DEGAS: de novo discovery of dysregulated pathways in human diseases

De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet

Background

With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem.

Results

We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software.

Conclusion

The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.

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.

Pathways to Parkinson's disease: a spotlight on 14-3-3 proteins

14-3-3s represent a family of highly conserved 30 kDa acidic proteins. 14-3-3s recognize and bind specific phospho-sequences on client partners and operate as molecular hubs to regulate their activity, localization, folding, degradation, and protein-protein interactions. 14-3-3s are also associated with the pathogenesis of several diseases, among which Parkinson's disease (PD). 14-3-3s are found within Lewy bodies (LBs) in PD patients, and their neuroprotective effects have been demonstrated in several animal models of PD. Notably, 14-3-3s interact with some of the major proteins known to be involved in the pathogenesis of PD. Here we first provide a detailed overview of the molecular composition and structural features of 14-3-3s, laying significant emphasis on their peculiar target-binding mechanisms. We then briefly describe the implication of 14-3-3s in the central nervous system and focus on their interaction with LRRK2, α-Synuclein, and Parkin, three of the major players in PD onset and progression. We finally discuss how different types of small molecules may interfere with 14-3-3s interactome, thus representing a valid strategy in the future of drug discovery.

14-3-3 Proteins: Novel Pharmacological Targets in Neurodegenerative Diseases

14-3-3 proteins are a family of proteins expressed throughout the body and implicated in many diseases, from cancer to neurodegenerative disorders. While these proteins do not have direct enzymatic activity, they form a hub for many signaling pathways via protein-protein interactions (PPIs). 14-3-3 interactions have proven difficult to target with traditional pharmacological methods due to the unique nature of their binding. However, recent advances in compound development utilizing a range of tools, from thermodynamic binding site analysis to computational molecular modeling techniques, have opened the door to targeting these interactions. Compounds are already being developed targeting 14-3-3 interactions with potential therapeutic implication for neurodegenerative disorders, but challenges still remain in optimizing specificity and target engagement to avoid unintended negative consequences arising from targeting 14-3-3 signaling networks.

Identification of conserved evolutionary trajectories in tumors

MOTIVATION

As multi-region, time-series and single-cell sequencing data become more widely available; it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with similar characteristics).

RESULTS

In this article, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 non-small-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories.

AVAILABILITY AND IMPLEMENTATION

CONETT is implemented in C++ and available at https://github.com/ehodzic/CONETT.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Identifying Drug Sensitivity Subnetworks with NETPHIX

Phenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. We develop a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction information, NETPHIX modules are functionally coherent and can thus provide important insights into drug action. In addition, we show that modules identified by NETPHIX together with their association patterns can be leveraged to suggest drug combinations.

Molecular networks in Network Medicine: Development and applications

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review

Next-generation sequencing approaches and genome-wide studies have become essential for characterizing the mechanisms of human diseases. Consequently, many researchers have applied these approaches to discover the genetic/genomic causes of common complex and rare human diseases, generating multiomics big data that span the continuum of genomics, proteomics, metabolomics, and many other system science fields. Therefore, there is a significant and unmet need for biological databases and tools that enable and empower the researchers to analyze, integrate, and make sense of big data. There are currently large number of databases that offer different types of biological information. In particular, the integration of gene expression profiles and protein-protein interaction networks provides a deeper understanding of the complex multilayered molecular architecture of human diseases. Therefore, there has been a growing interest in developing methodologies that integrate and contextualize big data from molecular interaction networks to identify biomarkers of human diseases at a subnetwork resolution as well. In this expert review, we provide a comprehensive summary of most popular biomolecular databases for molecular interactions (e.g., Biological General Repository for Interaction Datasets, Kyoto Encyclopedia of Genes and Genomes and Search Tool for The Retrieval of Interacting Genes/Proteins), gene-disease associations (e.g., Online Mendelian Inheritance in Man, Disease-Gene Network, MalaCards), and population-specific databases (e.g., Human Genetic Variation Database), and describe some examples of their usage and potential applications. We also present the most recent subnetwork identification approaches and discuss their main advantages and limitations. As the field of data science continues to emerge, the present analysis offers a deeper and contextualized understanding of the available databases in molecular biomedicine.

A comparison of mechanistic signaling pathway activity analysis methods

Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.

Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks

BACKGROUND

Advances in large-scale tumor sequencing have led to an understanding that there are combinations of genomic and transcriptomic alterations specific to tumor types, shared across many patients. Unfortunately, computational identification of functionally meaningful and recurrent alteration patterns within gene/protein interaction networks has proven to be challenging.

FINDINGS

We introduce a novel combinatorial method, cd-CAP (combinatorial detection of conserved alteration patterns), for simultaneous detection of connected subnetworks of an interaction network where genes exhibit conserved alteration patterns across tumor samples. Our method differentiates distinct alteration types associated with each gene (rather than relying on binary information of a gene being altered or not) and simultaneously detects multiple alteration profile conserved subnetworks.

CONCLUSIONS

In a number of The Cancer Genome Atlas datasets, cd-CAP identified large biologically significant subnetworks with conserved alteration patterns, shared across many tumor samples.

Towards a data-integrated cell

We are increasingly accumulating molecular data about a cell. The challenge is how to integrate them within a unified conceptual and computational framework enabling new discoveries. Hence, we propose a novel, data-driven concept of an integrated cell, iCell. Also, we introduce a computational prototype of an iCell, which integrates three omics, tissue-specific molecular interaction network types. We construct iCells of four cancers and the corresponding tissue controls and identify the most rewired genes in cancer. Many of them are of unknown function and cannot be identified as different in cancer in any specific molecular network. We biologically validate that they have a role in cancer by knockdown experiments followed by cell viability assays. We find additional support through Kaplan-Meier survival curves of thousands of patients. Finally, we extend this analysis to uncover pan-cancer genes. Our methodology is universal and enables integrative comparisons of diverse omics data over cells and tissues.

