Integrated Gene Network Analysis Research Articles

Differential Gene Expression Profiles and Pathways Highlight the Role of Osteoimmunology in Neurofibromatosis Type 1-Related Dystrophic Scoliosis With Osteopenia.

Microarray approach and integrated gene network analysis. To explore the differential genetic expression profile, Gene Ontology terms, and Kyoto Encyclopedia of Genes and Genomes pathways in human trabecular bone (HTB)-derived cells of dystrophic scoliosis secondary to neurofibromatosis type 1 (DS-NF1) and compare these to normal controls. The pathogenesis of DS-NF1 and the accompanying generalized osteopenia remain unclear. We hypothesized that HTBs may play a significant role in the etiology and pathogenesis of DS-NF1. Microarray analysis was used to identify differentially expressed genes of HTBs from patients with DS-NF1 compared with those from healthy individuals. Functional and pathway enrichment analysis were implemented through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway database. Then, the "search tool for the retrieval of interacting genes/proteins" database, Cytoscape, and "Molecular Complex Detection" were applied to construct the protein-protein interaction (PPI) network and screen hub genes. Pathway enrichment analysis was further performed for hub genes and gene clusters identified through module analysis. Six potential crucial genes were selected for validation by reverse transcription polymerase chain reaction. Bioinformatic analysis revealed that there are 401 previously unrecognized differentially expressed genes (238 up and 163 downregulated genes) in HTBs from patients with DS-NF1, and they were mainly enriched in terms of immune response, type-I interferon (IFN) signaling, TNF signaling pathway and etinoic acid inducible gene I-like receptor signaling pathway. Five hub genes, including signal transducer and activator of transcription 1, 2'-5'-oligoadenylate synthetase-like, IFN induced with helicase C domain 1, IFN regulatory factor 7, and MX dynamin-like GTPase 1 were identified through PPI network, which were mainly enriched in terms of Jak-STAT and etinoic acid inducible gene I-like receptor signaling pathway. An independently dysregulated protein cluster containing CCL2, CXCL1, CXCL3, CX3CL1, TLR1 , and CXCL12 was also identified through the PPI network. This indicated that the upper abnormally expressed genes may play essential roles in DS-NF1 pathogenesis and accompanied osteopenia. Six key genes were identified in the progression of DS-NF1-related osteopenia. Immune response might play a key role in the progression of osteopenia, whereas a CXCL12 -mediated osteogenic effect might play a protective role.

Integrative Transcriptomic Analyses of Hippocampal-Entorhinal System Subfields Identify Key Regulators in Alzheimer's Disease.

The hippocampal-entorhinal system supports cognitive function and is selectively vulnerable to Alzheimer's disease (AD). Little is known about global transcriptomic changes in the hippocampal-entorhinal subfields during AD. Herein, large-scale transcriptomic analysis is performed in five hippocampal-entorhinal subfields of postmortem brain tissues (262 unique samples). Differentially expressed genes are assessed across subfields and disease states, and integrated genotype data from an AD genome-wide association study. An integrative gene network analysis of bulk and single-nucleus RNA sequencing (snRNA-Seq) data identifies genes with causative roles in AD progression. Using a system-biology approach, pathology-specific expression patterns for cell types are demonstrated, notably upregulation of the A1-reactive astrocyte signature in the entorhinal cortex (EC) during AD. SnRNA-Seq data show that PSAP signaling is involved in alterations of cell- communications in the EC during AD. Further experiments validate the key role of PSAP in inducing astrogliosis and an A1-like reactive astrocyte phenotype. In summary, this study reveals subfield-, cell type-, and AD pathology-specific changes and demonstrates PSAP as a potential therapeutic target in AD.

An integrated nitrogen utilization gene network and transcriptome analysis reveal candidate genes in response to nitrogen deficiency in Brassica napus.

