Network-based Pathway Analysis Research Articles

Dysregulation of microRNAs and tRNA-derived ncRNAs in mesothelial and mesothelioma cell lines after asbestiform fiber exposure

Experimental evidence demonstrated that fluoro-edenite (FE) can develop chronic respiratory diseases and elicit carcinogenic effects. Environmental exposure to FE fibers is correlated with malignant pleural mesothelioma (MPM). An early diagnosis of MPM, and a comprehensive health monitoring of the patients exposed to FE fibers are two clinical issues that may be solved by the identification of specific biomarkers. We reported the microRNA (miRNA) and transfer RNA-derived non coding RNA (tRNA-derived ncRNA) transcriptome in human normal mesothelial and malignant mesothelioma cell lines exposed or not exposed to several concentration FE fibers. Furthermore, an interactive mesothelioma-based network was derived by using NetME tool. In untreated condition, the expression of miRNAs and tRNA-derived ncRNAs in tumor cells was significantly different with respect to non-tumor samples. Moreover, interesting and significant changes were found after the exposure of both cells lines to FE fibers. The network-based pathway analysis showed several signaling and metabolic pathways potentially involved in the pathogenesis of MPM. From papers analyzed by NetME, it is clear that many miRNAs can positively or negatively influence various pathways involved in MPM. For the first time, the analysis of tRNA-derived ncRNAs molecules in the context of mesothelioma has been made by using in vitro systems. Further studies will be designed to test and validate their diagnostic potential in high-risk individuals' liquid biopsies.

Benefits and Challenges of Pre-clustered Network-Based Pathway Analysis.

Functional analysis of gene sets derived from experiments is typically done by pathway annotation. Although many algorithms exist for analyzing the association between a gene set and a pathway, an issue which is generally ignored is that gene sets often represent multiple pathways. In such cases an association to a pathway is weakened by the presence of genes associated with other pathways. A way to counteract this is to cluster the gene set into more homogenous parts before performing pathway analysis on each module. We explored whether network-based pre-clustering of a query gene set can improve pathway analysis. The methods MCL, Infomap, and MGclus were used to cluster the gene set projected onto the FunCoup network. We characterized how well these methods are able to detect individual pathways in multi-pathway gene sets, and applied each of the clustering methods in combination with four pathway analysis methods: Gene Enrichment Analysis, BinoX, NEAT, and ANUBIX. Using benchmarks constructed from the KEGG pathway database we found that clustering can be beneficial by increasing the sensitivity of pathway analysis methods and by providing deeper insights of biological mechanisms related to the phenotype under study. However, keeping a high specificity is a challenge. For ANUBIX, clustering caused a minor loss of specificity, while for BinoX and NEAT it caused an unacceptable loss of specificity. GEA had very low sensitivity both before and after clustering. The choice of clustering method only had a minor effect on the results. We show examples of this approach and conclude that clustering can improve overall pathway annotation performance, but should only be used if the used enrichment method has a low false positive rate.

PathwAX II: network-based pathway analysis with interactive visualization of network crosstalk.

MotivationPathway annotation tools are indispensable for the interpretation of a wide range of experiments in life sciences. Network-based algorithms have recently been developed which are more sensitive than traditional overlap-based algorithms, but there is still a lack of good online tools for network-based pathway analysis.ResultsWe present PathwAX II—a pathway analysis web tool based on network crosstalk analysis using the BinoX algorithm. It offers several new features compared with the first version, including interactive graphical network visualization of the crosstalk between a query gene set and an enriched pathway, and the addition of Reactome pathways.Availability and implementationPathwAX II is available at http://pathwax.sbc.su.se.Supplementary information Supplementary data are available at Bioinformatics online.

Genomic and Biochemical Proteasomal Adaptations in Carfilzomib-Resistant Plasma Cells That Limit HLA-Desensitization in Transplantation Candidates

Bone marrow plasma cells (BMPCs) are the source of long-term humoral immunity including HLA antibodies, which promote organ rejection. End-stage renal disease (ESRD) affects more than 660,000 patients per year in the U.S. and is associated with significant morbidity and mortality. Renal transplantation remains the optimal treatment for a substantial proportion of ESRD patients and dramatically improves patient overall survival and quality-of-life. However, preformed human leukocyte antigen (HLA)-specific antibodies are associated with reduced transplantation rates, graft loss and poorer outcomes. Proteasome inhibitor (PI) therapies that target BMPCs to reduce HLA-antibody levels represent an effective means of enhancing transplantation outcomes but are limited by resistance mechanisms. Single antigen bead assays determined that the spectrum of antibody production by HLA-sensitized patient (BMPCs) was remarkably similar to that detected in matching serum samples, supporting the role of BMPCs as the primary source of circulating HLA antibodies. Desensitization therapy with the irreversible proteasome inhibitor carfilzomib reduced PCs in patient BM by ~70% and was associated with a profound reduction in HLA antibodies. We then used bulk RNA-seq transcriptomics to explore potential drug resistance mechanisms within carfilzomib-resistant BMPCs. Network-based pathway analysis revealed that genes encoding the immunoproteasome, a specialized proteasome variant with unique catalytic subunits, and the proteasome activator PA28 were upregulated in BMPCs that survived carfilzomib therapy. Native gel electrophoresis and in vitro functional studies confirmed that BMPCs isolated after carfilzomib therapy contained greater levels of immunoproteasomes as well as increased immunoproteasome-specific peptide-hydrolyzing activity relative to BMPCs from healthy donors or untreated HLA-sensitized patients. Supervised hierarchical clustering analysis (Fig. 1A) and western blotting (Fig. 1B) indicated that PSMB8, which encodes an immunoproteasome catalytic subunit, and PSME1 and PSME2 which encode the proteasome PA28 activator, were significantly upregulated in BMPCs isolated from patients following carfilzomib therapy. Assays then demonstrated that BMPCs that survived carfilzomib therapy were resistant to the FDA-approved PIs bortezomib, carfilzomib and ixazomib but were sensitive to an immunoproteasome-specific inhibitor KZR-616. Thus, we have uncovered proteasomal adaptations in BMPCs as a resistance mechanism underlying carfilzomib therapy and suggest novel strategies that could improve desensitization regimens and reduce antibody-mediated rejection in organ transplantation. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

PathBIX-a web server for network-based pathway annotation with adaptive null models.

Pathway annotation is a vital tool for interpreting and giving meaning to experimental data in life sciences. Numerous tools exist for this task, where the most recent generation of pathway enrichment analysis tools, network-based methods, utilize biological networks to gain a richer source of information as a basis of the analysis than merely the gene content. Network-based methods use the network crosstalk between the query gene set and the genes in known pathways, and compare this to a null model of random expectation. We developed PathBIX, a novel web application for network-based pathway analysis, based on the recently published ANUBIX algorithm which has been shown to be more accurate than previous network-based methods. The PathBIX website performs pathway annotation for 21 species, and utilizes prefetched and preprocessed network data from FunCoup 5.0 networks and pathway data from three databases: KEGG, Reactome, and WikiPathways. https://pathbix.sbc.su.se/. erik.sonnhammer@scilifelab.se. Supplementary data are available at Bioinformatics Advances online.

Combined use of protein biomarkers and network analysis unveils deregulated regulatory circuits in Duchenne muscular dystrophy.

Although the genetic basis of Duchenne muscular dystrophy has been known for almost thirty years, the cellular and molecular mechanisms characterizing the disease are not completely understood and an efficacious treatment remains to be developed. In this study we analyzed proteomics data obtained with the SomaLogic technology from blood serum of a cohort of patients and matched healthy subjects. We developed a workflow based on biomarker identification and network-based pathway analysis that allowed us to describe different deregulated pathways. In addition to muscle-related functions, we identified other biological processes such as apoptosis, signaling in the immune system and neurotrophin signaling as significantly modulated in patients compared with controls. Moreover, our network-based analysis identified the involvement of FoxO transcription factors as putative regulators of different pathways. On the whole, this study provided a global view of the molecular processes involved in Duchenne muscular dystrophy that are decipherable from serum proteome.

Genome-wide network-based pathway analysis of CSF t-tau/A\u03b21-42 ratio in the ADNI cohort

BackgroundThe cerebrospinal fluid (CSF) levels of total tau (t-tau) and Aβ1–42 are potential early diagnostic markers for probable Alzheimer’s disease (AD). The influence of genetic variation on these CSF biomarkers has been investigated in candidate or genome-wide association studies (GWAS). However, the investigation of statistically modest associations in GWAS in the context of biological networks is still an under-explored topic in AD studies. The main objective of this study is to gain further biological insights via the integration of statistical gene associations in AD with physical protein interaction networks.ResultsThe CSF and genotyping data of 843 study subjects (199 CN, 85 SMC, 239 EMCI, 207 LMCI, 113 AD) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were analyzed. PLINK was used to perform GWAS on the t-tau/Aβ1–42 ratio using quality controlled genotype data, including 563,980 single nucleotide polymorphisms (SNPs), with age, sex and diagnosis as covariates. Gene-level p-values were obtained by VEGAS2. Genes with p-value ≤ 0.05 were mapped on to a protein-protein interaction (PPI) network (9,617 nodes, 39,240 edges, from the HPRD Database). We integrated a consensus model strategy into the iPINBPA network analysis framework, and named it as CM-iPINBPA. Four consensus modules (CMs) were discovered by CM-iPINBPA, and were functionally annotated using the pathway analysis tool Enrichr. The intersection of four CMs forms a common subnetwork of 29 genes, including those related to tau phosphorylation (GSK3B, SUMO1, AKAP5, CALM1 and DLG4), amyloid beta production (CASP8, PIK3R1, PPA1, PARP1, CSNK2A1, NGFR, and RHOA), and AD (BCL3, CFLAR, SMAD1, and HIF1A).ConclusionsThis study coupled a consensus module (CM) strategy with the iPINBPA network analysis framework, and applied it to the GWAS of CSF t-tau/Aβ1-42 ratio in an AD study. The genome-wide network analysis yielded 4 enriched CMs that share not only genes related to tau phosphorylation or amyloid beta production but also multiple genes enriching several KEGG pathways such as Alzheimer’s disease, colorectal cancer, gliomas, renal cell carcinoma, Huntington’s disease, and others. This study demonstrated that integration of gene-level associations with CMs could yield statistically significant findings to offer valuable biological insights (e.g., functional interaction among the protein products of these genes) and suggest high confidence candidates for subsequent analyses.

A systems toxicology approach reveals the Wnt-MAPK crosstalk pathway mediated reproductive failure in Caenorhabditis elegans exposed to graphene oxide (GO) but not to reduced graphene oxide (rGO)

The potential hazards of graphene nanomaterials were investigated by exposing the nematode Caenorhabditis elegans to graphene oxide (GO) and reduced graphene oxide (rGO). The underlying mechanisms of the nano–bio interaction were addressed with an integrated systems toxicology approach using global transcriptomics, network-based pathway analysis, and experimental validation of the in-silico-derived hypotheses. Graphene oxide was found to reduce the worms’ reproductive health to a greater degree than rGO, but it did not affect survival (24 h endpoint). Comparative analysis of GO vs. rGO effects found that the wingless-type MMTV integration site family (Wnt) pathway and the mitogen-activated protein kinase (MAPK) pathway were evoked in GO- but not in rGO-exposed worms. We therefore hypothesized that crosstalk between the Wnt and MAPK pathways is responsible for C. elegans’ reproductive sensitivity to GO exposure. By targeting the individual components of the Wnt-MAPK crosstalk pathway (with qPCR gene expression and mutant reproduction analysis), we found a signaling cascade of MOM-2 → MOM-5 → MOM-4 → LIT-1 → POP-1 → EGL-5. Specifically, the activation of POP-1 (the TCF protein homolog) and subsequent repression of the Wnt/β-catenin target gene (EGL-5), analyzed with target-gene-specific RNAi in POP-1 mutant [pop-1(q645)] worms, were the central mechanisms of reduced reproductive potential in the worms exposed to GO. Our results highlight the distinct biological and molecular mechanisms of GO and rGO exposure and the role of Wnt-MAPK pathway crosstalk in regulating GO-induced reproductive failure in in vivo systems, and they will contribute to the development of efficient and innocuous graphene applications as well to improvements in mechanism-based risk assessment.

Network-based methods for identifying critical pathways of complex diseases: a survey

Biological pathways play important roles in the development of complex diseases, such as cancers, which are multifactorial complex diseases that are generally caused by mutation of multiple genes or dysregulation of pathways. It has become one of the most important issues to analyze pathways through combining multiple types of high-throughput data, such as genomics and proteomics, to understand the mechanisms of complex diseases. Currently, several network-based pathway analysis methods have been proposed. In this overview, we review seven major network-based pathway analysis methods and enumerate their benefits and limitations from an algorithmic perspective to provide a reference for the next generation of pathway analysis methods. Finally, we discuss the challenges that the next generation of methods faces.

Genome-wide network-based pathway analysis of CSF biomarker t-tau in the ADNI cohort

Genome-wide network-based pathway analysis of CSF biomarker t-tau in the ADNI cohort

Genetic associations with brain cortical thickness in multiple sclerosis.

Multiple sclerosis (MS) is characterized by temporal and spatial dissemination of demyelinating lesions in the central nervous system. Associated neurodegenerative changes contributing to disability have been recognized even at early disease stages. Recent studies show the importance of gray matter damage for the accrual of clinical disability rather than white matter where demyelination is easily visualized by magnetic resonance imaging (MRI). The susceptibility to MS is influenced by genetic risk, but genetic factors associated with the disability are not known. We used MRI data to determine cortical thickness in 557 MS cases and 75 controls and in another cohort of 219 cases. We identified nine areas showing different thickness between cases and controls (regions of interest, ROI) (eight of them were negatively correlated with Kurtzke's expanded disability status scale, EDSS) and conducted genome-wide association studies (GWAS) in 464 and 211 cases available from the two data sets. No marker exceeded genome-wide significance in the discovery cohort. We next combined nominal statistical evidence of association with physical evidence of interaction from a curated human protein interaction network, and searched for subnetworks enriched with nominally associated genes and for commonalities between the two data sets. This network-based pathway analysis of GWAS detected gene sets involved in glutamate signaling, neural development and an adjustment of intracellular calcium concentration. We report here for the first time gene sets associated with cortical thinning of MS. These genes are potentially correlated with disability of MS.

PINBPA: cytoscape app for network analysis of GWAS data.

Protein interaction network-based pathway analysis (PINBPA) for genome-wide association studies (GWAS) has been developed as a Cytoscape app, to enable analysis of GWAS data in a network fashion. Users can easily import GWAS summary-level data, draw Manhattan plots, define blocks, prioritize genes with random walk with restart, detect enriched subnetworks and test the significance of subnetworks via a user-friendly interface. PINBPA app is freely available in Cytoscape app store. Supplementary data are available at Bioinformatics online.

A network-based gene-weighting approach for pathway analysis

Classical algorithms aiming at identifying biological pathways significantly related to studying conditions frequently reduced pathways to gene sets, with an obvious ignorance of the constitutive non-equivalence of various genes within a defined pathway. We here designed a network-based method to determine such non-equivalence in terms of gene weights. The gene weights determined are biologically consistent and robust to network perturbations. By integrating the gene weights into the classical gene set analysis, with a subsequent correction for the "over-counting" bias associated with multi-subunit proteins, we have developed a novel gene-weighed pathway analysis approach, as implemented in an R package called "Gene Associaqtion Network-based Pathway Analysis" (GANPA). Through analysis of several microarray datasets, including the p53 dataset, asthma dataset and three breast cancer datasets, we demonstrated that our approach is biologically reliable and reproducible, and therefore helpful for microarray data interpretation and hypothesis generation.

Bayesian network based pathway analysis of microarray data

Bayesian network based pathway analysis of microarray data

Genetic variation influences glutamate concentrations in brains of patients with multiple sclerosis

Glutamate is the main excitatory neurotransmitter in the mammalian brain. Appropriate transmission of nerve impulses through glutamatergic synapses is required throughout the brain and forms the basis of many processes including learning and memory. However, abnormally high levels of extracellular brain glutamate can lead to neuroaxonal cell death. We have previously reported elevated glutamate levels in the brains of patients suffering from multiple sclerosis. Here two complementary analyses to assess the extent of genomic control over glutamate levels were used. First, a genome-wide association analysis in 382 patients with multiple sclerosis using brain glutamate concentration as a quantitative trait was conducted. In a second approach, a protein interaction network was used to find associated genes within the same pathway. The top associated marker was rs794185 (P < 6.44 x 10(-7)), a non-coding single nucleotide polymorphism within the gene sulphatase modifying factor 1. Our pathway approach identified a module composed of 70 genes with high relevance to glutamate biology. Individuals carrying a higher number of associated alleles from genes in this module showed the highest levels of glutamate. These individuals also showed greater decreases in N-acetylaspartate and in brain volume over 1 year of follow-up. Patients were then stratified by the amount of annual brain volume loss and the same approach was performed in the 'high' (n = 250) and 'low' (n = 132) neurodegeneration groups. The association with rs794185 was highly significant in the group with high neurodegeneration. Further, results from the network-based pathway analysis remained largely unchanged even after stratification. Results from these analyses indicated that variance in the activity of neurochemical pathways implicated in neurodegeneration is explained, at least in part, by the inheritance of common genetic polymorphisms. Spectroscopy-based imaging provides a novel quantitative endophenotype for genetic association studies directed towards identifying new factors that contribute to the heterogeneity of clinical expression of multiple sclerosis.

Comparative study of computational methods to detect the correlated reaction sets in biochemical networks

Correlated reaction sets (Co-Sets) are mathematically defined modules in biochemical reaction networks which facilitate the study of biological processes by decomposing complex reaction networks into conceptually simple units. According to the degree of association, Co-Sets can be classified into three types: perfect, partial and directional. Five approaches have been developed to calculate Co-Sets, including network-based pathway analysis, Monte Carlo sampling, linear optimization, enzyme subsets and hard-coupled reaction sets. However, differences in design and implementation of these methods lead to discrepancies in the resulted Co-Sets as well as in their use in biotechnology which need careful interpretation. In this paper, we provide a comparative study of the methods for Co-Sets computing in detail from four aspects: (i) sensitivity, (ii) completeness and soundness, (iii) flexibility and (iv) scalability. By applying them to Escherichia coli core metabolic network, the differences and relationships among these methods are clearly articulated which may be useful for potential users.

Genomic response of hypoxic Müller cells involves the very low density lipoprotein receptor as part of an angiogenic network

Müller cells have recently been found to produce select angiogenic substances. In choosing a more comprehensive approach, we wanted to study the genomic response of Müller cells to hypoxia to identify novel angiogenic genes. An established Müller cell line (rMC-1) was exposed to standard or hypoxic conditions. We analyzed gene expression with three independent microarrays and determined differential expression levels compared to normoxia. Selected genes were confirmed by real-time PCR (RTPCR). Subcellular localization of proteins was examined by immunocytochemistry. A network-based pathway analysis was performed to investigate how those genes may contribute to angiogenesis. We found 19 004 of 28 000 known rat genes expressed in Müller cells. 211 genes were upregulated by hypoxia 1.5 to 14.9-fold ( p < 0.001, FDR ≤ 5%) and 220 genes were downregulated 1.5–4.6-fold ( p < 0.001, FDR ≤ 5%). Unexpectedly, expression patterns of cell proliferation, differentiation and organogenesis were increased besides predictable declines in cell function. Very low density lipoprotein receptor (VLDLR) and tribbles 3 (TRIB3) were further analyzed because of recent implication in retinal neovascularization and macular degeneration (VLDLR) and in ocular mesodermal development and differentiation (TRIB3), respectively. VLDLR was upregulated 3.1-fold ( p = 0.001, RTPCR 3.0-fold) and TRIB3 2.8-fold ( p = 0.025, RTPCR 5.1-fold). VEGF was increased 3.1-fold ( p = 0.003, RTPCR 8.3-fold) and apelin, a novel factor of retinal angiogenesis, 5.6-fold ( p = 0.006, RTPCR 8.7-fold). A network of interacting angiogenic genes was identified in silico that included VLDLR as a surface receptor. VLDLR protein localized to the perinucleus, cytoplasm and cell membrane, while TRIB3 was found in nucleoli, the nucleus and cytoplasm. We conclude that hypoxia triggers an angiogenic network response in Müller cells with VLDLR as a novel node and gene expression patterns of proliferation, differentiation and organogenesis.

Stoichiometric modelling of cell metabolism

There are several methodologies based on representations of cell metabolism that share two characteristics: the use of a metabolic network and the assumption of pseudosteady state. These methodologies have different purposes, employ different mathematical tools and are based on different assumptions; however, they all exploit the properties of a similar mathematical description. In this article we use the term stoichiometric modelling to encompass all these methodologies and to describe them within a common framework. Although the information about reaction stoichiometry embedded in metabolic networks is highly important, the framework encompasses methodologies not limited to the use of stoichiometric information. To highlight this fact, the definition of the framework is approached from a constraint-based perspective. One of the reasons for the success of stoichiometric modelling is that it avoids the difficulties that arise in the development of kinetic models: a consequence of the lack of intracellular experimental measurements. Thus, it makes it possible to exploit the knowledge about the structure of cell metabolism, without considering the still not very well known intracellular kinetic processes. Stoichiometric models have been used to estimate the metabolic flux distribution under given circumstances in the cell at some given moment (metabolic flux analysis), to predict it on the basis of some optimality hypothesis (flux balance analysis), and as tools for the structural analysis of metabolism providing information about systemic characteristics of the cell under investigation (network-based pathway analysis).

Integration of enzyme activities into metabolic flux distributions by elementary mode analysis

BackgroundIn systems biology, network-based pathway analysis facilitates understanding or designing metabolic systems and enables prediction of metabolic flux distributions. Network-based flux analysis requires considering not only pathway architectures but also the proteome or transcriptome to predict flux distributions, because recombinant microbes significantly change the distribution of gene expressions. The current problem is how to integrate such heterogeneous data to build a network-based model.ResultsTo link enzyme activity data to flux distributions of metabolic networks, we have proposed Enzyme Control Flux (ECF), a novel model that integrates enzyme activity into elementary mode analysis (EMA). ECF presents the power-law formula describing how changes in enzyme activities between wild-type and a mutant are related to changes in the elementary mode coefficients (EMCs). To validate the feasibility of ECF, we integrated enzyme activity data into the EMCs of Escherichia coli and Bacillus subtilis wild-type. The ECF model effectively uses an enzyme activity profile to estimate the flux distribution of the mutants and the increase in the number of incorporated enzyme activities decreases the model error of ECF.ConclusionThe ECF model is a non-mechanistic and static model to link an enzyme activity profile to a metabolic flux distribution by introducing the power-law formula into EMA, suggesting that the change in an enzyme profile rather reflects the change in the flux distribution. The ECF model is highly applicable to the central metabolism in knockout mutants of E. coli and B. subtilis.

Comparison of network-based pathway analysis methods

Network-based definitions of biochemical pathways have emerged in recent years. These pathway definitions insist on the balanced use of a whole network of biochemical reactions. Two such related definitions, elementary modes and extreme pathways, have generated novel hypotheses regarding biochemical network function. The relationship between these two approaches can be illustrated by comparing and contrasting the elementary modes and extreme pathways of previously published metabolic reconstructions of the human red blood cell (RBC) and the human pathogen Helicobacter pylori. Descriptions of network properties generated by using these two approaches in the analysis of realistic metabolic networks need careful interpretation.