G2019S LRRK2 Increases Stress Susceptibility Through Inhibition of DAF-16 Nuclear Translocation in a 14-3-3 Associated-Manner in Caenorhabditis elegans

Mutations in leucine-rich repeat kinase 2 (LRRK2) are common causes of familial Parkinson’s disease (PD). Oxidative stress plays a key role in the pathogenesis of PD. Mutations in LRRK2 have been shown to increase susceptibility to oxidative stress. To explore mechanisms underlying susceptibility to oxidative stress in LRRK2 mutants, we generated stable Caenorhabditis elegans (C. elegans) strains in which human LRRK2 proteins including wild type LRRK2 (WT), G2019S LRRK2 (G2019S), and G2019S-D1994A kinase-dead LRRK2 (KD) were expressed in all neurons. Human 14-3-3 β was injected into LRRK2 transgenic worms to allow co-expression of 14-3-3 β and LRRK2 proteins. We found that G2019S transgenic worms had increased sensitivity to stress (heat and juglone treatment) and impaired stress-induced nuclear translocation of DAF-16. In addition, G2019S inhibited ftt2 (a 14-3-3 gene homolog in C. elegans) knockdown-associated nuclear translocation of DAF-16. Comparably, overexpression of human 14-3-3 β could attenuate G2019S-associated toxicity in response to stress and rescued G2019S-mediated inhibition of sod-3 and dod-3 expression. Taken together, our study provides evidence suggesting that 14-3-3-associated inhibition of DAF-16 nuclear translocation could be a mechanism for G2019S LRRK2-induced oxidative stress and cellular toxicity. Our findings may give a hint that the potential of 14-3-3 proteins as neuroprotective targets in PD patients carrying LRRK2 mutations.

Module-detection approaches for the integration of multilevel omics data highlight the comprehensive response of Aspergillus fumigatus to caspofungin

Background

Omics data provide deep insights into overall biological processes of organisms. However, integration of data from different molecular levels such as transcriptomics and proteomics, still remains challenging. Analyzing lists of differentially abundant molecules from diverse molecular levels often results in a small overlap mainly due to different regulatory mechanisms, temporal scales, and/or inherent properties of measurement methods. Module-detecting algorithms identifying sets of closely related proteins from protein-protein interaction networks (PPINs) are promising approaches for a better data integration.

Results

Here, we made use of transcriptome, proteome and secretome data from the human pathogenic fungus Aspergillus fumigatus challenged with the antifungal drug caspofungin. Caspofungin targets the fungal cell wall which leads to a compensatory stress response. We analyzed the omics data using two different approaches: First, we applied a simple, classical approach by comparing lists of differentially expressed genes (DEGs), differentially synthesized proteins (DSyPs) and differentially secreted proteins (DSePs); second, we used a recently published module-detecting approach, ModuleDiscoverer, to identify regulatory modules from PPINs in conjunction with the experimental data. Our results demonstrate that regulatory modules show a notably higher overlap between the different molecular levels and time points than the classical approach. The additional structural information provided by regulatory modules allows for topological analyses. As a result, we detected a significant association of omics data with distinct biological processes such as regulation of kinase activity, transport mechanisms or amino acid metabolism. We also found a previously unreported increased production of the secondary metabolite fumagillin by A. fumigatus upon exposure to caspofungin. Furthermore, a topology-based analysis of potential key factors contributing to drug-caused side effects identified the highly conserved protein polyubiquitin as a central regulator. Interestingly, polyubiquitin UbiD neither belonged to the groups of DEGs, DSyPs nor DSePs but most likely strongly influenced their levels.

Conclusion

Module-detecting approaches support the effective integration of multilevel omics data and provide a deep insight into complex biological relationships connecting these levels. They facilitate the identification of potential key players in the organism’s stress response which cannot be detected by commonly used approaches comparing lists of differentially abundant molecules.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0620-8) contains supplementary material, which is available to authorized users.

Identification of pathways significantly associated with spondyloarthropathy/ankylosing spondylitis using the sub‑pathway method

The aim of the present study was to extract potential sub-pathway biomarkers for spondyloarthropathy (SpA)/ankylosing spondylitis (AS) using a sub-pathway strategy. SpA/AS-relevant data, reference pathways and long non-coding (lnc)RNA-micro (mi)RNA-mRNA interactions were downloaded. The seed pathways based on Kyoto Encyclopedia of Genes and Genomes pathways and the mRNAs in the co-expressed lncRNA-mRNA interactions were extracted. Sub-pathways regulated by lncRNA were selected after establishing condition-specific lncRNA competitively regulated pathways (LCRP) network. Significant sub-pathways were further identified using the attract method. These significant sub-pathways were evaluated in the other independent published AS microarray data (E-GEOD-25101) using in silico validation. In addition, to uncover SpA/AS-relevant lncRNAs, the degree analysis for all nodes in the LCRP network was conducted. A total of 35 lncRNAs, 131 mRNAs and 145 co-expressed interactions were identified. When entering these 131 mRNAs into the reference pathways, 82 seed pathways were extracted, which were transformed into undirected graphs, and the 35 lncRNAs were mapped to the pathway graphs to further establish the condition-specific LCRP network. Based on degree analysis, four hub lncRNAs were selected, including C14orf169, LINC00242, LINC00116 and LINC00482. It was identified that 35 lncRNAs competitively regulating sub-pathways were involved in 56 complete pathways. Among these, the top three sub-pathways were path: 04010_1, which was a subregion of the mitogen-activated protein kinase (MAPK) signaling pathway; path: 04062-1, an important subregion in the chemokine signaling pathway; and path: 04066_2, was a part of HIF-1 signaling pathway. Furthermore, it was validated consistently in the separate microarray data set E-GEOD-25101. Cancer-associated pathways and hub node C14orf169 were identified in validation. Sub-pathways, including the MAPK signaling pathway and chemokine signaling pathway, and hub lncRNA (C14orf169) may serve important roles in SpA/AS.

