Insights into the metabolic specificities of pathogenic strains from the Ralstonia solanacearum species complex.

All the strains grouped under the species Ralstonia solanacearum represent a species complex responsible for many diseases on agricultural crops throughout the world. The strains have different lifestyles and host range. Here, we investigated whether specific metabolic pathways contribute to strain diversification. To this end, we carried out systematic comparisons on 11 strains representing the diversity of the species complex. We reconstructed the metabolic network of each strain from its genome sequence and looked for the metabolic pathways differentiating the different reconstructed networks and, by extension, the different strains. Finally, we conducted an experimental validation by determining the metabolic profile of each strain with the Biolog technology. Results revealed that the metabolism is conserved between strains, with a core metabolism composed of 82% of the pan-reactome. The three species composing the species complex could be distinguished according to the presence/absence of some metabolic pathways, in particular, one involving salicylic acid degradation. Phenotypic assays revealed that the trophic preferences on organic acids and several amino acids such as glutamine, glutamate, aspartate, and asparagine are conserved between strains. Finally, we generated mutants lacking the quorum-sensing-dependent regulator PhcA in four diverse strains, and we showed that the phcA-dependent trade-off between growth and production of virulence factors is conserved across the R. solanacearum species complex. IMPORTANCE Ralstonia solanacearum is one of the most important threats to plant health worldwide, causing disease on a very large range of agricultural crops such as tomato or potato. Behind the R. solanacearum name are hundreds of strains with different host range and lifestyle, classified into three species. Studying the differences between strains allows to better apprehend the biology of the pathogens and the specificity of some strains. None of the published genomic comparative studies have focused on the metabolism of the strains so far. We developed a new bioinformatic pipeline to build high-quality metabolic networks and used a combination of metabolic modeling and high-throughput phenotypic Biolog microplates to look for the metabolic differences between 11 strains across the three species. Our study revealed that genes encoding enzymes are overall conserved, with few variations between strains. However, more variations were observed when considering substrate usage. These variations probably result from regulation rather than the presence or absence of enzymes in the genome.

Rhizocarpon geographicum Lichen Discloses a Highly Diversified Microbiota Carrying Antibiotic Resistance and Persistent Organic Pollutant Tolerance.

As rock inhabitants, lichens are exposed to extreme and fluctuating abiotic conditions associated with poor sources of nutriments. These extreme conditions confer to lichens the unique ability to develop protective mechanisms. Consequently, lichen-associated microbes disclose highly versatile lifestyles and ecological plasticity, enabling them to withstand extreme environments. Because of their ability to grow in poor and extreme habitats, bacteria associated with lichens can tolerate a wide range of pollutants, and they are known to produce antimicrobial compounds. In addition, lichen-associated bacteria have been described to harbor ecological functions crucial for the evolution of the lichen holobiont. Nevertheless, the ecological features of lichen-associated microbes are still underestimated. To explore the untapped ecological diversity of lichen-associated bacteria, we adopted a novel culturomic approach on the crustose lichen Rhizocarpon geographicum. We sampled R. geographicum in French habitats exposed to oil spills, and we combined nine culturing methods with 16S rRNA sequencing to capture the greatest bacterial diversity. A deep functional analysis of the lichen-associated bacterial collection showed the presence of a set of bacterial strains resistant to a wide range of antibiotics and displaying tolerance to Persistent Organic Pollutants (POPs). Our study is a starting point to explore the ecological features of the lichen microbiota.

MERRIN: MEtabolic regulation rule INference from time series data.

MOTIVATION: Many techniques have been developed to infer Boolean regulations from a prior knowledge network (PKN) and experimental data. Existing methods are able to reverse-engineer Boolean regulations for transcriptional and signaling networks, but they fail to infer regulations that control metabolic networks. RESULTS: We present a novel approach to infer Boolean rules for metabolic regulation from time-series data and a PKN. Our method is based on a combination of answer set programming and linear programming. By solving both combinatorial and linear arithmetic constraints, we generate candidate Boolean regulations that can reproduce the given data when coupled to the metabolic network. We evaluate our approach on a core regulated metabolic network and show how the quality of the predictions depends on the available kinetic, fluxomics or transcriptomics time-series data. AVAILABILITY AND IMPLEMENTATION: Software available at https://github.com/bioasp/merrin. SUPPLEMENTARY INFORMATION: Supplementary data are available at https://doi.org/10.5281/zenodo.6670164.