Nitrogen (N) is an essential factor for crop yield. Here, we characterized 605 genes from 25 gene families that form the complex gene networks of N utilization pathway in Brassica napus. We found unequal gene distribution between the An- and Cn-sub-genomes, and that genes derived from Brassica rapa were more retained. Transcriptome analysis indicated that N utilization pathway gene activity shifted in a spatio-temporal manner in B. napus. A low N (LN) stress RNA-seq of B. napus seedling leaves and roots was generated, which proved that most N utilization related genes were sensitive to LN stress, thereby forming co-expression network modules. Nine candidate genes in N utilization pathway were confirmed to be significantly induced under N deficiency conditions in B. napus roots, indicating their potential roles in LN stress response process. Analyses of 22 representative species confirmed that the N utilization gene networks were widely present in plants ranging from Chlorophyta to angiosperms with a rapid expansion trend. Consistent with B. napus, the genes in this pathway commonly showed a wide and conserved expression profile in response to N stress in other plants. The network, genes, and gene-regulatory modules identified here represent resources that may enhance the N utilization efficiency or the LN tolerance of B. napus.

Integrated gene network analysis sheds light on understanding the progression of Osteosarcoma.

Osteosarcoma is a rare disorder among cancer, but the most frequently occurring among sarcomas in children and adolescents. It has been reported to possess the relapsing capability as well as accompanying collateral adverse effects which hinder the development process of an effective treatment plan. Using networks of omics data to identify cancer biomarkers could revolutionize the field in understanding the cancer. Cancer biomarkers and the molecular mechanisms behind it can both be understood by studying the biological networks underpinning the etiology of the disease. In our study, we aimed to highlight the hub genes involved in gene-gene interaction network to understand their interaction and how they affect the various biological processes and signaling pathways involved in Osteosarcoma. Gene interaction network provides a comprehensive overview of functional gene analysis by providing insight into how genes cooperatively interact to elicit a response. Because gene interaction networks serve as a nexus to many biological problems, their employment of it to identify the hub genes that can serve as potential biomarkers remain widely unexplored. A dynamic framework provides a clear understanding of biological complexity and a pathway from the gene level to interaction networks. Our study revealed various hub genes viz. TP53, CCND1, CDK4, STAT3, and VEGFA by analyzing various topological parameters of the network, such as highest number of interactions, average shortest path length, high cluster density, etc. Their involvement in key signaling pathways, such as the FOXM1 transcription factor network, FAK-mediated signaling events, and the ATM pathway, makes them significant candidates for studying the disease. The study also highlighted significant enrichment in GO terms (Biological Processes, Molecular Function, and Cellular Processes), such as cell cycle signal transduction, cell communication, kinase binding, transcription factor activity, nucleoplasm, PML body, nuclear body, etc. To develop better therapeutics, a specific approach toward the disease targeting the hub genes involved in various signaling pathways must have opted to unravel the complexity of the disease. Our study has highlighted the candidate hub genes viz. TP53, CCND1 CDK4, STAT3, VEGFA. Their involvement in the major signaling pathways of Osteosarcoma makes them potential candidates to be targeted for drug development. The highly enriched signaling pathways include FOXM1 transcription pathway, ATM signal-ling pathway, FAK mediated signaling events, Arf6 signaling events, mTOR signaling pathway, and Integrin family cell surface interactions. Targeting the hub genes and their associated functional partners which we have reported in our studies may be efficacious in developing novel therapeutic targets.

An integrated gene network analysis to decode the multi-drug resistance mechanism in Klebsiella pneumoniae

Antimicrobial resistance (AMR) among microorganisms has become one of the worldwide concerns of this century and continues to challenge us. To properly understand this problem, it is essential to know the genes that cause AMR and their resistance mechanisms. Our present study focused on Klebsiella pneumoniae, which possesses AMR genes conferring resistance against multiple antibiotics. A gene interaction network of 42 functional partners was constructed and analyzed to broaden our understanding. Three closely related clusters (C1–C3) having an association with multi-drug resistance mechanisms were identified by clustering analysis. The enrichment analysis illustrated 30 genes in biological processes, 24 genes in molecular function, and 25 genes in cellular components having a significant role. The analysis of the gene interaction network revealed genes birA2, folP, pabC, folA, gyrB, glmM, gyrA, thyA_2 had maximum no. of interactions with their functional partners viz. 26, 25, 25, 24, 23, 23, 23, 23 respectively and can be considered as hub genes. Analyzing the enriched pathways and Gene Ontologies provides insight into AMR's molecular basis. In addition, the proposed study could aid the researchers in developing new treatment options to combat multi-drug resistant K. pneumoniae.