14-3-3 Proteins Reduce Cell-to-Cell Transfer and Propagation of Pathogenic α-Synuclein

α-Synuclein (αsyn) is the key protein that forms neuronal aggregates in the neurodegenerative disorders Parkinson's disease (PD) and dementia with Lewy bodies. Recent evidence points to the prion-like spread of αsyn from one brain region to another. Propagation of αsyn is likely dependent on release, uptake, and misfolding. Under normal circumstances, this highly expressed brain protein functions normally without promoting pathology, yet the underlying endogenous mechanisms that prevent αsyn spread are not understood. 14-3-3 proteins are highly expressed brain proteins that have chaperone function and regulate protein trafficking. In this study, we investigated the potential role of the 14-3-3 proteins in the regulation of αsyn spread using two models of αsyn spread. In a paracrine αsyn model, 14-3-3θ promoted release of αsyn complexed with 14-3-3θ. Despite higher amounts of released αsyn, extracellular αsyn showed reduced oligomerization and seeding capability, reduced internalization, and reduced toxicity in primary mixed-gender mouse neurons. 14-3-3 inhibition reduced the amount of αsyn released, yet released αsyn was more toxic and demonstrated increased oligomerization, seeding capability, and internalization. In the preformed fibril model, 14-3-3 θ reduced αsyn aggregation and neuronal death, whereas 14-3-3 inhibition enhanced αsyn aggregation and neuronal death in primary mouse neurons. 14-3-3s blocked αsyn spread to distal chamber neurons not exposed directly to fibrils in multichamber, microfluidic devices. These findings point to 14-3-3s as a direct regulator of αsyn propagation, and suggest that dysfunction of 14-3-3 function may promote αsyn pathology in PD and related synucleinopathies.SIGNIFICANCE STATEMENT Transfer of misfolded aggregates of α-synuclein from one brain region to another is implicated in the pathogenesis of Parkinson's disease and other synucleinopathies. This process is dependent on active release, internalization, and misfolding of α-synuclein. 14-3-3 proteins are highly expressed chaperone proteins that interact with α-synuclein and regulate protein trafficking. We used two different models in which toxicity is associated with cell-to-cell transfer of α-synuclein to test whether 14-3-3s impact α-synuclein toxicity. We demonstrate that 14-3-3θ reduces α-synuclein transfer and toxicity by inhibiting oligomerization, seeding capability, and internalization of α-synuclein, whereas 14-3-3 inhibition accelerates the transfer and toxicity of α-synuclein in these models. Dysfunction of 14-3-3 function may be a critical mechanism by which α-synuclein propagation occurs in disease.

High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.

ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks

The identification of disease-associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. However, their identification is hampered by the detection of protein communities within large-scale, whole-genome PPINs. A presented successful strategy detects a PPIN's community structure based on the maximal clique enumeration problem (MCE), which is a non-deterministic polynomial time-hard problem. This renders the approach computationally challenging for large PPINs implying the need for new strategies. We present ModuleDiscoverer, a novel approach for the identification of regulatory modules from PPINs and gene expression data. Following the MCE-based approach, ModuleDiscoverer uses a randomization heuristic-based approximation of the community structure. Given a PPIN of Rattus norvegicus and public gene expression data, we identify the regulatory module underlying a rodent model of non-alcoholic steatohepatitis (NASH), a severe form of non-alcoholic fatty liver disease (NAFLD). The module is validated using single-nucleotide polymorphism (SNP) data from independent genome-wide association studies and gene enrichment tests. Based on gene enrichment tests, we find that ModuleDiscoverer performs comparably to three existing module-detecting algorithms. However, only our NASH-module is significantly enriched with genes linked to NAFLD-associated SNPs. ModuleDiscoverer is available at http://www.hki-jena.de/index.php/0/2/490 (Others/ModuleDiscoverer).

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.

Pathway Interaction Network Analysis Identifies Dysregulated Pathways in Human Monocytes Infected by Listeria monocytogenes

In our study, we aimed to extract dysregulated pathways in human monocytes infected by Listeria monocytogenes (LM) based on pathway interaction network (PIN) which presented the functional dependency between pathways. After genes were aligned to the pathways, principal component analysis (PCA) was used to calculate the pathway activity for each pathway, followed by detecting seed pathway. A PIN was constructed based on gene expression profile, protein-protein interactions (PPIs), and cellular pathways. Identifying dysregulated pathways from the PIN was performed relying on seed pathway and classification accuracy. To evaluate whether the PIN method was feasible or not, we compared the introduced method with standard network centrality measures. The pathway of RNA polymerase II pretranscription events was selected as the seed pathway. Taking this seed pathway as start, one pathway set (9 dysregulated pathways) with AUC score of 1.00 was identified. Among the 5 hub pathways obtained using standard network centrality measures, 4 pathways were the common ones between the two methods. RNA polymerase II transcription and DNA replication owned a higher number of pathway genes and DEGs. These dysregulated pathways work together to influence the progression of LM infection, and they will be available as biomarkers to diagnose LM infection.

Benchmarking selected computational gene network growing tools in context of virus-host interactions

Several available online tools provide network growing functions where an algorithm utilizing different data sources suggests additional genes/proteins that should connect an input gene set into functionally meaningful networks. Using the well-studied system of influenza host interactions, we compare the network growing function of two free tools GeneMANIA and STRING and the commercial IPA for their performance of recovering known influenza A virus host factors previously identified from siRNA screens. The result showed that given small (~30 genes) or medium (~150 genes) input sets all three network growing tools detect significantly more known host factors than random human genes with STRING overall performing strongest. Extending the networks with all the three tools significantly improved the detection of GO biological processes of known host factors compared to not growing networks. Interestingly, the rate of identification of true host factors using computational network growing is equal or better to doing another experimental siRNA screening study which could also be true and applied to other biological pathways/processes.

