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*

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*

OBJECTIVE: To investigate a Met-controlled allosteric module (AM) of neural generation as a potential therapeutic target for brain ischemia. METHODS: We selected Markov clustering algorithm (MCL) to mine functional modules in the related target networks. According to the topological similarity, one functional module was predicted in the modules of baicalin (BA), jasminoidin (JA), cholic acid (CA), compared with I/R model modules. This functional module included three genes: Inppl1, Met and Dapk3 (IMD). By gene ontology enrichment analysis, biological process related to this functional module was obtained. This functional module participated in generation of neurons. Western blotting was applied to present the compound-dependent regulation of IMD. Co-immunoprecipitation was used to reveal the relationship among the three members. We used IF to determine the number of newborn neurons between compound treatment group and ischemia/reperfusion group. The expressions of vascular endothelial growth factor (VEGF) and matrix metalloproteinase 9 (MMP-9) were supposed to show the changing circumstances for neural generation under cerebral ischemia. RESULTS: Significant reduction in infarction volume and pathological changes were shown in the compound treatment groups compared with the I/R model group (P<0.05). Three nodes in one novel module of IMD were found to exert diverse compound-dependent ischemic-specific excitatory regulatory activities. An anti-ischemic excitatory allosteric module (AM CONCLUSIONS AM
Animals ; Brain Ischemia/drug therapy* ; Gene Ontology ; Gene Regulatory Networks ; Rodentia ; Vascular Endothelial Growth Factor A

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

Transcriptional regulation is crucial for regulated gene expression. Due to the complexity, it has been difficult to engineer eukaryotic transcription factor (TF) and promoter pairs. The few availabilities of eukaryotic TF and promotor pairs limit their practical use for clinical or industrial applications. Here, we report a de novo construction of synthetic inhibitory transcription factor and promoter pairs for mammalian transcriptional regulation. The design of synthetic TF was based on the fusion of DNA binding domain and Kruppel associated box transcription regulating domain (KRAB). The synthetic promoter was constructed by inserting the corresponding TF response element after SV40 promoter. We constructed and tested five synthetic inhibitory transcription factor and promoter pairs in cultured mammalian cells. The inhibition capability and orthogonality were verified by flow cytometry. In summary, we demonstrate the feasibility of constructing mammalian inhibitory TF and promoter pairs, which could be standardized for advanced gene-circuit design and various applications in the mammalian synthetic biology.
Animals ; Gene Expression Regulation ; Gene Regulatory Networks ; Mammals ; Promoter Regions, Genetic ; Transcription Factors ; Transcription, 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.