The landscape of human tissue and cell type specific expression and co-regulation of senescence genes

BackgroundCellular senescence is a complex stress response that impacts cellular function and organismal health. Multiple developmental and environmental factors, such as intrinsic cellular cues, radiation, oxidative stress, oncogenes, and protein accumulation, activate genes and pathways that can lead to senescence. Enormous efforts have been made to identify and characterize senescence genes (SnGs) in stress and disease systems. However, the prevalence of senescent cells in healthy human tissues and the global SnG expression signature in different cell types are poorly understood.MethodsThis study performed an integrative gene network analysis of bulk and single-cell RNA-seq data in non-diseased human tissues to investigate SnG co-expression signatures and their cell-type specificity.ResultsThrough a comprehensive transcriptomic network analysis of 50 human tissues in the Genotype-Tissue Expression Project (GTEx) cohort, we identified SnG-enriched gene modules, characterized SnG co-expression patterns, and constructed aggregated SnG networks across primary tissues of the human body. Our network approaches identified 51 SnGs highly conserved across the human tissues, including CDKN1A (p21)-centered regulators that control cell cycle progression and the senescence-associated secretory phenotype (SASP). The SnG-enriched modules showed remarkable cell-type specificity, especially in fibroblasts, endothelial cells, and immune cells. Further analyses of single-cell RNA-seq and spatial transcriptomic data independently validated the cell-type specific SnG signatures predicted by the network analysis.ConclusionsThis study systematically revealed the co-regulated organizations and cell type specificity of SnGs in major human tissues, which can serve as a blueprint for future studies to map senescent cells and their cellular interactions in human tissues.

Comprehensive Transcriptomic Analysis Identifies Novel Antiviral Factors Against Influenza A Virus Infection.

Influenza A virus (IAV) has a higher genetic variation, leading to the poor efficiency of traditional vaccine and antiviral strategies targeting viral proteins. Therefore, developing broad-spectrum antiviral treatments is particularly important. Host responses to IAV infection provide a promising approach to identify antiviral factors involved in virus infection as potential molecular drug targets. In this study, in order to better illustrate the molecular mechanism of host responses to IAV and develop broad-spectrum antiviral drugs, we systematically analyzed mRNA expression profiles of host genes in a variety of human cells, including transformed and primary epithelial cells infected with different subtypes of IAV by mining 35 microarray datasets from the GEO database. The transcriptomic results showed that IAV infection resulted in the difference in expression of amounts of host genes in all cell types, especially those genes participating in immune defense and antiviral response. In addition, following the criteria of P<0.05 and |logFC|≥1.5, we found that some difference expression genes were overlapped in different cell types under IAV infection via integrative gene network analysis. IFI6, IFIT2, ISG15, HERC5, RSAD2, GBP1, IFIT3, IFITM1, LAMP3, USP18, and CXCL10 might act as key antiviral factors in alveolar basal epithelial cells against IAV infection, while BATF2, CXCL10, IFI44L, IL6, and OAS2 played important roles in airway epithelial cells in response to different subtypes of IAV infection. Additionally, we also revealed that some overlaps (BATF2, IFI44L, IFI44, HERC5, CXCL10, OAS2, IFIT3, USP18, OAS1, IFIT2) were commonly upregulated in human primary epithelial cells infected with high or low pathogenicity IAV. Moreover, there were similar defense responses activated by IAV infection, including the interferon-regulated signaling pathway in different phagocyte types, although the differentially expressed genes in different phagocyte types showed a great difference. Taken together, our findings will help better understand the fundamental patterns of molecular responses induced by highly or lowly pathogenic IAV, and the overlapped genes upregulated by IAV in different cell types may act as early detection markers or broad-spectrum antiviral targets.

DNA methylation analysis improves the prognostication of acute myeloid leukemia.

