Gene-gene Interaction Network Analysis Research Articles

Gene-gene interaction network analysis indicates CNTN2 is a candidate gene for idiopathic generalized epilepsy.

Twin and family studies have established the genetic contribution to idiopathic generalized epilepsy (IGE). The genetic architecture of IGE is generally complex and heterogeneous, and the majority of the genetic burden in IGE remains unsolved. We hypothesize that gene-gene interactions contribute to the complex inheritance of IGE. CNTN2 (OMIM* 615,400) variants have been identified in cases with familial adult myoclonic epilepsy and other epilepsies. To explore the gene-gene interaction network in IGE, we took the CNTN2 gene as an example and investigated its co-occurrent genetic variants in IGE cases. We performed whole-exome sequencing in 114 unrelated IGE cases and 296 healthy controls. Variants were qualified with sequencing quality, minor allele frequency, in silico prediction, genetic phenotype, and recurrent case numbers. The STRING_TOP25 gene interaction network analysis was introduced with the bait gene CNTN2 (denoted as A). The gene-gene interaction pair mode was presumed to be A + c, A + d, A + e, with a leading gene A, or A + B + f, A + B + g, A + B + h, with a double-gene A + B, or other combinations. We compared the number of gene interaction pairs between the case and control groups. We identified three pairs in the case group, CNTN2 + PTPN18, CNTN2 + CNTN1 + ANK2 + ANK3 + SNTG2, and CNTN2 + PTPRZ1, while we did not discover any pairs in the control group. The number of gene interaction pairs in the case group was much more than in the control group (p = 0.021). Taking together the genetic bioinformatics, reported epilepsy cases, and statistical evidence in the study, we supposed CNTN2 as a candidate pathogenic gene for IGE. The gene interaction network analysis might help screen candidate genes for IGE or other complex genetic disorders.

Characterization of the Heat Shock Transcription Factor Family in Lycoris radiata and Its Potential Roles in Response to Abiotic Stresses.

Heat shock transcription factors (HSFs) are an essential plant-specific transcription factor family that regulates the developmental and growth stages of plants, their signal transduction, and their response to different abiotic and biotic stresses. The HSF gene family has been characterized and systematically observed in various species; however, research on its association with Lycoris radiata is limited. This study identified 22 HSF genes (LrHSFs) in the transcriptome-sequencing data of L. radiata and categorized them into three classes including HSFA, HSFB, and HSFC, comprising 10, 8, and 4 genes, respectively. This research comprises basic bioinformatics analyses, such as protein sequence length, molecular weight, and the identification of its conserved motifs. According to the subcellular localization assessment, most LrHSFs were present in the nucleus. Furthermore, the LrHSF gene expression in various tissues, flower developmental stages, two hormones stress, and under four different abiotic stresses were characterized. The data indicated that LrHSF genes, especially LrHSF5, were essentially involved in L. radiata development and its response to different abiotic and hormone stresses. The gene-gene interaction network analysis revealed the presence of synergistic effects between various LrHSF genes' responses against abiotic stresses. In conclusion, these results provided crucial data for further functional analyses of LrHSF genes, which could help successful molecular breeding in L. radiata.

Network analysis of synovial RNA sequencing identifies gene-gene interactions predictive of response in rheumatoid arthritis

BackgroundTo determine whether gene-gene interaction network analysis of RNA sequencing (RNA-Seq) of synovial biopsies in early rheumatoid arthritis (RA) can inform our understanding of RA pathogenesis and yield improved treatment response prediction models.MethodsWe utilized four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We extracted specific gene-gene interaction networks in synovial RNA-Seq to characterize histologically defined pathotypes in early RA and leverage these synovial specific gene-gene networks to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). Differential interactions identified within each network were statistically evaluated through robust linear regression models. Ability to predict response to DMARD treatment was evaluated by receiver operating characteristic (ROC) curve analysis.ResultsAnalysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. We identified a key role for angiogenesis, observing significant statistical interactions between NOS3 (eNOS) and both CAMK1 and eNOS activator AKT3 when comparing responders and non-responders. The ratio of CAMKD2/NOS3 enhanced a prediction model of response improving ROC AUC from 0.63 to 0.73.ConclusionsWe demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

Predicted Cellular and Molecular Actions of Lithium in the Treatment of Bipolar Disorder: An In Silico Study.

