Shiny GATOM: omics-based identification of regulated metabolic modules in atom transition networks

Abstract

Multiple high-throughput omics techniques provide different angles on systematically quantifying and studying metabolic regulation of cellular processes. However, an unbiased analysis of such data and, in particular, integration of multiple types of data remains a challenge. Previously, for this purpose we developed GAM web-service for integrative metabolic network analysis. Here we describe an updated pipeline GATOM and the corresponding web-service Shiny GATOM, which takes as input transcriptional and/or metabolomic data and finds a metabolic subnetwork most regulated between the two conditions of interest. GATOM features a new metabolic network topology based on atom transition, which significantly improves interpretability of the analysis results. To address computational challenges arising with the new network topology, we introduce a new variant of the maximum weight connected subgraph problem and provide a corresponding exact solver. To make the used networks up-to-date we upgraded the KEGG-based network construction pipeline and developed one based on the Rhea database, which allows analysis of lipidomics data. Finally, we simplified local installation, providing R package mwcsr for solving relevant graph optimization problems and R package gatom, which implements the GATOM pipeline. The web-service is available at https://ctlab.itmo.ru/shiny/gatom and https://artyomovlab.wustl.edu/shiny/gatom.

Network analysis of large-scale ImmGen and Tabula Muris datasets highlights metabolic diversity of tissue mononuclear phagocytes

“…This can be addressed by introducing a more refined, atomistic structure of the metabolic network, as was recently done in the Shiny GATOM approach. 60 Finally, utilization of different clustering metrics (e.g., silhouette) may be added to assess the quality of derived modules and therefore improve clustering.…”

Inherent Metabolic Adaptations in Adult Spiny Mouse (Acomys) Cardiomyocytes Facilitate Enhanced Cardiac Recovery Following Myocardial Infarction

“…Further analysis of the metabolic state of aCM3 vs. mCM2 was performed using GATOM [29], a method of unbiasedly analyzing and integrating ‘omics data for understanding metabolic regulation in cellular processes. This analysis revealed distinct differences in the TCA cycle, amino acid metabolism, and antioxidant defense systems.…”

“…Furthermore, the expression of Redox genes was higher in Acomys , which enhances nucleic acid availability for cell proliferation and produces ROS scavengers ( Figure 3 ). Analysis of the metabolic state of Acomys and Mus cardiomyocytes was also assessed using GATOM [29], a method of unbiasedly analyzing and integrating ‘omics data for understanding metabolic regulation in cellular processes. Metabolic pathway analysis revealed distinct patterns of differential regulation on days 1, 3, and 7 after myocardial infarction between Acomys and Mus .…”

“…GATOM: The network analysis to elucidate significant interactions was performed using GATOM [29]. Overall, it consists of two major steps: 1) Creating a metabolic network using either genes and/or metabolites, 2) Identifying a subnetwork that encompasses the most significant changes.…”

LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis

“…Inspired by BioPAN ( 16 ), the second tool, the ‘Pathway Activity Network’, is designed to calculate flux changes that reflect the activity level between lipid classes within the lipid reaction network. The third tool, the ‘GATOM Network’, uses a bioinformatics approach to isolate sub-networks, elucidating crucial interactions among lipid species ( 17 ). Collectively, these advanced tools enable researchers to analyze their lipidomics data with greater depth and precision, offering a comprehensive view of the intricate interactions and dynamics within lipid networks.…”