Network biology discovers pathogen contact points in host protein-protein interactomes

Abstract

In all organisms, major biological processes are controlled by complex protein–protein interactions networks (interactomes), yet their structural complexity presents major analytical challenges. Here, we integrate a compendium of over 4300 phenotypes with Arabidopsis interactome (AI-1MAIN). We show that nodes with high connectivity and betweenness are enriched and depleted in conditional and essential phenotypes, respectively. Such nodes are located in the innermost layers of AI-1MAIN and are preferential targets of pathogen effectors. We extend these network-centric analyses to Cell Surface Interactome (CSILRR) and predict its 35 most influential nodes. To determine their biological relevance, we show that these proteins physically interact with pathogen effectors and modulate plant immunity. Overall, our findings contrast with centrality-lethality rule, discover fast information spreading nodes, and highlight the structural properties of pathogen targets in two different interactomes. Finally, this theoretical framework could possibly be applicable to other inter-species interactomes to reveal pathogen contact points.

EffectorK, a comprehensive resource to mine for pathogen effector targets in the Arabidopsis proteome

“…We have reinforced previously described intra- and interspecific convergence of effector targeting with effectors from two new species [11, 12], and showed at the same time that most effector targets are pathogen specific (Fig 2 and S2). Our analyses also supported the previously described tendency of effectors to interact with plant proteins better connected and central in the network [43, 45], and showed that this tendency is even stronger among effector hubs, multi-pathogen targets and bacterial core T3E targets (Table 2 and Fig S5).…”

“…In this work, by compiling our experimental interactomic data on Xcc and Rps and the literature-curated interactions from a wide variety of other pathogen effectors, we extended the notion that effectors tend to privilege interactions with host proteins with higher Ath degree and betweenness centrality [43, 45]. Furthermore, we showed that this tendency is stronger among effector hubs compared to single targets and among multi-pathogen effector targets compared to pathogen-specific targets.…”

“…There are two main types of key proteins in a network [43]: 1) proteins important for local network organization, typically showing high degree, and 2) proteins important for the global diffusion of the information through the network, characterized by high betweenness centrality. It had been previously reported in more limited networks that effectors tend to target host proteins with high degree and centrality [43–45]. We then analyzed whether this was the case in our network comparing effector targets with the rest of the Ath proteins (Fig 5).…”

Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis

“…This includes the above described degree, betweenness, information centrality, PageRank index and load centrality as well as additional features such as eigenvector centrality (a measure of the influence of a node in a network), closeness centrality (reciprocal of the sum of the length of the shortest paths between the node and network), harmonic centrality (reverses the sum and reciprocal operations of closeness centrality and weighted k -shell decomposition) (an edge weighting method based on adding the degree of two nodes in network partition). While weighted k -shell decomposition analysis was recently performed to increase the predictability of host targets of bacterial pathogens 35 , we showed that the top 5% of nodes reside in the inner layers of CSI (Fig. 3a, Supplementary Data 2).…”

Host_microbe_PPI - R package to analyse intra-species and inter-species protein-protein interactions in the model plant Arabidopsis thaliana

“…This could also show that target proteins are in general more connected to other proteins of the host species within the intra-species protein-protein network. This can be achieved by using approaches such as the centrality measurements [6] or K-shell decomposition [7]. In addition, several studies have used these datasets and applied them to other closely related species or species that are targeted by specific microbe that lakes PPI.…”