Pulmonary microRNA profiles identify involvement of Creb1 and Sec14l3 in bronchial epithelial changes in allergic asthma

Asthma is highly prevalent, but current therapies cannot influence the chronic course of the disease. It is thus important to understand underlying early molecular events. In this study, we aimed to use microRNAs (miRNAs) - which are critical regulators of signaling cascades - to identify so far uncharacterized asthma pathogenesis pathways. Therefore, deregulation of miRNAs was assessed in whole lungs from mice with ovalbumin (OVA)-induced allergic airway inflammation (AAI). In silico predicted target genes were confirmed in reporter assays and in house-dust-mite (HDM) induced AAI and primary human bronchial epithelial cells (NHBE) cultured at the air-liquid interface. We identified and validated the transcription factor cAMP-responsive element binding protein (Creb1) and its transcriptional co-activators (Crtc1-3) as targets of miR-17, miR-144, and miR-21. Sec14-like 3 (Sec14l3) - a putative target of Creb1 - was down-regulated in both asthma models and in NHBE cells upon IL13 treatment, while it’s expression correlated with ciliated cell development and decreased along with increasing goblet cell metaplasia. Finally, we propose that Creb1/Crtc1-3 and Sec14l3 could be important for early responses of the bronchial epithelium to Th2-stimuli. This study shows that miRNA profiles can be used to identify novel targets that would be overlooked in mRNA based strategies.

Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform

Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers' exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.

Age-Related Changes of 14-3-3 Isoforms in Midbrain of A53T-SNCA Overexpressing Mice

BACKGROUND

Parkinson's disease (PD) is characterized by loss of midbrain dopaminergic neurons, which are affected by cytoplasmic inclusions, named Lewy pathology. The main component is alpha-synuclein (SNCA), a protein modulating SNARE-complex dependent neurotransmission. SNCA mutations trigger dominantly inherited PD variants and sporadic cases of PD via aggregation and transmission. SNCA and isoforms of the 14-3-3 family show sequence homology, protein interaction and joint aggregation, so 14-3-3 s may be key molecules of pathogenesis.

OBJECTIVE

We aimed to identify the relevant isoforms in midbrain and to distinguish for the first time the changes that occur very early versus those that progress with pathology.

METHODS

We assessed expression of the 14-3-3 family with quantitative RT-PCR and immunoblots of differential solubility fractions in mice with A53T-SNCA overexpression longitudinally at different ages.

RESULTS

Transcript levels showed reductions at age 3 months with increases at later ages for the beta, eta and zeta isoforms. Protein levels at age 3 months exhibited a concordant reduction only for beta, while increased insolubility was observed for epsilon and zeta. At age 18 months only the reduction of 14-3-3 beta protein remained significant. Thus, the toxic gain-of-function of alpha-synuclein leads to early transitory alterations of several 14-3-3 isoforms. When the levels of soluble 14-3-3 proteins become apparently normal during later life, increasing amounts of beta, eta and zeta mRNA are produced, possibly to compensate for protein insolubility and aggregation in a SNCA/14-3-3 complex.

CONCLUSIONS

These data may contribute to identify key molecular events that reflect Parkinson's disease risk and progression.

A novel algorithm for network-based prediction of cancer recurrence

To develop accurate prognostic models is one of the biggest challenges in "omics"-based cancer research. Here, we propose a novel computational method for identifying dysregulated gene subnetworks as biomarkers to predict cancer recurrence. Applying our method to the DNA methylome of endometrial cancer patients, we identified a subnetwork consisting of differentially methylated (DM) genes, and non-differentially methylated genes, termed Epigenetic Connectors (EC), that are topologically important for connecting the DM genes in a protein-protein interaction network. The ECs are statistically significantly enriched in well-known tumorgenesis and metastasis pathways, and include known epigenetic regulators. Importantly, combining the DMs and ECs as features using a novel random walk procedure, we constructed a support vector machine classifier that significantly improved the prediction accuracy of cancer recurrence and outperformed several alternative methods, demonstrating the effectiveness of our network-based approach.

KeyPathwayMinerWeb: online multi-omics network enrichment

We present KeyPathwayMinerWeb, the first online platform for de novo pathway enrichment analysis directly in the browser. Given a biological interaction network (e.g. protein–protein interactions) and a series of molecular profiles derived from one or multiple OMICS studies (gene expression, for instance), KeyPathwayMiner extracts connected sub-networks containing a high number of active or differentially regulated genes (proteins, metabolites) in the molecular profiles. The web interface at (http://keypathwayminer.compbio.sdu.dk) implements all core functionalities of the KeyPathwayMiner tool set such as data integration, input of background knowledge, batch runs for parameter optimization and visualization of extracted pathways. In addition to an intuitive web interface, we also implemented a RESTful API that now enables other online developers to integrate network enrichment as a web service into their own platforms.

Understanding Genotype-Phenotype Effects in Cancer via Network Approaches

Cancer is now increasingly studied from the perspective of dysregulated pathways, rather than as a disease resulting from mutations of individual genes. A pathway-centric view acknowledges the heterogeneity between genomic profiles from different cancer patients while assuming that the mutated genes are likely to belong to the same pathway and cause similar disease phenotypes. Indeed, network-centric approaches have proven to be helpful for finding genotypic causes of diseases, classifying disease subtypes, and identifying drug targets. In this review, we discuss how networks can be used to help understand patient-to-patient variations and how one can leverage this variability to elucidate interactions between cancer drivers.

