Mapping Of Regulatory Networks Research Articles

Identification of key genes and pathways involved in vitiligo development based on integrated analysis.

Vitiligo is a chronic skin condition lack of melanocytes. However, researches on the aetiology and pathogenesis of vitiligo are still under debate. This study aimed to explore the key genes and pathways associated with occurrence and development of vitiligo.Weighted gene coexpression network analysis (WGCNA) was applied to reanalyze the gene expression dataset GSE65127 systematically. Functional enrichments of these modules were carried out at gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA). Then, a map of regulatory network was delineated according to pivot analysis and drug prediction. In addition, hub genes and crucial pathways were validated by an independent dataset GSE75819. The expressions of hub genes in modules were also tested by quantitative real-time polymerase chain reaction (qRT-PCR).Eight coexpressed modules were identified by WGCNA based on 5794 differentially expressed genes of vitiligo. Three modules were found to be significantly correlated with Lesional, Peri-Lesional, and Non-Lesional, respectively. The persistent maladjusted genes included 269 upregulated genes and 82 downregulated genes. The enrichments showed module genes were implicated in immune response, p53 signaling pathway, etc. According to GSEA and GSVA, dysregulated pathways were activated incessantly from Non-Lesional to Peri-Lesional and then to Lesional, 4 of which were verified by an independent dataset GSE75819. Finally, 42 transcription factors and 228 drugs were spotted. Focusing on the persistent maladjusted genes, a map of regulatory network was delineated. Hub genes (CACTIN, DCTN1, GPR143, HADH, MRPL47, NKTR, NUF2) and transcription factors (ITGAV, SYK, PDPK1) were validated by an independent dataset GSE75819. In addition, hub genes (CACTIN, DCTN1, GPR143, MRPL47, NKTR) were also confirmed by qRT-PCR.The present study, at least, might provide an integrated and in-depth insight for exploring the underlying mechanism of vitiligo and predicting potential diagnostic biomarkers and therapeutic targets.

Super-enhancer-guided mapping of regulatory networks controlling mouse trophoblast stem cells

Trophectoderm (TE) lineage development is pivotal for proper implantation, placentation, and healthy pregnancy. However, only a few TE-specific transcription factors (TFs) have been systematically characterized, hindering our understanding of the process. To elucidate regulatory mechanisms underlying TE development, here we map super-enhancers (SEs) in trophoblast stem cells (TSCs) as a model. We find both prominent TE-specific master TFs (Cdx2, Gata3, and Tead4), and >150 TFs that had not been previously implicated in TE lineage, that are SE-associated. Mapping targets of 27 SE-predicted TFs reveals a highly intertwined transcriptional regulatory circuitry. Intriguingly, SE-predicted TFs show 4 distinct expression patterns with dynamic alterations of their targets during TSC differentiation. Furthermore, depletion of a subset of TFs results in dysregulation of the markers for specialized cell types in placenta, suggesting a role during TE differentiation. Collectively, we characterize an expanded TE-specific regulatory network, providing a framework for understanding TE lineage development and placentation.

Differential Expression Profiles of the Transcriptome and miRNA Interactome in Synovial Fibroblasts of Rheumatoid Arthritis Revealed by Next Generation Sequencing.

Using next-generation sequencing to decipher the molecular mechanisms underlying aberrant rheumatoid arthritis synovial fibroblasts (RASF) activation, we performed transcriptome-wide RNA-seq and small RNA-seq on synovial fibroblasts from rheumatoid arthritis (RA) subject and normal donor. Differential expression of mRNA and miRNA was integrated with interaction analysis, functional annotation, regulatory network mapping and experimentally verified miRNA–target interaction data, further validated with microarray expression profiles. In this study, 3049 upregulated mRNA and 3552 downregulated mRNA, together with 50 upregulated miRNA and 35 downregulated miRNA in RASF were identified. Interaction analysis highlighted contribution of miRNA to altered transcriptome. Functional annotation revealed metabolic deregulation and oncogenic signatures of RASF. Regulatory network mapping identified downregulated FOXO1 as master transcription factor resulting in altered transcriptome of RASF. Differential expression in three miRNA and corresponding targets (hsa-miR-31-5p:WASF3, hsa-miR-132-3p:RB1, hsa-miR-29c-3p:COL1A1) were also validated. The interactions of these three miRNA–target genes were experimentally validated with past literature. Our transcriptomic and miRNA interactomic investigation identified gene signatures associated with RASF and revealed the involvement of transcription factors and miRNA in an altered transcriptome. These findings help facilitate our understanding of RA with the hope of serving as a springboard for further discoveries relating to the disease.

