Using a Network-Based Analysis Approach to Investigate the Involvement of S. aureus in the Pathogenesis of Granulomatosis with Polyangiitis

Chronic nasal carriage of Staphylococcus aureus (SA) has been shown to be significantly higher in GPA patients when compared to healthy subjects, as well as being associated with increased endonasal activity and disease relapse. The aim of this study was to investigate SA involvement in GPA by applying a network-based analysis (NBA) approach to publicly available nasal transcriptomic data. Using these data, our NBA pipeline generated a proteinase 3 (PR3) positive ANCA associated vasculitis (AAV) disease network integrating differentially expressed genes, dysregulated transcription factors (TFs), disease-specific genes derived from GWAS studies, drug-target and protein-protein interactions. The PR3+ AAV disease network captured genes previously reported to be dysregulated in AAV associated. A subnetwork focussing on interactions between SA virulence factors and enriched biological processes revealed potential mechanisms for SA's involvement in PR3+ AAV. Immunosuppressant treatment reduced differential expression and absolute TF activities in this subnetwork for patients with inactive nasal disease but not active nasal disease symptoms at the time of sampling. The disease network generated identified the key molecular signatures and highlighted the associated biological processes in PR3+ AAV and revealed potential mechanisms for SA to affect these processes.

Identification of Key Genes in 'Luang Pratahn', Thai Salt-Tolerant Rice, Based on Time-Course Data and Weighted Co-expression Networks

Salinity is an important environmental factor causing a negative effect on rice production. To prevent salinity effects on rice yields, genetic diversity concerning salt tolerance must be evaluated. In this study, we investigated the salinity responses of rice (Oryza sativa) to determine the critical genes. The transcriptomes of 'Luang Pratahn' rice, a local Thai rice variety with high salt tolerance, were used as a model for analyzing and identifying the key genes responsible for salt-stress tolerance. Based on 3' Tag-Seq data from the time course of salt-stress treatment, weighted gene co-expression network analysis was used to identify key genes in gene modules. We obtained 1,386 significantly differentially expressed genes in eight modules. Among them, six modules indicated a significant correlation within 6, 12, or 48h after salt stress. Functional and pathway enrichment analysis was performed on the co-expressed genes of interesting modules to reveal which genes were mainly enriched within important functions for salt-stress responses. To identify the key genes in salt-stress responses, we considered the two-state co-expression networks, normal growth conditions, and salt stress to investigate which genes were less important in a normal situation but gained more impact under stress. We identified key genes for the response to biotic and abiotic stimuli and tolerance to salt stress. Thus, these novel genes may play important roles in salinity tolerance and serve as potential biomarkers to improve salt tolerance cultivars.

Gene Expression Network Analysis of Precursor Lesions in Familial Pancreatic Cancer

Purpose: High-grade pancreatic intraepithelial neoplasia (PanIN) are aggressive premalignant lesions, associated with risk of progression to pancreatic ductal adenocarcinoma (PDAC). A depiction of co-dysregulated gene activity in high-grade familial pancreatic cancer (FPC)-related PanIN lesions may characterize the molecular events during the progression from familial PanIN to PDAC. Materials and Methods: We performed weighted gene coexpression network analysis (WGCNA) to identify clusters of coexpressed genes associated with FPC-related PanIN lesions in 13 samples with PanIN-2/3 from FPC predisposed individuals, 6 samples with PDAC from sporadic pancreatic cancer (SPC) patients, and 4 samples of normal donor pancreatic tissue. Results: WGCNA identified seven differentially expressed gene (DEG) modules and two commonly expressed gene (CEG) modules with significant enrichment for Gene Ontology (GO) terms in FPC and SPC, including three upregulated (p < 5e-05) and four downregulated (p < 6e-04) gene modules in FPC compared to SPC. Among the DEG modules, the upregulated modules include 14 significant genes (p < 1e-06): ALOX12-AS1, BCL2L11, EHD4, C4B, BTN3A3, NDUFA11, RBM4B, MYOC, ZBTB47, TTTY15, NAPRT, LOC102606465, LOC100505711, and PTK2. The downregulated modules include 170 genes (p < 1e-06), among them 13 highly significant genes (p < 1e-10): COL10A1, SAMD9, PLPP4, COMP, POSTN, IGHV4-31, THBS2, MMP9, FNDC1, HOPX, TMEM200A, INHBA, and SULF1. The DEG modules are enriched for GO terms related to mitochondrial structure and adenosine triphosphate metabolic processes, extracellular structure and binding properties, humoral and complement mediated immune response, ligand-gated ion channel activity, and transmembrane receptor activity. Among the CEG modules, IL22RA1, DPEP1, and BCAT1 were found as highly connective hub genes associated with both FPC and SPC. Conclusion: FPC-related PanIN lesions exhibit a common molecular basis with SPC as shown by gene network activities and commonly expressed high-connectivity hub genes. The differential molecular pathology of FPC and SPC involves multiple coexpressed gene clusters enriched for GO terms including extracellular activities and mitochondrion function.

uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes

Protein interaction networks provide a powerful framework for identifying genes causal for complex genetic diseases. Here, we introduce a general framework, uKIN, that uses prior knowledge of disease-associated genes to guide, within known protein-protein interaction networks, random walks that are initiated from newly identified candidate genes. In large-scale testing across 24 cancer types, we demonstrate that our network propagation approach for integrating both prior and new information not only better identifies cancer driver genes than using either source of information alone but also readily outperforms other state-of-the-art network-based approaches. We also apply our approach to genome-wide association data to identify genes functionally relevant for several complex diseases. Overall, our work suggests that guided network propagation approaches that utilize both prior and new data are a powerful means to identify disease genes. uKIN is freely available for download at: https://github.com/Singh-Lab/uKIN.

Target Identification Using Homopharma and Network-Based Methods for Predicting Compounds Against Dengue Virus-Infected Cells

Drug target prediction is an important method for drug discovery and design, can disclose the potential inhibitory effect of active compounds, and is particularly relevant to many diseases that have the potential to kill, such as dengue, but lack any healing agent. An antiviral drug is urgently required for dengue treatment. Some potential antiviral agents are still in the process of drug discovery, but the development of more effective active molecules is in critical demand. Herein, we aimed to provide an efficient technique for target prediction using homopharma and network-based methods, which is reliable and expeditious to hunt for the possible human targets of three phenolic lipids (anarcardic acid, cardol, and cardanol) related to dengue viral (DENV) infection as a case study. Using several databases, the similarity search and network-based analyses were applied on the three phenolic lipids resulting in the identification of seven possible targets as follows. Based on protein annotation, three phenolic lipids may interrupt or disturb the human proteins, namely KAT5, GAPDH, ACTB, and HSP90AA1, whose biological functions have been previously reported to be involved with viruses in the family Flaviviridae. In addition, these phenolic lipids might inhibit the mechanism of the viral proteins: NS3, NS5, and E proteins. The DENV and human proteins obtained from this study could be potential targets for further molecular optimization on compounds with a phenolic lipid core structure in anti-dengue drug discovery. As such, this pipeline could be a valuable tool to identify possible targets of active compounds.

Re-exploring the core genes and modules in the human frontal cortex during chronological aging: insights from network-based analysis of transcriptomic studies

Frontal cortical dysfunction is a fundamental pathology contributing to age-associated behavioral and cognitive deficits that predispose older adults to neurodegenerative diseases. It is established that aging increases the risk of frontal cortical dysfunction; however, the underlying molecular mechanism remains elusive. Here, we used an integrative meta-analysis to combine five frontal cortex microarray studies with a combined sample population of 161 younger and 155 older individuals. A network-based analysis was used to describe an outline of human frontal cortical aging to identify core genes whose expression changes with age and to reveal the interrelationships among these genes. We found that histone deacetylase 1 (HDAC1) and YES proto-oncogene 1 (YES1) are the two most upregulated genes, while cell division cycle 42 (CDC42) is the central regulatory gene decreased in the aged human frontal cortex. Quantitative PCR assays revealed corresponding changes in frontal cortical Hdac1, Yes1 and Cdc42 mRNA levels in an established aging mouse model. Moreover, analysis of the GSE48350 dataset confirmed similar changes in HDAC1, CDC42 and YES1 expression in Alzheimer's disease, thereby providing a molecular connection between aging and Alzheimer's disease (AD). This framework of network-based analysis could provide novel strategies for detecting and monitoring aging in the brain.

NGSEA: Network-Based Gene Set Enrichment Analysis for Interpreting Gene Expression Phenotypes with Functional Gene Sets

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

Identification of Novel Components of Target-of-Rapamycin Signaling Pathway by Network-Based Multi-Omics Integrative Analysis

Target of rapamycin (TOR) is a major signaling pathway and regulator of cell growth. TOR serves as a hub of many signaling routes, and is implicated in the pathophysiology of numerous human diseases, including cancer, diabetes, and neurodegeneration. Therefore, elucidation of unknown components of TOR signaling that could serve as potential biomarkers and drug targets has a great clinical importance. In this study, our aim is to integrate transcriptomics, interactomics, and regulomics data in Saccharomyces cerevisiae using a network-based multiomics approach to enlighten previously unidentified, potential components of TOR signaling. We constructed the TOR-signaling protein interaction network, which was used as a template to search for TOR-mediated rapamycin and caffeine signaling paths. We scored the paths passing from at least one component of TOR Complex 1 or 2 (TORC1/TORC2) using the co-expression levels of the genes in the transcriptome data of the cells grown in the presence of rapamycin or caffeine. The resultant network revealed seven hitherto unannotated proteins, namely, Atg14p, Rim20p, Ret2p, Spt21p, Ylr257wp, Ymr295cp, and Ygr017wp, as potential components of TOR-mediated rapamycin and caffeine signaling in yeast. Among these proteins, we suggest further deciphering of the role of Ylr257wp will be particularly informative in the future because it was the only protein whose removal from the constructed network hindered the signal transduction to the TORC1 effector kinase Npr1p. In conclusion, this study underlines the value of network-based multiomics integrative data analysis in discovering previously unidentified components of the signaling networks by revealing potential components of TOR signaling for future experimental validation.