Gene Expression Analysis Through Network Biology: Bioinformatics Approaches

Following the availability of high-throughput technologies, vast amounts of biological data have been generated. Gene expression is one example of the popular data that has been utilized for studying cellular systems in the transcriptional level. Several bioinformatics approaches have been developed to analyze such data. A typical expression analysis identifies a ranked list of individual significant differentially expressed genes between two conditions of interest. However, it has been accepted that biomolecules in a living organism are working together and interacting with each other. Study through network analysis could be complementary to typical expression analysis and provides more contexts to understanding the biological systems. Conversely, expression data could provide clues to functional links between biomolecules in biological networks. In this chapter, bioinformatics approaches to analyze expression data in network levels including basic concepts of network biology are described. Different concepts to integrate expression data with interactome data and example studies are explained.

Alzheimer's as a Systems-Level Disease Involving the Interplay of Multiple Cellular Networks

Alzheimer's disease (AD), and many neurodegenerative disorders, are multifactorial in nature. They involve a combination of genomic, epigenomic, interactomic and environmental factors. Progress is being made, and these complex diseases are beginning to be understood as having their origin in altered states of biological networks at the cellular level. In the case of AD, genomic susceptibility and mechanisms leading to (or accompanying) the impairment of the central Amyloid Precursor Protein (APP) processing and tau networks are widely accepted as major contributors to the diseased state. The derangement of these networks may result in both the gain and loss of functions, increased generation of toxic species (e.g., toxic soluble oligomers and aggregates) and imbalances, whose effects can propagate to supra-cellular levels. Although well sustained by empirical data and widely accepted, this global perspective often overlooks the essential roles played by the main counteracting homeostatic networks (e.g., protein quality control/proteostasis, unfolded protein response, protein folding chaperone networks, disaggregases, ER-associated degradation/ubiquitin proteasome system, endolysosomal network, autophagy, and other stress-protective and clearance networks), whose relevance to AD is just beginning to be fully realized. In this chapter, an integrative perspective is presented. Alzheimer's disease is characterized to be a result of: (a) intrinsic genomic/epigenomic susceptibility and, (b) a continued dynamic interplay between the deranged networks and the central homeostatic networks of nerve cells. This interplay of networks will underlie both the onset and rate of progression of the disease in each individual. Integrative Systems Biology approaches are required to effect its elucidation. Comprehensive Systems Biology experiments at different 'omics levels in simple model organisms, engineered to recapitulate the basic features of AD may illuminate the onset and sequence of events underlying AD. Indeed, studies of models of AD in simple organisms, differentiated cells in culture and rodents are beginning to offer hope that the onset and progression of AD, if detected at an early stage, may be stopped, delayed, or even reversed, by activating or modulating networks involved in proteostasis and the clearance of toxic species. In practice, the incorporation of next-generation neuroimaging, high-throughput and computational approaches are opening the way towards early diagnosis well before irreversible cell death. Thus, the presence or co-occurrence of: (a) accumulation of toxic Aβ oligomers and tau species; (b) altered splicing and transcriptome patterns; (c) impaired redox, proteostatic, and metabolic networks together with, (d) compromised homeostatic capacities may constitute relevant 'AD hallmarks at the cellular level' towards reliable and early diagnosis. From here, preventive lifestyle changes and tailored therapies may be investigated, such as combined strategies aimed at both lowering the production of toxic species and potentiating homeostatic responses, in order to prevent or delay the onset, and arrest, alleviate, or even reverse the progression of the disease.

14-3-3 Proteins regulate mutant LRRK2 kinase activity and neurite shortening

Mutations in leucine-rich repeat kinase 2 (LRRK2) are the most common known cause of inherited Parkinson's disease (PD), and LRRK2 is a risk factor for idiopathic PD. How LRRK2 function is regulated is not well understood. Recently, the highly conserved 14-3-3 proteins, which play a key role in many cellular functions including cell death, have been shown to interact with LRRK2. In this study, we investigated whether 14-3-3s can regulate mutant LRRK2-induced neurite shortening and kinase activity. In the presence of 14-3-3θ overexpression, neurite length of primary neurons from BAC transgenic G2019S-LRRK2 mice returned back to wild-type levels. Similarly, 14-3-3θ overexpression reversed neurite shortening in neuronal cultures from BAC transgenic R1441G-LRRK2 mice. Conversely, inhibition of 14-3-3s by the pan-14-3-3 inhibitor difopein or dominant-negative 14-3-3θ further reduced neurite length in G2019S-LRRK2 cultures. Since G2019S-LRRK2 toxicity is likely mediated through increased kinase activity, we examined 14-3-3θ's effects on LRRK2 kinase activity. 14-3-3θ overexpression reduced the kinase activity of G2019S-LRRK2, while difopein promoted the kinase activity of G2019S-LRRK2. The ability of 14-3-3θ to reduce LRRK2 kinase activity required direct binding of 14-3-3θ with LRRK2. The potentiation of neurite shortening by difopein in G2019S-LRRK2 neurons was reversed by LRRK2 kinase inhibitors. Taken together, we conclude that 14-3-3θ can regulate LRRK2 and reduce the toxicity of mutant LRRK2 through a reduction of kinase activity.

Network-Informed Gene Ranking Tackles Genetic Heterogeneity in Exome-Sequencing Studies of Monogenic Disease

Genetic heterogeneity presents a significant challenge for the identification of monogenic disease genes. Whole-exome sequencing generates a large number of candidate disease-causing variants and typical analyses rely on deleterious variants being observed in the same gene across several unrelated affected individuals. This is less likely to occur for genetically heterogeneous diseases, making more advanced analysis methods necessary. To address this need, we present HetRank, a flexible gene-ranking method that incorporates interaction network data. We first show that different genes underlying the same monogenic disease are frequently connected in protein interaction networks. This motivates the central premise of HetRank: those genes carrying potentially pathogenic variants and whose network neighbors do so in other affected individuals are strong candidates for follow-up study. By simulating 1,000 exome sequencing studies (20,000 exomes in total), we model varying degrees of genetic heterogeneity and show that HetRank consistently prioritizes more disease-causing genes than existing analysis methods. We also demonstrate a proof-of-principle application of the method to prioritize genes causing Adams-Oliver syndrome, a genetically heterogeneous rare disease. An implementation of HetRank in R is available via the Website http://sourceforge.net/p/hetrank/.

