Active Subnetworks Research Articles

Acute exposure to electronic cigarette components alters mRNA expression of pre-osteoblasts.

The use of traditional nicotine delivery products such as tobacco has long been linked to detrimental health effects. However, little work to date has focused on the emerging market of aerosolized nicotine delivery known as electronic nicotine delivery systems (ENDS) or electronic cigarettes, and their potential for new effects on human health. Challenges studying these devices include heterogeneity in the formulation of the common components of most available ENDS, including nicotine and a carrier (commonly composed of propylene glycol and vegetable glycerin, or PG/VG). In the present study, we report on experiments interrogating the effects of major identified components in e-cigarettes. Specifically, the potential concomitant effects of nicotine and common carrier ingredients in commercial "vape" products are explored invitro to inform the potential health effects on the craniofacial skeleton through novel vectors as compared to traditional tobacco products. MC3T3-E1 murine pre-osteoblast cells were cultured invitro with clinically relevant liquid concentrations of nicotine, propylene glycol (PG), vegetable glycerin (VG), Nicotine+PG/VG, and the vape liquid of a commercial product (Juul). Cells were treated acutely for 24 h and RNA-Seq was utilized to determine segregating alteration in mRNA signaling. Influential gene targets identified with sparse partial least squares discriminant analysis (sPLS-DA) implemented in mixOmics were assessed using the PANTHER Classification system for molecular functions, biological processes, cellular components, and pathways of effect. Additional endpoint functional analyses were used to confirm cell cycle changes. The initial excitatory concentration (EC50) studied defined a target concentration of carrier PG/VG liquid that altered the cell cycle of the calvarial cells. Initial sPLS-DA analysis demonstrated the segregation of nicotine and non-nicotine exposures utilized in our invitro modeling. Pathway analysis suggests a strong influence of nicotine exposures on cellular processes including metabolic processes and response to stimuli including autophagic flux. Further interrogation of the individual treatment conditions demonstrated segregation by treatment modality (Control, Nicotine, Carrier (PG+VG), Nicotine+PG/VG) along three dimensions best characterized by: latent variable 1 (PLSDA-1) showing strong segregation based on nicotine influence on cellular processes associated with cellular adhesion to collagen, osteoblast differentiation, and calcium binding and metabolism; latent variable 2 (PLSDA-2) showing strong segregation of influence based on PG+VG and Control influence on cell migration, survival, and cycle regulation; and latent variable 3 (PLSDA-3) showing strong segregation based on Nicotine and Control exposure influence on cell activity and growth and developmental processes. Further, gene co-expression network analysis implicates targets of the major pathway genes associated with bone growth and development, particularly craniofacial (FGF, Notch, TGFβ, WNT) and analysis of active subnetwork pathways found these additionally overrepresented in the Juul exposure relative to Nicotine+PG/VG. Finally, experimentation confirmed alterations in cell count, and increased evidence of cell stress (markers of autophagy), but no alteration in apoptosis. These data suggest concomitant treatment with Nicotine+PG/VG drives alterations in pre-osteoblast cell cycle signaling, specifically transcriptomic targets related to cell cycle and potentially cell stress. Although we suspected cell stress and well as cytotoxic effects of Nicotine+PG/VG, no great influence on apoptotic factors was observed. Further RNA-Seq analysis allowed for the direct interrogation of molecular targets of major pathways involved in bone and craniofacial development, each demonstrating segregation (altered signaling) due to e-cigarette-type exposure. These data have implications directed toward ENDS formulation as synergistic effects of Nicotine+PG/VG are evidenced here. Thus, future research will continue to interrogate how varied formulation of Nicotine+PG/VG affects overall cell functions in multiple vital systems.

Transcriptome Analysis in Patients With Muscle-invasive Bladder Cancer.

Bladder cancer (BC) is the most prevalent malignant tumor in the urinary tract, classified mainly into muscle-invasive BC (MIBC) and non-MIBC (NMIBC). Recent studies highlight the important role of changes in transcriptome activity in carcinogenesis, aiding in the identification of additional differentially regulated candidate genes, improving our understanding of the molecular basis of gene regulation in BC. This study aimed to evaluate the transcriptome of MIBC patients compared with normal subjects. mRNA sequencing was conducted using the Illumina NovaSeq 6000 Dx system in a case series comprising 11 subjects with MIBC and 19 healthy controls matched for age and sex. For functional analysis, the pathfindR package was utilized to comprehensively identify pathways enriched in omics data within active subnetworks. Our results demonstrated the presence of differentiated pathways, including spliceosome activity, oxidative phosphorylation, and chemical carcinogenesis due to reactive oxygen species, in MIBC patients compared with controls. The identification of novel molecular pathways in MIBC patients could prove useful in defining cancer predisposition factors and exploring potential therapeutic options.