Lithium remains the first-line treatment for bipolar disorder (BD), but patients respond to it variably. While a myriad of studies have attributed many genes and signaling pathways to lithium responsiveness, a comprehensive study with an integrated conclusion is still lacking. We aim to present an integrated mechanism for the therapeutic actions of lithium in BD. First, a list of lithium responsiveness-associated genes (LRAGs) was collected by searching in the literature. Thereafter, gene set enrichment analysis together with gene-gene interaction network analysis was performed, in order to find the cellular and molecular events related to the LRAGs. Gene set enrichment analyses showed that the chromosomal regions 3p26, 4p21, 5q34 and 7p13 could be novel associated loci for lithium responsiveness in BD. Also, expression pattern analysis of the LRAGs showed their enrichment in adulthood stages and different cell lineages of brain, blood and immune system. Most of the LRAGs exhibited enriched expression in central parts of human brain, suggesting major contribution of these parts in lithium responsiveness. Beside the prediction of several biological processes and signaling pathways related to lithium responsiveness, an interaction network between these processes was constructed that was found to be regulated by a set of microRNAs. Proteins of the network were mainly classified as transcription factors and kinases, which also highlighted the crucial role of glycogen synthase kinase 3β (GSK3β) in lithium responsiveness. The predicted cellular and molecular events in this study could be considered as mechanisms and also determinants of lithium responsiveness in BD.

Transcriptome of 17β-hydroxysteroid dehydrogenase type 2 plays both hormone-dependent and hormone-independent roles in MCF-7 breast cancer cells

Breast cancer is a major cause of cancer-related death for women in western countries. 17β-Hydroxysteroid dehydrogenases (17β-HSDs) play important roles in the last step of sex-hormone activation and the first step of sex-hormone inactivation. 17β-HSD2 is responsible for oxidizing the sex hormones. We used microarray technology to analyze the effect of 17β-HSD2 on the MCF-7 cell transcript profile after knocking down 17β-HSD2. Five hundred forty-two genes were regulated 1.5-fold or higher after treatment with 17β-HSD2 siRNA. Knocking down 17β-HSD2 interrupted nucleosome assembly. Pathway-Act-Network analysis showed that the MAPK and apoptosis signaling pathways were most regulated. In the gene-gene interaction network analysis, UGT2B15, which is involved in hormone metabolism, was the most regulated core gene. FOS, GREB1, and CXCL12 were the most regulated genes, and CXCL12 was related to tumor migration. Following 17β-HSD2 knock-down, the cell viability decreased to 75.9%. The S-phase percentage decreased by 19.4%, the Q2-phase percentage in cell apoptosis testing increased by 1.5 times, and cell migration decreased to 66.0%. These results were consistent with our gene chip analysis and indicated that 17β-HSD2 plays both hormone-dependent and hormone-independent enzymatic roles. In-depth investigations of this enzyme on the genomic level will help clarify its related molecular mechanisms.

Abstract 1252: RNA-Seq transcriptome analyses of human LNCaP prostate cancer cells treated with a novel bis-aspirinyl selenazolidine AS-10

