In this study, we propose a novel, intuitive method of constructing an expression quantitative trait (eQT) network that is related to the metabolic syndrome using LOD scores and peak loci for selected eQTs, based on the concept of gene-gene interactions. We selected 49 eQTs that were related to insulin resistance. A variance component linkage analysis was performed to explore the expression loci of each of the eQTs. The linkage peak loci were investigated, and the "support zone" was defined within boundaries of an LOD score of 0.5 from the peak. If one gene was located within the "support zone" of the peak loci for the eQT of another gene, the relationship was considered as a potential "directed causal pathway" from the former to the latter gene. SNP markers under the linkage peaks or within the support zone were searched for in the database to identify the genes at the loci. Two groups of gene networks were formed separately around the genes IRS2 and UGCGL2. The findings indicated evidence of networks between genes that were related to the metabolic syndrome. The use of linkage analysis enabled the construction of directed causal networks. This methodology showed that characterizing and locating eQTs can provide an effective means of constructing a genetic network.
Gene Regulatory Networks ; Insulin Resistance ; Lod Score

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

OBJECTIVES: Recently, comparison of drug responses on gene expression has been a major approach to identifying the functional similarity of drugs. Previous studies have mostly focused on a single feature, the expression differences of individual genes. We provide a more robust and accurate method to compare the functional similarity of drugs by diversifying the features of comparison in gene expression and considering the sample dependent variations. METHODS: For differentially expressed gene measurement, we modified the conventional t-test to normalize variations in diverse experimental conditions of individual samples. To extract significant differentially co-expressed gene modules, we searched maximal cliques among the co-expressed gene network. Finally, we calculated a combined similarity score by averaging the two scaled scores from the above two measurements. RESULTS: This method shows significant performance improvement in comparison to other approaches in the test with Connectivity Map data. In the test to find the drugs based on their own expression profiles with leave-one-out cross validation, the proposed method showed an area under the curve (AUC) score of 0.99, which is much higher than scores obtained with previous methods, ranging from 0.71 to 0.93. In the drug networks, we could find well clustered drugs having the same target proteins and novel relations among drugs implying the possibility of drug repurposing. CONCLUSIONS: Inclusion of the features of a co-expressed module provides more implications to infer drug action. We propose that this method be used to find collaborative cellular mechanisms associated with drug action and to simply identify drugs having similar responses.
Biomarkers, Pharmacological ; Drug Repositioning ; Gene Expression Regulation ; Gene Expression* ; Gene Regulatory Networks ; Methods ; Transcriptome*

PURPOSE: To better identify the physiology of triple-negative breast neoplasm (TNBN), we analyzed the TNBN gene regulatory network using gene expression data. METHODS: We collected TNBN gene expression data from The Cancer Genome Atlas to construct a TNBN gene regulatory network using least absolute shrinkage and selection operator regression. In addition, we constructed a triple-positive breast neoplasm (TPBN) network for comparison. Furthermore, survival analysis based on gene expression levels and differentially expressed gene (DEG) analysis were carried out to support and compare the network analysis results, respectively. RESULTS: The TNBN gene regulatory network, which followed a power-law distribution, had 10,237 vertices and 17,773 edges, with an average vertex-to-vertex distance of 8.6. The genes ZDHHC20 and RAPGEF6 were identified by centrality analysis to be important vertices. However, in the DEG analysis, we could not find meaningful fold changes in ZDHHC20 and RAPGEF6 between the TPBN and TNBN gene expression data. In the multivariate survival analysis, the hazard ratio for ZDHHC20 and RAPGEF6 was 1.677 (1.192–2.357) and 1.676 (1.222–2.299), respectively. CONCLUSION: Our TNBN gene regulatory network was a scale-free one, which means that the network would be easily destroyed if the hub vertices were attacked. Thus, it is important to identify the hub vertices in the network analysis. In the TNBN gene regulatory network, ZDHHC20 and RAPGEF6 were found to be oncogenes. Further study of these genes could help to reveal a novel method for treating TNBN in the future.
Breast Neoplasms ; Gene Expression* ; Gene Regulatory Networks* ; Genome ; Methods ; Oncogenes ; Physiology ; Triple Negative Breast Neoplasms*

The differentiation of neural precursor cells (NPCs) into neurons and astrocytes is a process that is tightly controlled by complicated and ill-defined gene networks. To extend our knowledge to gene networks, we performed a temporal analysis of gene expression during the differentiation (2, 4, and 8 days) of spinal cord-derived NPCs using oligonucleotide microarray technology. Out of 32,996 genes analyzed, 1878 exhibited significant changes in expression level (fold change>2, p<0.05) at least once throughout the differentiation process. These 1878 genes were classified into 12 groups by k-means clustering, based on their expression patterns. K-means clustering analysis revealed that the genes involved in astrogenesis were categorized into the clusters containing constantly upregulated genes, whereas the genes involved in neurogenesis were grouped to the cluster showing a sudden decrease in gene expression on Day 8. Functional analysis of the differentially expressedgenes indicated the enrichment of genes for Pax6- NeuroD signaling-TGFb-SMAD and BMP-SMAD-which suggest the implication of these genes in the differentiation of NPCs and, in particular, key roles for Nova1 and TGFBR1 in the neurogenesis/astrogenesis of mouse spinal cord.
Animals ; Astrocytes ; Gene Expression ; Gene Regulatory Networks ; Mice ; Neurogenesis ; Neurons ; Oligonucleotide Array Sequence Analysis ; Spinal Cord

Flowering, the floral transition from vegetative growth to reproductive growth, is induced by diverse endogenous and exogenous cues, such as photoperiod, temperature, hormones and age. Precise flowering time is critical to plant growth and evolution of species. The numerous renewal molecular and genetic results have revealed five flowering time pathways, including classical photoperiod pathway, vernalization pathway, autonomous pathway, gibberellins (GA) pathway and newly identified age pathway. These pathways take on relatively independent role, and involve extensive crosstalks and feedback loops. This review describes the complicated regulatory network of this floral transition to understand the molecular mechanism of flowering and provide references for further research in more plants.
Arabidopsis ; physiology ; Flowers ; physiology ; Gene Expression Regulation, Plant ; Gene Regulatory Networks

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

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

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