Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis

Abstract

Motivation Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism’s metabolism, yet their integration to achieve biological insight remains challenging. Results We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO. Availability and implementation The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE. Supplementary information Supplementary data are available at Bioinformatics online.

Anti-adipogenic signals at the onset of obesity-related inflammation in white adipose tissue

“…Our work is based on unbiased analyses of multiple genome-wide datasets that were obtained from vWAT and scWAT samples with a rigorously controlled study design. Besides applying classical pathway enrichment tools to our data, we further deepened our analyses by adapting emerging approaches that use modeling of cellular metabolism, such as metaboGSE [45] and genome-scale metabolic network (GSMN). Because of their totally different methodology, such novel tools enabled us to identify other key features that were not highlighted with classical pathway analyses and that could contribute to the different timing of the adipogenic program in vWAT and scWAT.…”

“…The results obtained so far highlighted important biological features, but only a few related to metabolism. We, thus, attempted to investigate further the RNA-seq data with metaboGSE [45], which is a recently published algorithm relying on a genome-scale metabolic network (GSMN) to simulate the cellular metabolism. Albeit limited to metabolic reactions, this method emphasizes the low expressed genes and produces alternative gene set enrichment results, which are not necessarily identified by classical algorithms like GSEA and topGO.…”

“…As described in Tran et al [45], for each RNA-seq sample, a series of metabolic sub-networks was built using the expression-based ranking of genes and a comprehensive genomescale metabolic network (GSMN). Each sub-network was evaluated by a fitness score, which estimates its degree of alteration from the comprehensive GSMN.…”

MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models

“…MNXref is now used within tools for testing GSMNs such as MEMOTE ( 40 ), and has been proposed as a reference for minimal standard content for metabolic network reconstruction ( 41 ). Finally, the ability to accurately reconcile metabolites and reactions within GSMNs paves the way for applications such as multi-omics data interpretation ( 42 , 43 ), and white-box AI models ( 44 ). Moving forward, MetaNetX/MNXref efforts to not only unify metabolites, reactions and subcellular compartments but also genes and proteins ( 45 ) into a single comprehensive namespace will continue to provide a strong basis for such key systems biology endeavors in the future.…”

MetaNetX/MNXref - unified namespace for metabolites and biochemical reactions in the context of metabolic models

“…MNXref is now used within tools for testing GSMNs such as MEMOTE (49) , and has been proposed as a reference for minimal standard content for metabolic network reconstruction (50) . Finally, the ability to accurately reconcile metabolites and reactions within GSMNs paves the way for applications such as multi-omics data interpretation (51,52) , and white-box AI models (53) . Moving forward, MetaNetX/MNXref efforts to not only unify metabolites, reactions and subcellular compartments but also genes and proteins (54) into a single comprehensive namespace will continue to provide a strong basis for such key systems biology endeavors in the future.…”