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Although originally primarily a model for functional biology, the thale cress Arabidopsis thaliana has, owing to its broad geographical distribution and adaptation to diverse abiotic and biotic environments, developed into a powerful model in population genomics. Here, we present chromosome-level genome assemblies of 72 diverse accessions located across the global species range. We found that genomic co-linearity is remarkably conserved, even among geographically and genetically distant accessions. Along chromosome arms, megabase-scale rearrangements are rare, typically present only in a single accession. This indicates that the highly re-arranged karyotype of Arabidopsis thaliana (as compared to other Brassicaceae species), is quasi-fixed and that rearrangements in chromosome arms are counter-selected. In contrast, centromeric regions show much higher structural dynamics, and divergences in core centromeres account for most of the variation in genome size. Pan-genome analyses of the gene space uncovered 40,382 distinct genes, 52% being present in all accessions and 48% appearing to be dispensable, including 23% private to a single accession, indicating a vast unexplored genic diversity. The availability of 72 new Arabidopsis thaliana genome assemblies will empower future genetic research.

Crop wild relatives represent valuable sources of alleles for crop improvement, including adaptation to climate change and emerging diseases. However, introgressions from wild relatives might have deleterious effects on desirable traits, including yield, due to linkage drag. Here, we analyzed the genomic and phenotypic impacts of wild introgressions in inbred lines of cultivated sunflower to estimate the impacts of linkage drag. First, we generated reference sequences for seven cultivated and one wild sunflower genotype, as well as improved assemblies for two additional cultivars. Next, relying on previously generated sequences from wild donor species, we identified introgressions in the cultivated reference sequences, as well as the sequence and structural variants they contain. We then used a ridge-regression best linear unbiased prediction (BLUP) model to test the effects of the introgressions on phenotypic traits in the cultivated sunflower association mapping population. We found that introgression has introduced substantial sequence and structural variation into the cultivated sunflower gene pool, including >3,000 new genes. While introgressions reduced genetic load at protein-coding sequences, they mostly had negative impacts on yield and quality traits. Introgressions found at high frequency in the cultivated gene pool had larger effects than low-frequency introgressions, suggesting that the former likely were targeted by artificial selection. Also, introgressions from more distantly related species were more likely to be maladaptive than those from the wild progenitor of cultivated sunflower. Thus, breeding efforts should focus, as far as possible, on closely related and fully compatible wild relatives.

Phytopathogens are a continuous threat for global food production and security. Emergence or re-emergence of plant pathogens is highly dependent on the environmental conditions affecting pathogen spread and survival. Under climate change, a geographic expansion of pathogen distribution poleward has been observed, potentially resulting in disease outbreaks on crops and wild plants. Therefore, estimating the adaptive potential of plants to novel epidemics and describing its underlying genetic architecture, is a primary need to propose agricultural management strategies reducing pathogen outbreaks and to breed novel plant cultivars adapted to pathogens that might spread in novel habitats under climate change. To address this challenge, we inoculated Pseudomonas syringae strains isolated from Arabidopsis thaliana populations located in south-west of France on the highly genetically polymorphic TOU-A A. thaliana population located east-central France. While no adaptive potential was identified in response to most P. syringae strains, the TOU-A population displays a variable disease response to the P. syringae strain JACO-CL belonging to the phylogroup 7 (PG7). This strain carried a reduced T3SS characteristic of the PG7 as well as flexible genomic traits and potential novel effectors. GWA mapping on 192 TOU-A accessions inoculated with JACO-CL revealed a polygenic architecture. The main QTL region encompasses two R genes and the AT5G18310 gene encoding for ubiquitin hydrolase, a target of the AvrRpt2 P. syringae effector. Altogether, our results pave the way for a better understanding of the genetic and molecular basis of the adaptive potential in an ecologically relevant A. thaliana – P. syringae pathosystem.

Crop wild relatives represent valuable sources of alleles for crop improvement, including adaptation to climate change and emerging diseases. However, introgressions from wild relatives might have deleterious effects on desirable traits, including yield, due to linkage drag. Here we comprehensively analyzed the genomic and phenotypic impacts of wild introgressions into cultivated sunflower to estimate the impacts of linkage drag. First, we generated new reference sequences for seven cultivated and one wild sunflower genotype, as well as improved assemblies for two additional cultivars. Next, relying on previously generated sequences from wild donor species, we identified introgressions in the cultivated reference sequences, as well as the sequence and structural variants they contain. We then used a ridge regression model to test the effects of the introgressions on phenotypic traits in the cultivated sunflower association mapping population. We found that introgression has introduced substantial sequence and structural variation into the cultivated sunflower gene pool, including > 3,000 new genes. While introgressions reduced genetic load at protein-coding sequences and positively affected traits associated with abiotic stress resistance, they mostly had negative impacts on yield and quality traits. Introgressions found at high frequency in the cultivated gene pool had larger effects than low frequency introgressions, suggesting that the former likely were targeted by artificial selection. Also, introgressions from more distantly related species were more likely to be maladaptive than those from the wild progenitor of cultivated sunflower. Thus, pre-breeding efforts should focus, as far as possible, on closely related and fully compatible wild relatives.

