The pathological hallmarks of psoriasis involve alterations in T cell genes associated with transcriptional levels, which are determined by chromatin accessibility. However, to what extent these alterations in T cell transcriptional levels recapitulate the epigenetic features of psoriasis remains unknown. Here, we systematically profiled chromatin accessibility on Th1, Th2, Th1-17, Th17, and Treg cells and found that chromatin remodeling contributes significantly to the pathogenesis of the disease. The chromatin remodeling tendency of different subtypes of Th cells were relatively consistent. Next, we profiled chromatin accessibility and transcriptional dynamics on memory Th/Treg cells. In the memory Th cells, 803 increased and 545 decreased chromatin-accessible regions were identified. In the memory Treg cells, 713 increased and 1206 decreased chromatin-accessible regions were identified. A total of 54 and 53 genes were differentially expressed in the peaks associated with the memory Th and Treg cells. FOSL1, SPI1, ATF3, NFKB1, RUNX, ETV4, ERG, FLI1, and ETC1 were identified as regulators in the development of psoriasis. The transcriptional regulatory network showed that NFKB1 and RELA were highly connected and central to the network. NFKB1 regulated the genes of CCL3, CXCL2, and IL1RN. Our results provided candidate transcription factors and a foundational framework of the regulomes of the disease.
Chromatin/genetics* ; Chromatin Assembly and Disassembly ; Gene Regulatory Networks ; Humans ; Psoriasis/genetics* ; T-Lymphocytes, Regulatory

Genetic circuits are collections of basic elements that interact to produce a particular behavior. By constructing biochemical logic circuits and embedding them in cells, one can extend or modify the behavior of cells. To date, several small synthetic gene networks have been built that accomplish specific genetic regulatory functions in vivo: the autorepressor, in which a repressor regulates its own production to reduce noise in gene expression; the toggle-switch, in which two repressors inhibit each other's production to achieve a bistable system; the repressilator, in which three repressors are connected in a ring topology to produce repeated oscillation. "Rational" and "directed evolution" are currently used Genetic-circuit design tools. Someday we may be able to program cell behavior as easily as we program computers.
Computer Simulation ; Gene Expression Regulation ; genetics ; Gene Regulatory Networks ; genetics ; Humans ; Models, Genetic

OBJECTIVES: To explore the differential expression of messenger RNA (mRNA) in myocardial tissues of rats with sudden coronary death (SCD), and to provide ideas for the forensic identification of SCD. METHODS: The rat SCD model was established, and the transcriptome sequencing was performed by next-generation sequencing technology. Differentially expressed genes (DEGs) in myocardial tissues of SCD rats were screened by using the R package limma. A protein-protein interaction (PPI) network was constructed by using the STRING database and Cytoscape 3.8.2 on DEG, and hub genes were screened based on cytoHubba plug-in. Finally, the R package clusterProfiler was used to analyze the biological function and signal pathway enrichment of the selected DEG. RESULTS: A total of 177 DEGs were associated with SCD and were mainly involved in the renin-angiotensin system and PI3K-Akt signaling pathway. The genes including angiotensinogen (AGT), complement component 4a (C4a), Fos proto-oncogene (FOS) and others played key roles in the development of SCD. CONCLUSIONS Genes such as AGT, C4a, FOS and other genes are expected to be potential biomarkers for forensic identification of SCD. The study based on mRNA expression profile can provide a reference for forensic identification of SCD.
Rats ; Animals ; RNA, Messenger/genetics* ; Gene Regulatory Networks ; Gene Expression Profiling ; Phosphatidylinositol 3-Kinases/genetics* ; Biomarkers

BACKGROUNDAsthma is a heterogeneous disease for which a strong genetic basis has been firmly established. Until now no studies have been undertaken to systemically explore the network of asthma-related genes using an internally developed literature-based discovery approach. This study was to explore asthma-related genes by using literature-based mining and network centrality analysis.

METHODSLiterature involving asthma-related genes were searched in PubMed from 2001 to 2011. Integration of natural language processing with network centrality analysis was used to identify asthma susceptibility genes and their interaction network. Asthma susceptibility genes were classified into three functional groups by gene ontology (GO) analysis and the key genes were confirmed by establishing asthma-related networks and pathways.