Identification of diagnostic markers of pancreatic ductal adenocarcinoma using transcriptomic tumour and blood sample data

AbstractBackgroundPancreatic ductal adenocarcinoma (PDAC) is the most frequently diagnosed form of pancreatic cancer worldwide. PDAC is associated with a poor survival rate mainly due to the disease being usually diagnosed at late stages.MethodsPublicly available gene expression data from 10 studies with tumour tissue (448 samples) and/or blood samples (128 samples) from PDAC patients were pooled together and analyzed for the identification of stage‐specific and global diagnostic markers using differential gene expression analysis. The list of statistically significant () differentially expressed genes were used to carry out enrichment analysis via active subnetworks and miRNA enrichment analysis. We then used the results from these analyses to identify the most significant genes and pathways and map these to marketed drugs’ pharmacological targets. The same process was replicated for studies with blood samples and results were compared to those from the tissue analysis. A set of consistently deregulated genes (pancreatic tumour signature, PTS) in both tissue and blood samples was derived and validated in external cohorts and The Cancer Genome Atlas (TCGA) data.ResultsNotable gene expression deregulation was found in all tumour stages with significant overlap. We identified 820 consistently deregulated genes (PTS) in tissue samples of all stages and blood samples. Active subnetwork analysis revealed enriched ribosome, proteasome, adherens junction and cell cycle pathways across all stages and blood samples. Our findings suggest that microRNA (miRNA) contribution to PDAC pathology plays a significant role and is probably mediated by distinct miRNAs across stages of PDAC. Stage‐specific enriched miRNAs with diagnostic potential included miR‐21, miR‐29, miR‐124 and miR‐30, for stages 1–4, respectively. By investigating the pharmacogenetic interactions of the identified targets with clinically approved drugs, we outline potential paths for personalized interventions. Importantly, the PTS showed a significant association with survival in TCGA data.ConclusionThus, we present a compilation of protein‐coding markers and miRNAs that hold potential as a diagnostic tool for the early detection of PDAC, as well as for designing novel therapeutic strategies aimed at improving patient outcomes.

SEMtree: tree-based structure learning methods with structural equation models.

With the exponential growth of expression and protein-protein interaction (PPI) data, the identification of functional modules in PPI networks that show striking changes in molecular activity or phenotypic signatures becomes of particular interest to reveal process-specific information that is correlated with cellular or disease states. This requires both the identification of network nodes with reliability scores and the availability of an efficient technique to locate the network regions with the highest scores. In the literature, a number of heuristic methods have been suggested. We propose SEMtree(), a set of tree-based structure discovery algorithms, combining graph and statistically interpretable parameters together with a user-friendly R package based on structural equation models framework. Condition-specific changes from differential expression and gene-gene co-expression are recovered with statistical testing of node, directed edge, and directed path difference between groups. In the end, from a list of seed (i.e. disease) genes or gene P-values, the perturbed modules with undirected edges are generated with five state-of-the-art active subnetwork detection methods. The latter are supplied to causal additive trees based on Chu-Liu-Edmonds' algorithm (Chow and Liu, Approximating discrete probability distributions with dependence trees. IEEE Trans Inform Theory 1968;14:462-7) in SEMtree() to be converted in directed trees. This conversion allows to compare the methods in terms of directed active subnetworks. We applied SEMtree() to both Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) and simulated datasets with various differential expression patterns. Compared to existing methods, SEMtree() is able to capture biologically relevant subnetworks with simple visualization of directed paths, good perturbation extraction, and classifier performance. SEMtree() function is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph.

Identifying Network Biomarkers for Alzheimer's Disease Using Single-Cell RNA Sequencing Data.

System-level network-based approaches are an emerging field in the biomedical domain since biological networks can be used to analyze complicated biological processes and complex human disorders more efficiently. Network biomarkers are groups of interconnected molecular components causing perturbations in the entire network topology that can be used as indicators of pathogenic biological processes when studying a given disease. Although in the last years computational systems-based approaches have gained ground on the path to discovering new network biomarkers, in complex diseases like Alzheimer's disease (AD), this approach has still much to offer. Especially the adoption of single-cell RNA sequencing (scRNA-seq) has now become the dominant technology for the study of stochastic gene expression. Toward this orientation, we propose an R workflow that extracts disease-perturbed subpathways within a pathway network. We construct a gene-gene interaction network integrated with scRNA-seq expression profiles, and after network processing and pruning, the most active subnetworks are isolated from the entire network topology. The proposed methodology was applied on a real AD-based scRNA-seq data, providing already existing and new potential AD biomarkers in gene network context.