Identification of cancer omics commonality and difference via community fusion

The analysis of cancer omics data is a "classic" problem; however, it still remains challenging. Advancing from early studies that are mostly focused on a single type of cancer, some recent studies have analyzed data on multiple "related" cancer types/subtypes, examined their commonality and difference, and led to insightful findings. In this article, we consider the analysis of multiple omics datasets, with each dataset on one type/subtype of "related" cancers. A Community Fusion (CoFu) approach is developed, which conducts marker selection and model building using a novel penalization technique, informatively accommodates the network community structure of omics measurements, and automatically identifies the commonality and difference of cancer omics markers. Simulation demonstrates its superiority over direct competitors. The analysis of TCGA lung cancer and melanoma data leads to interesting findings.

Network-Based Coverage of Mutational Profiles Reveals Cancer Genes

A central goal in cancer genomics is to identify the somatic alterations that underpin tumor initiation and progression. While commonly mutated cancer genes are readily identifiable, those that are rarely mutated across samples are difficult to distinguish from the large numbers of other infrequently mutated genes. We introduce a method, nCOP, that considers per-individual mutational profiles within the context of protein-protein interaction networks in order to identify small connected subnetworks of genes that, while not individually frequently mutated, comprise pathways that are altered across (i.e., "cover") a large fraction of individuals. By analyzing 6,038 samples across 24 different cancer types, we demonstrate that nCOP is highly effective in identifying cancer genes, including those with low mutation frequencies. Overall, our work demonstrates that combining per-individual mutational information with interaction networks is a powerful approach for tackling the mutational heterogeneity observed across cancers.

Meta-analysis on blood transcriptomic studies identifies consistently coexpressed protein-protein interaction modules as robust markers of human aging

The bodily decline that occurs with advancing age strongly impacts on the prospects for future health and life expectancy. Despite the profound role of age in disease etiology, knowledge about the molecular mechanisms driving the process of aging in humans is limited. Here, we used an integrative network-based approach for combining multiple large-scale expression studies in blood (2539 individuals) with protein–protein Interaction (PPI) data for the detection of consistently coexpressed PPI modules that may reflect key processes that change throughout the course of normative aging. Module detection followed by a meta-analysis on chronological age identified fifteen consistently coexpressed PPI modules associated with chronological age, including a highly significant module (P = 3.5 × 10⁻³⁸) enriched for ‘T-cell activation’ marking age-associated shifts in lymphocyte blood cell counts (R² = 0.603; P = 1.9 × 10⁻¹⁰). Adjusting the analysis in the compendium for the ‘T-cell activation’ module showed five consistently coexpressed PPI modules that robustly associated with chronological age and included modules enriched for ‘Translational elongation’, ‘Cytolysis’ and ‘DNA metabolic process’. In an independent study of 3535 individuals, four of five modules consistently associated with chronological age, underpinning the robustness of the approach. We found three of five modules to be significantly enriched with aging-related genes, as defined by the GenAge database, and association with prospective survival at high ages for one of the modules including ASF1A. The hereby-detected age-associated and consistently coexpressed PPI modules therefore may provide a molecular basis for future research into mechanisms underlying human aging.

Brain galanin system genes interact with life stresses in depression-related phenotypes

Galanin is a stress-inducible neuropeptide and cotransmitter in serotonin and norepinephrine neurons with a possible role in stress-related disorders. Here we report that variants in genes for galanin (GAL) and its receptors (GALR1, GALR2, GALR3), despite their disparate genomic loci, conferred increased risk of depression and anxiety in people who experienced childhood adversity or recent negative life events in a European white population cohort totaling 2,361 from Manchester, United Kingdom and Budapest, Hungary. Bayesian multivariate analysis revealed a greater relevance of galanin system genes in highly stressed subjects compared with subjects with moderate or low life stress. Using the same method, the effect of the galanin system genes was stronger than the effect of the well-studied 5-HTTLPR polymorphism in the serotonin transporter gene (SLC6A4). Conventional multivariate analysis using general linear models demonstrated that interaction of galanin system genes with life stressors explained more variance (1.7%, P = 0.005) than the life stress-only model. This effect replicated in independent analysis of the Manchester and Budapest subpopulations, and in males and females. The results suggest that the galanin pathway plays an important role in the pathogenesis of depression in humans by increasing the vulnerability to early and recent psychosocial stress. Correcting abnormal galanin function in depression could prove to be a novel target for drug development. The findings further emphasize the importance of modeling environmental interaction in finding new genes for depression.