Gene-gene Interaction Network Research Articles

SMISS: a protein function prediction server by integrating multiple sources

SMISS is a novel web server for protein function prediction. Three different predictors can be selected for different usage. It integrates different sources to improve the protein function prediction accuracy, including the query protein sequence, protein-protein interaction network, gene-gene interaction network and the rules mined from protein function associations. SMISS automatically switch to ab initio protein function prediction based on the query sequence when there is no homolog's in the database. It takes fasta format sequences as input; and several sequences can be submitted together without influencing the computation speed too much. PHP and Perl are two primary programming language used in the server. The CodeIgniter MVC PHP web framework and bootstrap front-end framework are used for building the server. It can be used in different platforms in standard web browser, such as Windows, Mac OS X, Linux and iOS. No plug-ins is needed for our website (availability: http://tulip.rnet.missouri.edu/profunc/).

Analyzing Functional Pathways and constructing gene-gene network for Narcolepsy based on candidate genes.

Aims: To investigate the interactions among narcolepsy-associated genes and reveal the pathways these genes involved through bioinformatics analyses.Methods: The study was performed with the following steps: 1) Selected the previously discovered narcolepsy risk genes through literature review, 2) pathway enrichment analysis, and construction of gene-gene and protein-protein interaction (PPI) networks for narcolepsy.Results: 1) GO analysis revealed the positive regulation of interferon-gamma production as the most enriched terms in biological process, and C-C chemokine receptor activity as the most enriched term in molecular function, 2) KEGG pathway enrichment analysis revealed selective enrichment of genes in cytokine-cytokine receptor interaction signaling pathways, and 3) five hub genes were identified (IFNAR1, IL10RB, DNMT1, TNFSF4 and NFATC2).Conclusion: The bioinformatics results provide new insights into the molecular pathogenesis of narcolepsy and the identification of potential therapeutic targets for narcolepsy treatment.

VD-Analysis: A Dynamic Network Framework for Analyzing Disease Progressions

The progression of a disease associates with changes in genomic activity, but it remains a challenge to screen genetic biomarkers for clinical applications. The disease progression, in dynamic network methods (DNM), can be analogous to an animated film composed of discrete frames, where each frame represents a temporary state of the time-varying gene-gene interaction network. The major shortage therein is that the transition between two neighboring temporary states was beyond investigation. Here, we develop an updated computational methodology named after VD-analysis. Because single-gene biomarkers were not approved capable of representing a complex biological process, we firstly introduce V-structure - a gene module composed of three genes and two interactions among them - and define it as unit module. We then identify the perturbed pathways that mark the disease progression, followed with the V-structures identified which drive the pathway perturbations. Such driver V-structures can be taken as eligible biomarkers for clinical applications. To test the feasibility of this method, we apply it to a time course dataset of gene expression related to mouse type-II diabetes (T2D). Result indicates that the whole process of T2D is exactly divided into 3 stages and that the driver V-structures inferred for each stage are qualified biomarkers. In summary, our method contributes to the description of dynamic disease progression and the V-structure biomarkers facilitate the treatments of disease.

Prognostic model for multiple myeloma progression integrating gene expression and clinical features.

BackgroundMultiple myeloma (MM) is a hematological cancer caused by abnormal accumulation of monoclonal plasma cells in bone marrow. With the increase in treatment options, risk-adapted therapy is becoming more and more important. Survival analysis is commonly applied to study progression or other events of interest and stratify the risk of patients.ResultsIn this study, we present the current state-of-the-art model for MM prognosis and the molecular biomarker set for stratification: the winning algorithm in the 2017 Multiple Myeloma DREAM Challenge, Sub-Challenge 3. Specifically, we built a non-parametric complete hazard ranking model to map the right-censored data into a linear space, where commonplace machine learning techniques, such as Gaussian process regression and random forests, can play their roles. Our model integrated both the gene expression profile and clinical features to predict the progression of MM. Compared with conventional models, such as Cox model and random survival forests, our model achieved higher accuracy in 3 within-cohort predictions. In addition, it showed robust predictive power in cross-cohort validations. Key molecular signatures related to MM progression were identified from our model, which may function as the core determinants of MM progression and provide important guidance for future research and clinical practice. Functional enrichment analysis and mammalian gene-gene interaction network revealed crucial biological processes and pathways involved in MM progression. The model is dockerized and publicly available at https://www.synapse.org/#!Synapse:syn11459638. Both data and reproducible code are included in the docker.ConclusionsWe present the current state-of-the-art prognostic model for MM integrating gene expression and clinical features validated in an independent test set.