Integrated analysis of numerous heterogeneous gene expression profiles for detecting robust disease-specific biomarkers and proposing drug targets

Genome-wide expression profiling has revolutionized biomedical research; vast amounts of expression data from numerous studies of many diseases are now available. Making the best use of this resource in order to better understand disease processes and treatment remains an open challenge. In particular, disease biomarkers detected in case-control studies suffer from low reliability and are only weakly reproducible. Here, we present a systematic integrative analysis methodology to overcome these shortcomings. We assembled and manually curated more than 14,000 expression profiles spanning 48 diseases and 18 expression platforms. We show that when studying a particular disease, judicious utilization of profiles from other diseases and information on disease hierarchy improves classification quality, avoids overoptimistic evaluation of that quality, and enhances disease-specific biomarker discovery. This approach yielded specific biomarkers for 24 of the analyzed diseases. We demonstrate how to combine these biomarkers with large-scale interaction, mutation and drug target data, forming a highly valuable disease summary that suggests novel directions in disease understanding and drug repurposing. Our analysis also estimates the number of samples required to reach a desired level of biomarker stability. This methodology can greatly improve the exploitation of the mountain of expression profiles for better disease analysis.

Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets

An important goal of cancer genomic research is to identify the driving pathways underlying disease mechanisms and the heterogeneity of cancers. It is well known that somatic genome alterations (SGAs) affecting the genes that encode the proteins within a common signaling pathway exhibit mutual exclusivity, in which these SGAs usually do not co-occur in a tumor. With some success, this characteristic has been utilized as an objective function to guide the search for driver mutations within a pathway. However, mutual exclusivity alone is not sufficient to indicate that genes affected by such SGAs are in common pathways. Here, we propose a novel, signal-oriented framework for identifying driver SGAs. First, we identify the perturbed cellular signals by mining the gene expression data. Next, we search for a set of SGA events that carries strong information with respect to such perturbed signals while exhibiting mutual exclusivity. Finally, we design and implement an efficient exact algorithm to solve an NP-hard problem encountered in our approach. We apply this framework to the ovarian and glioblastoma tumor data available at the TCGA database, and perform systematic evaluations. Our results indicate that the signal-oriented approach enhances the ability to find informative sets of driver SGAs that likely constitute signaling pathways.

An important goal of studying cancer genomics is to identify critical pathways that, when perturbed by somatic genomic alterations (SGAs) such as somatic mutations, copy number alterations and epigenomic alterations, cause cancers and underlie different clinical phenotypes. In this study, we present a framework for discovering perturbed signaling pathways in cancers by integrating genome alteration data and transcriptomic data from the Cancer Genome Atlas (TCGA) project. Since gene expression in a cell is regulated by cellular signaling systems, we used transcriptomic changes to reveal perturbed cellular signals in each tumor. We then combined the genomic alteration data to search for SGA events across multiple tumors that affected a common signal, thus identifying the candidate members of cancer pathways. Our results demonstrate the advantage of the signal-oriented pathway approach over previous methods.

14-3-3 inhibition promotes dopaminergic neuron loss and 14-3-3θ overexpression promotes recovery in the MPTP mouse model of Parkinson's disease

14-3-3s are a highly conserved protein family that plays important roles in cell survival and interact with several proteins implicated in Parkinson's disease (PD). Disruption of 14-3-3 expression and function has been implicated in the pathogenesis of PD. We have previously shown that increasing the expression level of 14-3-3θ is protective against rotenone and 1-methyl-4-phenylpyridinium (MPP(+)) in cultured cells. Here, we extend our studies to examine the effects of 14-3-3s in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model of PD. We first investigated whether targeted nigral 14-3-3θ overexpression mediated by adeno-associated virus offers neuroprotection against MPTP-induced toxicity. 14-3-3θ overexpression using this approach did not reduce MPTP-induced dopaminergic cell loss in the substantia nigra nor the depletion of dopamine (DA) and its metabolites in the striatum at three weeks after MPTP administration. However, 14-3-3θ-overexpressing mice showed a later partial recovery in striatal DA metabolites at eight weeks after MPTP administration compared to controls, suggesting that 14-3-3θ overexpression may help in the functional recovery of those dopaminergic neurons that survive. Conversely, we investigated whether disrupting 14-3-3 function in transgenic mice expressing the pan 14-3-3 inhibitor difopein exacerbates MPTP-induced toxicity. We found that difopein expression promoted dopaminergic cell loss in response to MPTP treatment. Together, these findings suggest that 14-3-3θ overexpression promotes recovery of DA metabolites whereas 14-3-3 inhibition exacerbates neuron loss in the MPTP mouse model of PD.

An argument for mechanism-based statistical inference in cancer

Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning biomarkers, metabolism, cell signaling, network inference and tumorigenesis.