Integration of orthogonal data could provide new opportunities to pinpoint the underlying molecular mechanisms of hematologic disorders. Using a novel gene network approach, we integrated DNA methylation data from The Cancer Genome Atlas (n = 194 cases) with the corresponding gene expression profile. Our integrated gene network analysis classified AML patients into low‐, intermediate‐, and high‐risk groups. The identified high‐risk group had significantly shorter overall survival compared to the low‐risk group (p‐value ≤10−11). Specifically, our approach identified a particular subgroup of nine high‐risk AML cases that died within 2 years after diagnosis. These high‐risk cases otherwise would be incorrectly classified as intermediate‐risk solely based on cytogenetics, mutation profiles, and common molecular characteristics of AML. We confirmed the prognostic value of our integrative gene network approach using two independent datasets, as well as through comparison with European LeukemiaNet and LSC17 criteria. Our approach could be useful in the prognostication of a subset of borderline AML cases. These cases would not be classified into appropriate risk groups by other approaches that use gene expression, but not DNA methylation data. Our findings highlight the significance of epigenomic data, and they indicate integrating DNA methylation data with gene coexpression networks can have a synergistic effect.

Descriptive and functional characterization of epidermal growth factor‑like domain 8 in mouse cortical thymic epithelial cells by integrated analysis of gene expression signatures and networks.

Epidermal growth factor-like domain 8 (EGFL8), a newly identified member of the EGFL family, and plays negative regulatory roles in mouse thymic epithelial cells (TECs) and thymocytes. However, the role of EGFL8 in these cells remains poorly understood. In the present study, in order to characterize the function of EGFL8, genome-wide expression profiles in EGFL8-overexpressing or -silenced mouse cortical TECs (cTECs) were analyzed. Microarray analysis revealed that 458 genes exhibited a >2-fold change in expression levels in the EGFL8-overexpressing vs. the EGFL8-silenced cTECs. Several genes involved in a number of cellular processes, such as the cell cycle, proliferation, growth, migration and differentiation, as well as in apoptosis, reactive oxygen species generation, chemotaxis and immune responses, were differentially expressed in the EGFL8-overexpressing or -silenced cTECs. WST-1 analysis revealed that that the overexpression of EGFL8 inhibited cTEC proliferation. To investigate the underlying mechanisms of EGFL8 in the regulation of cTEC function, genes related to essential cellular functions were selected. Reverse transcription-polymerase chain reaction analysis revealed that EGFL8 knockdown upregulated the expression of cluster differentiation 74 (CD74), Fas ligand (FasL), C-X-C motif chemokine ligand 5 (CXCL5), CXCL10, CXCL16, C-C motif chemokine ligand 20 (CCL20), vascular endothelial growth factor-A (VEGF-A), interferon regulatory factor 7 (Irf7), insulin-like growth factor binding protein-4 (IGFBP-4), thrombospondin 1 (Thbs1) and nuclear factor κB subunit 2 (NF-κB2) genes, and downregulated the expression of angiopoietin-like 1 (Angptl1), and neuropilin-1 (Nrp1) genes. Additionally, EGFL8 silencing enhanced the expression of anti-apoptotic molecules, such as B-cell lymphoma-2 (Bcl-2) and Bcl-extra large (Bcl-xL), and that of cell cycle-regulating molecules, such as cyclin-dependent kinase 1 (CDK1), CDK4, CDK6 and cyclin D1. Moreover, gene network analysis revealed that EGFL8 exerted negative effects on VEGF-A gene expression. Hence, the altered expression of several genes associated with EGFL8 expression in cTECs highlights the important physiological processes in which EGFL8 is involved, and provides insight into its biological functions.

Comprehensive Transcriptomic Analysis Identifies Cellular Immune Response Against Influenza A Virus Infection

