This article presents a hyperspectral imaging (HSI) database of healthy leaves and leaves infected with Zymoseptoria tritici fungal pathogen responsible for leaf blotch (Lb) disease. Leaves of two durum wheat genotypes were studied under controlled conditions to track the evolution of Lb disease and capture significant spectral and spatial differences until the onset of symptoms. Hyperspectral image acquisitions were purchased with two cameras in visible-near infrared (VNIR) and short-wave infrared (SWIR) spectral ranges on eighteen dates between one day before inoculation and twenty days after inoculation. For each wavelength range studied, a total of 1175 images provided information on 3326 leaves measured throughout the experiment. These data are valuable since they can be used as a basis to monitor disease's development over time, to build leaf classification models according to their infection status per genotype per day, to develop prediction models related to symptoms' appearance, or to test imaging and spectral analysis methods.

Competition between plants can lead to a tragedy of the commons (TOC), where excessive investment in resource-harvesting organs reduces collective performance. Mixing crop varieties could resolve such TOCs through niche complementarity-if varieties differ in resource use-or selection effects, where competitive varieties benefit from weaker neighbours. While most studies on varietal mixtures focus on above-ground traits, below-ground interactions remain poorly understood. We grew 36 durum wheat (Triticum turgidum ssp. durum) varieties in pure stands and 54 binary mixtures using a high-throughput root phenotyping platform, under both non-limiting (R+) and limiting (R-) water and nutrient conditions, to assess early-stage root competition. In R-, mixtures produced less biomass than expected based on pure stands, largely due to a negative complementarity effect. This was mostly explained by the average projected root area of the two varieties. Rather than indicating a negative interaction, the effect reflected a relaxation of competition: varieties with larger root systems benefited from having weaker competitors, disengaging from the arms race for biomass accumulation. These findings suggest that root area is a promising breeding target for mitigating intra-specific competition and a critical trait for assembling optimal varietal mixtures.

BACKGROUND:Genotyping of individuals plays a pivotal role in various biological analyses, with technology choice influenced by multiple factors including genomic constraints, number of targeted loci and individuals, cost considerations, and the ease of sample preparation and data processing. Target enrichment capture of specific polymorphic regions has emerged as a flexible and cost-effective genomic reduction method for genotyping, especially adapted to the case of very large genomes. However, this approach necessitates complex bioinformatics treatment to extract genotyping data from raw reads. Existing workflows predominantly cater to phylogenetic inference, leaving a gap in user-friendly tools for genotyping analysis based on capture methods. In response to these challenges, we have developed GeCKO (Genotyping Complexity Knocked-Out). To assess the effectiveness of combining target enrichment capture with GeCKO, we conducted a case study on durum wheat domestication history, involving sequencing, processing, and analyzing variants in four relevant durum wheat groups. RESULTS:GeCKO encompasses four distinct workflows, each designed for specific steps of genomic data processing: (i) read demultiplexing and trimming for data cleaning, (ii) read mapping to align sequences to a reference genome, (iii) variant calling to identify genetic variants, and (iv) variant filtering. Each workflow in GeCKO can be easily configured and is executable across diverse computational environments. The workflows generate comprehensive HTML reports including key summary statistics and illustrative graphs, ensuring traceable, reproducible results and facilitating straightforward quality assessment. A specific innovation within GeCKO is its 'targeted remapping' feature, specifically designed for efficient treatment of targeted enrichment capture data. This process consists of extracting reads mapped to the targeted regions, constructing a smaller sub-reference genome, and remapping the reads to this sub-reference, thereby enhancing the efficiency of subsequent steps. CONCLUSIONS:The case study results showed the expected intra-group diversity and inter-group differentiation levels, confirming the method's effectiveness for genotyping and analyzing genetic diversity in species with complex genomes. GeCKO streamlined the data processing, significantly improving computational performance and efficiency. The targeted remapping enabled straightforward SNP calling in durum wheat, a task otherwise complicated by the species' large genome size. This illustrates its potential applications in various biological research contexts.