Identification of a prognostic alternative splicing signature in oral squamous cell carcinoma.

Alternative splicing (AS) is critically associated with tumorigenesis and patient's prognosis. Here, we systematically analyzed survival-associated AS signatures in oral squamous cell carcinoma (OSCC) and evaluated their prognostic predictive values. Survival-related AS events were identified by univariate and multivariate Cox regression analyses using OSCC data from the TCGA head neck squamous cell carcinoma data set. ThePercent Spliced In calculated by SpliceSeq from 0 to 1 was used to quantify seven types of AS events. A predictive model based on AS events was constructed by least absolute shrinkage and selection operator Cox regression assay and further validated using a training-testing cohort design. Patient survival was estimated using the Kaplan-Meier method and compared with Log-rank test. The receiver operating characteristics curve area under the curves was used to evaluate the predictive abilities of these predictive models. Furthermore, gene-gene interaction networks and the splicing factors (SFs)-AS regulatory network was generated by Cytoscape. A total of 825 survival-related AS events within 719 genes were identified in OSCC samples. The integrative predictive model was betteratpredicting outcomes of patients as compared to those models built with the individual AS event. The predictive model based on three AS-related genes also effectively predicted patients' survival. Moreover, seven survival-related SFs were detected in OSCC including RBM4, HNRNPD, and HNRNPC, which have been linked to tumorigenesis. The SF-AS network revealed a significant correlation between survival-related AS genes and these SFs. Our findings revealed a systemic portrait of survival-associated AS events and the splicing network in OSCC, suggesting that AS events might serve as novel prognostic biomarkers and therapeutic targets for OSCC.

1985PD - Aspartate-β-hydroxylase drives hepatocelluar carcinoma progression to metastasis fueling glutamine via HIF1α-mediated mitophagy

BackgroundAspartame (asparagynal)-β-hydroxylase (ASPH) leads to hepatocellular carcinoma (HCC) progression, but the potential molecular mechanism of ASPH on HCC progression to metastasis is still obscure. Our aim was to explore the correlation between ASPH and HIF1α-mediated mitophagy fueling via glutamine during tumor progression to metastasis in HCC. MethodsBioinformatics analysis constructed a composite gene-gene functional interaction network for ASPH regulatory genes to predict potential cellular mechanisms and then verified in vivo and vitro. ASPH+/-expressing stable cell line of HepG2 and Huh7 cell lines were structured by lentivirus encoding the wild-type ASPH open reading frame and ASPH-CRISPR/Cas9 system. 140 cases of HCC patients and a sbcutaneous xenograft HCC tumor model were used to evaluate HCC tumor growth and progression in vivo. The effect of ASPH on mitophagy, glutamine metabolism, angiogenesis and metastasis in HCC were tested via western blot, RT-PCR, immunohistochemical staining, electron microscopy, hematoxylin-eosin (HE) and immunocytofluorescent staining. The HIF1α inhibitor 2-Methoxyestradiol (2-MeOE2) and the serum free cultivation were used to conduct the related rescue experiments in vitro. ResultsIn this study, ASPH improved the expression of LC3-II/LC3-I,Atg7, Atg5 and Beclin1 and down-regulated the expression of TOMM20 and P62. Meanwhile, ASPH also significantly increased the expression of GLS1, which reflects the glutamine metabolism level of tumor cells. In addition, ASPH also significantly inhibited the expressions of HIF1α, BNIP3 and NIX proteins in HCC. Furthermore, a significant association between ASPH and CD34 expression was observed in HCC patients (P=0.024). ConclusionsOur finding suggests that ASPH fuels glutamine via HIF1α-mediated mitophagy to drive cancer angiogenesis and metastasis in HCC. ASPH may act as a promising therapeutic target in HCC patients. Legal entity responsible for the studyThe authors. FundingNational Natural Science Foundation of China (No. 81470877) Beijing Natural Science Foundation (No.7192085). DisclosureAll authors have declared no conflicts of interest.

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.