The principles of heredity state that the two alleles carried by a heterozygote are equally transmitted to the progeny. However, genomic regions that escape this rule have been reported in many organisms. It is notably the case of genetic loci referred to as gamete killers, where one allele enhances its transmission by causing the death of the gametes that do not carry it. Gamete killers are of great interest, particularly to understand mechanisms of evolution and speciation. Although being common in plants, only a few, all in rice, have so far been deciphered to the causal genes. Here, we studied a pollen killer found in hybrids between two accessions of Arabidopsis thaliana. Exploring natural variation, we observed this pollen killer in many crosses within the species. Genetic analyses revealed that three genetically linked elements are necessary for pollen killer activity. Using mutants, we showed that this pollen killer works according to a poison-antidote model, where the poison kills pollen grains not producing the antidote. We identified the gene encoding the antidote, a chimeric protein addressed to mitochondria. De novo genomic sequencing in 12 natural variants with different behaviors regarding the pollen killer revealed a hyper variable locus, with important structural variations particularly in killer genotypes, where the antidote gene recently underwent duplications. Our results strongly suggest that the gene has newly evolved within A. thaliana. Finally, we identified in the protein sequence polymorphisms related to its antidote activity.

Understanding the genetic basis of phenotypic plasticity is crucial for predicting and managing climate change effects on wild plants and crops. Here, we combined crop modeling and quantitative genetics to study the genetic control of oil yield plasticity for multiple abiotic stresses in sunflower. First we developed stress indicators to characterize 14 environments for three abiotic stresses (cold, drought and nitrogen) using the SUNFLO crop model and phenotypic variations of three commercial varieties. The computed plant stress indicators better explain yield variation than descriptors at the climatic or crop levels. In those environments, we observed oil yield of 317 sunflower hybrids and regressed it with three selected stress indicators. The slopes of cold stress norm reaction were used as plasticity phenotypes in the following genome-wide association study. Among the 65,534 tested SNP, we identified nine QTL controlling oil yield plasticity to cold stress. Associated SNP are localized in genes previously shown to be involved in cold stress responses: oligopeptide transporters, LTP, cystatin, alternative oxidase, or root development. This novel approach opens new perspectives to identify genomic regions involved in genotype-by-environment interaction of a complex traits to multiple stresses in realistic natural or agronomical conditions.

This article describes how RNA expression data of 173 genes were produced on 384 sunflowerhybrids grown infield conditions. Sunflower hybrids were selected to represent genetic diversity withincultivated sunflower. The RNA was extracted from mature leaves at one time seven days after anthesis.These data allow to differentiate the different genotype behaviours and constitute a valuable resource to thecommunity to study the adaptation of crops tofield conditions and the molecular basis of heterosis. It isavailable on data.inra.fr repository.; Cet article décrit la production des niveaux d’expression de 173 gènes dans 384 hybrides de tournesol cultivés en conditions de champ. Les hybrides sont issus de parents choisis pour représenter la diversité génétique dans le tournesol cultivé. Les ARN ont été extraits à partir de feuilles matures environ sept jours après la floraison. Ces données permettent de différencier les comportements des différents génotypes et constituent une ressource importante pour les chercheurs intéressés dans l’adaptation des espèces cultivées aux conditions agronomiques et aux bases moléculaires de l’hétérosis. Elles sont disponibles sur le portail Data INRAE : data.inra.fr.

