Domestication provides durum wheat with protection from locust herbivory.

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.

A genome-wide analysis suggests pleiotropic effects of Green Revolution genes on shade avoidance in wheat.

A classic example of phenotypic plasticity in plants is the suit of phenotypic responses induced by a change in the ratio of red to far-red light (R∶FR) as a result of shading, also known as the shade avoidance syndrome (SAS). While the adaptive consequences of this syndrome have been extensively discussed in natural ecosystems, how SAS varies within crop populations and how SAS evolved during crop domestication and breeding remain poorly known. In this study, we grew a panel of 180 durum wheat (Triticum turgidum ssp. durum) genotypes spanning diversity from wild, early domesticated, and elite genetic compartments under two light treatments: low R:FR light (shaded treatment) and high R:FR light (unshaded treatment). We first quantified the genetic variability of SAS, here measured as a change in plant height at the seedling stage. We then dissected the genetic basis of this variation through genome-wide association mapping. Genotypes grown in shaded conditions were taller than those grown under unshaded conditions. Interaction between light quality and genotype did not affect plant height. We found six QTLs affecting plant height. Three significantly interacted with light quality among which the well-known Rht1 gene introgressed in elite germplasm during the Green Revolution. Interestingly at three loci, short genotypes systematically expressed reduced SAS, suggesting a positive genetic correlation between plant height and plant height plasticity. Overall, our study sheds light on the evolutionary history of crops and illustrates the relevance of genetic approaches to tackle agricultural challenges.

Interest of phenomic prediction as an alternative to genomic prediction in grapevine.

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.

Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case.

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.

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy.

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.

Rapid phenotyping of the tomato fruit model, Micro-Tom, with a portable VIS-NIR spectrometer.

Tomato (Solanum lycopersicum) quality traits such as juice soluble solid content (Brix), juice pH, color parameters (Hue and Chroma), firmness and water content, are critical factors for fruit quality assessment. The need for screening very large numbers of fruit has led to the development of a high-throughput method using visible-near infrared (VIS-NIR) spectrometry. We are reporting here a set of results obtained with a portable spectrometer using the 350-2500 nm range, showing good prediction of the quality traits cited above, over a wide range of developmental stages from immature green to ripe tomato fruit, cv. Micro-Tom. This is a rather good set of quality traits compared to previous publications predicting tomato quality with VIS-NIR spectrometry, and the prediction is robust, as it was obtained by grouping sets of different operators. This would be a useful tool to phenotype hundreds of Micro-Tom per day, making it possible to follow the dynamics of the described parameters on growing fruits. Thus the method can be used to study the biochemistry and physiology of fruit development in planta.