iTraNet: A Web-Based Platform for integrated Trans-Omics Network Visualization and Analysis

Abstract

A major goal in biology is to comprehensively understand molecular interactions within living systems. Visualization and analysis of biological networks play crucial roles in understanding these biochemical processes. Biological networks include diverse types, from gene regulatory networks and protein–protein interactions (PPIs) to metabolic networks. Metabolic networks include substrates, products, and enzymes, which are regulated by allosteric mechanisms and gene expression. Given this complexity, there is a pressing need to investigate trans-omics networks that include these various regulations to understand living systems. However, analyzing various omics layers is laborious due to the diversity of databases and the intricate nature of network analysis. We developed iTraNet, a user-friendly interactive web application that visualizes and analyzes trans-omics networks involving four major types of networks: gene regulatory networks (including transcription factor, microRNA, and mRNA); PPIs; metabolic networks (including enzyme, mRNA, and metabolite); and metabolite exchange networks (including transporter, mRNA, and metabolite). Using iTraNet, we found that in wild-type mice, hub molecules within the network tended to respond to glucose administration, whereas inob/obmice, this tendency disappeared. With its ability to facilitate network visualization and analysis, we anticipate that iTraNet will help researchers gain insights into biological systems. iTraNet is available at (https://transomics.streamlit.app/).GRAPHICAL ABSTRACT

Structural robustness and temporal vulnerability of the starvation-responsive metabolic network in liver of healthy and obese mice

“…2) 40 . Scaling parameters of the trans-omic networks, representing the heterogeneity of the network, were different during oral glucose ingestion but similar during starvation between WT and ob / ob mice 42 . Taken together, glucose ingestion requires distinct responsive molecules and heterogeneities of trans-omic network between WT and ob / ob mice, suggesting metabolic network is more vulnerable to temporally rapid perturbations (oral glucose ingestion) than slow (starvation) or chronic (cancer) perturbations.…”

“…If the metabolic network is temporally vulnerable, the structural robustness observed in starvation (several hours) and cancers (∼years) could be disrupted in a rapid metabolic shift such as glucose ingestion because of the lack of time for rescuing the structural properties. Previously, we conducted a trans-omic analysis of liver in WT and ob / ob mice during oral glucose ingestion, in which we measured metabolome, lipidome, and transcriptome 40,42 . Small fraction of the responsive metabolites (15 out of 88, 17.0%) and transcripts (732 out of 5,091, 14.4%) was common between WT and ob / ob mice during oral glucose ingestion, while more than one-third of the responsive molecules were common during starvation (Fig.…”

“…Biological networks, such as metabolic networks [43][44][45] , are often scale-free, which means that a small number of "hub" nodes interact with many other nodes, and large number of "leaf" nodes interact with a few other nodes 46 . We showed that the trans-omic network is scale-free and structural properties of the networks were different between healthy and obese mice during oral glucose ingestion 42 . However, the relationships between the structural properties and temporal orders of the trans-omic network has not been investigated, as well as temporal co-regulations, the correlated temporal patterns of nodes in a biological network.…”

“…With this approach, we can integrate multi-omic data into a trans-omic network, offering a global landscape of biochemical reactions. Recently, we showed that analysis of the structural properties of the trans-omic network aids in their interpretation during oral glucose ingestion 42 . A network is composed of nodes and edges: nodes are entities such as molecules; edges are relationships between nodes such as regulatory events.…”