The Pseudomonas syringae complex is composed of numerous genetic lineages of strains from both agricultural and environmental habitats including habitats closely linked to the water cycle. The new insights from the discovery of this bacterial species in habitats outside of agricultural contexts per se have led to the revelation of a wide diversity of strains in this complex beyond what was known from agricultural contexts. Here, through Multi Locus Sequence Typing (MLST) of 216 strains, we identified 23 clades within 13 phylogroups among which the seven previously described P. syringae phylogroups were included. The phylogeny of the core genome of 29 strains representing nine phylogroups was similar to the phylogeny obtained with MLST thereby confirming the robustness of MLST-phylogroups. We show that phenotypic traits rarely provide a satisfactory means for classification of strains even if some combinations are highly probable in some phylogroups. We demonstrate that the citrate synthase (cts) housekeeping gene can accurately predict the phylogenetic affiliation for more than 97% of strains tested. We propose a list of cts sequences to be used as a simple tool for quickly and precisely classifying new strains. Finally, our analysis leads to predictions about the diversity of P. syringae that is yet to be discovered. We present here an expandable framework mainly based on cts genetic analysis into which more diversity can be integrated.

The emergence and re-emergence of plant pathogenic microorganisms are processes that imply perturbations in both host and pathogen ecological niches. Global change is largely assumed to drive the emergence of new etiological agents by altering the equilibrium of the ecological habitats which in turn places hosts more in contact with pathogen reservoirs. In this context, the number of epidemics is expected to increase dramatically in the next coming decades both in wild and crop plants. Under these considerations, the identification of the genetic variants underlying natural variation of resistance is a pre-requisite to estimate the adaptive potential of wild plant populations and to develop new breeding resistant cultivars. On the other hand, the prediction of pathogen’s genetic determinants underlying disease emergence can help to identify plant resistance alleles. In the genomic era, whole genome sequencing combined with the development of statistical methods led to the emergence of Genome Wide Association (GWA) mapping, a powerful tool for detecting genomic regions associated with natural variation of disease resistance in both wild and cultivated plants. However, GWA mapping has been less employed for the detection of genetic variants associated with pathogenicity in microbes. Here, we reviewed GWA studies performed either in plants or in pathogenic microorganisms (bacteria, fungi and oomycetes). In addition, we highlighted the benefits and caveats of the emerging joint GWA mapping approach that allows for the simultaneous identification of genes interacting between genomes of both partners. Finally, based on co-evolutionary processes in wild populations, we highlighted a phenotyping-free joint GWA mapping approach as a promising tool for describing the molecular landscape underlying plant - microbe interactions.

A current challenge in microbial pathogenesis is to identify biological control agents that may prevent and/or limit host invasion by microbial pathogens. In natura, hosts are often infected by multiple pathogens. However, most of the current studies have been performed under laboratory controlled conditions and by taking into account the interaction between a single commensal species and a single pathogenic species. The next step is therefore to explore the relationships between host–microbial communities (microbiota) and microbial members with potential pathogenic behavior (pathobiota) in a realistic ecological context. In the present study, we investigated such relationships within root-associated and leaf-associated bacterial communities of 163 ecologically contrasted Arabidopsis thaliana populations sampled across two seasons in southwest of France. In agreement with the theory of the invasion paradox, we observed a significant humped-back relationship between microbiota and pathobiota α-diversity that was robust between both seasons and plant organs. In most populations, we also observed a strong dynamics of microbiota composition between seasons. Accordingly, the potential pathobiota composition was explained by combinations of season-specific microbiota operational taxonomic units. This result suggests that the potential biomarkers controlling pathogen’s invasion are highly dynamic.

Understanding the genetic bases underlying climate adaptation is a key element to predict the potential of species to face climate warming. Although substantial climate variation is observed at a micro-geographic scale, most genomic maps of climate adaptation have been established at broader geographical scales. Here, by using a Pool-Seq approach combined with a Bayesian hierarchical model that control for confounding by population structure, we performed a genome–environment association (GEA) analysis to investigate the genetic basis of adaptation to six climate variables in 168 natural populations of Arabidopsis thaliana distributed in south-west of France. Climate variation among the 168 populations represented up to 24% of climate variation among 521 European locations where A. thaliana inhabits. We identified neat and strong peaks of association, with most of the associated SNPs being significantly enriched in likely functional variants and/or in the extreme tail of genetic differentiation among populations. Furthermore, genes involved in transcriptional mechanisms appear predominant in plant functions associated with local climate adaptation. Globally, our results suggest that climate adaptation is an important driver of genomic variation in A. thaliana at a small spatial scale and mainly involves genome-wide changes in fundamental mechanisms of gene regulation. The identification of climate-adaptive genetic loci at a micro-geographic scale also highlights the importance to include within-species genetic diversity in ecological niche models for projecting potential species distributional shifts over short geographic distances.

Significance: Genome-wide association (GWA) mapping is a powerful tool for identification of genetic variants underlying complex traits. However, existing methods typically perform GWA mapping within a single species; methods allowing the description of the genomic landscape of interspecies interactions are only beginning to be developed. Here, we present a method to simultaneously perform GWA mapping on two interacting species. We applied our approach to the Arabidopsis thaliana–Xanthomonas arboricola pathosystem and identified candidate genes conferring host–pathogen specificity. By integrating the whole-genome sequence data available for pairs of interacting species, we can decipher the genetic architecture of complex traits in finer detail than has previously been possible.