Metabolome Based Reaction Graphs of M. tuberculosis and M. leprae: A Comparative Network Analysis

Abstract

BackgroundSeveral types of networks, such as transcriptional, metabolic or protein-protein interaction networks of various organisms have been constructed, that have provided a variety of insights into metabolism and regulation. Here, we seek to exploit the reaction-based networks of three organisms for comparative genomics. We use concepts from spectral graph theory to systematically determine how differences in basic metabolism of organisms are reflected at the systems level and in the overall topological structures of their metabolic networks.Methodology/Principal FindingsMetabolome-based reaction networks of Mycobacterium tuberculosis, Mycobacterium leprae and Escherichia coli have been constructed based on the KEGG LIGAND database, followed by graph spectral analysis of the network to identify hubs as well as the sub-clustering of reactions. The shortest and alternate paths in the reaction networks have also been examined. Sub-cluster profiling demonstrates that reactions of the mycolic acid pathway in mycobacteria form a tightly connected sub-cluster. Identification of hubs reveals reactions involving glutamate to be central to mycobacterial metabolism, and pyruvate to be at the centre of the E. coli metabolome. The analysis of shortest paths between reactions has revealed several paths that are shorter than well established pathways.ConclusionsWe conclude that severe downsizing of the leprae genome has not significantly altered the global structure of its reaction network but has reduced the total number of alternate paths between its reactions while keeping the shortest paths between them intact. The hubs in the mycobacterial networks that are absent in the human metabolome can be explored as potential drug targets. This work demonstrates the usefulness of constructing metabolome based networks of organisms and the feasibility of their analyses through graph spectral methods. The insights obtained from such studies provide a broad overview of the similarities and differences between organisms, taking comparative genomics studies to a higher dimension.

Protein–protein interaction networks suggest different targets have different propensities for triggering drug resistance

“…Each drug target exhibits different properties. Nevertheless, as expected from protein-protein interaction networks of this type (Verkhedkar et al 2007; Barabasi and Oltvai 2004) the clustering coefficient is very low, Fig. 1 Flow chart shows here the methodology followed to derive individual target to resistance paths network, form curation of the drug targets for each drug in the TB regimen and proteins involved in the resistance machineries known, building interactome network of shortest paths with incorporation of the specific drug exposed microarray data to score and finally to rank the paths Protein-protein interaction networks 315…”

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

“…We removed the 24 currency metabolites (proton, water, oxygen molecule, NADP + , NADPH, ATP, diphosphate, carbon dioxide, phosphate, ADP, coA, UDP, NAD + , NADH, AMP, ammonia, hydrogen peroxide, oxidized electron acceptor, reduced electron acceptor, 3-5-ADP, GDP, carbon monoxide, GTP, and FAD; Supplemental Table S5) from the network because they are associated with many reactions in the cell, thus creating many biologically unrealistic shortcuts in the metabolic network Zeng, 2003a, 2003b;Verkhedkar et al, 2007). For example, water is an essential compound involved in most biochemical reactions.…”

“…Because they take part in many reactions in the cell, thereby creating many biologically unrealistic shortcuts in the metabolic network, any path-based measurement of the network could be biased Zeng, 2003a, 2003b;Verkhedkar et al, 2007). To select currency metabolites, we prioritized metabolites by the connectivity (degree) of metabolites in the reconstructed metabolic network and then manually checked the links created by them by evaluating whether they were functionally meaningful.…”

Integration of Metabolic Knowledge for Genome‐Scale Metabolic Reconstruction

“…Comparison of different organisms and finding similarities among them are other applications of GSMR [44,70,83,95,[141][142][143][144][145]. Based on complex systems theory, a holistic approach helps better understand some network properties which are not accessible in the reduction procedure.…”