Abstract Purpose: Our goal is to develop novel selenium-containing aspirin compound(s) for the chemoprevention of prostate cancer. Our current studies have shown significant inhibitory effects of bis-aspirinyl selenazolidine AS-10 on androgen receptor (AR) signaling, cell proliferation and survival (apoptosis) of human prostate cancer LNCaP cells in the single micromolar range of continued exposure. Immunoblot analyses of the AS-10 exposed cells showed the upregulation or activation of DNA damage response (DDR) protein p53 and p21Cip1 as well as DNA double strand break marker histone H2A.X associated with the apoptotic metrics. The objectives of the current work were: 1) to cross-validate involvement of transcriptional action of AS-10 to regulate key molecules related to AR signaling, cell cycle arrest, DDR and apoptosis responses, and 2) to discover potential transcriptome signatures and pathway networks induced by AS-10 that help to delineate its mechanisms of action. Experimental Design: RNA-seq was performed for LNCaP cells that were treated with AS-10 at 5 and 10 μM, with DMSO as a control for 3, 6 or 12 h. The sequencing results were analyzed by Ingenuity Pathway Analysis (IPA) software with several bioinformatic approaches, including functional enrichment analysis, gene-gene interaction network analysis and upstream regulator analysis. Results: A total of 12055 genes were positively aligned. At AS-10 exposure concentration of 5 and 10 μM, 2535 and 4556 genes were affected by two-fold, respectively (p<0.01). For targeted analyses of mRNA abundance changes of AR and its best known transcriptional target prostate specific antigen (PSA/KLK3), AS-10 decreased them in synchrony with their protein changes. For un-biased profiling, IPA functional enrichment analysis revealed that many of the differentially expressed genes were associated with cell cycle-associated functions, cell death, and P53 signaling. For gene-gene network interactions, the top ranked networks from all doses and treatment time-points revealed significant links with cancer, organismal injury and abnormalities, cellular development, growth and proliferation, cell death and survival. At AS-10 exposure concentration of 5μM, MYC and PSA were initially targeted, followed by AR and P53. At AS-10 exposure concentration of 10μM, MYC and AR were initially targeted, followed by AR. As expected, higher concentration of AS-10 induced the genes related to cell death earlier than lower level. Consistent with RNA-seq data, our protein assay showed that AS-10 10μM induced cell death at 12 hr. Conclusions: The RNA-seq data on AR/PSA and p53/p21 identify transcriptional as well as post-transcriptional regulation by AS-10 to affect their protein levels in the LNCaP cells. The transcriptome profiling approaches reveal comprehensive network interactions and cross-talks to contribute to cell cycle arrest and apoptosis. Citation Format: Sangyub Kim, Deepkamal N. Karelia, Yuka Imamura, Srinivasa Ramisetti, Cheng Jiang, Shantu Amin, Arun K. Sharma, Junxuan Lu. RNA-Seq transcriptome analyses of human LNCaP prostate cancer cells treated with a novel bis-aspirinyl selenazolidine AS-10 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1252.

Exploring the Key Genes and Pathways of Osteoarthritis in Knee Cartilage in a Rat Model Using Gene Expression Profiling.

PurposeTo compare differentially expressed genes (DEGs) mediating osteoarthritis (OA) in knee cartilage and in normal knee cartilage in a rat model of OA and to identify their impact on molecular pathways associated with OA.Materials and MethodsA gene expression profile was downloaded from the Gene Expression Omnibus database. Analysis of DEGs was carried out using GEO2R. Enrichment analyses were performed on the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway using the Search Tool for the Retrieval of Interacting Genes database (http://www.string-db.org/). Subsequently, the regulatory interaction network of OA-associated genes was visualized using Cytoscape software (version 3.4.0; www.cytoscape.org).ResultsIn the gene expression profile GSE103416, a total of 99 DEGs were identified. Among them, 76 DEGs (76.77%) were overexpressed, and the remaining 23 DEGs (23.23%) were underexpressed. GO and pathway enrichment analyses of target genes were performed. Using gene-gene interaction network analysis, relevant core genes, including MET, UBB, GNAI3, and GNA13, were shown to hold a potential relationship with the development of OA in cartilage. Using quantitative real-time PCR, the Gna13/cGMP-PKG signaling pathway was identified as a potential research target for therapy and for further understanding the development of OA.ConclusionThe results of the present study provide a comprehensive understanding of the roles of DEGs in knee cartilage in relation to the development of OA.

Retraction note: Gene-gene interaction network analysis of ovarian cancer using TCGA data.

The Publisher and Editor regretfully retract this article (Ying H, Lv J, Ying T, Jin S, Shao J, Wang L, et al. Gene-gene interaction network analysis of ovarian cancer using TCGA data. J Ovarian Res. 2013;6:88) because the peer-review process was inappropriately influenced and compromised. As a result, the scientific integrity of the article cannot be guaranteed. A systematic and detailed investigation suggests that a third party was involved in supplying fabricated details of potential peer reviewers for a large number of manuscripts submitted to different journals. In accordance with recommendations from COPE we have retracted all affected published articles, including this one. It was not possible to determine beyond doubt that the authors of this particular article were aware of any third party attempts to manipulate peer review of their manuscript.