Influenza A virus (IAV) has the higher genetic variation and reassortment, the traditional vaccine and virus-directed antiviral strategies have hardly shown successful efficacy and even lead to serious resistance, therefor developing the broad-spectrum antiviral treatments is particularly important. Host response to IAV infection provides a promising alternative approach to find host factors that are involved in the replication of viruses as molecular drug target. In this study, in order to better illustrate how IAV infection triggers cellular response and understand virus-host interaction, we systematically studied the expression profiles of host gene in a variety of human cells (including epithelial cells, primary epithelial cells) infected with different subtypes of IAV in this research by collecting and excavating 40 microarray data from Gene Expression Omnibus (GEO). The transcriptome analysis results showed that each of influenza strains led to the certain amounts of genes differentially regulated in all cell types, especially those genes belonging to immune defense and antiviral response, but we found that the overlapping regulated genes accounted for only a few in all cell types with different subtypes of IAV infection following the the criteria of P<0.05 and |logFC|≥1.5, because of the difference of IAV strains and infected cell types. Integrative gene network analysis found that IFI6, IFIT2, ISG15, HERC5, RSAD2, GBP1, IFIT3, IFITM1, LAMP3, USP18 and CXCL10 may act as key signatures in alveolar basal epithelial cells for IAV infection, while BATF2, CXCL10, IFI44L, IL6 and OAS2 played a key role in airway epithelial cell response to different subtypes of IAV infection. In addition, we also revealed that some overlaps (BATF2, IFI44L, IFI44, HERC5, OASL, CXCL10, MX2, OAS2, XAF1, IFIT3, USP18, IFIH1, OAS1, IFIT2 and IFIT1) were commonly upregulated in human primary epithelial cells infected by different influenza strains of high or low pathogenicity. Additionally, in the phagocytes, the difference gene expression pattern in different subtypes of IAV infection was found, but phagocytes could elicit the similar defence response against virus infection. Our findings will help us understand the fundamental patterns of molecular responses induced by highly or lowly pathogenic IAV originating from different species and the overlapped genes upregulated by IAV in different cell types may be as key biomarkers to improve effective early detection and treatment. Funding Statement: This work was supported by Key Lab of Process Analysis and Control of Sichuan Universities (No.2018001), The Project-sponsored by Sichuan Province for ROCS (0903/00021728) and NSFC (81902073). Declaration of Interests: No potential conflict of interest was reported by the authors.

Selective association of neurogranin gene expression with amyloid and tau pathology in the parahippocampal gyrus in Alzheimer’s disease

AbstractBackgroundBrain region‐specific neuronal and synaptic damage is associated with clinical symptomatology and pathogenesis of Alzheimer’s disease (AD). The mechanism underlying such regional vulnerability in AD remains elusive.MethodPublic access datasets from the Mount Sinai NIH NeuroBioBank (MSBB), The Religious order Study and the Memory and Aging Project(ROSMAP) and the Harvard Brain Tissue Resource Center (HBTRC) were used in this study. Using a large‐scale transcriptomic dataset from the prefrontal cortex (BA 10, 261 samples), the superior temporal gyrus (BA 22, 240 samples), the parahippocampal gyrus (BA 36, 215 samples) and the pars opercularis (BA 44, 222 samples) of MBSS post‐mortem brains of AD and normal control subjects, we studied the association of the post‐synaptic protein neurogranin (NRGN) gene expression with amyloid and tau pathology (plaque and tangle counts), clinical dementia scale (CDR), and pathological diagnosis of AD. Integrative gene network analysis was conducted by using a combination of the datasets from three brain banks.ResultWe show that NRGN gene expression in post‐mortem brain is associated with the certainty of pathological diagnosis of AD, and negatively associated with amyloid and tau pathology only in parahippocampal gyrus among the four brain regions examined. NRGN gene expression in parahippocampal gyrus is also negatively associated with CDR scores. Integrative gene network analysis reveals that the positively correlated genes with NRGN in AD are associated with synaptic transmission and cation channel pathways.ConclusionOur results suggest that synaptic damage in selective brain regions contributes to the clinical phenotype in AD. Understanding of synaptic functional pathways associated with synaptic damage may provide another avenue to prioritize targets for AD.

Identification of key regulators in parathyroid adenoma using an integrative gene network analysis.

Parathyroid adenoma (PA) is marked by a certain benign outgrowth in the surface of parathyroid glands. The transcriptome analysis of parathyroid adenomas can provide a deep insight into actively expressed genes and transcripts. Hence, we analyzed and compared the gene expression profiles of parathyroid adenomas and healthy parathyroid gland tissues from Gene Expression Omnibus (GEO) database. We identified a total of 280 differentially expressed genes (196 up-regulated, 84 down-regulated), which are involved in a wide array of biological processes. We further constructed a gene interaction network and analyzed its topological properties to know the network structure and its hidden mechanism. This will help to understand the molecular mechanisms underlying parathyroid adenoma development. We thus identified 13 key regulators (PRPF19, SMC3, POSTN, SNIP1, EBF1, MEIS2, PAX9, SCUBE2, WNT4, ARHGAP10, DOCK5, CAV1 and VSIR), which are deep-rooted from top to bottom in the gene interaction network forming a backbone for the network. The structural features of the network are probably maintained by crosstalk between important genes within the network along with associated functional modules.Thus, gene-expression profiling and network approach could be used to provide an independent platform to glen insights from available clinical data.

