follicle fate in obese mice to elucidate the potential mechanism of primordial follicle depletion. Experimental study and transcriptomic analysis. Healthy (n=15) and obese (n=15) female mice. Six-week-old CD-1 mice were divided into healthy and high-fat diet groups and fed continuously for 12 weeks. The diet of healthy mice contained 10% fat. The diet of high-fat mice contained 60% fat. Primordial to primary follicle transition rate, gene expression changes, enriched Kyoto Encyclopedia of Genes and Genomes pathways, and ferroptosis. Primordial follicle depletion was increased in the ovaries of obese mice. We found that deposited fat around primordial and primary follicles of obese mice was higher than that for healthy mice. The proliferation of granulosa cells around primary follicles was increased in obese mice. In addition, we uncovered specific gene signatures associated with the primordial to primary follicle transition (PPT) in obese mice using laser capture microdissection RNA sequencing analysis. Gene set enrichment analysis indicated that ferroptosis, cell oxidation, vascular endothelial growth factor, and mammalian target of rapamycin signaling were increased significantly in the primordial follicles of obese mice. Notably, the ferritin, acyl CoA synthetase long-chain family member 4, and solid carrier family 7 member 11 associated proteins of the ferroptosis signaling pathway were significantly increased in the PPT phase of obese mice. Our work suggests that ferroptosis is a key pathway activated within immature ovarian follicles in the context of obesity and that the process may be involved in the physiological regulation of the PPT as well.

Structural controllability of dynamic transcriptional regulatory networks for Saccharomyces cerevisiae

Biological networks can dynamically change their structures to adapt to various biotic and abiotic stimuli. However, state-of-the-art studies on large-scale biological networks mainly focus on static networks. Here, we applied the sophisticated structural controllability tool to analyze one static and five dynamic transcriptional regulatory networks (TRNs) for Saccharomyces cerevisiae (S. cerevisiae), the five dynamic networks include two endogenous and three exogenous ones. Structural controllability allows us to classify nodes into different categories according to their control roles, and we clarify their topological features. Our results reveal that biological networks are rather difficult to be fully controlled. However, the dynamic networks are relatively easier to be controlled than the static one, and one needs relatively more external inputs to control the exogenous networks than the endogenous ones. Moreover, we found that nodes consisting of the single input module (SIM) and multiple input module (MIM) tend to be critical driver nodes, and those consisting of the feed-forward loop (FFL) in dynamic networks are prone to be critical drivers in comparison with those in the static network. Furthermore, GO enrichment analysis reveals that driver nodes but not critical driver nodes contribute to functional differences between the static and dynamic networks. Critical drivers, indispensable and neutral nodes are all enriched with transcriptional factors (TFs). On the contrary, essential genes tend to be enriched as driver nodes and dispensable nodes. The obtained results have certain robustness against perturbations. We declare that control roles of TFs and essential genes facilitate the identification of them via structural controllability theory. Our investigations provide some insights into real-world control of biological networks.

Genome-Wide Mapping of Binding Sites Reveals Multiple Biological Functions of the Transcription Factor Cst6p in Saccharomyces cerevisiae.

ABSTRACTIn the model eukaryote Saccharomyces cerevisiae, the transcription factor Cst6p has been reported to play important roles in several biological processes. However, the genome-wide targets of Cst6p and its physiological functions remain unknown. Here, we mapped the genome-wide binding sites of Cst6p at high resolution. Cst6p binds to the promoter regions of 59 genes with various biological functions when cells are grown on ethanol but hardly binds to the promoter at any gene when cells are grown on glucose. The retarded growth of the CST6 deletion mutant on ethanol is attributed to the markedly decreased expression of NCE103, encoding a carbonic anhydrase, which is a direct target of Cst6p. The target genes of Cst6p have a large overlap with those of stress-responsive transcription factors, such as Sko1p and Skn7p. In addition, a CST6 deletion mutant growing on ethanol shows hypersensitivity to oxidative stress and ethanol stress, assigning Cst6p as a new member of the stress-responsive transcriptional regulatory network. These results show that mapping of genome-wide binding sites can provide new insights into the function of transcription factors and highlight the highly connected and condition-dependent nature of the transcriptional regulatory network in S. cerevisiae.

PlantPAN 2.0: an update of plant promoter analysis navigator for reconstructing transcriptional regulatory networks in plants.

Transcription factors (TFs) are sequence-specific DNA-binding proteins acting as critical regulators of gene expression. The Plant Promoter Analysis Navigator (PlantPAN; http://PlantPAN2.itps.ncku.edu.tw) provides an informative resource for detecting transcription factor binding sites (TFBSs), corresponding TFs, and other important regulatory elements (CpG islands and tandem repeats) in a promoter or a set of plant promoters. Additionally, TFBSs, CpG islands, and tandem repeats in the conserve regions between similar gene promoters are also identified. The current PlantPAN release (version 2.0) contains 16 960 TFs and 1143 TF binding site matrices among 76 plant species. In addition to updating of the annotation information, adding experimentally verified TF matrices, and making improvements in the visualization of transcriptional regulatory networks, several new features and functions are incorporated. These features include: (i) comprehensive curation of TF information (response conditions, target genes, and sequence logos of binding motifs, etc.), (ii) co-expression profiles of TFs and their target genes under various conditions, (iii) protein-protein interactions among TFs and their co-factors, (iv) TF-target networks, and (v) downstream promoter elements. Furthermore, a dynamic transcriptional regulatory network under various conditions is provided in PlantPAN 2.0. The PlantPAN 2.0 is a systematic platform for plant promoter analysis and reconstructing transcriptional regulatory networks.

ProTF: a comprehensive data and phylogenomics resource for prokaryotic transcription factors

Investigation of transcription factors (TFs) is of extreme significance for gleaning more information about the mechanisms underlying the dynamic transcriptional regulatory network. Herein, proTF is constructed to serve as a comprehensive data resource and phylogenomics analysis platform for prokaryotic TFs. It has many prominent characteristics: (i) detailed annotation information, including basic sequence features, domain organization, sequence homolog and sequence composition, was extensively collected, and then visually displayed for each TF entry in all prokaryotic genomes; (ii) workset was employed as the basic frame to provide an efficient way to organize the retrieved data and save intermediate records; and (iii) a number of elaborated tools for phylogenomics analysis were implemented to investigate the evolutionary roles of specific TFs. In conclusion, proTF dedicates to the prokaryotic TFs with integrated multi-function, which will become a valuable resource for prokaryotic transcriptional regulatory network in the post-genomic era. http://centre.bioinformatics.zj.cn/proTF.

Transcriptional responses to fatty acid are coordinated by combinatorial control

In transcriptional regulatory networks, the coincident binding of a combination of factors to regulate a gene implies the existence of complex mechanisms to control both the gene expression profile and specificity of the response. Unraveling this complexity is a major challenge to biologists. Here, a novel network topology-based clustering approach was applied to condition-specific genome-wide chromatin localization and expression data to characterize a dynamic transcriptional regulatory network responsive to the fatty acid oleate. A network of four (predicted) regulators of the response (Oaf1p, Pip2p, Adr1p and Oaf3p) was investigated. By analyzing trends in the network structure, we found that two groups of multi-input motifs form in response to oleate, each controlling distinct functional classes of genes. This functionality is contributed in part by Oaf1p, which is a component of both types of multi-input motifs and has two different regulatory activities depending on its binding context. The dynamic cooperation between Oaf1p and Pip2p appears to temporally synchronize the two different responses. Together, these data suggest a network mechanism involving dynamic combinatorial control for coordinating transcriptional responses.