Network based analysis of microarray gene expression profiles in response to electroacupuncture

Electroacupuncture (EA) has been extensively considered as a tool for treating diseases and relieving various pains. However, understanding the molecular mechanisms underlying its effect is of high importance. In this study, we performed a weighted gene co-expression network analysis (WGCNA) on data collected from a microarray experiment to investigate the relationship underlying EA within three factors, time, frequency and tissue regions (periaqueductal grey (PAG) and spinal dorsal horn (DH)) as well as the biological implication of gene expression changes. Gene expression on rats in PAG-DH regions induced by EA with 2 Hz and 100 Hz at l h and 24 h were measured using microarray technology. The WGCNA was performed to identify distinct network modules related to EA effects. To find the biological function of genes and pathways, the gene ontology (GO) Consortium was applied and the gene-gene interaction network of top genes in important modules was visualized. We identified one network module (466 genes) which is significantly associated with time, another module (402 genes) significantly related to frequency, and three modules each consisting of 1144, 402 and 3148 genes that are significantly associated with tissue regions. Furthermore, meaningful biological pathways were enriched in association with each of the experimental factors during EA stimulation.Our analysis showed the robustness of WGCNA and revealed important genes within specific modules and pathways which might be activated in response to EA analgesia. The findings may help to clarify the underlying mechanisms of EA and provide references for future verification of this study.

Transcriptome sequencing of lncRNA, miRNA, mRNA and interaction network constructing in coronary heart disease

BackgroundNon-coding RNA has been shown to participate in numerous biological and pathological processes and has attracted increasing attention in recent years. Recent studies have demonstrated that long non-coding RNA and micro RNA can interact through various mechanisms to regulate mRNA. Yet the gene-gene interaction has not been investigated in coronary heart disease (CHD).MethodsHigh throughput sequencing were used to identify differentially expressed (DE) lncRNA, miRNA, and mRNA profiles between CHD and healthy control. Gene Oncology (GO), KEGG enrichment analysis were performed. Gene-gene interaction network were constructed and pivotal genes were screened out. Lentivirus-induced shRNA infection and qRT-PCR were performed to validated the gene-gene interactions.ResultsA total of 62 lncRNAs, 332 miRNAs and 366 mRNAs were differentially expressed between CHD and healthy control. GO and KEGG analysis show that immune related molecular mechanisms and biological processes play a role in CHD. The gene-gene interaction network were constructed and visualized based on Pearson correlation coefficients and starBase database. 6 miRNAs in the network were significantly correlated to left ventricular ejection fraction, total choleterol and homocysteine. 2 lncRNAs (CTA-384D8.35 and CTB-114C7.4 (refseq entry LOC100128059)), 1 miRNA (miR-4497), and 1 mRNA (NR4A1) were the pivotal genes. Lentivirus-induced shRNA infection and qRT-PCR had validated the pivotal gene-gene interactions.ConclusionsThese results have shown the potential of lncRNA, miRNA, and mRNA as clinical biomarkers and in elucidating pathological mechanisms of CHD from a transcriptomic perspective.

Robust kernel canonical correlation analysis to detect gene-gene co-associations: A case study in genetics.

The kernel canonical correlation analysis based U-statistic (KCCU) is being used to detect nonlinear gene-gene co-associations. Estimating the variance of the KCCU is however computationally intensive. In addition, the kernel canonical correlation analysis (kernel CCA) is not robust to contaminated data. Using a robust kernel mean element and a robust kernel (cross)-covariance operator potentially enables the use of a robust kernel CCA, which is studied in this paper. We first propose an influence function-based estimator for the variance of the KCCU. We then present a non-parametric robust KCCU, which is designed for dealing with contaminated data. The robust KCCU is less sensitive to noise than KCCU. We investigate the proposed method using both synthesized and real data from the Mind Clinical Imaging Consortium (MCIC). We show through simulation studies that the power of the proposed methods is a monotonically increasing function of sample size, and the robust test statistics bring incremental gains in power. To demonstrate the advantage of the robust kernel CCA, we study MCIC data among 22,442 candidate Schizophrenia genes for gene-gene co-associations. We select 768 genes with strong evidence for shedding light on gene-gene interaction networks for Schizophrenia. By performing gene ontology enrichment analysis, pathway analysis, gene-gene network and other studies, the proposed robust methods can find undiscovered genes in addition to significant gene pairs, and demonstrate superior performance over several of current approaches.

ScNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data

BackgroundSingle-cell RNA-sequencing (scRNA-seq) is fast becoming a powerful tool for profiling genome-scale transcriptomes of individual cells and capturing transcriptome-wide cell-to-cell variability. However, scRNA-seq technologies suffer from high levels of technical noise and variability, hindering reliable quantification of lowly and moderately expressed genes. Since most downstream analyses on scRNA-seq, such as cell type clustering and differential expression analysis, rely on the gene-cell expression matrix, preprocessing of scRNA-seq data is a critical preliminary step in the analysis of scRNA-seq data.ResultsWe presented scNPF, an integrative scRNA-seq preprocessing framework assisted by network propagation and network fusion, for recovering gene expression loss, correcting gene expression measurements, and learning similarities between cells. scNPF leverages the context-specific topology inherent in the given data and the priori knowledge derived from publicly available molecular gene-gene interaction networks to augment gene-gene relationships in a data driven manner. We have demonstrated the great potential of scNPF in scRNA-seq preprocessing for accurately recovering gene expression values and learning cell similarity networks. Comprehensive evaluation of scNPF across a wide spectrum of scRNA-seq data sets showed that scNPF achieved comparable or higher performance than the competing approaches according to various metrics of internal validation and clustering accuracy. We have made scNPF an easy-to-use R package, which can be used as a versatile preprocessing plug-in for most existing scRNA-seq analysis pipelines or tools.ConclusionsscNPF is a universal tool for preprocessing of scRNA-seq data, which jointly incorporates the global topology of priori interaction networks and the context-specific information encapsulated in the scRNA-seq data to capture both shared and complementary knowledge from diverse data sources. scNPF could be used to recover gene signatures and learn cell-to-cell similarities from emerging scRNA-seq data to facilitate downstream analyses such as dimension reduction, cell type clustering, and visualization.

NoMAS: A Computational Approach to Find Mutated Subnetworks Associated With Survival in Genome-Wide Cancer Studies.

Next-generation sequencing technologies allow to measure somatic mutations in a large number of patients from the same cancer type: one of the main goals in their analysis is the identification of mutations associated with clinical parameters. The identification of such relationships is hindered by extensive genetic heterogeneity in tumors, with different genes mutated in different patients, due, in part, to the fact that genes and mutations act in the context of pathways: it is therefore crucial to study mutations in the context of interactions among genes. In this work we study the problem of identifying subnetworks of a large gene-gene interaction network with mutations associated with survival time. We formally define the associated computational problem by using a score for subnetworks based on the log-rank statistical test to compare the survival of two given populations. We propose a novel approach, based on a new algorithm, called Network of Mutations Associated with Survival (NoMAS) to find subnetworks of a large interaction network whose mutations are associated with survival time. NoMAS is based on the color-coding technique, that has been previously employed in other applications to find the highest scoring subnetwork with high probability when the subnetwork score is additive. In our case the score is not additive, so our algorithm cannot identify the optimal solution with the same guarantees associated to additive scores. Nonetheless, we prove that, under a reasonable model for mutations in cancer, NoMAS identifies the optimal solution with high probability. We also design a holdout approach to identify subnetworks significantly associated with survival time. We test NoMAS on simulated and cancer data, comparing it to approaches based on single gene tests and to various greedy approaches. We show that our method does indeed find the optimal solution and performs better than the other approaches. Moreover, on three cancer datasets our method identifies subnetworks with significant association to survival when none of the genes has significant association with survival when considered in isolation.

Integrated analysis of lncRNA and mRNA repertoires in Marek\u2019s disease infected spleens identifies genes relevant to resistance

BackgroundMarek’s disease virus (MDV) is an oncogenic herpesvirus that can cause T-cell lymphomas in chicken. Long noncoding RNA (lncRNA) is strongly associated with various cancers and many other diseases. In chickens, lncRNAs have not been comprehensively identified. Here, we profiled mRNA and lncRNA repertoires in three groups of spleens from MDV-infected and non-infected chickens, including seven tumorous spleens (TS) from MDV-infected chickens, five spleens from the survivors (SS) without lesions after MDV infection, and five spleens from noninfected chickens (NS), to explore the underlying mechanism of host resistance in Marek’s disease (MD).ResultsBy using a precise lncRNA identification pipeline, we identified 1315 putative lncRNAs and 1166 known lncRNAs in spleen tissue. Genomic features of putative lncRNAs were characterized. Differentially expressed (DE) mRNAs, putative lncRNAs, and known lncRNAs were profiled among three groups. We found that several specific intergroup differentially expressed genes were involved in important biological processes and pathways, including B cell activation and the Wnt signaling pathway; some of these genes were also found to be the hub genes in the co-expression network analyzed by WGCNA. Network analysis depicted both intergenic correlation and correlation between genes and MD traits. Five DE lncRNAs including MSTRG.360.1, MSTRG.6725.1, MSTRG.6754.1, MSTRG.15539.1, and MSTRG.7747.5 strongly correlated with MD-resistant candidate genes, such as IGF-I, CTLA4, HDAC9, SWAP70, CD72, JCHAIN, CXCL12, and CD8B, suggesting that lncRNAs may affect MD resistance and tumorigenesis in chicken spleens through their target genes.ConclusionsOur results provide both transcriptomic and epigenetic insights on MD resistance and its pathological mechanism. The comprehensive lncRNA and mRNA transcriptomes in MDV-infected chicken spleens were profiled. Co-expression analysis identified integrated lncRNA-mRNA and gene-gene interaction networks, implying that hub genes or lncRNAs exert critical influence on MD resistance and tumorigenesis.