Modified from the protocol of Baptiste Mayjonade, Jérome Gouzy, Cécile Donnadieu, Nicolas Pouilly, William Marande, Caroline Callot, Nicolas Langlade and Stéphane Munos, High molecular weight gDNA extraction, Bio Techniques, Vol. 61, No. 4, October 2016, pp. 203-205. BioTechniques 61:203-205 (October 2016) doi 10.2144/000114460 https://www.future-science.com/doi/10.2144/000114460 The original protocol failed to extract High Molecular Weight genomic DNA from orchids samples (poor yield and low purity). Therefore modifications (mainly/mostly BME addition during the lysis step and a Phenol:Chloroform purification step) were made to create a new version of this protocol. This new version allowed to successfully extract DNA from orchid samples. This DNA was then sequenced on a Nanopore PromethION platform and good results in term of total yield and read length were obtained. This protocol was also successfully applied to a wide range of plant species (See attached document/excel sheet). ACKNOWLEDGEMENT Sequencing of the Arabidopsis thaliana samples listed in the attached reports was performed in collaboration with the GeT core facility, Toulouse, France (http://get.genotoul.fr), and was supported by France Génomique National infrastructure, funded as part of “Investissement d’avenir” program managed by Agence Nationale pour la Recherche (contract ANR-10-INBS-09). We particularly want to thank Lisa Gil and Céline Lopez-Roques for the sequencing part and Roxane Boyer for the data processing. Sampling and DNA Extraction for the Ophrys sphegodes samples were financially supported by the University of Zürich Research Priority Programme URPP "Evolution in Action" (PMS/AR). Sequencing was performed by Daniel Frei in the Department of Molecular Diagnostics, Genomics and Bioinformatics, Agroscope Wädenswil, Switzerland. We are thankful to Jürg E. Frey, Agroscope, for financial support. We are grateful to Philipp M. Schlüter and Ueli Grossniklaus for support and helpful discussion. Sequencing of Lolium multiflorium was performed in Agroscope by Daniel Frei. We thank Daniel, Jürg E. Frei, Dario Copetti and Bruno Studer, Department of Environmental Systems Science, ETH Zürich, for sharing the sequencing results. Sequencing of Brassica incana was performed at FGCZ Zürich. We are thankful to Léa Frachon, Department of Systematic and Evolutionary Botany, UZH Zürich, to share the data. DNA extraction of Gorteria diffusa was performed by Roman T. Kellenberger, Department of Plant Sciences, University of Cambridge. We wish to thank him for sharing his sequencing results.

Associate Editor: Stephen Wright; International audience; Despite the importance of plant-plant interactions on plant community dynamics and crop yield, our understanding of the adaptive genetics underlying these interactions is still limited and deserves to be investigated in the context of complex and diffuse interactions occurring in plant assemblages. Here, based on 145 natural populations of Arabidopsis thaliana located in south-west of France and characterized for plant communities, we conducted a Genome-Environment Association analysis to finely map adaptive genomic regions of A. thaliana associated with plant community descriptors. To control for correlated abiotic environment effects, we also characterized the populations for a set of biologically meaningful climate and soil variables. A nonnegligible fraction of top single nucleotide polymorphisms was associated with both plant community descriptors and abiotic variables, highlighting the importance of considering the actual abiotic drivers of plant communities to disentangle genetic variants for biotic adaptation from genetic variants for abiotic adaptation. The adaptive loci associated with species abundance were highly dependent on the identity of the neighboring species suggesting a high degree of biotic specialization of A. thaliana to members of its plant interaction network. Moreover, the identification of adaptive loci associated with a-diversity and composition of plant communities supports the ability of A. thaliana to interact simultaneously with multiple plant neighbors, which in turn can help to understand the role of community-wide selection. Altogether, our study highlights that dissecting the genetic basis underlying plant-plant interactions at a regional scale while controlling for abiotic confounding factors can help understanding the adaptive mechanisms modulating natural plant assemblages.

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 and leaf associated bacterial communities of 163 ecologically contrasted Arabidopsis thaliana populations sampled across two seasons in South-West 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 OTUs. This result suggests that the potential biomarkers controlling pathogen’s invasion are highly dynamic.

This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession PSQE00000000.; Advances in deciphering the functional architecture of eukaryotic genomes have been facilitated by recent breakthroughs in sequencing technologies, enabling a more comprehensive representation of genes and repeat elements in genome sequence assemblies, as well as more sensitive and tissue-specific analyses of gene expression. Here we show that PacBio sequencing has led to a substantially improved genome assembly of Medicago truncatula A17, a legume model species notable for endosymbiosis studies1, and has enabled the identification of genome rearrangements between genotypes at a near-base-pair resolution. Annotation of the new M. truncatula genome sequence has allowed for a thorough analysis of transposable elements and their dynamics, as well as the identification of new players involved in symbiotic nodule development, in particular 1,037 upregulated long non-coding RNAs (lncRNAs). We have also discovered that a substantial proportion (~35% and 38%, respectively) of the genes upregulated in nodules or expressed in the nodule differentiation zone colocalize in genomic clusters (270 and 211, respectively), here termed symbiotic islands. These islands contain numerous expressed lncRNA genes and display differentially both DNA methylation and histone marks. Epigenetic regulations and lncRNAs are therefore attractive candidate elements for the orchestration of symbiotic gene expression in the M. truncatula genome.