Increased 14-3-3 phosphorylation observed in Parkinson's disease reduces neuroprotective potential of 14-3-3 proteins

14-3-3 proteins are key regulators of cell survival. We have previously demonstrated that 14-3-3 levels are decreased in an alpha-synuclein (αsyn) mouse model of Parkinson's disease (PD), and that overexpression of certain 14-3-3 isoforms is protective in several PD models. Here we examine whether changes in 14-3-3 phosphorylation may contribute to the neurodegenerative process in PD. We examine three key 14-3-3 phosphorylation sites that normally regulate 14-3-3 function, including serine 58 (S58), serine 184 (S184), and serine/threonine 232 (S/T232), in several models of PD and in human PD brain. We observed that an increase in S232 phosphorylation is observed in rotenone-treated neuroblastoma cells, in cells overexpressing αsyn, and in human PD brains. Alterations in S58 phosphorylation were less consistent in these models, and we did not observe any phosphorylation changes at S184. Phosphorylation at S232 induced by rotenone is reduced by casein kinase inhibitors, and is not dependent on αsyn. Mutation of the S232 site affected 14-3-3θ's neuroprotective effects against rotenone and 1-methyl-4-phenylpyridinium (MPP(+)), with the S232D mutant lacking any protective effect compared to wildtype or S232A 14-3-3θ. The S232D mutant partially reduced the ability of 14-3-3θ to inhibit Bax activation in response to rotenone. Based on these findings, we propose that phosphorylation of 14-3-3s at serine 232 contributes to the neurodegenerative process in PD.

Graphical algorithm for integration of genetic and biological data: proof of principle using psoriasis as a model

MOTIVATION

Pathway analysis to reveal biological mechanisms for results from genetic association studies have great potential to better understand complex traits with major human disease impact. However, current approaches have not been optimized to maximize statistical power to identify enriched functions/pathways, especially when the genetic data derives from studies using platforms (e.g. Immunochip and Metabochip) customized to have pre-selected markers from previously identified top-rank loci. We present here a novel approach, called Minimum distance-based Enrichment Analysis for Genetic Association (MEAGA), with the potential to address both of these important concerns.

RESULTS

MEAGA performs enrichment analysis using graphical algorithms to identify sub-graphs among genes and measure their closeness in interaction database. It also incorporates a statistic summarizing the numbers and total distances of the sub-graphs, depicting the overlap between observed genetic signals and defined function/pathway gene-sets. MEAGA uses sampling technique to approximate empirical and multiple testing-corrected P-values. We show in simulation studies that MEAGA is more powerful compared to count-based strategies in identifying disease-associated functions/pathways, and the increase in power is influenced by the shortest distances among associated genes in the interactome. We applied MEAGA to the results of a meta-analysis of psoriasis using Immunochip datasets, and showed that associated genes are significantly enriched in immune-related functions and closer with each other in the protein-protein interaction network.

AVAILABILITY AND IMPLEMENTATION

http://genome.sph.umich.edu/wiki/MEAGA CONTACT: : tsoi.teen@gmail.com or goncalo@umich.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Assessment of subnetwork detection methods for breast cancer

Subnetwork detection is often used with differential expression analysis to identify modules or pathways associated with a disease or condition. Many computational methods are available for subnetwork analysis. Here, we compare the results of eight methods: simulated annealing–based jActiveModules, greedy search–based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker. These methods represent distinctly different computational strategies and are among the most widely used. Each of these methods was used to analyze gene expression data consisting of paired tumor and normal samples from 50 breast cancer patients. While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods. Within the core set, 12 genes were found to be known breast cancer genes.

Systems level analysis and identification of pathways and networks associated with liver fibrosis

Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.

The discovery of integrated gene networks for autism and related disorders

Despite considerable genetic heterogeneity underlying neurodevelopmental diseases, there is compelling evidence that many disease genes will map to a much smaller number of biological subnetworks. We developed a computational method, termed MAGI (merging affected genes into integrated networks), that simultaneously integrates protein–protein interactions and RNA-seq expression profiles during brain development to discover “modules” enriched for de novo mutations in probands. We applied this method to recent exome sequencing of 1116 patients with autism and intellectual disability, discovering two distinct modules that differ in their properties and associated phenotypes. The first module consists of 80 genes associated with Wnt, Notch, SWI/SNF, and NCOR complexes and shows the highest expression early during embryonic development (8–16 post-conception weeks [pcw]). The second module consists of 24 genes associated with synaptic function, including long-term potentiation and calcium signaling with higher levels of postnatal expression. Patients with de novo mutations in these modules are more significantly intellectually impaired and carry more severe missense mutations when compared to probands with de novo mutations outside of these modules. We used our approach to define subsets of the network associated with higher functioning autism as well as greater severity with respect to IQ. Finally, we applied MAGI independently to epilepsy and schizophrenia exome sequencing cohorts and found significant overlap as well as expansion of these modules, suggesting a core set of integrated neurodevelopmental networks common to seemingly diverse human diseases.

Edge biomarkers for classification and prediction of phenotypes

In general, a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network, which can be considered as a set of interactions or edges among molecules. Thus, instead of individual molecules, networks or edges are stable forms to reliably characterize complex diseases. This paper reviews both traditional node biomarkers and edge biomarkers, which have been newly proposed. These biomarkers are classified in terms of their contained information. In particular, we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample. First, we categorize the biomarkers based on the information used in the learning and prediction steps. We then briefly introduce conventional node biomarkers, or molecular biomarkers without network information, and their computational approaches. The main focus of this paper is edge and network biomarkers, which exploit network information to improve the accuracy of diagnosis and prognosis. Moreover, by extracting both network and dynamic information from the data, we can develop dynamical network and edge biomarkers. These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state. The identified critical molecules can be used as drug targets, and the critical state indicates the critical point of disease control. The paper also discusses representative biomarker-based methods.

Structural bioinformatics of the interactome

The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.

KeyPathwayMiner 4.0: condition-specific pathway analysis by combining multiple omics studies and networks with Cytoscape

Background

Over the last decade network enrichment analysis has become popular in computational systems biology to elucidate aberrant network modules. Traditionally, these approaches focus on combining gene expression data with protein-protein interaction (PPI) networks. Nowadays, the so-called omics technologies allow for inclusion of many more data sets, e.g. protein phosphorylation or epigenetic modifications. This creates a need for analysis methods that can combine these various sources of data to obtain a systems-level view on aberrant biological networks.