Drug repositioning by merging active subnetworks validated in cancer and COVID-19

Computational drug repositioning aims at ranking and selecting existing drugs for novel diseases or novel use in old diseases. In silico drug screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of disease active subnetworks. DrugMerge uses differential transcriptomic data on drugs and diseases in the context of a large gene co-expression network. Experiments with four benchmark diseases demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Application of DrugMerge to COVID-19 found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge can mimic human expert judgment.

Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)

Integrating -omics data with biological networks such as protein–protein interaction networks is a popular and useful approach to interpret expression changes of genes in changing conditions, and to identify relevant cellular pathways, active subnetworks or network communities. Yet, most -omics data integration tools are restricted to static networks and therefore cannot easily be used for analyzing time-series data. Determining regulations or exploring the network structure over time requires time-dependent networks which incorporate time as one component in their structure. Here, we present a method to project time-series data on sequential layers of a multilayer network, thus creating a temporal multilayer network (tMLN). We implemented this method as a Cytoscape app we named TimeNexus. TimeNexus allows to easily create, manage and visualize temporal multilayer networks starting from a combination of node and edge tables carrying the information on the temporal network structure. To allow further analysis of the tMLN, TimeNexus creates and passes on regular Cytoscape networks in form of static versions of the tMLN in three different ways: (i) over the entire set of layers, (ii) over two consecutive layers at a time, (iii) or on one single layer at a time. We combined TimeNexus with the Cytoscape apps PathLinker and AnatApp/ANAT to extract active subnetworks from tMLNs. To test the usability of our app, we applied TimeNexus together with PathLinker or ANAT on temporal expression data of the yeast cell cycle and were able to identify active subnetworks relevant for different cell cycle phases. We furthermore used TimeNexus on our own temporal expression data from a mouse pain assay inducing hindpaw inflammation and detected active subnetworks relevant for an inflammatory response to injury, including immune response, cell stress response and regulation of apoptosis. TimeNexus is freely available from the Cytoscape app store at https://apps.cytoscape.org/apps/TimeNexus.

Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma.

Lung cancer is the second most frequently diagnosed cancer type and responsible for the highest number of cancer deaths worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are subtypes of non-small-cell lung cancer which has the highest frequency of lung cancer cases. We aimed to analyze genomic and transcriptomic variations including simple nucleotide variations (SNVs), copy number variations (CNVs) and differential expressed genes (DEGs) in order to find key genes and pathways for diagnostic and prognostic prediction for lung adenocarcinoma and lung squamous cell carcinoma. We performed a univariate Cox model and then lasso-regularized Cox model with leave-one-out cross-validation using The Cancer Genome Atlas (TCGA) gene expression data in tumor samples. We generated 35- and 33-gene signatures for prognostic risk prediction based on the overall survival time of the patients with LUAD and LUSC, respectively. When we clustered patients into high- and low-risk groups, the survival analysis showed highly significant results with high prediction power for both training and test datasets. Then, we characterized the differences including significant SNVs, CNVs, DEGs, active subnetworks, and the pathways. We described the results for the risk groups and cancer subtypes separately to identify specific genomic alterations between both high-risk groups and cancer subtypes. Both LUAD and LUSC high-risk groups have more downregulated immune pathways and upregulated metabolic pathways. On the other hand, low-risk groups have both up- and downregulated genes on cancer-related pathways. Both LUAD and LUSC have important gene alterations such as CDKN2A and CDKN2B deletions with different frequencies. SOX2 amplification occurs in LUSC and PSMD4 amplification in LUAD. EGFR and KRAS mutations are mutually exclusive in LUAD samples. EGFR, MGA, SMARCA4, ATM, RBM10, and KDM5C genes are mutated only in LUAD but not in LUSC. CDKN2A, PTEN, and HRAS genes are mutated only in LUSC samples. The low-risk groups of both LUAD and LUSC tend to have a higher number of SNVs, CNVs, and DEGs. The signature genes and altered genes have the potential to be used as diagnostic and prognostic biomarkers for personalized oncology.