Platelet-specific collagen receptor glycoprotein VI gene variants affect recurrent pregnancy loss

To determine whether platelet-specific collagen receptor glycoprotein VI (GP6) gene variants are associated with recurrent miscarriages (RM). Genetic association study. Tertiary care referral hospital. A total of 200 women with at least three unexplained spontaneous abortions before 20weeks of gestation and 300 healthy parous women. Determination of variants of GP6 single-nucleotide polymorphisms (SNPs) namely; rs1671153, rs1654410, rs1654419, and rs1613662 was based on polymerase chain reaction-restriction fragment-length polymorphism. Genotypes and haplotypes frequencies were compared in RM case subjects versus control subjects. We observed significantly higher occurrence of rare alleles of SNPs in GP6, namely, rs1671153, rs1654410, rs1654419, and rs1613662, among RM cases, revealing risk association for fetal losses. The synergistic effects of haplotype combinations were also evaluated and showed that four haplotypes G-T-G-G, T-C-A-A, G-C-G-A, and G-T-A-A were more prevalent among RM cases, revealing increased risk for fetal losses. In silico analysis revealed that GP6 has an impact on biologic pathways and significant influence in collagen binding. Gene-gene interaction network analysis revealed that GP6 consisted of a total of 25 interactions with 13 genes in the human genome. These results suggest that variants of GP6 SNPs, namely, rs1671153, rs1654410, rs1654419, and rs1613662, may be associated with risk of recurrent miscarriage. In silico analyses demonstrated the influence of GP6 in biologic pathways, molecular function, including collagen binding, and gene-gene interaction in the human genome.

Gene-gene interaction network analysis of ovarian cancer using TCGA data.

BackgroundThe Cancer Genome Atlas (TCGA) Data portal provides a platform for researchers to search, download, and analysis data generated by TCGA. The objective of this study was to explore the molecular mechanism of ovarian cancer pathogenesis.MethodsMicroarray data of ovarian cancer were downloaded from TCGA database, and Limma package in R language was used to identify the differentially expressed genes (DEGs) between ovarian cancer and normal samples, followed by the function and pathway annotations of the DEGs. Next, NetBox software was used to for the gene-gene interaction (GGI) network construction and the corresponding modules identification, and functions of genes in the modules were screened using DAVID.ResultsOur studies identified 332 DEGs, including 146 up-regulated genes which mainly involved in the cell cycle related functions and cell cycle pathway, and 186 down-regulated genes which were enriched in extracellular region par function, and Ether lipid metabolism pathway. GGI network was constructed by 127 DEGs and their significantly interacted 209 genes (LINKERs). In the top 10 nodes ranked by degrees in the network, 5 were LINKERs. Totally, 7 functional modules in the network were selected, and they were enriched in different functions and pathways, such as mitosis process, DNA replication and DNA double-strand synthesis, lipid synthesis processes and metabolic pathways. AR, BRCA1, TFDP1, FOXM1, CDK2, and DBF4 were identified as the transcript factors of the 7 modules.Conclusionour data provides a comprehensive bioinformatics analysis of genes, functions, and pathways which may be involved in the pathogenesis of ovarian cancer.

Intermittent hypoxia activates temporally coordinated transcriptional programs in visceral adipose tissue

Obstructive sleep apnea (OSA) is a prevalent disorder characterized by intermittent hypoxia (IH) during sleep. OSA is strongly associated with obesity and dysregulation of metabolism-yet the molecular pathways linking the effects of IH on adipocyte biology remain unknown. We hypothesized that exposure to IH would activate distinct, time-dependent transcriptional programs in visceral adipose tissue of mice. We exposed 36 mice to IH or normoxia for up to 13days. We transcriptionally profiled visceral fat tissue harvested from the animals and performed functional enrichment and network analysis on differentially expressed genes. We identified over 3,000 genes with significant expression patterns during the time course of IH exposure. The most enriched pathways mapped to metabolic processes, mitochondrion, and oxidative stress responses. We confirmed the pathophysiological relevance of these findings by demonstrating that mice exposed to chronic IH developed dyslipidemia and underwent significant lipid and protein oxidation within their visceral adipose depots. We applied gene-gene interaction network analysis to identify critical controllers of IH-induced transcriptional programs in adipocytes-these network hubs represent putative targets to modulate the effects of chronic IH on adipose tissue. Our approach to integrate computational methods with gene expression profiling of visceral fat tissue during IH exposure shows promise in helping unravel the mechanistic links between OSA and adipocyte biology.