Genome-Wide Regulatory Network Mapping of miRNA and Transcription Factors in Banana Roots

MicroRNA (miRNA) are important regulators of gene expression. Plant miRNA have been reported both to target and to be regulated by transcription factors, however, genomic distribution of miRNA, transcription factor targets for miRNA, transcription factor binding sites (TFBS) of miRNA promoters and their regulatory networks have not been systematically mapped in banana. In this study, genome-wide annotation of miRNA in the most recently published banana genome sequence was used to predict miRNA promoter regions and to map TFBS of miRNA genes. A total of 183 mature miRNAs, comprising 144 orthologous miRNA and 39 Musa-specific miRNA were predicted. Following this, banana root degradome data was used to confirm miRNA targets and the transcription factor targets were placed into a predicted network together with their targeting miRNA using cytoscape. Gene ontology of the 20 transcription factors among the predicted miRNA targets, showed predominance for auxin-activated signalling and developmental processes. Profiling of TFBS motifs across miRNA promoter regions showed that binding site motifs for TCP, AP2/ERF, GATA, NF-YB, DOF, B3, bZIP, trihelix, ZF-HD, bHLH and Dehydrin transcription factor families are abundant in the Musa acuminata genome. Finally, we propose a regulatory network for the miRNA families miR156, miR164, miR166, miR171, miR319, miR396, miR528, mac-miR-new14 and mac-miR-new20 and their respective transcription factor targets.

Gene regulatory network mapping in late-onset Alzheimer's disease (AD)

Gene regulatory network mapping in late-onset Alzheimer's disease (AD)

Integrative miRNA-mRNA Profiling of Adipose Tissue Unravels Transcriptional Circuits Induced by Sleep Fragmentation

Obstructive sleep apnea (OSA) is a prevalent condition and strongly associated with metabolic disorders. Sleep fragmentation (SF) is a major consequence of OSA, but its contribution to OSA-related morbidities is not known. We hypothesized that SF causes specific perturbations in transcriptional networks of visceral fat cells, leading to systemic metabolic disturbances. We simultaneously profiled visceral adipose tissue mRNA and miRNA expression in mice exposed to 6 hours of SF during sleep, and developed a new computational framework based on gene set enrichment and network analyses to merge these data. This approach leverages known gene product interactions and biologic pathways to interrogate large-scale gene expression profiling data. We found that SF induced the activation of several distinct pathways, including those involved in insulin regulation and diabetes. Our integrative methodology identified putative controllers and regulators of the metabolic response during SF. We functionally validated our findings by demonstrating altered glucose and lipid homeostasis in sleep-fragmented mice. This is the first study to link sleep fragmentation with widespread disruptions in visceral adipose tissue transcriptome, and presents a generalizable approach to integrate mRNA-miRNA information for systematic mapping of regulatory networks.

Yeast One-hybrid Assays for Regulatory Network Mapping

Yeast One-hybrid Assays for Regulatory Network Mapping

Yeast One-hybrid Assays for Regulatory Network Mapping

Yeast One-hybrid Assays for Regulatory Network Mapping

Transcript and protein profiling analysis of OTA-induced cell death reveals the regulation of the toxicity response process in Arabidopsis thaliana

Ochratoxin A (OTA) is a toxic isocoumarin derivative produced by various species of mould which mainly grow on grain, coffee, and nuts. Recent studies have suggested that OTA induces cell death in plants. To investigate possible mechanisms of OTA phytotoxicity, both digital gene expression (DGE) transcriptomic and two-dimensional electrophoresis proteomic analyses were used, through which 3118 genes and 23 proteins were identified as being up- or down-regulated at least 2-fold in Arabidopsis leaf in response to OTA treatment. First, exposure of excised Arabidopsis thaliana leaves to OTA rapidly causes the hypersensitive reponse, significantly accelerates the increase of reactive oxygen species and malondialdehyde, and enhances antioxidant enzyme defence responses and xenobiotic detoxification. Secondly, OTA stimulation causes dynamic changes in transcription factors and activates the membrane transport system dramatically. Thirdly, a concomitant persistence of compromised photosynthesis and photorespiration is indicative of a metabolic shift from a highly active to a weak state. Finally, the data revealed that ethylene, salicylic acid, jasmonic acid, and mitogen-activated protein kinase signalling molecules mediate the process of toxicity caused by OTA. Profiling analyses on Arabidopsis in response to OTA will provide new insights into signalling transduction that modulates the OTA phytotoxicity mechanism, facilitate mapping of regulatory networks, and extend the ability to improve OTA tolerance in Arabidopsis.

Enhanced yeast one-hybrid assays for high-throughput gene-centered regulatory network mapping.

A major challenge in systems biology is to understand the gene regulatory networks that drive development, physiology and pathology. Interactions between transcription factors and regulatory genomic regions provide the first level of gene control. Gateway-compatible yeast one-hybrid (Y1H) assays present a convenient method to identify and characterize the repertoire of transcription factors that can bind a DNA sequence of interest. To delineate genome-scale regulatory networks, however, large sets of DNA fragments need to be processed at high throughput and high coverage. Here, we present “enhanced” Y1H (eY1H) assays that utilize a robotic mating platform with a set of improved Y1H reagents and automated readout quantification. We demonstrate that eY1H assays provide excellent coverage and identify interacting transcription factors for multiple DNA fragments in a short amount of time. eY1H assays will be an important tool for gene regulatory network mapping in Caenorhabditis elegans and other model organisms, as well as humans.