Identification of pathways associated with chemosensitivity through network embedding.

Basal gene expression levels have been shown to be predictive of cellular response to cytotoxic treatments. However, such analyses do not fully reveal complex genotype- phenotype relationships, which are partly encoded in highly interconnected molecular networks. Biological pathways provide a complementary way of understanding drug response variation among individuals. In this study, we integrate chemosensitivity data from a large-scale pharmacogenomics study with basal gene expression data from the CCLE project and prior knowledge of molecular networks to identify specific pathways mediating chemical response. We first develop a computational method called PACER, which ranks pathways for enrichment in a given set of genes using a novel network embedding method. It examines a molecular network that encodes known gene-gene as well as gene-pathway relationships, and determines a vector representation of each gene and pathway in the same low-dimensional vector space. The relevance of a pathway to the given gene set is then captured by the similarity between the pathway vector and gene vectors. To apply this approach to chemosensitivity data, we identify genes whose basal expression levels in a panel of cell lines are correlated with cytotoxic response to a compound, and then rank pathways for relevance to these response-correlated genes using PACER. Extensive evaluation of this approach on benchmarks constructed from databases of compound target genes and large collections of drug response signatures demonstrates its advantages in identifying compound-pathway associations compared to existing statistical methods of pathway enrichment analysis. The associations identified by PACER can serve as testable hypotheses on chemosensitivity pathways and help further study the mechanisms of action of specific cytotoxic drugs. More broadly, PACER represents a novel technique of identifying enriched properties of any gene set of interest while also taking into account networks of known gene-gene relationships and interactions.

Fuzzy logic based approaches for gene regulatory network inference.

The rapid advancements in high-throughput techniques have fueled large-scale production of biological data at very affordable costs. Some of these techniques are microarrays and next-generation sequencing that provide genome level insight of living cells. As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, etc., is growing exponentially. These biological data are analyzed using various computational techniques for knowledge discovery - which is also one of the objectives of bioinformatics research. Gene regulatory network (GRN) is a gene-gene interaction network which plays a pivotal role in understanding gene regulation processes and disease mechanism at the molecular level. From last couple of decades, researchers are interested in developing computational algorithms for GRN inference (GRNI) from high-throughput experimental data. Several computational approaches have been proposed for inferring GRN from gene expression data including statistical techniques (correlation coefficient), information theory (mutual information), regression-based approaches, probabilistic approaches (Bayesian networks, naïve byes), artificial neural networks and fuzzy logic. The fuzzy logic, along with its hybridization with other intelligent approaches, is a well-studied technique in GRNI due to its several advantages. In this paper, we present a consolidated review on fuzzy logic and its hybrid approaches developed during last two decades for GRNI.

Leukemia inhibitory factor inhibits erythropoietin-induced myelin gene expression in oligodendrocytes

BackgroundThe pro-myelinating effects of leukemia inhibitory factor (LIF) and other cytokines of the gp130 family, including oncostatin M (OSM) and ciliary neurotrophic factor (CNTF), have long been known, but controversial results have also been reported. We recently overexpressed erythropoietin receptor (EPOR) in rat central glia-4 (CG4) oligodendrocyte progenitor cells (OPCs) to study the mechanisms mediating the pro-myelinating effects of erythropoietin (EPO). In this study, we investigated the effect of co-treatment with EPO and LIF.MethodsGene expression in undifferentiated and differentiating CG4 cells in response to EPO and LIF was analysed by DNA microarrays and by RT-qPCR. Experiments were performed in biological replicates of N ≥ 4. Functional annotation and biological term enrichment was performed using DAVID (Database for Annotation, Visualization and Integrated Discovery). The gene-gene interaction network was visualised using STRING (Search Tool for the Retrieval of Interacting Genes).ResultsIn CG4 cells treated with 10 ng/ml of EPO and 10 ng/ml of LIF, EPO-induced myelin oligodendrocyte glycoprotein (MOG) expression, measured at day 3 of differentiation, was inhibited ≥4-fold (N = 5, P < 0.001). Inhibition of EPO-induced MOG was also observed with OSM and CNTF. Analysis of the gene expression profile of CG4 differentiating cells treated for 20 h with EPO and LIF revealed LIF inhibition of EPO-induced genes involved in lipid transport and metabolism, previously identified as positive regulators of myelination in this system. In addition, among the genes induced by LIF, and not by differentiation or by EPO, the role of suppressor of cytokine signaling 3 (SOCS3) and toll like receptor 2 (TLR2) as negative regulators of myelination was further explored. LIF-induced SOCS3 was associated with MOG inhibition; Pam3, an agonist of TLR2, inhibited EPO-induced MOG expression, suggesting that TLR2 is functional and its activation decreases myelination.ConclusionsCytokines of the gp130 family may have negative effects on myelination, depending on the cytokine environment.