Global gene network exploration based on explainable artificial intelligence approach.

In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statistical and machine learning approaches have been developed and applied to reveal sample-specific regulatory pathways, integrative understanding of the massive multilayer networks remains a challenge. To resolve this problem, we propose a novel artificial intelligence (AI) strategy for comprehensive gene regulatory network analysis. In our strategy, personalized gene networks corresponding specific clinical characteristic are constructed and the constructed network is considered as a second-order tensor. Then, an explainable AI method based on deep learning is applied to decompose the multilayer networks, thus we can reveal all-encompassing gene regulatory systems characterized by clinical features of patients. To evaluate the proposed methodology, we apply our method to the multilayer gene networks under varying conditions of an epithelial–mesenchymal transition (EMT) process. From the comprehensive analysis of multilayer networks, we identified novel markers, and the biological mechanisms of the identified genes and their reciprocal mechanisms are verified through the literature. Although any biological knowledge about the identified genes was not incorporated in our analysis, our data-driven approach based on AI approach provides biologically reliable results. Furthermore, the results provide crucial evidences to reveal biological mechanism related to various diseases, e.g., keratinocyte proliferation. The use of explainable AI method based on the tensor decomposition enables us to reveal global and novel mechanisms of gene regulatory system from the massive multiple networks, which cannot be demonstrated by existing methods. We expect that the proposed method provides a new insight into network biology and it will be a useful tool to integrative gene network analysis related complex architectures of diseases.

Transcriptomic analysis at organ and time scale reveals gene regulatory networks controlling the sulfate starvation response of Solanum lycopersicum

BackgroundSulfur is a major component of biological molecules and thus an essential element for plants. Deficiency of sulfate, the main source of sulfur in soils, negatively influences plant growth and crop yield. The effect of sulfate deficiency on plants has been well characterized at the physiological, transcriptomic and metabolomic levels in Arabidopsis thaliana and a limited number of crop plants. However, we still lack a thorough understanding of the molecular mechanisms and regulatory networks underlying sulfate deficiency in most plants. In this work we analyzed the impact of sulfate starvation on the transcriptome of tomato plants to identify regulatory networks and key transcriptional regulators at a temporal and organ scale.ResultsSulfate starvation reduces the growth of roots and leaves which is accompanied by major changes in the organ transcriptome, with the response being temporally earlier in roots than leaves. Comparative analysis showed that a major part of the Arabidopsis and tomato transcriptomic response to sulfate starvation is conserved between these plants and allowed for the identification of processes specifically regulated in tomato at the transcript level, including the control of internal phosphate levels. Integrative gene network analysis uncovered key transcription factors controlling the temporal expression of genes involved in sulfate assimilation, as well as cell cycle, cell division and photosynthesis during sulfate starvation in tomato roots and leaves. Interestingly, one of these transcription factors presents a high identity with SULFUR LIMITATION1, a central component of the sulfate starvation response in Arabidopsis.ConclusionsTogether, our results provide the first comprehensive catalog of sulfate-responsive genes in tomato, as well as novel regulatory targets for future functional analyses in tomato and other crops.

Leveraging multiple gene networks to prioritize GWAS candidate genes via network representation learning.