An effective method for computer network anomaly detection

With the increasing network threat, network anomaly detection has become a very challenging and indispensable task. In this article, we propose an anomaly detection algorithm through modeling computer network as temporal network. The active subnetwork is extracted from the original computer communication network and then projected to an undirected weighted network series, finally the abnormal behavior patterns of network series are detected based on eigenvectors. Data test experiment shows that the proposed algorithm has achieved very good results.

Finding disease modules for cancer and COVID-19 in gene co-expression networks with the Core&Peel method

Genes are organized in functional modules (or pathways), thus their action and their dysregulation in diseases may be better understood by the identification of the modules most affected by the disease (aka disease modules, or active subnetworks). We describe how an algorithm based on the Core&Peel method is used to detect disease modules in co-expression networks of genes. We first validate Core&Peel for the general task of functional module detection by comparison with 42 methods participating in the Disease Module Identification DREAM challenge. Next, we use four specific disease test cases (colorectal cancer, prostate cancer, asthma, and rheumatoid arthritis), four state-of-the-art algorithms (ModuleDiscoverer, Degas, KeyPathwayMiner, and ClustEx), and several pathway databases to validate the proposed algorithm. Core&Peel is the only method able to find significant associations of the predicted disease module with known validated relevant pathways for all four diseases. Moreover, for the two cancer datasets, Core&Peel detects further eight relevant pathways not discovered by the other methods used in the comparative analysis. Finally, we apply Core&Peel and other methods to explore the transcriptional response of human cells to SARS-CoV-2 infection, finding supporting evidence for drug repositioning efforts at a pre-clinical level.

Insights Into the Mechanism of Anticancer Drug Imatinib Revealed Through Multi-Omic Analyses in Yeast.

Imatinib mesylate is a receptor tyrosine kinase inhibitor drug with broad applications in cancer therapeutics, for example, in chronic myeloid leukemia and gastrointestinal stromal tumors. In this study, new multi-omics findings in yeast on the mechanism of imatinib are reported, using the model organism Saccharomyces cerevisiae. Whole-genome analysis of the transcriptional response of yeast cells following long-term exposure to imatinib, flux-balance analysis (FBA), and modular analysis of protein/protein interaction network consisting of proteins encoded by differentially expressed genes (DEGs) were performed. DEGs indicated that carbon, nitrogen, starch, sucrose, glyoxylate/dicarboxylate metabolism, valine and leucine degradation, and tricarboxylic acid cycle (TCA) were significantly upregulated. By contrast, ribosome biogenesis, pentose/glucuronate interconversion, tryptophan/pyruvate metabolic pathways, and meiosis were significantly downregulated. FBA revealed that a large set of metabolic pathways was altered by imatinib to compensate cancer-associated metabolic changes. Integration of transcriptome and interactome (protein/protein interactions) data helped to identify the core regulatory genes and pathways through elucidation of the active subnetworks. It appears that imatinib may also contribute to antitumoral immune response in the tumor microenvironment and most of the metabolic rearrangements are at posttranscriptional level. Furthermore, additional support for possible contribution of thiamine/pyridoxal phosphate biosynthesis and mitogen-activated protein kinase pathway to drug resistance is noted. This report advances multi-omics understanding of the mechanism of imatinib, and by extension, offers new molecular avenues toward precision medicine and discovery of novel drug targets in cancer therapeutics.

Computational Systems Biology Sheds Insights on to Metabolic Syndrome and its Component Disorders

The interconnected physiological and metabolism factors, such as obesity, hypertension, insulin resistance, etc. that increases the risk of cardiovascular disorders (CVD), type2 diabetes mellitus (T2DM) that causes mortality is the description of metabolic syndrome (MetS). It remains unclear how molecular mechanisms are typical between MetS, CVD, T2DM, obesity, and hypertension. In this study, we compiled 27 common genes by mapping miRNAs and TFs as active seed nodes into the regulatory TF-miRNAs and TF-miRNA networks by the integration of target prediction. By merging these networks, the gene-based miRNA and TF mediated regulatory network common for MetS and its associated diseases were constructed. As a result, we obtained a potential active sub-network based on degree analysis. Next, by using the breadth-first-search approach, 46 regulatory pathways, which are the gene-based regulatory cascade of TFs and miRNAs, were identified. In order to identify the hub regulators in the original network constructed, based on degree and betweenness analysis, 16 genes (VEGFA, KLF2, PNPLA3, GRK2, HMOX1, EDN1, IL6, TGFB1, NOS3, TNF, SERPINA1, SPP1, AGTR1, ADRB3), 3 miRNAs(has-miR-335-5p, has-miR-124-3p, has-miR-181a-5p), 4TFs(NFKB, FOXO1, RELA, SP1) are over-represented in the following significantly enriched functional pathway groups such as AGE-RAGE signaling pathway, Renin-Angiotensin System, HIF-1 signaling pathway. The majority of the regulatory relationships from published literature studies demonstrated the reliability and validity of these miRNA and TF mediated regulatory network. Hence, our study has aided in deciphering the complex regulatory mechanisms involved in MetS and will provide putative therapeutic targets by further validation of these pathways by biological experiments.

Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma

BackgroundLung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patient-based biomarkers which are the candidates of molecular targeting.ResultsWe built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories.We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets.ConclusionsThis 12-gene signature could predict the prognostic risk of the patients with lung adenocarcinoma in TCGA and they are potential predictors for the survival-based risk clustering of the patients with lung adenocarcinoma. These genes can be used to cluster patients based on molecular nature and the best candidates of drugs for the patient clusters can be proposed. These genes also have a high potential for targeted cancer therapy of patients with lung adenocarcinoma.

Dynamics of the gene regulatory networks of the inner and rosette leaves in Brassica rapa

Chinese cabbage (Brassica rapa ssp. pekinensis) is one of the most important crops in Asian countries. In the heading formation of Chinese cabbage, differential regulation of production and growth between the inner and rosette leaves is the most important developmental process. In general, it is well known that the developmental process is determined by complex interactions among genes. Therefore, it is important to understand the developmental process of Chinese cabbage by studying the crucial genes involved in the cells constituting the inner and rosette leaves and to clarify their dynamic characteristics. A recent study showed that the gene expression patterns of inner and rosette leaves are quite different but the tissue-specific gene regulatory networks of the two types of leaves are still largely unknown. Based on the RNA sequencing profiles of inner, rosette, and young leaves from Chinese cabbage and the gene regulatory network of Arabidopsis thaliana, we reconstructed tissue-specific active subnetworks of inner and rosette leaves of Chinese cabbage. Then, we analyzed the structural and functional characteristics of the two tissue-specific active subnetworks. Notably, we found that coherent feedforward loops were prominent in the inner leaf, whereas incoherent feedforward loops were abundant in the rosette leaf. By exploring the dynamic and functional characteristics of the feedforward loops, we found that the coherent feedforward loops are necessary for maintaining the molecular states, whereas the incoherent feedforward loops are crucial for adaptation. Overall, we concluded that the exclusive pattern of the network motifs is closely associated with the biological traits of each tissue.

Multiple Spatiotemporal Characteristics-Based Zonal Voltage Control for High Penetrated PVs in Active Distribution Networks

The penetration of photovoltaic (PV) outputs brings great challenges to optimal operation of active distribution networks (ADNs), especially leading to more serious overvoltage problems. This study proposes a zonal voltage control scheme based on multiple spatiotemporal characteristics for highly penetrated PVs in ADNs. In the spatial domain, a community detection algorithm using a reactive/ active power quality function was introduced to partition an ADN into sub-networks. In the time domain, short-term zonal scheduling (SZS) with 1 h granularity was drawn up based on a cluster. The objective was to minimize the supported reactive power and the curtailed active power in reactive and active power sub-networks. Additionally, a real-time zonal voltage control scheme (RZVC) with 1 min granularity was proposed to correct the SZS rapidly by choosing and controlling the key PV inverter to regulate the supported reactive power and the curtailed active power of the inverters to prevent the overvoltage in each sub-network. With the time domain cooperation, the proposed method could achieve economic control and avoid overvoltage caused by errors in the forecast data of the PVs. For the spatial domain, zonal scheduling and zonal voltage control were carried out in each cluster, and the short-term scheduling and voltage controlling problem of the ADN could then be decomposed into several sub-problems. This could simplify the optimization and control which can reduce the computing time. Finally, an actual 10kV, 103-node network in Zhejiang Province of China is employed to verify the effectiveness and feasibility of the proposed approach.

PathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks.

