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.

Systems Biology of Plant-Microbiome Interactions

“…Genome-scale metabolic modeling has been used to study the metabolism of an individual organism, and modeling of community level reactions is progressing but challenging (reviewed by Kruger and Ratcliffe, 2015;Topfer et al, 2015). Metabolic modeling of prokaryotes is routine nowadays (Heavner and Price, 2015); on the plant side, metabolic models have been generated for Arabidopsis, barley, maize, sorghum, sugarcane, and canola (Botero et al, 2018). Thus, the metabolic capabilities of beneficials and pathogens can be analyzed by networks comparison.…”

Bioimaging structural signatures of the oomycete pathogen Sclerospora graminicola in pearl millet using different microscopic techniques

“…Oomycetes are multinucleate with cell wall consisting of β-1,3- and β-1,6-glucans 21 . Studies of the intimate association between hosts and pathogens at cellular level and elucidation of the roles of different host and pathogen genotypes can shed light on understanding of the nature of disease resistance and advance our understanding of the host-pathogen interactions 23 . In the present report, the use of different types of microscopy to study pathogenesis and morphological features has enhanced our understanding of the morphological features of the infective propagules of the S. graminicola pathogen.…”

Understanding Host–Pathogen Interactions in Brassica napus in the Omics Era

“…The huge number of data collected from multi-omics technologies will be useful in the construction of network biology, where mathematical models and computational approaches are implemented to predict the pathogenicity and virulence mechanisms in plant–pathogen interactions [ 217 ]. Metabolic pathways supported by mathematical modelling can be used to study how cells within a multicellular organism work cooperatively to carry out a particular function.…”