Rhizobium leguminosarum symbiovar viciae strains are natural wheat endophytes that can stimulate root development.

Although rhizobia that establish a nitrogen-fixing symbiosis with legumes are also known to promote growth in non-legumes, studies on rhizobial associations with wheat roots are scarce. We searched for Rhizobium leguminosarum symbiovar viciae (Rlv) strains naturally competent to endophytically colonize wheat roots. We isolated 20 strains from surface-sterilized wheat roots and found a low diversity of Rlv compared to that observed in the Rlv species complex. We tested the ability of a subset of these Rlv for wheat root colonization when co-inoculated with other Rlv. Only a few strains, including those isolated from wheat roots, and one strain isolated from pea nodules, were efficient in colonizing roots in co-inoculation conditions, while all the strains tested in single strain inoculation conditions were found to colonize the surface and interior of roots. Furthermore, Rlv strains isolated from wheat roots were able to stimulate root development and early arbuscular mycorrhizal fungi colonization. These responses were strain and host genotype dependent. Our results suggest that wheat can be an alternative host for Rlv; nevertheless, there is a strong competition between Rlv strains for wheat root colonization. In addition, we showed that Rlv are endophytic wheat root bacteria with potential ability to modify wheat development.

An integrated approach reveals how lipo-chitooligosaccharides interact with the lysin motif receptor-like kinase MtLYR3.

N-acetylglucosamine containing compounds acting as pathogenic or symbiotic signals are perceived by plant-specific Lysin Motif Receptor-Like Kinases (LysM-RLKs). The molecular mechanisms of this perception are not fully understood, notably those of lipo-chitooligosaccharides (LCOs) produced during root endosymbioses with nitrogen-fixing bacteria or arbuscular mycorrhizal fungi. In Medicago truncatula, we previously identified the LysM-RLK LYR3 (MtLYR3) as a specific LCO-binding protein. We also showed that the absence of LCO binding to LYR3 of the non-mycorrhizal Lupinus angustifolius, (LanLYR3), was related to LysM3, which differs from that of MtLYR3 by several amino acids and, particularly, by a critical tyrosine residue absent in LanLYR3. Here, we aimed to define the LCO binding site of MtLYR3 by using molecular modelling and simulation approaches, combined with site-directed mutagenesis and LCO binding experiments. 3D models of MtLYR3 and LanLYR3 ectodomains were built, and homology modelling and molecular dynamics (MD) simulations were performed. Molecular docking and MD simulation on the LysM3 identified potential key residues for LCO binding. We highlighted by steered MD simulations that in addition to the critical tyrosine, two other residues were important for LCO binding in MtLYR3. Substitution of these residues in LanLYR3-LysM3 by those of MtLYR3-LysM3 allowed the recovery of high-affinity LCO binding in experimental radioligand-binding assays. An analysis of selective constraints revealed that the critical tyrosine has experienced positive selection pressure and is absent in some LYR3 proteins. These findings now pave the way to uncover the functional significance of this specific evolutionary pattern.

MtEFD and MtEFD2: Two transcription factors with distinct neofunctionalization in symbiotic nodule development.