Lower plant resistance to herbivores following domestication has been suggested as the main cause for higher feeding damage in crops than in wild progenitors. While herbivore compensatory feeding has also been proposed as a possible mechanism for raised damage in crops with low nutritional quality, predictions regarding the effects of plant domestication on nutritional quality for herbivores remain unclear. In particular, data on primary metabolites, even major macronutrients, measured in the organs consumed by herbivores, are scarce. In this study, we used a collection of 10 accessions of wild ancestors and 10 accessions of modern progenies of Triticum turgidum to examine whether feeding damage and selectivity by nymphs of Locusta migratoria primarily depended on five leaf traits related to structural resistance or nutrient profiles. Our results unexpectedly showed that locusts favored wild ancestors over domesticated accessions and that leaf toughness and nitrogen and soluble protein contents increased with the domestication process. Furthermore, the quantitative relationship between soluble protein and digestible carbohydrates was found to poorly meet the specific requirements of the herbivore, in all wheat accessions, both wild and modern. The increase in leaf structural resistance to herbivores in domesticated tetraploid wheat accessions suggested that resource allocation trade-offs between growth and herbivory resistance may have been disrupted by domestication in the vegetative organs of this species. Since domestication did not result in a loss of nutritional quality in the leaves of the tetraploid wheat, our results rather provides evidence for a role of the content of plants in nonnutritive nitrogenous secondary compounds, possibly deterrent or toxic, at least for grasshopper herbivores.

Roots are essential for water and nutrient uptake but are rarely the direct target of breeding efforts. To characterize the genetic variability of wheat root architecture, the root and shoot traits of 200 durum and 715 bread wheat varieties were measured at a young stage on a high-throughput phenotyping platform. Heritability of platform traits ranged from 0.40 for root biomass in durum wheat to 0.82 for the number of tillers. Field phenotyping data for yield components and SNP genotyping were already available for all the genotypes. Taking differences in earliness into account, several significant correlations between root traits and field agronomic performances were found, suggesting that plants investing more resources in roots in some stressed environments favored water and nutrient uptake, with improved wheat yield. We identified 100 quantitative trait locus (QTLs) of root traits in the bread wheat panels and 34 in the durum wheat panel. Most colocalized with QTLs of traits measured in field conditions, including yield components and earliness for bread wheat, but only in a few environments. Stress and climatic indicators explained the differential effect of some platform QTLs on yield, which was positive, null, or negative depending on the environmental conditions. Modern breeding has led to deeper rooting but fewer seminal roots in bread wheat. The number of tillers has been increased in bread wheat, but decreased in durum wheat, and while the root-shoot ratio for bread wheat has remained stable, for durum wheat it has been increased. Breeding for root traits or designing ideotypes might help to maintain current yield while adapting to specific drought scenarios.

BACKGROUND:Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum provides information on the biochemical composition within a tissue, itself being under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been mainly applied in several annual crop species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour. A major novelty of this study was to collect spectra and phenotypes several years apart from each other. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability.RESULTS:For the first time, we showed that the similarity between spectra and genomic relationship matrices was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Applying a mixed model on spectra data increased phenomic predictive ability, while using spectra collected on wood or leaves from one year or another had less impact. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, a significant positive correlation was found across traits between predictive ability of genomic and phenomic predictions.CONCLUSION:NIRS is a new low-cost alternative to genotyping for predicting complex traits in perennial species such as grapevine. Having spectra and phenotypes from different years allowed us to exclude genotype-by-environment interactions and confirms that phenomic prediction can rely only on genetics.