RESULTSThree hundred and twenty-six genes related with asthma such as IGHE (IgE), interleukin (IL)-4, 5, 6, 10, 13, 17A, and tumor necrosis factor (TNF)-alpha were identified. GO analysis indicated some biological processes (developmental processes, signal transduction, death, etc.), cellular components (non-structural extracellular, plasma membrane and extracellular matrix), and molecular functions (signal transduction activity) that were involved in asthma. Furthermore, 22 asthma-related pathways such as the Toll-like receptor signaling pathway, hematopoietic cell lineage, JAK-STAT signaling pathway, chemokine signaling pathway, and cytokine-cytokine receptor interaction, and 17 hub genes, such as JAK3, CCR1-3, CCR5-7, CCR8, were found.

CONCLUSIONSOur study provides a remarkably detailed and comprehensive picture of asthma susceptibility genes and their interacting network. Further identification of these genes and molecular pathways may play a prominent role in establishing rational therapeutic approaches for asthma.

The transcriptome represents the whole complement of RNA transcripts in cells or tissues and reflects the expressed genes at various life stages, tissue types, physiological states, and environmental conditions. Transcriptomics study concerning medicinal plants has become the most active area in medicinal plant genome research. Transcriptome analysis provides a comprehensive understanding of gene expression and its regulation. The study of its transcriptome has great significance in solving the questions of genetic evolution, genetic breeding, ecology and so on. Here we report the application status of transcriptomics in medicinal plants based on emergence, development and methodology of transcriptomics.
Gene Expression Regulation, Plant ; Gene Regulatory Networks ; Plants, Medicinal ; genetics ; Sequence Analysis, RNA ; Transcriptome

Harnessing industrial microorganisms to utilize renewable feedstocks and meanwhile produce biofuels, bulk chemicals, food ingredients, nutraceuticals, pharmaceuticals, industrial enzymes, etc. is the basis for successful biological industries. Robust traits of industrial microorganisms including high yield and productivity as well as stress tolerance are controlled by sophisticated genetic regulatory networks. Engineering robustness of industrial microorganisms requires systematic and global perturbations at the genome-wide scale to accelerate the accumulation of diversified genotypic mutations, thus generating desirable phenotypes. We review heve the mechanisms of genetic regulation and stress response in robust industrial organisms, the global perturbations and multiplex accelerated evolution at the genome-wide scale, as well as the global perturbation of cellular redox balance. In the future, based on system biology and synthetic biology, more efforts should be further devoted to understanding the mechanisms behind robust traits in industrial microorganisms under industrial niches for modeling and prediction as well as systematic engineering.
Environment ; Gene Expression Regulation, Bacterial ; Gene Regulatory Networks ; genetics ; Industrial Microbiology ; Metabolic Engineering ; Synthetic Biology

In case/control gene expression data, differential expression (DE) represents changes in gene expression levels across various biological conditions, whereas differential co-expression (DC) represents an alteration of correlation coefficients between gene pairs. Both DC and DE genes have been studied extensively in human diseases. However, effective approaches for integrating DC-DE analyses are lacking. Here, we report a novel analytical framework named DC&DEmodule for integrating DC and DE analyses and combining information from multiple case/control expression datasets to identify disease-related gene co-expression modules. This includes activated modules (gaining co-expression and up-regulated in disease) and dysfunctional modules (losing co-expression and down-regulated in disease). By applying this framework to microarray data associated with liver, gastric and colon cancer, we identified two, five and two activated modules and five, five and one dysfunctional module(s), respectively. Compared with the other methods, pathway enrichment analysis demonstrated the superior sensitivity of our method in detecting both known cancer-related pathways and those not previously reported. Moreover, we identified 17, 69, and 11 module hub genes that were activated in three cancers, which included 53 known and three novel cancer prognostic markers. Random forest classifiers trained by the hub genes showed an average of 93% accuracy in differentiating tumor and adjacent normal samples in the TCGA and GEO database. Comparison of the three cancers provided new insights into common and tissue-specific cancer mechanisms. A series of evaluations demonstrated the framework is capable of integrating the rapidly accumulated expression data and facilitating the discovery of dysregulated processes.
Gene Expression Profiling ; Gene Regulatory Networks ; Humans ; Microarray Analysis ; Neoplasms/genetics*