Rhizobium-legume nitrogen-fixing symbiosis involves the formation of a specific organ, the root nodule, which provides bacteria with the proper cellular environment for atmospheric nitrogen fixation. Coordinated differentiation of plant and bacterial cells is an essential step of nodule development, for which few transcriptional regulators have been characterized. Medicago truncatula ETHYLENE RESPONSE FACTOR REQUIRED FOR NODULE DIFFERENTIATION (MtEFD) encodes an APETALA2/ETHYLENE RESPONSIVE FACTOR (ERF) transcription factor, the mutation of which leads to both hypernodulation and severe defects in nodule development. MtEFD positively controls a negative regulator of cytokinin signaling, the RESPONSE REGULATOR 4 (MtRR4) gene. Here we showed that that the Mtefd-1 mutation affects both plant and bacterial endoreduplication in nodules, as well as the expression of hundreds of genes in young and mature nodules, upstream of known regulators of symbiotic differentiation. MtRR4 expressed with the MtEFD promoter complemented Mtefd-1 hypernodulation but not the nodule differentiation phenotype. Unexpectedly, a nonlegume homolog of MtEFD, AtERF003 in Arabidopsis (Arabidopsis thaliana), could efficiently complement both phenotypes of Mtefd-1, in contrast to the MtEFD paralog MtEFD2 expressed in the root and nodule meristematic zone. A domain swap experiment showed that MtEFD2 differs from MtEFD by its C-terminal fraction outside the DNA binding domain. Furthermore, clustered regularly interspaced short palindromic repeats-CRISPR associated protein 9 (CRISPR-Cas9) mutagenesis of MtEFD2 led to a reduction in the number of nodules formed in Mtefd-1, with downregulation of a set of genes, including notably NUCLEAR FACTOR-YA1 (MtNF-YA1) and MtNF-YB16, which are essential for nodule meristem establishment. We, therefore, conclude that nitrogen-fixing symbiosis recruited two proteins originally expressed in roots, MtEFD and MtEFD2, with distinct functions and neofunctionalization processes for each of them.

A multi-organ metabolic model of tomato predicts plant responses to nutritional and genetic perturbations.

Predicting and understanding plant responses to perturbations require integrating the interactions between nutritional sources, genes, cell metabolism, and physiology in the same model. This can be achieved using metabolic modeling calibrated by experimental data. In this study, we developed a multi-organ metabolic model of a tomato (Solanum lycopersicum) plant during vegetative growth, named Virtual Young TOmato Plant (VYTOP) that combines genome-scale metabolic models of leaf, stem and root and integrates experimental data acquired from metabolomics and high-throughput phenotyping of tomato plants. It is composed of 6,689 reactions and 6,326 metabolites. We validated VYTOP predictions on five independent use cases. The model correctly predicted that glutamine is the main organic nutrient of xylem sap. The model estimated quantitatively how stem photosynthetic contribution impacts exchanges between the different organs. The model was also able to predict how nitrogen limitation affects vegetative growth and the metabolic behavior of transgenic tomato lines with altered expression of core metabolic enzymes. The integration of different components, such as a metabolic model, physiological constraints, and experimental data, generates a powerful predictive tool to study plant behavior, which will be useful for several other applications, such as plant metabolic engineering or plant nutrition.

EffectorK, a comprehensive resource to mine for Ralstonia, Xanthomonas, and other published effector interactors 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 defence 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 invertebrate 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 interactors, suggesting dual roles as modulators of generic and adaptive host processes. Network analyses revealed that effector interactors, particularly "effector hubs" and bacterial core effector interactors, 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.

Genome-Scale Investigation of the Metabolic Determinants Generating Bacterial Fastidious Growth.

High proliferation rate and robustness are vital characteristics of bacterial pathogens that successfully colonize their hosts. The observation of drastically slow growth in some pathogens is thus paradoxical and remains unexplained. In this study, we sought to understand the slow (fastidious) growth of the plant pathogen Xylella fastidiosa Using genome-scale metabolic network reconstruction, modeling, and experimental validation, we explored its metabolic capabilities. Despite genome reduction and slow growth, the pathogen's metabolic network is complete but strikingly minimalist and lacking in robustness. Most alternative reactions were missing, especially those favoring fast growth, and were replaced by less efficient paths. We also found that the production of some virulence factors imposes a heavy burden on growth. Interestingly, some specific determinants of fastidious growth were also found in other slow-growing pathogens, enriching the view that these metabolic peculiarities are a pathogenicity strategy to remain at a low population level.IMPORTANCE Xylella fastidiosa is one of the most important threats to plant health worldwide, causing disease in the Americas on a range of agricultural crops and trees, and recently associated with a critical epidemic affecting olive trees in Europe. A main challenge for the detection of the pathogen and the development of physiological studies is its fastidious growth, as the generation time can vary from 10 to 100 h for some strains. This physiological peculiarity is shared with several human pathogens and is poorly understood. We performed an analysis of the metabolic capabilities of X. fastidiosa through a genome-scale metabolic model of the bacterium. This model was reconstructed and manually curated using experiments and bibliographical evidence. Our study revealed that fastidious growth most probably results from different metabolic specificities such as the absence of highly efficient enzymes or a global inefficiency in virulence factor production. These results support the idea that the fragility of the metabolic network may have been shaped during evolution to lead to the self-limiting behavior of X. fastidiosa.