Abstract Background As a rapid and non-destructive method, Near Infrared Spectroscopy is classically proposed to assess plant traits in many scientific fields, to observe enlarged genotype panels and to document the temporal kinetic of some biological processes. Most often, supervised models are used. The signal is calibrated thanks to reference measurements, and dedicated models are generated to predict biological traits. An alternative unsupervised approach considers the whole spectra information in order to point out various matrix changes. Although more generic, and faster to implement, as it does not require a reference data set, this latter approach is rarely used to document biological processes, and does requires more information of the process. Methods In our work, an unsupervised model was used to document the flag leaf senescence of durum wheat (Triticum turgidum durum). Leaf spectra changes were observed using Moving Window Principal Component Analysis (MWPCA). The dates related to earlier and later spectra changes were compared to two key points on the senescence time course: senescence onset (T0) and the end of the leaf span (T1) derived from a supervised strategy. Results For almost all leaves and whatever the signal pre-treatments and window size considered, the MWPCA found significant spectral changes. The latter was highly correlated with T1 (0.59 ≤ r ≤ 0.86) whereas the correlations between the first significant spectrum changes and T0 were lower (0.09 ≤ r ≤ 0.56). These different relationships are discussed below since they define the potential as well as the limitations of MWPCA to model biological processes. Conclusion Overall, our study demonstrates that the information contained in the spectra can be used when applying an unsupervised method, here the MWPCA, to characterize a complex biological phenomenon such leaf senescence. It also means that using whole spectra may be relevant in agriculture and plant biology.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Deciphering wheat grain protein content : Genetıc analysis of temporal dynamic nitrogen content resorption in flag leaves Ingrid Vilmus, Pierre Roumet, Jacques David, Laurent Gervais, Martin Ecarnot

For each plant, we obtained 30-40 spectra that were converted into value of nitrogen content thanks to a robust calibra(cid:1)on of the device (R²=0.93, Ecarnot and Roumet, submi(cid:1) ed). The curves were modelled using a non linear method based on a Gauss Newton algorithm. Grain protein content (GPC) is a targeted trait in breeding for durum wheat since it has a strong impact in industrial transforma(cid:1)on. However, its improvement is compromised by the complexity of its gene(cid:1) c architecture (many QTL have been highlighted) and by a nega(cid:1) ve correla(cid:1) on with the yield . Nevertheless, in view of the necessity to feed more and more people, reducing the fer(cid:1) liza-(cid:1) on inputs and preserving our environment, the gene(cid:1)c improvement of the GPC appears as a good way to maintain the product’s quality and to ensure a good produc(cid:1)on to farmers. In response to this economical and environmental context, we propose to decipher this trait and to focus on an underlying mechanism to the elabora(cid:1)on of the GPC, the nitrogen remobiliza(cid:1) on in the flag

Data and code for the study "Multifaceted functional diversity for multifaceted crop yield: towards ecological assembly rules for varietal mixtures" Two data files are available: "CWM_D.csv" contains one row per experimental plot with community-weighted mean (CWM) and Rao quadratic diversity (D) indices computed on the 19 functional traits. The first two columns ("genotype_1" & "genotype_2") are the identity of the two genotypes in the plot, which are identical in single-variety plots. The third column ("assoc") refers to the plot type: single-variety ("M") or mixed-variety ("P") plot. Then, all trait CWMs and Ds are reported as "CWM_trait_name" and "D_trait_name" , respectively. For single-variety plots, only CWMs are reported but in this case they correspond to unweighted-averaged trait values across replicated measurements within plots. Root trait names are followed by "sem" or "adv" depending if they were measured on seminal or adventitious roots. Reported traits are: "Angle_aer" (Aerial angle, °), "Angle_root" (Root angle, °), "Diam_sem/adv" (mean root diameter, mm), "SRL_sem/adv" (specific root length, m/g), "RTD_sem/adv" (root tissue density, g/cm3), "RBI_sem/adv" (root branching intensity, nb of root tips/cm), "RLD_sem/adv" (root length density, cm root/cm3 soil), "Till_nb" (tiller number per capita), "Ear_bio" (early biomass per capita, g), "SLA" (specific leaf area, m²/kg), "LNC" (leaf nitrogen content, %), "Height" (plant height, cm), "Heading" (heading date, Growing Degree Days), and "Maturity" (maturity date, Growing Degree Days). "RAW_RYT.csv" contains one row per experimental plot with absolute and relative measures of performance on several agronomic variables. The first two columns ("genotype_1" & "genotype_2") are the identity of the two genotypes in the plot, which are identical in single-variety plots. The third column ("assoc") refers to the plot type: single-variety ("M") or mixed-variety ("P") plot. Then, all absolute and relative measures of agronomic performance are reported as "RAW_performance_variable_name" and "RYT_performance_variable_name", respectively. For single-variety plots, only absolute performances are reported. Reported performance variable are "GY" (Grain yield, g/m²), "GNb" (Grain number per m²), "SY" (Spike yield, g/m²), "SNb" (Spike number per m²), "BY" (Biomass yield, g/m²), "PY" (Protein yield, g/m²), "TKW" (Thousand kernel weight, g), "SeY" (Semolina yield, %), "GPC" (Grain protein content, %), "TW" (Test weight, kg/hL), "RLVA" (Rate of loss of vitreous aspect, %), "YI" (Yellowness index), "GPD" (Grain protein deviation, %). One R code file is available: "Mu_FD_Mu_CY_Analysis.R" contains all statistical analysis performed to produce the results presented in the main text and in the Supplementary Information of the study. It uses "CWM_D.csv" and "RAW_RYT.csv" files as inputs.