This article describes how the transcriptomic data were produced on sunflower plants subjected to water deficit. Twenty-four sunflower (Helianthus annuus) genotypes were selected to represent genetic diversity within cultivated sunflower and included both inbred lines and their hybrids. Drought stress was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen. Here, we provide transcriptomic data from sunflower leaves. These data differentiate both plant water status and the different genotypes. They constitute a valuable resource to the community to study adaptation of crops to drought and the transcriptomic basis of heterosis.; Données transcriptomiques de feuilles de huit lignées de tournesol et de leurs 16 hybrides soumis à un stress hydrique. Cet article décrit la production de données transcriptomiques sur des plantes de tournesol soumises à un stress hydrique. Vingt-quatre génotypes de tournesol (Helianthus annuus) ont été sélectionnés pour représenter la diversité génétique parmi le tournesol cultivé et comprennent à la fois des lignées et leurs hybrides. Le stress hydrique a été appliqué sur des plantes en pots au stade végétatif grâce à la plateforme de phénotypage haut-débit Heliaphen. Ici, nous mettons à disposition les données transcritomiques de feuilles de ces plantes. Les données permettent de différencier à la fois le statut hydrique et les différents génotypes. Elles constituent une ressource importante pour la communauté pour étudier l'adaptation des plantes à la sécheresse et les bases de l'hétérosis au niveau transcriptomique.

Plant or soil water status are required in many scientific fields to understand plant responses to drought. Because the transcriptomic response to abiotic conditions, such as water deficit, reflects plant water status, genomic tools could be used to develop a new type of molecular biomarker. Using the sunflower (Helianthus annuus L.) as a model species to study the transcriptomic response to water deficit both in greenhouse and field conditions, we specifically identified three genes that showed an expression pattern highly correlated to plant water status as estimated by the pre-dawn leaf water potential, fraction of transpirable soil water, soil water content or fraction of total soil water in controlled conditions. We developed a generalized linear model to estimate these classical water status indicators from the expression levels of the three selected genes under controlled conditions. This estimation was independent of the four tested genotypes and the stage (pre- or post-flowering) of the plant. We further validated this gene expression biomarker under field conditions for four genotypes in three different trials, over a large range of water status, and we were able to correct their expression values for a large diurnal sampling period.

Understanding the genetic basis of phenotypic plasticity is crucial for predicting and managing climate change effects on wild plants and crops. Here, we combined crop modelling and quantitative genetics to study the genetic control of oil yield plasticity for multiple abiotic stresses in sunflower. First, we developed stress indicators to characterize 14 environments for three abiotic stresses (cold, drought and nitrogen) using the SUNFLO crop model and phenotypic variations of three commercial varieties. The computed plant stress indicators better explain yield variation than descriptors at the climatic or crop levels. In those environments, we observed oil yield of 317 sunflower hybrids and regressed it with three selected stress indicators. The slopes of cold stress norm reaction were used as plasticity phenotypes in the following genome-wide association study. Among the 65 534 tested Single Nucleotide Polymorphisms (SNPs), we identified nine quantitative trait loci controlling oil yield plasticity to cold stress. Associated single nucleotide polymorphisms are localized in genes previously shown to be involved in cold stress responses: oligopeptide transporters, lipid transfer protein, cystatin, alternative oxidase or root development. This novel approach opens new perspectives to identify genomic regions involved in genotype-by-environment interaction of a complex traits to multiple stresses in realistic natural or agronomical conditions.

Plant or soil water status is required in many scientific fields to understand plant responses to drought. Because the transcriptomic response to abiotic conditions, such as water deficit, reflects plant water status, genomic tools could be used to develop a new type of molecular biomarker. Using the sunflower (Helianthus annuus L.) as a model species to study the transcriptomic response to water deficit both in greenhouse and field conditions, we specifically identified three genes that showed an expression pattern highly correlated to plant water status as estimated by the pre-dawn leaf water potential, fraction of transpirable soil water, soil water content or fraction of total soil water in controlled conditions. We developed a generalized linear model to estimate these classical water status indicators from the expression levels of the three selected genes under controlled conditions. This estimation was independent of the four tested genotypes and the stage (pre- or post-flowering) of the plant. We further validated this gene expression biomarker under field conditions for four genotypes in three different trials, over a large range of water status, and we were able to correct their expression values for a large diurnal sampling period.