Identification and Characterization of Novel Sources of Resistance to Rust Caused by Uromyces pisi in Pisum spp

Identification of Sources of Resistance to Aphanomyces Root Rot in Pisum

Aphanomyces root rot (ARR), caused by Aphanomyces euteiches, is one of the most devastating diseases that affect the production of peas. Several control strategies such as crop rotation, biocontrol, and fungicides have been proposed, but none provides a complete solution. Therefore, the deployment of resistant cultivars is fundamental. ARR resistance breeding is hampered by the moderate levels of resistance identified so far. The available screening protocols require post-inoculation root assessment, which is destructive, time-consuming, and tedious. In an attempt to address these limitations, we developed a non-destructive screening protocol based on foliar symptoms and used it to identify new sources of resistance in a Pisum spp. germplasm collection. Accessions were root inoculated separately with two A. euteiches isolates, and leaf symptoms were assessed at 5, 10, 14, 17, and 20 days after inoculation (DAI). Although the majority of accessions exhibited high levels of susceptibility, thirty of them exhibited moderate resistance. These thirty accessions were selected for a second experiment, in which they were inoculated with both A. euteiches isolates at two inoculum doses. The objective of this second trial was to confirm the resistance of these accessions by evaluating root and biomass loss, as well as foliar symptoms, and to compare root and foliar evaluations. As a result, a high correlation (R2 = 0.75) between foliar and root evaluations was observed, validating the foliar evaluation method. Notably, accessions from P.s. subsp. humile exhibited the lowest symptomatology across all evaluation methods, representing valuable genetic resources for breeding programs aimed at developing pea varieties resistant to ARR.

RGB image-based method for phenotyping rust disease progress in pea leaves using R

BACKGROUND

Rust is a damaging disease affecting vital crops, including pea, and identifying highly resistant genotypes remains a challenge. Accurate measurement of infection levels in large germplasm collections is crucial for finding new resistance sources. Current evaluation methods rely on visual estimation of disease severity and infection type under field or controlled conditions. While they identify some resistance sources, they are error-prone and time-consuming. An image analysis system proves useful, providing an easy-to-use and affordable way to quickly count and measure rust-induced pustules on pea samples. This study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection.

RESULTS

A highly efficient and automatic image-based method for assessing rust disease in pea leaves was developed using R. The method's optimization and validation involved testing different segmentation indices and image resolutions on 600 pea leaflets with rust symptoms. The approach allows automatic estimation of parameters like pustule number, pustule size, leaf area, and percentage of pustule coverage. It reconstructs time series data for each leaf and integrates daily estimates into disease progression parameters, including latency period and area under the disease progression curve. Significant variation in disease responses was observed between genotypes using both visual ratings and image-based analysis. Among assessed segmentation indices, the Normalized Green Red Difference Index (NGRDI) proved fastest, analysing 600 leaflets at 60% resolution in 62 s with parallel processing. Lin's concordance correlation coefficient between image-based and visual pustule counting showed over 0.98 accuracy at full resolution. While lower resolution slightly reduced accuracy, differences were statistically insignificant for most disease progression parameters, significantly reducing processing time and storage space. NGRDI was optimal at all time points, providing highly accurate estimations with minimal accumulated error.

CONCLUSIONS

A new image-based method for monitoring pea rust disease in detached leaves, using RGB spectral indices segmentation and pixel value thresholding, improves resolution and precision. It rapidly analyses hundreds of images with accuracy comparable to visual methods and higher than other image-based approaches. This method evaluates rust progression in pea, eliminating rater-induced errors from traditional methods. Implementing this approach to evaluate large germplasm collections will improve our understanding of plant-pathogen interactions and aid future breeding for novel pea cultivars with increased rust resistance.

Genetic Diversity and Population Structure of a Wide Pisum spp. Core Collection

Peas (Pisum sativum) are the fourth most cultivated pulses worldwide and a critical source of protein in animal feed and human food. Developing pea core collections improves our understanding of pea evolution and may ease the exploitation of their genetic diversity in breeding programs. We carefully selected a highly diverse pea core collection of 325 accessions and established their genetic diversity and population structure. DArTSeq genotyping provided 35,790 polymorphic DArTseq markers, of which 24,279 were SilicoDArT and 11,511 SNP markers. More than 90% of these markers mapped onto the pea reference genome, with an average of 2787 SilicoDArT and 1644 SNP markers per chromosome, and an average LD50 distance of 0.48 and 1.38 Mbp, respectively. The pea core collection clustered in three or six subpopulations depending on the pea subspecies. Many admixed accessions were also detected, confirming the frequent genetic exchange between populations. Our results support the classification of Pisum genus into two species, P. fulvum and P. sativum (including subsp. sativum, arvense, elatius, humile, jomardii and abyssinicum). In addition, the study showed that wild alleles were incorporated into the cultivated pea through the intermediate P. sativum subsp. jomardii and P. sativum subsp. arvense during pea domestication, which have important implications for breeding programs. The high genetic diversity found in the collection and the high marker coverage are also expected to improve trait discovery and the efficient implementation of advanced breeding approaches.

MGIDI: a powerful tool to analyze plant multivariate data

BACKGROUND

Commonly, several traits are assessed in agronomic experiments to better understand the factors under study. However, it is also common to see that even when several traits are available, researchers opt to follow the easiest way by applying univariate analyses and post-hoc tests for mean comparison for each trait, which arouses the hypothesis that the benefits of a multi-trait framework analysis may have not been fully exploited in this area.

RESULTS

In this paper, we extended the theoretical foundations of the multi-trait genotype-ideotype distance index (MGIDI) to analyze multivariate data either in simple experiments (e.g., one-way layout with few treatments and traits) or complex experiments (e.g., with a factorial treatment structure). We proposed an optional weighting process that makes the ranking of treatments that stands out in traits with higher weights more likely. Its application is illustrated using (1) simulated data and (2) real data from a strawberry experiment that aims to select better factor combinations (namely, cultivar, transplant origin, and substrate mixture) based on the desired performance of 22 phenological, productive, physiological, and qualitative traits. Our results show that most of the strawberry traits are influenced by the cultivar, transplant origin, cultivation substrates, as well as by the interaction between cultivar and transplant origin. The MGIDI ranked the Albion cultivar originated from Imported transplants and the Camarosa cultivar originated from National transplants as the better factor combinations. The substrates with burned rice husk as the main component (70%) showed satisfactory physical proprieties, providing higher water use efficiency. The strengths and weakness view provided by the MGIDI revealed that looking for an ideal treatment should direct the efforts on increasing fruit production of Albion transplants from Imported origin. On the other hand, this treatment has strengths related to productive precocity, total soluble solids, and flesh firmness.

CONCLUSIONS

Overall, this study opens the door to the use of MGIDI beyond the plant breeding context, providing a unique, practical, robust, and easy-to-handle multi-trait-based framework to analyze multivariate data. There is an exciting possibility for this to open up new avenues of research, mainly because using the MGIDI in future studies will dramatically reduce the number of tables/figures needed, serving as a powerful tool to guide researchers toward better treatment recommendations.