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

Pathogens deploy effector proteins that interact with host proteins to manipulate the host physiology to the pathogen’s own benefit. However, effectors can also be recognized by host immune proteins leading to the activation of defense responses. Effectors are thus essential components in determining the outcome of plant-pathogen interactions. Despite major efforts to decipher effector functions, our current knowledge on effector biology is scattered and often limited. In this study, we conducted two systematic large-scale yeast two-hybrid screenings to detect interactions between Arabidopsis thaliana proteins and effectors from two vascular bacterial pathogens: Ralstonia pseudosolanacearum and Xanthomonas campestris. We then constructed an interactomic network focused on Arabidopsis and effector proteins from a wide variety of bacterial, oomycete, fungal and animal pathogens. This network contains our experimental data and protein-protein interactions from 2,035 peer-reviewed publications (48,200 Arabidopsis-Arabidopsis and 1,300 Arabidopsis-effector protein interactions). Our results show that effectors from different species interact with both common and specific Arabidopsis targets suggesting dual roles as modulators of generic and adaptive host processes. Network analyses revealed that effector targets, particularly effector hubs and bacterial core effector targets, occupy important positions for network organization as shown by their larger number of protein interactions and centrality. These interactomic data were incorporated in EffectorK, a new graph-oriented knowledge database that allows users to navigate the network, search for homology or find possible paths between host and/or effector proteins. EffectorK is available at www.effectork.org and allows users to submit their own interactomic data. Author summary Plant pests and diseases caused by bacteria, oomycetes, fungi or animals are threatening food security worldwide. Understanding how these pathogens infect and manipulate the host is key to develop sustainable crop resistance in the long term. Effector proteins are secreted by pathogens to subvert the host immune responses. The roles of several effector proteins have been described; however, it is yet poorly understood how effectors interact with host proteins at a global level. To address this issue, we have generated EffectorK, an interactive database focused on the model plant species Arabidopsis thaliana. This database contains manually curated Arabidopsis-effector protein interactions from the available literature on a wide variety of pathogens. It also contains new experimental data on effectors from two vascular pathogens: Ralstonia pseudosolanacearum and Xanthomonas campestris. This work integrates all the gathered knowledge over the last decades and allows to identify general patterns of how effectors interact with the host proteome. This knowledge is easily accessible and searchable at www.effectork.org.

These random rewiring simulations also allowed us to determine whether effectors from 140 different species interact randomly or convergently with Ath proteins. For this, the number of 141 common interactors of effectors from different species was compared with the experiment data 142 ( Fig 2B). When comparing all three kingdoms, the number of common targets observed was 143 significantly higher than expected by random rewiring. We then analyzed all possible binary, 144 ternary, quaternary and quinary combinations of species and in all cases, the number of common 145 targets observed was higher than expected randomly ( Fig 2C). In order to gather more knowledge on Ath-effector protein interactions, we conducted an 160 extensive literature search compiling data from a wider spectrum of bacterial, fungal, oomycete 161 and animal effector proteins. We only considered published direct protein-protein interactions that 162 had been confirmed by classic techniques such as Y2H, co-immunoprecipitation, pull-down, 163 protein-fragment complementation, fluorescence resonance energy transfer or mass spectrometry. 164 We compiled 287 interactions found in 80 peer-reviewed publications involving 218 Ath proteins 165 and 72 effectors from 22 pathogen species (S2 Table). Among these 22 pathogens, there were nine 166 bacterial species, mostly proteobacteria but also a phytoplasma species; eight animal species 186 what we defined as Ath proteins interacting with two or more effectors (Fig 4). The definition of 187 hub has been debated and it has been traditionally associated with proteins that are highly 188 connected in interactomic networks [19]. Our definition of "effector hub" came from the need to 189 designate the Ath proteins that interact with several effectors and is based exclusively on the 190 number of interacting effector proteins. We identified 100 new effector hubs and increased the 191 degree of 42 previously described effector hubs (S3 Table).

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To evaluate the potential relevance of the newly identified effector hubs in plant immunity, 193 we conducted a second literature survey to check if the corresponding Ath genes had been 194 previously characterized to be involved in plant immunity or pathogen fitness in planta (Table 1). 14 208 In

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To further investigate the potential impact of effectors on the plant interactome, we 227 evaluated the importance of their targets for the organization of the network. We focused on two 228 main network topology parameters: degree and betweenness centrality (Fig 4). The degree of a 15 229 protein represents the number of proteins that it interacts with. In this study we differentiated two 230 types of degrees depending on the nature of the interacting proteins: the Ath degree of a given 231 effector or Ath protein (i.e., number of interacting Ath proteins) and effector degree for a given Ath  (Table 2).
245 Effectively, the area under the curve value of effector targets was higher than the value of the rest 246 of Ath proteins. This indicates that effector targets present generally higher Ath degree than the 247 rest of Ath proteins. Similarly, we compared the betweenness centrality of these two groups of 248 proteins (Table 2 and Fig S5). Effector targets also presented significantly higher betweenness 249 centrality values than the rest of Ath proteins. Altogether, these results indicate that effectors 16 250 preferentially interact with Ath proteins that are more connected to other Ath proteins and that 251 occupy more central positions in the interactomic network.
252  Fig S7). Our data showed that core and variable T3Es from the three  (Fig 1). We have reinforced previously described intra-330 and interspecific convergence of effector targeting with effectors from two new species [11,12], 331 and showed at the same time that most effector targets are pathogen specific (Fig 2 and S2). Our 332 analyses also supported the previously described tendency of effectors to interact with plant 333 proteins better connected and central in the network [43,45], and showed that this tendency is even 334 stronger among effector hubs, multi-pathogen targets and bacterial core T3E targets (Table 2 and 335 Fig S5).

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Our data showed that most effector targets were pathogen-specific (S2 Fig) but at the same 338 time, effectors converge interspecifically onto a small subset of Ath proteins (Fig 2B-C). These a 369 Extensive work will be required to characterize further effector-host protein interactions in other 370 pathosystems. As one of the simplest yet powerful high throughput techniques for protein-protein 371 interaction detection, our work, like others before, highlights the potential of such large-scale Y2H 372 screenings in the identification of novel effector targets in an easy, cheap and systematic manner.
373 EffectorK, an entry point to explore and make sense of plant-effector interactomics

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To conclude, our work also provides valuable resources for the plant-pathogen interaction 375 community. We described 540 new Ath-Rps and Ath-Xcc effector protein interactions that allowed 376 us to identify 166 new effector targets (S1 Table). We also manually curated several publications 377 to assemble a collection of 287 Ath-effector protein interactions from a wide variety of pathogens 23 378 (S2 Table). All this, allowed us to identify 100 novel effector hubs (S3 Table). The contribution to 379 plant immunity of these effector hubs has been described for 19 of them, but remains untested for 380 the majority (Table 1) simulations where the number of common targets between species was higher or equal than the 468 experimentally observed is divided by the number of simulations. When the number of simulations 469 with more common targets than observed was zero, the p-value was set to < 0.001.

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Ascorbic acid deficiency in arabidopsis induces constitutive priming that is dependent on