An ancestral signalling pathway is conserved in intracellular symbioses-forming plant lineages.

Plants are the foundation of terrestrial ecosystems, and their colonization of land was probably facilitated by mutualistic associations with arbuscular mycorrhizal fungi. Following this founding event, plant diversification has led to the emergence of a tremendous diversity of mutualistic symbioses with microorganisms, ranging from extracellular associations to the most intimate intracellular associations, where fungal or bacterial symbionts are hosted inside plant cells. Here, through analysis of 271 transcriptomes and 116 plant genomes spanning the entire land-plant diversity, we demonstrate that a common symbiosis signalling pathway co-evolved with intracellular endosymbioses, from the ancestral arbuscular mycorrhiza to the more recent ericoid and orchid mycorrhizae in angiosperms and ericoid-like associations of bryophytes. By contrast, species forming exclusively extracellular symbioses, such as ectomycorrhizae, and those forming associations with cyanobacteria, have lost this signalling pathway. This work unifies intracellular symbioses, revealing conservation in their evolution across 450 million yr of plant diversification.

Computational analysis of the Plasmodiophora brassicae genome: mitochondrial sequence description and metabolic pathway database design.

Plasmodiophora brassicae is an obligate biotrophic pathogenic protist responsible for clubroot, a root gall disease of Brassicaceae species. In addition to the reference genome of the P. brassicae European e3 isolate and the draft genomes of Canadian or Chinese isolates, we present the genome of eH, a second European isolate. Refinement of the annotation of the eH genome led to the identification of the mitochondrial genome sequence, which was found to be bigger than that of Spongospora subterranea, another plant parasitic Plasmodiophorid phylogenetically related to P. brassicae. New pathways were also predicted, such as those for the synthesis of spermidine, a polyamine up-regulated in clubbed regions of roots. A P. brassicae pathway genome database was created to facilitate the functional study of metabolic pathways in transcriptomics approaches. These available tools can help in our understanding of the regulation of P. brassicae metabolism during infection and in response to diverse constraints.

Sunflower resistance to multiple downy mildew pathotypes revealed by recognition of conserved effectors of the oomycete Plasmopara halstedii.

Over the last 40 years, new sunflower downy mildew isolates (Plasmopara halstedii) have overcome major gene resistances in sunflower, requiring the identification of additional and possibly more durable broad-spectrum resistances. Here, 354 RXLR effectors defined in silico from our new genomic data were classified in a network of 40 connected components sharing conserved protein domains. Among 205 RXLR effector genes encoding conserved proteins in 17 P. halstedii pathotypes of varying virulence, we selected 30 effectors that were expressed during plant infection as potentially essential genes to target broad-spectrum resistance in sunflower. The transient expression of the 30 core effectors in sunflower and in Nicotiana benthamiana leaves revealed a wide diversity of targeted subcellular compartments, including organelles not so far shown to be targeted by oomycete effectors such as chloroplasts and processing bodies. More than half of the 30 core effectors were able to suppress pattern-triggered immunity in N. benthamiana, and five of these induced hypersensitive responses (HR) in sunflower broad-spectrum resistant lines. HR triggered by PhRXLRC01 co-segregated with Pl22 resistance in F3 populations and both traits localized in 1.7 Mb on chromosome 13 of the sunflower genome. Pl22 resistance was physically mapped on the sunflower genome recently sequenced, unlike all the other downy mildew resistances published so far. PhRXLRC01 and Pl22 are proposed as an avirulence/resistance gene couple not previously described in sunflower. Core effector recognition is a successful strategy to accelerate broad-spectrum resistance gene identification in complex crop genomes such as sunflower.

LysM Receptor-Like Kinase and LysM Receptor-Like Protein Families: An Update on Phylogeny and Functional Characterization.

Members of plant specific families of receptor-like kinases (RLKs) and receptor-like proteins (RLPs), containing 3 extracellular LysMs have been shown to directly bind and/or to be involved in perception of lipo-chitooligosaccharides (LCO), chitooligosaccharides (CO), and peptidoglycan (PGN), three types of GlcNAc-containing molecules produced by microorganisms. These receptors are involved in microorganism perception by plants and can activate different plant responses leading either to symbiosis establishment or to defense responses against pathogens. LysM-RLK/Ps belong to multigenic families. Here, we provide a phylogeny of these families in eight plant species, including dicotyledons and monocotyledons, and we discuss known or putative biological roles of the members in each of the identified phylogenetic groups. We also report and discuss known biochemical properties of the LysM-RLK/Ps.

Evolutionary History of Plant LysM Receptor Proteins Related to Root Endosymbiosis.

LysM receptor-like kinases (LysM-RLKs), which are specific to plants, can control establishment of both the arbuscular mycorrhizal (AM) and the rhizobium-legume (RL) symbioses in response to signal molecules produced, respectively, by the fungal and bacterial symbiotic partners. While most studies on these proteins have been performed in legume species, there are also important findings that demonstrate the roles of LysM-RLKs in controlling symbiosis in non-legume plants. Phylogenomic studies, which have revealed the presence or absence of certain LysM-RLKs among different plant species, have provided insight into the evolutionary mechanisms underlying both the acquisition and the loss of symbiotic properties. The role of a key nodulation LysM-RLK, NFP/NFR5, in legume plants has thus probably been co-opted from an ancestral role in the AM symbiosis, and has been lost in most plant species that have lost the ability to establish the AM or the RL symbiosis. Another LysM-RLK, LYK3/NFR1, that controls the RL symbiosis probably became neo-functionalised following two rounds of gene duplication. Evidence suggests that a third LysM-RLK, LYR3/LYS12, is also implicated in perceiving microbial symbiotic signals, and this protein could have roles in symbiosis and/or plant immunity in different plant species. By focusing on these three LysM-RLKs that are widespread in plants we review their evolutionary history and what this can tell us about the evolution of both the RL and the AM symbioses.

MetExplore: collaborative edition and exploration of metabolic networks.

Metabolism of an organism is composed of hundreds to thousands of interconnected biochemical reactions responding to environmental or genetic constraints. This metabolic network provides a rich knowledge to contextualize omics data and to elaborate hypotheses on metabolic modulations. Nevertheless, performing this kind of integrative analysis is challenging for end users with not sufficiently advanced computer skills since it requires the use of various tools and web servers. MetExplore offers an all-in-one online solution composed of interactive tools for metabolic network curation, network exploration and omics data analysis. In particular, it is possible to curate and annotate metabolic networks in a collaborative environment. The network exploration is also facilitated in MetExplore by a system of interactive tables connected to a powerful network visualization module. Finally, the contextualization of metabolic elements in the network and the calculation of over-representation statistics make it possible to interpret any kind of omics data. MetExplore is a sustainable project maintained since 2010 freely available at https://metexplore.toulouse.inra.fr/metexplore2/.

The Rosa genome provides new insights into the domestication of modern roses.

Roses have high cultural and economic importance as ornamental plants and in the perfume industry. We report the rose whole-genome sequencing and assembly and resequencing of major genotypes that contributed to rose domestication. We generated a homozygous genotype from a heterozygous diploid modern rose progenitor, Rosa chinensis 'Old Blush'. Using single-molecule real-time sequencing and a meta-assembly approach, we obtained one of the most comprehensive plant genomes to date. Diversity analyses highlighted the mosaic origin of 'La France', one of the first hybrids combining the growth vigor of European species and the recurrent blooming of Chinese species. Genomic segments of Chinese ancestry identified new candidate genes for recurrent blooming. Reconstructing regulatory and secondary metabolism pathways allowed us to propose a model of interconnected regulation of scent and flower color. This genome provides a foundation for understanding the mechanisms governing rose traits and should accelerate improvement in roses, Rosaceae and ornamentals.

Control of primary metabolism by a virulence regulatory network promotes robustness in a plant pathogen.

