Dataset Information

Network Analyses in Plant Pathogens.

ABSTRACT: Even in the age of big data in Biology, studying the connections between the biological processes and the molecular mechanisms behind them is a challenging task. Systems biology arose as a transversal discipline between biology, chemistry, computer science, mathematics, and physics to facilitate the elucidation of such connections. A scenario, where the application of systems biology constitutes a very powerful tool, is the study of interactions between hosts and pathogens using network approaches. Interactions between pathogenic bacteria and their hosts, both in agricultural and human health contexts are of great interest to researchers worldwide. Large amounts of data have been generated in the last few years within this area of research. However, studies have been relatively limited to simple interactions. This has left great amounts of data that remain to be utilized. Here, we review the main techniques in network analysis and their complementary experimental assays used to investigate bacterial-plant interactions. Other host-pathogen interactions are presented in those cases where few or no examples of plant pathogens exist. Furthermore, we present key results that have been obtained with these techniques and how these can help in the design of new strategies to control bacterial pathogens. The review comprises metabolic simulation, protein-protein interactions, regulatory control of gene expression, host-pathogen modeling, and genome evolution in bacteria. The aim of this review is to offer scientists working on plant-pathogen interactions basic concepts around network biology, as well as an array of techniques that will be useful for a better and more complete interpretation of their data.

SUBMITTER: Botero D

PROVIDER: S-EPMC5797656 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Network Analyses in Plant Pathogens.

Botero David D Alvarado Camilo C Bernal Adriana A Danies Giovanna G Restrepo Silvia S

Frontiers in microbiology 20180130

Even in the age of big data in Biology, studying the connections between the biological processes and the molecular mechanisms behind them is a challenging task. Systems biology arose as a transversal discipline between biology, chemistry, computer science, mathematics, and physics to facilitate the elucidation of such connections. A scenario, where the application of systems biology constitutes a very powerful tool, is the study of interactions between hosts and pathogens using network approach ...[more]

PMID: 29441045

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Project description:BACKGROUND:Genomic studies demonstrate that components of virulence mechanisms in filamentous eukaryotic pathogens (FEPs, fungi and oomycetes) of plants are often highly conserved, or found in gene families that include secreted hydrolytic enzymes (e.g., cellulases and proteases) and secondary metabolites (e.g., toxins), central to the pathogenicity process. However, very few large-scale genomic comparisons have utilized complete proteomes from dozens of FEPs to reveal lifestyle-associated virulence mechanisms. Providing a powerful means for exploration, and the discovery of trends in large-scale datasets, network analysis has been used to identify core functions of the primordial cyanobacteria, and ancient evolutionary signatures in oxidoreductases. RESULTS:We used a sequence-similarity network to study components of virulence mechanisms of major pathogenic lifestyles (necrotroph (ic), N; biotroph (ic), B; hemibiotroph (ic), H) in complete pan-proteomes of 65 FEPs and 17 saprobes. Our comparative analysis highlights approximately 190 core functions found in 70% of the genomes of these pathogenic lifestyles. Core functions were found mainly in: transport (in H, N, B cores); carbohydrate metabolism, secondary metabolite synthesis, and protease (H and N cores); nucleic acid metabolism and signal transduction (B core); and amino acid metabolism (H core). Taken together, the necrotrophic core contains functions such as cell wall-associated degrading enzymes, toxin metabolism, and transport, which are likely to support their lifestyle of killing prior to feeding. The biotrophic stealth growth on living tissues is potentially controlled by a core of regulatory functions, such as: small G-protein family of GTPases, RNA modification, and cryptochrome-based light sensing. Regulatory mechanisms found in the hemibiotrophic core contain light- and CO2-sensing functions that could mediate important roles of this group, such as transition between lifestyles. CONCLUSIONS:The selected set of enriched core functions identified in our work can facilitate future studies aimed at controlling FEPs. One interesting example would be to facilitate the identification of the pathogenic potential of samples analyzed by metagenomics. Finally, our analysis offers potential evolutionary scenarios, suggesting that an early-branching saprobe (identified in previous studies) has probably evolved a necrotrophic lifestyle as illustrated by the highest number of shared gene families between saprobes and necrotrophs.

Dataset Information

Network Analyses in Plant Pathogens.

Publications

Network Analyses in Plant Pathogens.

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