A Computation Method Based on the Combination of Chlorophyll Fluorescence Parameters to Improve the Discrimination of Visually Similar Phenotypes Induced by Bacterial Virulence Factors

Phenotyping biotic stresses in plant-pathogen interactions studies is often hindered by phenotypes that can hardly be discriminated by visual assessment. Particularly, single gene mutants in virulence factors could lack visible phenotypes. Chlorophyll fluorescence (CF) imaging is a valuable tool to monitor plant-pathogen interactions. However, while numerous CF parameters can be measured, studies on plant-pathogen interactions often focus on a restricted number of parameters. It could result in limited abilities to discriminate visually similar phenotypes. In this study, we assess the ability of the combination of multiple CF parameters to improve the discrimination of such phenotypes. Such an approach could be of interest for screening and discriminating the impact of bacterial virulence factors without prior knowledge. A computation method was developed, based on the combination of multiple CF parameters, without any parameter selection. It involves histogram Bhattacharyya distance calculations and hierarchical clustering, with a normalization approach to take into account the inter-leaves and intra-phenotypes heterogeneities. To assess the efficiency of the method, two datasets were analyzed the same way. The first dataset featured single gene mutants of a Xanthomonas strain which differed only by their abilities to secrete bacterial virulence proteins. This dataset displayed expected phenotypes at 6 days post-inoculation and was used as ground truth dataset to setup the method. The efficiency of the computation method was demonstrated by the relevant discrimination of phenotypes at 3 days post-inoculation. A second dataset was composed of transient expression (agrotransformation) of Type 3 Effectors. This second dataset displayed phenotypes that cannot be discriminated by visual assessment and no prior knowledge can be made on the respective impact of each Type 3 Effectors on leaf tissues. Using the computation method resulted in clustering the leaf samples according to the Type 3 Effectors, thereby demonstrating an improvement of the discrimination of the visually similar phenotypes. The relevant discrimination of visually similar phenotypes induced by bacterial strains differing only by one virulence factor illustrated the importance of using a combination of CF parameters to monitor plant-pathogen interactions. It opens a perspective for the identification of specific signatures of biotic stresses.

High throughput phenotyping dataset related to seed and seedling traits of sugar beet genotypes

Several seed and seedling traits are measured to evaluate germination and emergence potential in relation with environmental conditions. More generally, these traits are also measured in the field of ecology as simple traits that can be correlated to other adaptative traits more difficult to measure on adult plants, as for example traits of the rooting system. Methods were developed for deep high throughput phenotyping of hundreds of genotypes from dry seed to the end of heterotrophic growth. The present dataset comes from a project on genotyping and phenotyping of populations of genotypes, with different geographic and genetic origins so as to increase genotypic diversity of sugar beet in terms of germination and early growth traits, evaluated at low temperatures. Data were collected in relation to the creation of the first sugar beet crop ontology. This dataset corresponds to the first automated phenotyping of a population of 198 genotypes and 4 commercial control varieties and is hosted on INRAE public depository under the reference number doi.org/10.15,454/AKNF4Q. The equipment and methods presented here are available on a phenotyping platform opened to collaborative research and adaptable for specific services for characterizing thousands of genotypes on different crops or other species. The phenotyping values can also be linked to genomic information to study the genetic determinism of the trait values.

Quantitative trait loci analysis reveals a correlation between the ratio of sucrose/raffinose family oligosaccharides and seed vigour in Medicago truncatula

Seed vigour is important for successful establishment and high yield, especially under suboptimal environmental conditions. In legumes, raffinose oligosaccharide family (RFO) sugars have been proposed as an easily available energy reserve for seedling establishment. In this study, we investigated whether the composition or amount of soluble sugars (sucrose and RFO) is part of the genetic determinants of seed vigour of Medicago truncatula using two recombinant inbred line (RIL) populations. Quantitative trait loci (QTL) mapping for germination rate, hypocotyl and radicle growth under water deficit and nutritional stress, seed weight and soluble sugar content was performed using RIL populations LR1 and LR4. Seven of the 12 chromosomal regions containing QTL for germination rate or post-germinative radicle growth under optimal or stress conditions co-located with Suc/RFO QTL. A significant negative correlation was also found between seed vigour traits and Suc/RFO. In addition, one QTL that explained 80% of the variation in the ratio stachyose/verbascose co-located with a stachyose synthase gene whose expression profile in the parental lines could explain the variation in oligosaccharide composition. The correlation and co-location of Suc/RFO ratio with germination and radicle growth QTL suggest that an increased Suc/RFO ratio in seeds of M. truncatula might negatively affect seed vigour.