Abstract
Network analysis is a systems biology-oriented approach based on graph theory that has been recently adopted in various fields of life sciences. Starting from mitochondrial proteomes purified from roots of Cucumis sativus plants grown under single or combined iron (Fe) and molybdenum (Mo) starvation, we reconstructed and analyzed at the topological level the protein–protein interaction (PPI) and co-expression networks. Besides formate dehydrogenase (FDH), already known to be involved in Fe and Mo nutrition, other potential mitochondrial hubs of Fe and Mo homeostasis could be identified, such as the voltage-dependent anion channel VDAC4, the beta-cyanoalanine synthase/cysteine synthase CYSC1, the aldehyde dehydrogenase ALDH2B7, and the fumaryl acetoacetate hydrolase. Network topological analysis, applied to plant proteomes profiled in different single or combined nutritional conditions, can therefore assist in identifying novel players involved in multiple homeostatic interactions.
Highlights
Living organisms are increasingly viewed as integrated and communicating molecular networks, thanks to the diffusion of data-derived Systems Biology approaches (Barabási and Oltvai, 2004)
Cytoscape’s Apps; the network was visualized by the Cytoscape platform, and node color code indicates upregulated and downregulated proteins based on Spectral count (SpC) normalization (SpC normalized in the range 0–100, by setting to 100 the higher SpC value per protein)
The analysis of root mitochondria of C. sativus plants grown under Mo and/or Fe starvation led to the identification of 1419 proteins (Vigani et al, 2017)
Summary
Living organisms are increasingly viewed as integrated and communicating molecular networks, thanks to the diffusion of data-derived Systems Biology approaches (Barabási and Oltvai, 2004). Such approaches are well established in biomedical and pharmaceutical research (Zhou et al, 2014; Guney et al, 2016) but not widely used in plant science. Fewer studies rely on protein– protein interaction (PPI) networks, mainly due to the lack of accurate plant models (Di Silvestre et al, 2018). The computational prediction of PPI is usually inferred by transferring interactions from model plant orthologs, like
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