Convergent targeting of a common host protein-network by pathogen effectors from three kingdoms of life.

While conceptual principles governing plant immunity are becoming clear, its systems-level organization and the evolutionary dynamic of the host-pathogen interface are still obscure. We generated a systematic protein-protein interaction network of virulence effectors from the ascomycete pathogen Golovinomyces orontii and Arabidopsis thaliana host proteins. We combined this data set with corresponding data for the eubacterial pathogen Pseudomonas syringae and the oomycete pathogen Hyaloperonospora arabidopsidis. The resulting network identifies host proteins onto which intraspecies and interspecies pathogen effectors converge. Phenotyping of 124 Arabidopsis effector-interactor mutants revealed a correlation between intraspecies and interspecies convergence and several altered immune response phenotypes. Several effectors and the most heavily targeted host protein colocalized in subnuclear foci. Products of adaptively selected Arabidopsis genes are enriched for interactions with effector targets. Our data suggest the existence of a molecular host-pathogen interface that is conserved across Arabidopsis accessions, while evolutionary adaptation occurs in the immediate network neighborhood of effector targets.

Comprehensive and relaxed search for oligonucleotide signatures in hierarchically clustered sequence datasets.

MOTIVATION: PCR, hybridization, DNA sequencing and other important methods in molecular diagnostics rely on both sequence-specific and sequence group-specific oligonucleotide primers and probes. Their design depends on the identification of oligonucleotide signatures in whole genome or marker gene sequences. Although genome and gene databases are generally available and regularly updated, collections of valuable signatures are rare. Even for single requests, the search for signatures becomes computationally expensive when working with large collections of target (and non-target) sequences. Moreover, with growing dataset sizes, the chance of finding exact group-matching signatures decreases, necessitating the application of relaxed search methods. The resultant substantial increase in complexity is exacerbated by the dearth of algorithms able to solve these problems efficiently. RESULTS: We have developed CaSSiS, a fast and scalable method for computing comprehensive collections of sequence- and sequence group-specific oligonucleotide signatures from large sets of hierarchically clustered nucleic acid sequence data. Based on the ARB Positional Tree (PT-)Server and a newly developed BGRT data structure, CaSSiS not only determines sequence-specific signatures and perfect group-covering signatures for every node within the cluster (i.e. target groups), but also signatures with maximal group coverage (sensitivity) within a user-defined range of non-target hits (specificity) for groups lacking a perfect common signature. An upper limit of tolerated mismatches within the target group, as well as the minimum number of mismatches with non-target sequences, can be predefined. Test runs with one of the largest phylogenetic gene sequence datasets available indicate good runtime and memory performance, and in silico spot tests have shown the usefulness of the resulting signature sequences as blueprints for group-specific oligonucleotide probes. AVAILABILITY: Software and Supplementary Material are available at http://cassis.in.tum.de/.