Genome-wide association studies (GWAS) have successfully discovered a number of disease-associated genetic variants in the past decade, providing an unprecedented opportunity for deciphering genetic basis of human inherited diseases. However, it is still a challenging task to extract biological knowledge from the GWAS data, due to such issues as missing heritability and weak interpretability. Indeed, the fact that the majority of discovered loci fall into noncoding regions without clear links to genes has been preventing the characterization of their functions and appealing for a sophisticated approach to bridge genetic and genomic studies. Towards this problem, network-based prioritization of candidate genes, which performs integrated analysis of gene networks with GWAS data, has emerged as a promising direction and attracted much attention. However, most existing methods overlook the sparse and noisy properties of gene networks and thus may lead to suboptimal performance. Motivated by this understanding, we proposed a novel method called REGENT for integrating multiple gene networks with GWAS data to prioritize candidate genes for complex diseases. We leveraged a technique called the network representation learning to embed a gene network into a compact and robust feature space, and then designed a hierarchical statistical model to integrate features of multiple gene networks with GWAS data for the effective inference of genes associated with a disease of interest. We applied our method to six complex diseases and demonstrated the superior performance of REGENT over existing approaches in recovering known disease-associated genes. We further conducted a pathway analysis and showed that the ability of REGENT to discover disease-associated pathways. We expect to see applications of our method to a broad spectrum of diseases for post-GWAS analysis. REGENT is freely available at https://github.com/wmmthu/REGENT.

GIANT 2.0: genome-scale integrated analysis of gene networks in tissues.

GIANT2 (Genome-wide Integrated Analysis of gene Networks in Tissues) is an interactive web server that enables biomedical researchers to analyze their proteins and pathways of interest and generate hypotheses in the context of genome-scale functional maps of human tissues. The precise actions of genes are frequently dependent on their tissue context, yet direct assay of tissue-specific protein function and interactions remains infeasible in many normal human tissues and cell-types. With GIANT2, researchers can explore predicted tissue-specific functional roles of genes and reveal changes in those roles across tissues, all through interactive multi-network visualizations and analyses. Additionally, the NetWAS approach available through the server uses tissue-specific/cell-type networks predicted by GIANT2 to re-prioritize statistical associations from GWAS studies and identify disease-associated genes. GIANT2 predicts tissue-specific interactions by integrating diverse functional genomics data from now over 61 400 experiments for 283 diverse tissues and cell-types. GIANT2 does not require any registration or installation and is freely available for use at http://giant-v2.princeton.edu.

Integrative gene ontology and network analysis of coronary artery disease associated genes suggests potential role of ErbB pathway gene EGFR.

Coronary artery disease (CAD) is a major cause of mortality in India, more importantly the young Indians. Combinatorial and integrative approaches to evaluate pathways and genes to gain an improved understanding and potential biomarkers for risk assessment are required. Therefore, 608 genes from the CADgene database version 2.0, classified into 12 functional classes representing the atherosclerotic disease process, were analyzed. Homology analysis of the unique list of gene ontologies (GO) from each functional class gave 8 GO terms represented in 11 and 10 functional classes. Using disease ontology analysis 80 genes belonging to 8 GO terms, using FunDO suggested that 29 of them were identified to be associated with CAD. Extended network analysis of these genes using STRING version 9.1 gave 328 nodes and 4,525 interactions of which the top 5% had a node degree of ≥75 associated with pathways including the ErbB signaling pathway with epidermal growth factor receptor (EGFR) gene as the central hub. Evaluation of EFGR protein levels in age and gender-matched 342 CAD patients vs. 342 control subjects demonstrated significant differences [controls=149.76±2.47 pg/ml and CAD patients stratified into stable angina (SA)=161.65±3.40 pg/ml and myocardial infarction (MI)=171.51±4.26 pg/ml]. Logistic regression analysis suggested that increased EGFR levels exhibit 3-fold higher risk of CAD [odds ratio (OR) 3.51, 95% confidence interval [CI] 1.96–6.28, P≤0.001], upon adjustment for hypertension, diabetes and smoking. A unit increase in EGFR levels increased the risk by 2-fold for SA (OR 2.58, 95% CI 1.25–5.33, P=0.01) and 3.8-fold for MI (OR 3.82, 95% CI 1.94–7.52, P≤0.001) following adjustment. Thus, the use of ontology mapping and network analysis in an integrative manner aids in the prioritization of biomarkers of complex disease.