Results

We present a new release of KeyPathwayMiner (version 4.0) that is not limited to analyses of single omics data sets, e.g. gene expression, but is able to directly combine several different omics data types. Version 4.0 can further integrate existing knowledge by adding a search bias towards sub-networks that contain (avoid) genes provided in a positive (negative) list. Finally the new release now also provides a set of novel visualization features and has been implemented as an app for the standard bioinformatics network analysis tool: Cytoscape.

Conclusion

With KeyPathwayMiner 4.0, we publish a Cytoscape app for multi-omics based sub-network extraction. It is available in Cytoscape’s app store http://apps.cytoscape.org/apps/keypathwayminer or via http://keypathwayminer.mpi-inf.mpg.de.

Network-based analysis reveals distinct association patterns in a semantic MEDLINE-based drug-disease-gene network

BACKGROUND

A huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among different biological entities in the context of personalized medicine and translational research. Recently, network-based computational approaches have gained popularity in investigating such heterogeneous data, proposing novel therapeutic targets and deciphering disease mechanisms. However, little effort has been devoted to investigating associations among drugs, diseases, and genes in an integrative manner.

RESULTS

We propose a novel network-based computational framework to identify statistically over-expressed subnetwork patterns, called network motifs, in an integrated disease-drug-gene network extracted from Semantic MEDLINE. The framework consists of two steps. The first step is to construct an association network by extracting pair-wise associations between diseases, drugs and genes in Semantic MEDLINE using a domain pattern driven strategy. A Resource Description Framework (RDF)-linked data approach is used to re-organize the data to increase the flexibility of data integration, the interoperability within domain ontologies, and the efficiency of data storage. Unique associations among drugs, diseases, and genes are extracted for downstream network-based analysis. The second step is to apply a network-based approach to mine the local network structure of this heterogeneous network. Significant network motifs are then identified as the backbone of the network. A simplified network based on those significant motifs is then constructed to facilitate discovery. We implemented our computational framework and identified five network motifs, each of which corresponds to specific biological meanings. Three case studies demonstrate that novel associations are derived from the network topology analysis of reconstructed networks of significant network motifs, further validated by expert knowledge and functional enrichment analyses.

CONCLUSIONS

We have developed a novel network-based computational approach to investigate the heterogeneous drug-gene-disease network extracted from Semantic MEDLINE. We demonstrate the power of this approach by prioritizing candidate disease genes, inferring potential disease relationships, and proposing novel drug targets, within the context of the entire knowledge. The results indicate that such approach will facilitate the formulization of novel research hypotheses, which is critical for translational medicine research and personalized medicine.

An integrative network algorithm identifies age-associated differential methylation interactome hotspots targeting stem-cell differentiation pathways

Epigenetic changes have been associated with ageing and cancer. Identifying and interpreting epigenetic changes associated with such phenotypes may benefit from integration with protein interactome models. We here develop and validate a novel integrative epigenome-interactome approach to identify differential methylation interactome hotspots associated with a phenotype of interest. We apply the algorithm to cancer and ageing, demonstrating the existence of hotspots associated with these phenotypes. Importantly, we discover tissue independent age-associated hotspots targeting stem-cell differentiation pathways, which we validate in independent DNA methylation data sets, encompassing over 1000 samples from different tissue types. We further show that these pathways would not have been discovered had we used a non-network based approach and that the use of the protein interaction network improves the overall robustness of the inference procedure. The proposed algorithm will be useful to any study seeking to identify interactome hotspots associated with common phenotypes.

Functional module search in protein networks based on semantic similarity improves the analysis of proteomics data

The continuously evolving field of proteomics produces increasing amounts of data while improving the quality of protein identifications. Albeit quantitative measurements are becoming more popular, many proteomic studies are still based on non-quantitative methods for protein identification. These studies result in potentially large sets of identified proteins, where the biological interpretation of proteins can be challenging. Systems biology develops innovative network-based methods, which allow an integrated analysis of these data. Here we present a novel approach, which combines prior knowledge of protein-protein interactions (PPI) with proteomics data using functional similarity measurements of interacting proteins. This integrated network analysis exactly identifies network modules with a maximal consistent functional similarity reflecting biological processes of the investigated cells. We validated our approach on small (H9N2 virus-infected gastric cells) and large (blood constituents) proteomic data sets. Using this novel algorithm, we identified characteristic functional modules in virus-infected cells, comprising key signaling proteins (e.g. the stress-related kinase RAF1) and demonstrate that this method allows a module-based functional characterization of cell types. Analysis of a large proteome data set of blood constituents resulted in clear separation of blood cells according to their developmental origin. A detailed investigation of the T-cell proteome further illustrates how the algorithm partitions large networks into functional subnetworks each representing specific cellular functions. These results demonstrate that the integrated network approach not only allows a detailed analysis of proteome networks but also yields a functional decomposition of complex proteomic data sets and thereby provides deeper insights into the underlying cellular processes of the investigated system.

Outlier Analysis and Top Scoring Pair for Integrated Data Analysis and Biomarker Discovery

Pathway deregulation has been identified as a key driver of carcinogenesis, with proteins in signaling pathways serving as primary targets for drug development. Deregulation can be driven by a number of molecular events, including gene mutation, epigenetic changes in gene promoters, overexpression, and gene amplifications or deletions. We demonstrate a novel approach that identifies pathways of interest by integrating outlier analysis within and across molecular data types with gene set analysis. We use the results to seed the top-scoring pair algorithm to identify robust biomarkers associated with pathway deregulation. We demonstrate this methodology on pediatric acute myeloid leukemia (AML) data. We develop a biomarker in primary AML tumors, demonstrate robustness with an independent primary tumor data set, and show that the identified biomarkers also function well in relapsed pediatric AML tumors.