Pathway analysis is often the first choice for studying the mechanisms underlying a phenotype. However, conventional methods for pathway analysis do not take into account complex protein-protein interaction information, resulting in incomplete conclusions. Previously, numerous approaches that utilize protein-protein interaction information to enhance pathway analysis yielded superior results compared to conventional methods. Hereby, we present pathfindR, another approach exploiting protein-protein interaction information and the first R package for active-subnetwork-oriented pathway enrichment analyses for class comparison omics experiments. Using the list of genes obtained from an omics experiment comparing two groups of samples, pathfindR identifies active subnetworks in a protein-protein interaction network. It then performs pathway enrichment analyses on these identified subnetworks. To further reduce the complexity, it provides functionality for clustering the resulting pathways. Moreover, through a scoring function, the overall activity of each pathway in each sample can be estimated. We illustrate the capabilities of our pathway analysis method on three gene expression datasets and compare our results with those obtained from three popular pathway analysis tools. The results demonstrate that literature-supported disease-related pathways ranked higher in our approach compared to the others. Moreover, pathfindR identified additional pathways relevant to the conditions that were not identified by other tools, including pathways named after the conditions.

Enrichment of damaging missense variants in genes related with axonal guidance signalling in sporadic Meniere’s disease

IntroductionMeniere’s disease (MD) is a rare inner ear disorder with a significant genetic contribution defined by a core phenotype: episodic vertigo, sensorineural hearing loss and tinnitus. It has been mostly...

A system-wide network reconstruction of gene regulation and metabolism in Escherichia coli.

Genome-scale metabolic models have become a fundamental tool for examining metabolic principles. However, metabolism is not solely characterized by the underlying biochemical reactions and catalyzing enzymes, but also affected by regulatory events. Since the pioneering work of Covert and co-workers as well as Shlomi and co-workers it is debated, how regulation and metabolism synergistically characterize a coherent cellular state. The first approaches started from metabolic models, which were extended by the regulation of the encoding genes of the catalyzing enzymes. By now, bioinformatics databases in principle allow addressing the challenge of integrating regulation and metabolism on a system-wide level. Collecting information from several databases we provide a network representation of the integrated gene regulatory and metabolic system for Escherichia coli, including major cellular processes, from metabolic processes via protein modification to a variety of regulatory events. Besides transcriptional regulation, we also take into account regulation of translation, enzyme activities and reactions. Our network model provides novel topological characterizations of system components based on their positions in the network. We show that network characteristics suggest a representation of the integrated system as three network domains (regulatory, metabolic and interface networks) instead of two. This new three-domain representation reveals the structural centrality of components with known high functional relevance. This integrated network can serve as a platform for understanding coherent cellular states as active subnetworks and to elucidate crossover effects between metabolism and gene regulation.

A Comprehensive Survey of Tools and Software for Active Subnetwork Identification.

A recent focus of computational biology has been to integrate the complementary information available in molecular profiles as well as in multiple network databases in order to identify connected regions that show significant changes under different conditions. This allows for capturing dynamic and condition-specific mechanisms of the underlying phenomena and disease stages. Here we review 22 such integrative approaches for active module identification published over the last decade. This article only focuses on tools that are currently available for use and are well-maintained. We compare these methods focusing on their primary features, integrative abilities, network structures, mathematical models, and implementations. We also provide real-world scenarios in which these methods have been successfully applied, as well as highlight outstanding challenges in the field that remain to be addressed. The main objective of this review is to help potential users and researchers to choose the best method that is suitable for their data and analysis purpose.

Beyond Pathway Analysis: Identification of Active Subnetworks in Rett Syndrome.

Pathway and network approaches are valuable tools in analysis and interpretation of large complex omics data. Even in the field of rare diseases, like Rett syndrome, omics data are available, and the maximum use of such data requires sophisticated tools for comprehensive analysis and visualization of the results. Pathway analysis with differential gene expression data has proven to be extremely successful in identifying affected processes in disease conditions. In this type of analysis, pathways from different databases like WikiPathways and Reactome are used as separate, independent entities. Here, we show for the first time how these pathway models can be used and integrated into one large network using the WikiPathways RDF containing all human WikiPathways and Reactome pathways, to perform network analysis on transcriptomics data. This network was imported into the network analysis tool Cytoscape to perform active submodule analysis. Using a publicly available Rett syndrome gene expression dataset from frontal and temporal cortex, classical enrichment analysis, including pathway and Gene Ontology analysis, revealed mainly immune response, neuron specific and extracellular matrix processes. Our active module analysis provided a valuable extension of the analysis prominently showing the regulatory mechanism of MECP2, especially on DNA maintenance, cell cycle, transcription, and translation. In conclusion, using pathway models for classical enrichment and more advanced network analysis enables a more comprehensive analysis of gene expression data and provides novel results.