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A Quantitative Approach to Analyzing Genome Reductive Evolution Using Protein-Protein Interaction Networks: A Case Study of Mycobacterium leprae.

ABSTRACT: The advance in high-throughput sequencing technologies has yielded complete genome sequences of several organisms, including complete bacterial genomes. The growing number of these available sequenced genomes has enabled analyses of their dynamics, as well as the molecular and evolutionary processes which these organisms are under. Comparative genomics of different bacterial genomes have highlighted their genome size and gene content in association with lifestyles and adaptation to various environments and have contributed to enhancing our understanding of the mechanisms of their evolution. Protein-protein functional interactions mediate many essential processes for maintaining the stability of the biological systems under changing environmental conditions. Thus, these interactions play crucial roles in the evolutionary processes of different organisms, especially for obligate intracellular bacteria, proven to generally have reduced genome sizes compared to their nearest free-living relatives. In this study, we used the approach based on the Renormalization Group (RG) analysis technique and the Maximum-Excluded-Mass-Burning (MEMB) model to investigate the evolutionary process of genome reduction in relation to the organization of functional networks of two organisms. Using a Mycobacterium leprae (MLP) network in comparison with a Mycobacterium tuberculosis (MTB) network as a case study, we show that reductive evolution in MLP was as a result of removal of important proteins from neighbors of corresponding orthologous MTB proteins. While each orthologous MTB protein had an increase in number of interacting partners in most instances, the corresponding MLP protein had lost some of them. This work provides a quantitative model for mapping reductive evolution and protein-protein functional interaction network organization in terms of roles played by different proteins in the network structure.

SUBMITTER: Akinola RO

PROVIDER: S-EPMC4809885 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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A Quantitative Approach to Analyzing Genome Reductive Evolution Using Protein-Protein Interaction Networks: A Case Study of Mycobacterium leprae.

Akinola Richard O RO Mazandu Gaston K GK Mulder Nicola J NJ

Frontiers in genetics 20160329

The advance in high-throughput sequencing technologies has yielded complete genome sequences of several organisms, including complete bacterial genomes. The growing number of these available sequenced genomes has enabled analyses of their dynamics, as well as the molecular and evolutionary processes which these organisms are under. Comparative genomics of different bacterial genomes have highlighted their genome size and gene content in association with lifestyles and adaptation to various envir ...[more]

PMID: 27066064

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Project description:Upon infection, Mycobacterium leprae, an obligate intracellular bacillus, induces accumulation of cholesterol-enriched lipid droplets (LDs) in Schwann cells (SCs). LDs are promptly recruited to M. leprae-containing phagosomes, and inhibition of this process decreases bacterial survival, suggesting that LD recruitment constitutes a mechanism by which host-derived lipids are delivered to intracellular M. leprae. We previously demonstrated that M. leprae has preserved only the capacity to oxidize cholesterol to cholestenone, the first step of the normal cholesterol catabolic pathway. In this study we investigated the biochemical relevance of cholesterol oxidation on bacterial pathogenesis in SCs. Firstly, we showed that M. leprae increases the uptake of LDL-cholesterol by infected SCs. Moreover, fluorescence microscopy analysis revealed a close association between M. leprae and the internalized LDL-cholesterol within the host cell. By using Mycobacterium smegmatis mutant strains complemented with M. leprae genes, we demonstrated that ml1942 coding for 3β-hydroxysteroid dehydrogenase (3β-HSD), but not ml0389 originally annotated as cholesterol oxidase (ChoD), was responsible for the cholesterol oxidation activity detected in M. leprae. The 3β-HSD activity generates the electron donors NADH and NADPH that, respectively, fuel the M. leprae respiratory chain and provide reductive power for the biosynthesis of the dominant bacterial cell wall lipids phthiocerol dimycocerosate (PDIM) and phenolic glycolipid (PGL)-I. Inhibition of M. leprae 3β-HSD activity with the 17β-[N-(2,5-di-t-butylphenyl)carbamoyl]-6-azaandrost-4-en-3one (compound 1), decreased bacterial intracellular survival in SCs. In conclusion, our findings confirm the accumulation of cholesterol in infected SCs and its potential delivery to the intracellular bacterium. Furthermore, we provide strong evidence that cholesterol oxidation is an essential catabolic pathway for M. leprae pathogenicity and point to 3β-HSD as a prime drug target that may be used in combination with current multidrug regimens to shorten leprosy treatment and ameliorate nerve damage.

Project description:BackgroundUnderstanding the information-processing capabilities of signal transduction networks, how those networks are disrupted in disease, and rationally designing therapies to manipulate diseased states require systematic and accurate reconstruction of network topology. Data on networks central to human physiology, such as the inflammatory signalling networks analyzed here, are found in a multiplicity of on-line resources of pathway and interactome databases (Cancer CellMap, GeneGo, KEGG, NCI-Pathway Interactome Database (NCI-PID), PANTHER, Reactome, I2D, and STRING). We sought to determine whether these databases contain overlapping information and whether they can be used to construct high reliability prior knowledge networks for subsequent modeling of experimental data.ResultsWe have assembled an ensemble network from multiple on-line sources representing a significant portion of all machine-readable and reconcilable human knowledge on proteins and protein interactions involved in inflammation. This ensemble network has many features expected of complex signalling networks assembled from high-throughput data: a power law distribution of both node degree and edge annotations, and topological features of a "bow tie" architecture in which diverse pathways converge on a highly conserved set of enzymatic cascades focused around PI3K/AKT, MAPK/ERK, JAK/STAT, NFκB, and apoptotic signaling. Individual pathways exhibit "fuzzy" modularity that is statistically significant but still involving a majority of "cross-talk" interactions. However, we find that the most widely used pathway databases are highly inconsistent with respect to the actual constituents and interactions in this network. Using a set of growth factor signalling networks as examples (epidermal growth factor, transforming growth factor-beta, tumor necrosis factor, and wingless), we find a multiplicity of network topologies in which receptors couple to downstream components through myriad alternate paths. Many of these paths are inconsistent with well-established mechanistic features of signalling networks, such as a requirement for a transmembrane receptor in sensing extracellular ligands.ConclusionsWide inconsistencies among interaction databases, pathway annotations, and the numbers and identities of nodes associated with a given pathway pose a major challenge for deriving causal and mechanistic insight from network graphs. We speculate that these inconsistencies are at least partially attributable to cell, and context-specificity of cellular signal transduction, which is largely unaccounted for in available databases, but the absence of standardized vocabularies is an additional confounding factor. As a result of discrepant annotations, it is very difficult to identify biologically meaningful pathways from interactome networks a priori. However, by incorporating prior knowledge, it is possible to successively build out network complexity with high confidence from a simple linear signal transduction scaffold. Such reduced complexity networks appear suitable for use in mechanistic models while being richer and better justified than the simple linear pathways usually depicted in diagrams of signal transduction.

Dataset Information

A Quantitative Approach to Analyzing Genome Reductive Evolution Using Protein-Protein Interaction Networks: A Case Study of Mycobacterium leprae.

Publications

A Quantitative Approach to Analyzing Genome Reductive Evolution Using Protein-Protein Interaction Networks: A Case Study of Mycobacterium leprae.

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