Integrative gene network analysis identifies key signatures, intrinsic networks and host factors for influenza virus A infections

Influenza A virus, with the limited coding capacity of 10–14 proteins, requires the host cellular machinery for many aspects of its life cycle. Knowledge of these host cell requirements not only reveals molecular pathways exploited by the virus or triggered by the immune system, but also provides further targets for antiviral drug development. To uncover novel pathways and key targets of influenza infection, we assembled a large amount of data from 12 cell-based gene-expression studies of influenza infection for an integrative network analysis. We systematically identified differentially expressed genes and gene co-expression networks induced by influenza infection. We revealed the dedicator of cytokinesis 5 (DOCK5) played potentially an important role for influenza virus replication. CRISPR/Cas9 knockout of DOCK5 reduced influenza virus replication, indicating that DOCK5 is a key regulator for the viral life cycle. DOCK5’s targets determined by the DOCK5 knockout experiments strongly validated the predicted gene signatures and networks. This study systematically uncovered and validated fundamental patterns of molecular responses, intrinsic structures of gene co-regulation, and novel key targets in influenza virus infection.

Common gene-network signature of different neurological disorders and their potential implications to neuroAIDS.

The neurological complications of AIDS (neuroAIDS) during the infection of human immunodeficiency virus (HIV) are symptomized by non-specific, multifaceted neurological conditions and therefore, defining a specific diagnosis/treatment mechanism(s) for this neuro-complexity at the molecular level remains elusive. Using an in silico based integrated gene network analysis we discovered that HIV infection shares convergent gene networks with each of twelve neurological disorders selected in this study. Importantly, a common gene network was identified among HIV infection, Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and age macular degeneration. An mRNA microarray analysis in HIV-infected monocytes showed significant changes in the expression of several genes of this in silico derived common pathway which suggests the possible physiological relevance of this gene-circuit in driving neuroAIDS condition. Further, this unique gene network was compared with another in silico derived novel, convergent gene network which is shared by seven major neurological disorders (Alzheimer’s disease, Parkinson’s disease, Multiple Sclerosis, Age Macular Degeneration, Amyotrophic Lateral Sclerosis, Vascular Dementia, and Restless Leg Syndrome). These networks differed in their gene circuits; however, in large, they involved innate immunity signaling pathways, which suggests commonalities in the immunological basis of different neuropathogenesis. The common gene circuits reported here can provide a prospective platform to understand how gene-circuits belonging to other neuro-disorders may be convoluted during real-time neuroAIDS condition and it may elucidate the underlying–and so far unknown–genetic overlap between HIV infection and neuroAIDS risk. Also, it may lead to a new paradigm in understanding disease progression, identifying biomarkers, and developing therapies.

Transcriptional Effects of E3 Ligase Nrdp1 on Hypertrophy in Neonatal Rat Cardiomyocytes by Microarray and Integrated Gene Network Analysis

Objective: Neuregulin receptor degradation protein-1 (Nrdp1) is a novel E3 ubiquitin ligase, and we have previously shown that overexpression of Nrdp1 increased cardiomyocyte injury. However, the role of Nrdp1 in myocardial hypertrophy is unclear. In the present study, we clarified the molecular mechanisms of angiotensin II (Ang II)-induced cardiomyocyte hypertrophy regulated by Nrdp1 based on genome-wide transcriptional analysis. Methods: Neonatal rat cardiomyocytes were infected with adenoviruses containing green fluorescent protein (Ad-GFP) or wild-type Nrdp1 (Ad-Nrdp1), and then treated with Ang II for 36 h. Detection of differentially expressed genes was achieved with an Affymetrix Rat Gene 2.0 Array and Cluster and Java TreeView software. Results and Conclusion: Microarray data analysis demonstrated that Nrdp1 overexpression affected the expression of 12,140 mRNA genes in Ang II-induced cardiomyocyte hypertrophy, including the upregulation of 12,044 and the downregulation of 96. Gene ontology and globe signal transduction network analysis showed that Nrdp1 affected the expression of many genes related to stimulus response, the cell receptor pathway, and cell growth. Pathway network analysis identified myocardial metabolism, DNA replication, and the cell cycle as the most important pathways targeted by Nrdp1. lncRNA-mRNA coexpression network analysis showed that two core lncRNAs, NONRATT057160 and NONRATT054243, were involved in cardiomyotrophy regulated by Nrdp1 in cardiomyocytes. Taken together, these data provide compelling clues for further exploration of the function of Nrdp1 in heart disease.