Despite the large morphological and physiological changes that plants have undergone through domestication, little is known about their impact on their microbiome. Here we characterized rhizospheric bacterial and fungal communities as well as the abundance of N-cycling microbial guilds across thirty-nine accessions of tetraploid wheat, Triticum turgidum, from four domestication groups ranging from the wild subspecies to the semi dwarf elite cultivars. We identified several microbial phylotypes displaying significant variation in their relative abundance depending on the wheat domestication group with a stronger impact of domestication on fungi. The relative abundance of potential fungal plant pathogens belonging to the Sordariomycetes class decreased in domesticated compared to wild emmer while the opposite was found for members of the Glomeromycetes, which are obligate plant symbionts. The depletion of nitrifiers and of arbuscular mycorrhizal fungi in elite wheat cultivars compared to primitive domesticated forms suggests that the Green Revolution has decreased the coupling between plant and rhizosphere microbes that are potentially important for plant nutrient availability. Both plant diameter and fine root percentage exhibited the highest number of associations with microbial taxa, highlighting their putative role in shaping the rhizosphere microbiota during domestication. Aside from domestication, significant variation of bacterial and fungal community composition was found among accessions within each domestication group. In particular, the relative abundances of Ophiostomataceae and of Rhizobiales were strongly dependent on the host accession, with heritability estimates of ~ 27% and ~ 25%, indicating that there might be room for genetic improvement via introgression of ancestral plant rhizosphere-beneficial microbe associations.

Few resistance genes providing defence against the major fungal diseases septoria tritici blotch (STB), septoria nodorum blotch, leaf rust (LR), and an emerging wheat blast disease have been identified in durum wheat. We identified sixteen fungal disease-associated QTL through genome-wide association mapping of 180 inbred lines sampled from a durum wheat Composite Cross-population. Two STB resistance-associated QTL mapped to chromosome 3A, one of which colocalizes with Stb6, a known resistance gene previously identified in bread wheat. This partial resistance could be conferred by a new allele of Stb6 or another paralogous gene. The second locus is associated with a reduction in pycnidia density, a recently identified and poorly understood form of resistance. A resistance QTL strongly associated with LR, and colocalizing with Lr61, was observed in a 3.24 Mbp region on chromosome 6B. QTL mapping of LR resistance following treatment by chitin used in the context of inducer treatment was also investigated. Using a combination of resistance alleles at these loci could confer durable resistance to multiple fungal diseases and aid durum wheat breeders in their fight against these fungal pathogens.

Variety mixtures, the cultivation of different genotypes within a field, have been proposed as a way to increase within-crop diversity, allowing the development of more sustainable agricultural systems with reduced environmental costs. Although mixtures have often been shown to over-yield the average of component varieties in pure stands, decreased yields in mixtures have also been documented. Kin selection may explain such pattern, whenever plants direct helping behaviors preferentially toward relatives and thus experience stronger competition when grown with less related neighbors, lowering seed production of mixtures. Using varieties of durum wheat originating from traditional Moroccan agrosystems, we designed a greenhouse experiment to address whether plants reduced competition for light by limiting stem elongation when growing with kin and whether such phenotypic response resulted in higher yield of kin groups. Seeds were sown in groups of siblings and nonkin, each group containing a focal plant surrounded by four neighbors. At the group level, mean plant height and yield did not depend upon relatedness among competing plants. At the individual level, plant height was not affected by genetic relatedness to neighbors, after accounting for direct genetic effects that might induce among-genotype differences in the ability to capture resources that do not depend on relatedness. Moreover, in contrast to our predictions, shorter plants had lower inclusive fitness. Phenotypic plasticity in height was very limited in response to neighbor genotypes. This suggests that human selection in crops may have attenuated shade-avoidance responses to competition for light. Future research on preferential helping to relatives in crops might thus target social traits that drive competition for other resources than light. Overall, our study illustrates the relevance of tackling agricultural issues from an evolutionary standpoint and calls for extending such approaches to a larger set of crop species.

In-field hyperspectral imagery is a promising tool for crop phenotyping or monitoring. In association with partial least square regression (PLS-R), it allows building high spatial resolution maps of the chemical content of plant leaves. However, several optical phenomena must be taken into account, due to their influence on collected spectral data. The most challenging is multiple scattering, produced when a leaf is partly illuminated by light reflection or transmission from neighboring leaves. It can induce bias in prediction results. This paper presents a method for multi-scattering correction. Its development has been based on simulation tools: a 3D canopy model of winter wheat was combined with light propagation modeling, in order to simulate the apparent reflectance of every visible leaf in the canopy for a given actual reflectance. Leaf nitrogen content (LNC) prediction has been considered. A data set of reflectance spectra associated with LNC values has been issued from real leaf measurements. A theoretical disturbance subspace representing the spectrum dispersion in the spectral space due to multi-scattering has then been built by considering polynomial combinations of the initial spectra, and a projection along this subspace has been applied to every simulated spectra. Using this strategy, a PLS-R model built on initial spectra was still satisfactory when applied to simulated spectra with multiple scattering. The method has then been applied to real plants in greenhouse and field conditions, and its prediction results compared with those of a standard PLS-R, confirming its efficiency in the presence of various lighting environments.

GnpIS is a data repository for plant phenomics that stores whole field and greenhouse experimental data including environment measures. It allows long-term access to datasets following the FAIR principles: Findable, Accessible, Interoperable, and Reusable, by using a flexible and original approach. It is based on a generic and ontology driven data model and an innovative software architecture that uncouples data integration, storage, and querying. It takes advantage of international standards including the Crop Ontology, MIAPPE, and the Breeding API. GnpIS allows handling data for a wide range of species and experiment types, including multiannual perennial plants experimental network or annual plant trials with either raw data, i.e., direct measures, or computed traits. It also ensures the integration and the interoperability among phenotyping datasets and with genotyping data. This is achieved through a careful curation and annotation of the key resources conducted in close collaboration with the communities providing data. Our repository follows the Open Science data publication principles by ensuring citability of each dataset. Finally, GnpIS compliance with international standards enables its interoperability with other data repositories hence allowing data links between phenotype and other data types. GnpIS can therefore contribute to emerging international federations of information systems.

ArchiRac : Caracterisation de la diversite genetique de l’architecture racinaire chez le ble tendre et le ble dur. Journee scientifique de l'Association des Selectionneurs Francais