Dataset Information

Establishment of preliminary regulatory network of TRPV1 and related cytokines.

ABSTRACT: Our purpose was to investigate the regulatory mechanism of TRPV1 and related cytokines on children bronchial asthma. TRPV1 mRNA level and two SNP genotypes of children in case group and control group were detected by real-time quantitative PCR. Western blot and ELISA were used to measure the levels of cytokines like IgE, IL-2, etc. Their correlations were analyzed by Logistic regression and KEGG analysis. Moreover, tertiary structure of protein and miRNA binding sites were also predicted by online tools. Case group was obviously different from control group in TRPV1 mRNA level, the two SNP genotypes distribution and the related cytokines levels. Logistic regression analysis further demonstrated that TRPV1 mRNA level, EOS, IL-4 and IL-5 may be risk factors for children bronchial asthma. And based on that, the preliminary regulatory network of children bronchial asthma was drawn. What's more, mutation of rs4790521 and rs4790522 in TRPV1 gene both induced its corresponding miRNA binding site's change. The preliminary regulatory network of TRPV1 and related cytokines on children bronchial asthma established in this study provides certain theoretical basis for pathogenesis and treatment of children bronchial asthma.

SUBMITTER: Zhang J

PROVIDER: S-EPMC5372391 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Establishment of preliminary regulatory network of TRPV1 and related cytokines.

Zhang Jianhua J Zhou Zheng Z Zhang Ning N Jin Wenwen W Ren Yafeng Y Chen Chuanliang C

Saudi journal of biological sciences 20170126 3

Our purpose was to investigate the regulatory mechanism of TRPV1 and related cytokines on children bronchial asthma. TRPV1 mRNA level and two SNP genotypes of children in case group and control group were detected by real-time quantitative PCR. Western blot and ELISA were used to measure the levels of cytokines like IgE, IL-2, etc. Their correlations were analyzed by Logistic regression and KEGG analysis. Moreover, tertiary structure of protein and miRNA binding sites were also predicted by onli ...[more]

PMID: 28386183

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Project description:Background: Glioblastoma multiforme (GBM), the most common and aggressive human malignant brain tumor, is notorious for its limited treatment options and poor prognosis. MicroRNAs (miRNAs) are found to be involved in tumorigenesis of GBM. However, a comprehensive miRNA-mRNA regulatory network has still not been established. Methods: A miRNA microarray dataset (GSE90603) was obtained from GEO database. Then, we employed GEO2R tool to perform differential expression analysis. Potential transcription factors and target genes of screened differentially expressed miRNAs (DE-miRNAs) were predicted. The GBM mRNA dataset were downloaded from TCGA database for identifying differentially expressed genes (DEGs). Next, GO annotation and KEGG pathway enrichment analysis was conducted. PPI network was then established, and hub genes were identified via Cytoscape software. The expression and prognostic roles of hub genes was further evaluated. Results: Total 33 DE-miRNAs, consisting of 10 upregulated DE-miRNAs and 23 downregulated DE-miRNAs, were screened. SP1 was predicted to potentially regulate most of screened DE-miRNAs. Three thousand and twenty seven and 3,879 predicted target genes were obtained for upregulated and downregulated DE-miRNAs, respectively. Subsequently, 1,715 upregulated DEGs and 1,259 downregulated DEGs were identified. Then, 149 and 295 potential downregulated and upregulated genes commonly appeared in target genes of DE-miRNAs and DEGs were selected for GO annotation and KEGG pathway enrichment analysis. The downregulated genes were significantly enriched in cGMP-PKG signaling pathway and calcium signaling pathway whereas the upregulated genes were enriched in pathways in cancer and PI3K-Akt signaling pathway. Construction and analysis of PPI network showed that STXBP1 and TP53 were recognized as hub genes with the highest connectivity degrees. Expression analytic result of the top 20 hub genes in GBM using GEPIA database was generally identical with previous differential expression analysis for TCGA data. EGFR, PPP3CB, and MYO5A expression was significantly associated with patients' OS. Conclusions: In this study, we established a potential GBM-related miRNA-mRNA regulatory network, which explores a comprehensive understanding of the molecular mechanisms and provides key clues in seeking novel therapeutic targets for GBM. In the future, more experiments need to be performed to validate our current findings.

Project description:BackgroundNetwork Component Analysis (NCA) is a network structure-driven framework for deducing regulatory signal dynamics. In contrast to principal component analysis, which can be employed to select the high-variance genes, NCA makes use of the connectivity structure from transcriptional regulatory networks to infer dynamics of transcription factor activities. Using the budding yeast Saccharomyces cerevisiae as a model system, we aim to deduce regulatory actions of cytokinesis-related genes, using precise spatial proximity (midbody) and/or temporal synchronicity (cytokinesis) to avoid full-scale computation from genome-wide databases.ResultsNCA was applied to infer regulatory actions of transcription factor activity from microarray data and partial transcription factor-gene connectivity information for cytokinesis-related genes, which were a subset of genome-wide datasets. No literature has so far discussed the inferred results through NCA are independent of the scale of the gene expression dataset. To avoid full-scale computation from genome-wide databases, four cytokinesis-related gene cases were selected for NCA by running computational analysis over the transcription factor database to confirm the approach being scale-free. The inferred dynamics of transcription factor activity through NCA were independent of the scale of the data matrix selected from the four cytokinesis-related gene sets. Moreover, the inferred regulatory actions were nearly identical to published observations for the selected cytokinesis-related genes in the budding yeast; namely, Mcm1, Ndd1, and Fkh2, which form a transcription factor complex to control expression of the CLB2 cluster (i.e. BUD4, CHS2, IQG1, and CDC5).ConclusionIn this study, using S. cerevisiae as a model system, NCA was successfully applied to infer similar regulatory actions of transcription factor activities from two various microarray databases and several partial transcription factor-gene connectivity datasets for selected cytokinesis-related genes independent of data sizes. The regulated action for four selected cytokinesis-related genes (BUD4, CHS2, IQG1, and CDC5) belongs to the M-phase or M/G1 phase, consistent with the empirical observations that in S. cerevisiae, the Mcm1-Ndd1-Fkh2 transcription factor complex can regulate expression of the cytokinesis-related genes BUD4, CHS2, IQG1, and CDC5. Since Bud4, Iqg1, and Cdc5 are highly conserved between human and yeast, results obtained from NCA for cytokinesis in the budding yeast can lead to a suggestion that human cells should have the transcription regulator(s) as the budding yeast Mcm1-Ndd1-Fkh2 transcription factor complex in controlling occurrence of cytokinesis.

Dataset Information

Establishment of preliminary regulatory network of TRPV1 and related cytokines.

Publications

Establishment of preliminary regulatory network of TRPV1 and related cytokines.

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