Pathway-based gene-gene interaction network modelling to predict potential biomarkers of essential hypertension

Essential hypertension (EH) is a major risk factor for cardiovascular disease. Despite considerable efforts to elucidate the pathogenesis of EH, there is an imperious need for novel indicators of EH. This study aimed to develop a method to predict potential biomarkers of EH from the point of view of network. A pathway-based gene-gene interaction (GGI) network model was constructed and analyzed, containing 116 nodes and 1272 connections. The nodes represented EH-related genes, and that connections represented their interactions. The network showed a small-world property and uneven degree distribution, suggesting that a few highly interconnected hubs played a vital role in EH. An inherent hierarchy and assortative mixing pattern were also observed in the network. GNAS, GNB3, PF4 and PPBP showed the highest values of degrees and centrality indices, and were chosen as potential biomarkers of EH. A two-mode network model based on the potential biomarkers demonstrated that hemostasis and GPCR ligand binding pathway were key pathways contributing to EH. Results of this study improve our current understanding of the molecular mechanisms driving EH. The selected genes and pathways have the potential to be used in the diagnosis and treatment of EH. Moreover, the combination of pathway analysis and complex network methodology provides a novel strategy for searching new genetic indicators of complex diseases.

Identifying lncRNA and mRNA Co-Expression Modules from Matched Expression Data in Ovarian Cancer.

Long non-coding RNAs (lncRNAs) have been shown to be involved in multiple biological processes and play critical roles in tumorigenesis. Numerous lncRNAs have been discovered in diverse species, but the functions of most lncRNAs still remain unclear. Meanwhile, their expression patterns and regulation mechanisms are also far from being fully understood. With the advances of high-throughput technologies, the increasing availability of genomic data creates opportunities for deciphering the molecular mechanism and underlying pathogenesis of human diseases. Here, we develop an integrative framework called JONMF to identify lncRNA-mRNA co-expression modules based on the sample-matched lncRNA and mRNA expression profiles. We formulate the module detection task as an optimization problem with joint orthogonal non-negative matrix factorization that could effectively prevent multicollinearity and produce a good modularity interpretation. The constructed lncRNA-mRNA co-expression network and the gene-gene interaction network are used as the network-regularized constraints to improve the module accuracy, while the sparsity constraints are simultaneously utilized to achieve modular sparse solutions. We applied JONMF to human ovarian cancer dataset and the experiment results demonstrate that the proposed method can effectively discover biologically functional co-expression modules, which may provide insights into the function of lncRNAs and molecular mechanism of human diseases.

Innate Immune Cells Are Regulated by Axl in Hypertensive Kidney

The balance between adaptive and innate immunity in kidney damage in salt-dependent hypertension is unclear. We investigated early renal dysfunction and the influence of Axl, a receptor tyrosine kinase, on innate immune response in hypertensive kidney in mice with lymphocyte deficiency (Rag1-/-). The data suggest that increased presence of CD11b+ myeloid cells in the medulla might explain intensified salt and water retention as well as initial hypertensive response in Rag1-/- mice. Global deletion of Axl on Rag1-/- background reversed kidney dysfunction and accumulation of myeloid cells in the kidney medulla. Chimeric mice that lack Axl in innate immune cells (in the absence of lymphocytes) significantly improved kidney function and abolished early hypertensive response. The bioinformatics analyses of Axl-related gene-gene interaction networks established tissue-specific variation in regulatory pathways. It was confirmed that complement C3 is important for Axl-mediated interactions between myeloid and vascular cells in hypertensive kidney. In summary, innate immunity is crucial for renal dysfunction in early hypertension, and is highly influenced by the presence of Axl.

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&amp;lt;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.