Dataset Information

Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding.

ABSTRACT: Late leaf spot (LLS), caused by Nothopassalora personata (Berk. & M.A Curt.), and groundnut rosette disease (GRD), [caused by groundnut rosette virus (GRV)], represent the most important biotic constraints to groundnut production in Uganda. Application of visual scores in selection for disease resistance presents a challenge especially when breeding experiments are large because it is resource-intensive, subjective, and error-prone. High-throughput phenotyping (HTP) can alleviate these constraints. The objective of this study is to determine if HTP derived indices can replace visual scores in a groundnut breeding program in Uganda. Fifty genotypes were planted under rain-fed conditions at two locations, Nakabango (GRD hotspot) and NaSARRI (LLS hotspot). Three handheld sensors (RGB camera, GreenSeeker, and Thermal camera) were used to collect HTP data on the dates visual scores were taken. Pearson correlation was made between the indices and visual scores, and logistic models for predicting visual scores were developed. Normalized difference vegetation index (NDVI) (r = -0.89) and red-green-blue (RGB) color space indices CSI (r = 0.76), v* (r = -0.80), and b* (r = -0.75) were highly correlated with LLS visual scores. NDVI (r = -0.72), v* (r = -0.71), b* (r = -0.64), and GA (r = -0.67) were best related to the GRD visual symptoms. Heritability estimates indicated NDVI, green area (GA), greener area (GGA), a*, and hue angle having the highest heritability (H ² > 0.75). Logistic models developed using these indices were 68% accurate for LLS and 45% accurate for GRD. The accuracy of the models improved to 91 and 84% when the nearest score method was used for LLS and GRD, respectively. Results presented in this study indicated that use of handheld remote sensing tools can improve screening for GRD and LLS resistance, and the best associated indices can be used for indirect selection for resistance and improve genetic gain in groundnut breeding.

SUBMITTER: Chapu I

PROVIDER: S-EPMC9238324 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Late leaf spot (LLS) caused by fungi Passalora personata is generally more destructive and difficult to control than early leaf spot. The aim of this study was to decipher biochemical defense mechanism in groundnut genotypes against P. personata by identifying resistance specific biomarkers and metabolic pathways induced during host-pathogen interaction. Metabolomics of non-infected and infected leaves of moderately resistant (GPBD4 and ICGV86590), resistant (KDG128 and RHRG06083) and susceptible (GG20, JL24 and TMV2) genotypes was carried out at 5 days after infection (65 days after sowing). Non-targeted metabolite analysis using GC-MS revealed total 77 metabolites including carbohydrates, sugar alcohols, amino acids, fatty acids, polyamines, phenolics, terpenes and sterols. Variable importance in projection (VIP) measure of partial least squares-discriminant analysis (PLS-DA) showed that resistant and moderately resistant genotypes possessed higher intensities of ribonic acid, cinnamic acid, malic acid, squalene, xylulose, galactose, fructose, glucose, β-amyrin and hydroquinone while susceptible genotypes had higher amount of gluconic acid 2-methoxime, ribo-hexose-3-ulose and gluconic acid. Heat map analysis showed that resistant genotypes had higher intensities of β-amyrin, hydroquinone in non-infected and malic acid, squalene, putrescine and 2,3,4-trihydroxybutyric acid in infected leaves. Dendrogram analysis further separated resistant genotypes in the same cluster along with infected moderately resistant genotypes. The most significant pathways identified are: linoleic acid metabolism, flavone and flavonol biosynthesis, cutin, suberin and wax biosynthesis, pentose and glucuronate interconversions, starch and sucrose metabolism, stilbenoid biosynthesis and ascorbate and aldarate metabolism. Targeted metabolite analysis further confirmed that resistant genotypes possessed higher content of primary metabolites sucrose, glucose, fructose, malic acid and citric acid. Moreover, resistant genotypes possessed higher content of salicylic, coumaric, ferulic, cinnamic, gallic acid (phenolic acids) and kaempferol, quercetin and catechin (flavonols). Thus metabolites having higher accumulation in resistant genotypes can be used as biomarkers for screening of LSS resistant germplasm. These results unravel that higher amount of primary metabolites leads to stimulate the accumulation of more amounts of secondary metabolites such as phenolic acid, flavanols, stilbenes and terpenoids (squalene and β-amyrin) biosynthesis which are ultimately involved in defense mechanism against LLS pathogen.Supplementary informationThe online version contains supplementary material available at 10.1007/s12298-021-00985-5.

Dataset Information

Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets