MetaboSignal: a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways.
Ontology highlight
ABSTRACT: Summary
MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes.Availability and implementation
MetaboSignal is available from Bioconductor: https://bioconductor.org/packages/MetaboSignal/.Contact
m.dumas@imperial.ac.uk .Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Rodriguez-Martinez A
PROVIDER: S-EPMC5408820 | biostudies-literature | 2017 Mar
REPOSITORIES: biostudies-literature
Publications
MetaboSignal: a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways.
Rodriguez-Martinez Andrea A Ayala Rafael R Posma Joram M JM Neves Ana L AL Gauguier Dominique D Nicholson Jeremy K JK Dumas Marc-Emmanuel ME
Bioinformatics (Oxford, England) 20170301 5
<h4>Summary</h4>MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes.<h4>Availability and implementation</h4>MetaboSignal is available from Bioconductor: https://bioco ...[more]
Similar Datasets
Project description:BackgroundBy studying genome-wide expression patterns in healthy and diseased tissues across a wide range of pathophysiological conditions, DNA microarrays have revealed unique insights into complex diseases. However, the high-dimensionality of microarray data makes interpretation of heterogeneous gene expression studies inherently difficult.ResultsUsing a large-scale analysis of more than 40 microarray studies encompassing ~2400 mammalian tissue samples, we identified a common theme across heterogeneous microarray studies evident by a robust genome-wide inverse regulation of metabolic and cell signaling pathways: We found that upregulation of cell signaling pathways was invariably accompanied by downregulation of cell metabolic transcriptional activity (and vice versa). Several findings suggest that this characteristic gene expression pattern represents a new principle of mammalian transcriptional regulation. First, this coordinated transcriptional pattern occurred in a wide variety of physiological and pathophysiological conditions and was identified across all 20 human and animal tissue types examined. Second, the differences in metabolic gene expression predicted the magnitude of differences for signaling and all other pathways, i.e. tissue samples with similar expression levels of metabolic transcripts did not show any differences in gene expression for all other pathways. Third, this transcriptional pattern predicted a profound effect on the proteome, evident by differences in structure, stability and post-translational modifications of proteins belonging to signaling and metabolic pathways, respectively.ConclusionsOur data suggest that in a wide range of physiological and pathophysiological conditions, gene expression changes exhibit a recurring pattern along a transcriptional axis, characterized by an inverse regulation of major metabolic and cell signaling pathways. Given its widespread occurrence and its predicted effects on protein structure, protein stability and post-translational modifications, we propose a new principle for transcriptional regulation in mammalian biology.
| S-EPMC2861677 | biostudies-literature
Project description:MotivationBiological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module's information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information.ResultsIn this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation.Availability and implementationR code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9831052 | biostudies-literature
Project description:Metabolic syndrome is a pathological condition characterized by obesity, hyperglycemia, hypertension, elevated levels of triglycerides and low levels of high-density lipoprotein cholesterol that increase cardiovascular disease risk and type 2 diabetes. Although numerous predisposing genetic risk factors have been identified, the biological mechanisms underlying this complex phenotype are not fully elucidated. Here we introduce a systems biology approach based on network analysis to investigate deregulated biological processes and subsequently identify drug repurposing candidates. A proximity score describing the interaction between drugs and pathways is defined by combining topological and functional similarities. The results of this computational framework highlight a prominent role of the immune system in metabolic syndrome and suggest a potential use of the BTK inhibitor ibrutinib as a novel pharmacological treatment. An experimental validation using a high fat diet-induced obesity model in zebrafish larvae shows the effectiveness of ibrutinib in lowering the inflammatory load due to macrophage accumulation.
| S-EPMC6861239 | biostudies-literature
Project description:The crosstalk between signaling and metabolic pathways has been known to play key roles in human diseases and plant biological processes. The integration of signaling and metabolic pathways can provide an essential reference framework for crosstalk analysis. However, current databases use distinct structures to present signaling and metabolic pathways, which leads to the chaos in the integrated networks. Moreover, for the metabolic pathways, the metabolic enzymes and the reactions are disconnected by the current widely accepted layout of edges and nodes, which hinders the topological analysis of the integrated networks. Here, we propose a novel "meta-pathway" structure, which uses the uniformed structure to display the signaling and metabolic pathways, and resolves the difficulty in linking the metabolic enzymes to the reactions topologically. We compiled a comprehensive collection of global integrative networks (GINs) by merging the meta-pathways of 7077 species. We demonstrated the assembly of the signaling and metabolic pathways using the GINs of four species-human, mouse, Arabidopsis, and rice. Almost all of the nodes were assembled into one major network for each of the four species, which provided opportunities for robust crosstalk and topological analysis, and knowledge graph construction.Supplementary informationThe online version contains supplementary material available at 10.1007/s42994-022-00078-1.
| S-EPMC9755797 | biostudies-literature
Project description:Finding non-standard or new metabolic pathways has important applications in metabolic engineering, synthetic biology and the analysis and reconstruction of metabolic networks. Branched metabolic pathways dominate in metabolic networks and depict a more comprehensive picture of metabolism compared to linear pathways. Although progress has been developed to find branched metabolic pathways, few efforts have been made in identifying branched metabolic pathways via atom group tracking. In this paper, we present a pathfinding method called BPFinder for finding branched metabolic pathways by atom group tracking, which aims to guide the synthetic design of metabolic pathways. BPFinder enumerates linear metabolic pathways by tracking the movements of atom groups in metabolic network and merges the linear atom group conserving pathways into branched pathways. Two merging rules based on the structure of conserved atom groups are proposed to accurately merge the branched compounds of linear pathways to identify branched pathways. Furthermore, the integrated information of compound similarity, thermodynamic feasibility and conserved atom groups is also used to rank the pathfinding results for feasible branched pathways. Experimental results show that BPFinder is more capable of recovering known branched metabolic pathways as compared to other existing methods, and is able to return biologically relevant branched pathways and discover alternative branched pathways of biochemical interest. The online server of BPFinder is available at http://114.215.129.245:8080/atomic/. The program, source code and data can be downloaded from https://github.com/hyr0771/BPFinder.
| S-EPMC7880430 | biostudies-literature
Project description:Biological pathways are thought to be robust against a variety of internal and external perturbations. Fail-safe mechanisms allow for compensation of perturbations to maintain the characteristic function of a pathway. Pathways can undergo changes during aging, which may lead to changes in their stability. Less stable or less robust pathways may be consequential to or increase the susceptibility of the development of diseases. Among others, NF- κ B signaling is a crucial pathway in the process of aging. The NF- κ B system is involved in the immune response and dealing with various internal and external stresses. Boolean networks as models of biological pathways allow for simulation of signaling behavior. They can help to identify which proposed mechanisms are biologically representative and which ones function but do not mirror physical processes-for instance, changes of signaling pathways during the aging process. Boolean networks can be inferred from time-series of gene expression data. This allows us to get insights into the changes of behavior of pathways such as NF- κ B signaling in aged organisms in comparison to young ones.
| S-EPMC5745451 | biostudies-literature
Project description:All organisms need to be capable of adapting to changes in the availability and composition of nutrients. Over 75 years ago, researchers discovered that a calorie restricted (CR) diet could significantly extend the lifespan of rats, and since then a CR diet has been shown to increase lifespan and healthspan in model organisms ranging from yeast to non-human primates. In this review, we discuss the effects of a CR diet on metabolism and healthspan, and highlight emerging evidence that suggests that dietary composition - the precise macronutrients that compose the diet - may be just as important as caloric content. In particular, we discuss recent evidence that suggests protein quality may influence metabolic health. Finally, we discuss key metabolic pathways which may influence the response to CR diets and altered macronutrient composition. Understanding the molecular mechanisms responsible for the effects of CR and dietary composition on health and longevity may allow the design of novel therapeutic approaches to age-related diseases.
| S-EPMC5440210 | biostudies-other
Project description:Metabolomics has been increasingly applied to discovering biomarkers and identifying perturbed pathways. Berberine has been shown to exhibit anti-inflammatory, antioxidant, and anticancer properties, but its mechanisms for treating nonbacterial prostatitis (NBP) remain unclear completely. We developed the untargeted metabolomics approach based on UPLC-Q-TOF-HDMS to profile the metabolite changes in urine samples in order to discover novel potential biomarkers to clarify mechanisms of berberine in treating a rat model of capsaicin-induced nonbacterial prostatitis (NBP). The changes in metabolic profiling were restored to their base-line values after berberine treatment according to the principal component analysis (PCA) score plots. Fourteen different potential biomarkers and five acutely perturbed metabolic pathways contributing to the treatment of NBP were discovered and identified. Specifically, the berberine-treated rats are located closer to the normal group, indicating that the NBP-induced disturbances to the metabolic profile were partially reversed by berberine treatment. After treatment with berberine, the relative contents of 12 potential biomarkers were effectively regulated, which suggested that the therapeutic effects of berberine on NBP may involve regulating disturbances to the metabolism. Our results show that the protective effect of berberine occurs in part through a reversal of the NBP-caused disturbances.
| S-EPMC4356222 | biostudies-literature
Project description:Conditions of metabolic distress, from malnutrition to obesity, impact, via as yet ill-defined mechanisms, the timing of puberty, whose alterations can hamper later cardiometabolic health and even life expectancy. AMP-activated protein kinase (AMPK), the master cellular energy sensor activated in conditions of energy insufficiency, has a major central role in whole-body energy homeostasis. However, whether brain AMPK metabolically modulates puberty onset remains unknown. We report here that central AMPK interplays with the puberty-activating gene, Kiss1, to control puberty onset. Pubertal subnutrition, which delayed puberty, enhanced hypothalamic pAMPK levels, while activation of brain AMPK in immature female rats substantially deferred puberty. Virogenetic overexpression of a constitutively active form of AMPK, selectively in the hypothalamic arcuate nucleus (ARC), which holds a key population of Kiss1 neurons, partially delayed puberty onset and reduced luteinizing hormone levels. ARC Kiss1 neurons were found to express pAMPK, and activation of AMPK reduced ARC Kiss1 expression. The physiological relevance of this pathway was attested by conditional ablation of the AMPKα1 subunit in Kiss1 cells, which largely prevented the delay in puberty onset caused by chronic subnutrition. Our data demonstrate that hypothalamic AMPK signaling plays a key role in the metabolic control of puberty, acting via a repressive modulation of ARC Kiss1 neurons in conditions of negative energy balance.
| S-EPMC6233121 | biostudies-literature
Project description:Disruption of signaling pathways that plays a role in the normal development and cellular homeostasis may lead to the dysregulation of cellular signaling and bring about the onset of different diseases, including cancer. In addition to genetic aberrations, DNA methylation also acts as an epigenetic modifier to drive the onset and progression of cancer by mediating the reversible transcription of related genes. Although the role of DNA methylation as an alternative driver of carcinogenesis has been well-established, the global effects of DNA methylation on oncogenic signaling pathways and the presentation of cancer is only emerging. In this article, we introduced a differential methylation parsing pipeline (MethylMine) which mined for epigenetic biomarkers based on feature selection. This pipeline was used to mine for biomarkers, which presented a substantial difference in methylation between the tumor and the matching normal tissue samples. Combined with the Data Integration Analysis for Biomarker discovery (DIABLO) framework for machine learning and multi-omic analysis, we revisited the TCGA DNA methylation and RNA-Seq datasets for breast, colorectal, lung, and prostate cancer, and identified differentially methylated genes within the NRF2-KEAP1/PI3K oncogenic pathway, which regulates the expression of cytoprotective genes, that serve as potential therapeutic targets to treat different cancers.
| S-EPMC7692324 | biostudies-literature