Yeast one-hybrid assays for gene-centered human gene regulatory network mapping.

Gateway-compatible yeast one-hybrid (Y1H) assays provide a convenient gene-centered (DNA-to-protein) approach to identify the repertoire of transcription factors that can bind a DNA sequence of interest. We present a set of Y1H resources, including clones for 988 of 1,434 (69%) predicted human transcription factors, for the interrogation of interactions using either low or high-throughput settings. These approaches detect both known and novel interactions between human DNA regions and transcription factors.

Chapter 10 - Gene-Centered Regulatory Network Mapping

The Caenorhabditis elegans hermaphrodite is a complex multicellular animal that is composed of 959 somatic cells. The C. elegans genome contains ∼20,000 protein-coding genes, 940 of which encode regulatory transcription factors (TFs). In addition, the worm genome encodes more than 100 microRNAs and many other regulatory RNA and protein molecules. Most C. elegans genes are subject to regulatory control, most likely by multiple regulators, and combined, this dictates the activation or repression of the gene and corresponding protein in the relevant cells and under the appropriate conditions. A major goal in C. elegans research is to determine the spatiotemporal expression pattern of each gene throughout development and in response to different signals, and to determine how this expression pattern is accomplished. Gene regulatory networks describe physical and/or functional interactions between genes and their regulators that result in specific spatiotemporal gene expression. Such regulators can act at transcriptional or post-transcriptional levels. Here, I will discuss the methods that can be used to delineate gene regulatory networks in C. elegans. I will mostly focus on gene-centered yeast one-hybrid (Y1H) assays that are used to map interactions between non-coding genic regions, such as promoters, and regulatory TFs. The approaches discussed here are not only relevant to C. elegans biology, but can also be applied to other model organisms and humans.

Matrix and Steiner-triple-system smart pooling assays for high-performance transcription regulatory network mapping

Yeast one-hybrid (Y1H) assays provide a gene-centered method for the identification of interactions between gene promoters and regulatory transcription factors (TFs). To date, Y1H assays have involved library screens that are relatively expensive and laborious. We present two Y1H strategies that allow immediate prey identification: matrix assays that use an array of 755 individual Caenorhabditis elegans TFs, and smart-pool assays that use TF multiplexing. Both strategies simplify the Y1H pipeline and reduce the cost of protein-DNA interaction identification. We used a Steiner triple system (STS) to create smart pools of 4-25 TFs. Notably, we uniplexed a small number of highly connected TFs to allow efficient assay deconvolution. Both strategies outperform library screens in terms of coverage, confidence and throughput. These versatile strategies can be adapted both to TFs in other systems and, likely, to other biomolecules and assays as well.

Comprehensive transcriptional profiling of NaCl-stressed Arabidopsis roots reveals novel classes of responsive genes

BackgroundRoots are an attractive system for genomic and post-genomic studies of NaCl responses, due to their primary importance to agriculture, and because of their relative structural and biochemical simplicity. Excellent genomic resources have been established for the study of Arabidopsis roots, however, a comprehensive microarray analysis of the root transcriptome following NaCl exposure is required to further understand plant responses to abiotic stress and facilitate future, systems-based analyses of the underlying regulatory networks.ResultsWe used microarrays of 70-mer oligonucleotide probes representing 23,686 Arabidopsis genes to identify root transcripts that changed in relative abundance following 6 h, 24 h, or 48 h of hydroponic exposure to 150 mM NaCl. Enrichment analysis identified groups of structurally or functionally related genes whose members were statistically over-represented among up- or down-regulated transcripts. Our results are consistent with generally observed stress response themes, and highlight potentially important roles for underappreciated gene families, including: several groups of transporters (e.g. MATE, LeOPT1-like); signalling molecules (e.g. PERK kinases, MLO-like receptors), carbohydrate active enzymes (e.g. XTH18), transcription factors (e.g. members of ZIM, WRKY, NAC), and other proteins (e.g. 4CL-like, COMT-like, LOB-Class 1). We verified the NaCl-inducible expression of selected transcription factors and other genes by qRT-PCR.ConclusionMicorarray profiling of NaCl-treated Arabidopsis roots revealed dynamic changes in transcript abundance for at least 20% of the genome, including hundreds of transcription factors, kinases/phosphatases, hormone-related genes, and effectors of homeostasis, all of which highlight the complexity of this stress response. Our identification of these transcriptional responses, and groups of evolutionarily related genes with either similar or divergent transcriptional responses to stress, will facilitate mapping of regulatory networks and extend our ability to improve salt tolerance in plants.