Robustness is a key system-level property of living organisms to maintain their functions while tolerating perturbations. We investigate here how a regulatory network controlling multiple virulence factors impacts phenotypic robustness of a bacterial plant pathogen. We reconstruct a cell-scale model of Ralstonia solanacearum connecting a genome-scale metabolic network, a virulence macromolecule network, and a virulence regulatory network, which includes 63 regulatory components. We develop in silico methods to quantify phenotypic robustness under a broad set of conditions in high-throughput simulation analyses. This approach reveals that the virulence regulatory network exerts a control of the primary metabolism to promote robustness upon infection. The virulence regulatory network plugs into the primary metabolism mainly through the control of genes likely acquired via horizontal gene transfer, which results in a functional overlay with ancestral genes. These results support the view that robustness may be a selected trait that promotes pathogenic fitness upon infection.

MetExploreViz: web component for interactive metabolic network visualization.

SUMMARY: MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context. AVAILABILITY AND IMPLEMENTATION: Documentation and link to GIT code repository (GPL 3.0 license) are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc/.

Genomic Prediction of Sunflower Hybrids Oil Content.

Prediction of hybrid performance using incomplete factorial mating designs is widely used in breeding programs including different heterotic groups. Based on the general combining ability (GCA) of the parents, predictions are accurate only if the genetic variance resulting from the specific combining ability is small and both parents have phenotyped descendants. Genomic selection (GS) can predict performance using a model trained on both phenotyped and genotyped hybrids that do not necessarily include all hybrid parents. Therefore, GS could overcome the issue of unknown parent GCA. Here, we compared the accuracy of classical GCA-based and genomic predictions for oil content of sunflower seeds using several GS models. Our study involved 452 sunflower hybrids from an incomplete factorial design of 36 female and 36 male lines. Re-sequencing of parental lines allowed to identify 468,194 non-redundant SNPs and to infer the hybrid genotypes. Oil content was observed in a multi-environment trial (MET) over 3 years, leading to nine different environments. We compared GCA-based model to different GS models including female and male genomic kinships with the addition of the female-by-male interaction genomic kinship, the use of functional knowledge as SNPs in genes of oil metabolic pathways, and with epistasis modeling. When both parents have descendants in the training set, the predictive ability was high even for GCA-based prediction, with an average MET value of 0.782. GS performed slightly better (+0.2%). Neither the inclusion of the female-by-male interaction, nor functional knowledge of oil metabolism, nor epistasis modeling improved the GS accuracy. GS greatly improved predictive ability when one or both parents were untested in the training set, increasing GCA-based predictive ability by 10.4% from 0.575 to 0.635 in the MET. In this scenario, performing GS only considering SNPs in oil metabolic pathways did not improve whole genome GS prediction but increased GCA-based prediction ability by 6.4%. Our results show that GS is a major improvement to breeding efficiency compared to the classical GCA modeling when either one or both parents are not well-characterized. This finding could therefore accelerate breeding through reducing phenotyping efforts and more effectively targeting for the most promising crosses.

The sunflower genome provides insights into oil metabolism, flowering and Asterid evolution.

The domesticated sunflower, Helianthus annuus L., is a global oil crop that has promise for climate change adaptation, because it can maintain stable yields across a wide variety of environmental conditions, including drought. Even greater resilience is achievable through the mining of resistance alleles from compatible wild sunflower relatives, including numerous extremophile species. Here we report a high-quality reference for the sunflower genome (3.6 gigabases), together with extensive transcriptomic data from vegetative and floral organs. The genome mostly consists of highly similar, related sequences and required single-molecule real-time sequencing technologies for successful assembly. Genome analyses enabled the reconstruction of the evolutionary history of the Asterids, further establishing the existence of a whole-genome triplication at the base of the Asterids II clade and a sunflower-specific whole-genome duplication around 29 million years ago. An integrative approach combining quantitative genetics, expression and diversity data permitted development of comprehensive gene networks for two major breeding traits, flowering time and oil metabolism, and revealed new candidate genes in these networks. We found that the genomic architecture of flowering time has been shaped by the most recent whole-genome duplication, which suggests that ancient paralogues can remain in the same regulatory networks for dozens of millions of years. This genome represents a cornerstone for future research programs aiming to exploit genetic diversity to improve biotic and abiotic stress resistance and oil production, while also considering agricultural constraints and human nutritional needs.