OBJECTIVES: Laryngeal cancer (LC) is a globally prevalent and highly lethal tumor. Despite extensive efforts, the underlying mechanisms of LC remain inadequately understood. This study aims to conduct an innovative bioinformatic analysis to identify hub genes that could potentially serve as biomarkers or therapeutic targets in LC. METHODS: We acquired a dataset consisting of 117 LC patient samples, 16 746 LC gene RNA sequencing data points, and 9 clinical features from the Cancer Genome Atlas (TCGA) database in the United States. We employed weighted gene co-expression network analysis (WGCNA) to construct multiple co-expression gene modules. Subsequently, we assessed the correlations between these co-expression modules and clinical features to validate their associations. We also explored the interplay between modules to identify pivotal genes within disease pathways. Finally, we used the Kaplan-Meier plotter to validate the correlation between enriched genes and LC prognosis. RESULTS: WGCNA analysis led to the creation of a total of 16 co-expression gene modules related to LC. Four of these modules (designated as the yellow, magenta, black, and brown modules) exhibited significant correlations with 3 clinical features: The age of initial pathological diagnosis, cancer status, and pathological N stage. Specifically, the yellow and magenta gene modules displayed negative correlations with the age of pathological diagnosis (r=-0.23, P<0.05; r=-0.33, P<0.05), while the black and brown gene modules demonstrated negative associations with cancer status (r=-0.39, P<0.05; r=-0.50, P<0.05). The brown gene module displayed a positive correlation with pathological N stage. Gene Ontology (GO) enrichment analysis identified 77 items, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis identified 30 related signaling pathways, including the calcium signaling pathway, cytokine-cytokine receptor interaction, neuro active ligand-receptor interaction, and regulation of lipolysis in adipocytes, etc. Consequently, central genes within these modules that were significantly linked to the overall survival rate of LC patients were identified. Central genes included CHRNB4, FOXL2, KCNG1, LOC440173, ADAMTS15, BMP2, FAP, and KIAA1644. CONCLUSIONS This study, utilizing WGCNA and subsequent validation, pinpointed 8 genes with potential as gene biomarkers for LC. These findings offer valuable references for the clinical diagnosis, prognosis, and treatment of LC.
Humans ; Laryngeal Neoplasms/genetics* ; Rosaniline Dyes ; Biomarkers ; Adipocytes ; Gene Regulatory Networks ; Gene Expression Profiling

We propose that locations of genes on chromosomes can contribute to the prediction of gene regulatory relationships. We constructed a time-based gene regulatory network of zebrafish cardiogenesis on the basis of a spatio-temporal neighborhood method. Through the network, specific regulatory pathways and order of gene expression during zebrafish cardiogenesis were obtained. By comparing the order with locations of these genes on chromosomes, we discovered that there exists a reversal phenomenon between the order and order of gene locations. The discovery provides an inherent rule to instruct exploration of gene regulatory relationships. Specifically, the discovery can help to predict if regulatory relationships between genes exist and contribute to evaluating the correctness of discovered gene regulatory relationships.
Algorithms ; Animals ; Chromosome Mapping ; Chromosomes ; Gene Expression ; Gene Regulatory Networks ; Heart/physiology* ; Zebrafish/genetics*

Wound healing, as one of the important public health issues, has been a worldwide problem. Due to the unique biological wound environment, wound healing is a very complex process with current treatments requiring long cycles, being poorly effective, and bringing high economic burden to patients. An increasing number of studies have shown that non-coding RNAs (ncRNAs) play important roles in wound healing process. The competing endogenous RNAs (ceRNAs) hypothesis in recent years is a new proposal on the inter-regulation of RNAs, which suggests a "mode of communication" between different RNAs. ceRNA regulatory network (ceRNET) combines the functions of protein-coding mRNA with ncRNA (e.g., microRNA, long non-coding RNA, pseudogenes, and circular RNA). Recent studies have shown that ceRNAs play important roles in wound healing, which may provide new effective therapeutic targets for wound healing. This paper starting with ceRNET systematically reviewed the research progress on the effects of various ceRNAs in wound healing and the future research challenges, with the aim to deeply explore the molecular mechanisms and clinical significance of ceRNAs in the process of wound healing.
Gene Regulatory Networks ; Humans ; MicroRNAs/genetics* ; RNA, Circular ; RNA, Long Noncoding ; Wound Healing/genetics*