From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

Abstract

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

Systems Biology of Plant-Microbiome Interactions

“…These techniques have been applied in A. thaliana and other plants such as maize and wheat (Kim et al, 2018) to explore their interactions with microbes. The identified modules provide a first insight into genes sharing the same functionalities (Vella et al, 2017), and can help to achieve a better understanding of processes relevant for infection or commensalism.…”

Functional homogeneity and specificity of topological modules in human proteome

“…But homogeneity and heterogeneity values are more varied (high standard deviation) for functional PPIN than physical and combined. Thus, it is advantageous to integrate physical protein interactions with expression based networks for functional analyses as attempted in some reported studies [29,38].…”

“…We combine functional interactions inferred from expression data [26,27] and physical interactions of PPIN [6,16] to provide holistic functional attributes to protein nodes and interactions of the network for the determination of functional modules [28][29][30]. Though several studies have reported characteristics of resulting modules of different biological networks [13,17,21], there is a need of a systematic study elucidating the effects of using both functional and physical interactions of PPIN on detecting topological and functional modules.…”

Evolutionary Distance of Gene-Gene Interactions: Estimation under Statistical Uncertainty

“…Note that transcriptome (RNA-seq) can be also viewed as the gene-tissue interactions. Both PPI and GRN networks are analyzed through the identification of subnetworks, or modules, showing specific topological and/or functional characteristics (Barabási et al 2011;Vella et al 2017;Hartwell et al 1999;Gursoy et al 2008;Fraser 2005). For instance, a PPI module represents a group of proteins taking part in specific, separable functions such as protein complexes, metabolic pathways or signal transduction systems.…”