Dataset Information

A Mechanistic Weather-Driven Model for Ascochyta rabiei Infection and Disease Development in Chickpea.

ABSTRACT: Ascochyta blight caused by Ascochyta rabiei is an important disease of chickpea. By using systems analysis, we retrieved and analyzed the published information on A. rabiei to develop a mechanistic, weather-driven model for the prediction of Ascochyta blight epidemics. The ability of the model to predict primary infections was evaluated using published data obtained from trials conducted in Washington (USA) in 2004 and 2005, Israel in 1996 and 1998, and Spain from 1988 to 1992. The model showed good accuracy and specificity in predicting primary infections. The probability of correctly predicting infections was 0.838 and the probability that there was no infection when not predicted was 0.776. The model's ability to predict disease progress during the growing season was also evaluated by using data collected in Australia from 1996 to 1998 and in Southern Italy in 2019; a high concordance correlation coefficient (CCC = 0.947) between predicted and observed data was obtained, with an average distance between real and fitted data of root mean square error (RMSE) = 0.103, indicating that the model was reliable, accurate, and robust in predicting seasonal dynamics of Ascochyta blight epidemics. The model could help growers schedule fungicide treatments to control Ascochyta blight on chickpea.

SUBMITTER: Salotti I

PROVIDER: S-EPMC8000037 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Using microarray technology and a set of chickpea (Cicer arietinum L.) unigenes, grasspea (Lathyrus sativus L.) ESTs and lentil (Lens culinaris Med.) resistance gene analogs, the ascochyta blight (Ascochyta rabiei (Pass.) L.) resistance response was studied in four chickpea genotypes, including resistant, moderately resistant, susceptible and wild relative (Cicer echinospermum L.) genotypes. The experimental system minimized environmental effects and was conducted in reference design, where samples from mock-inoculated controls acted as references against post-inoculation samples. Robust data quality was achieved through the use of three biological replicates (including a dye-swap), the inclusion of negative controls, and strict selection criteria for differentially expressed genes including a fold change cutoff determined by self-self hybridizations, Students t test and multiple testing correction (P<0.05). Microarray observations were also validated by quantitative RT-PCR. The time-course expression patterns of 756 microarray features resulted in differential expression of 97 genes in at least one genotype at one time-point. K-means clustering grouped the genes into clusters of similar observations for each genotype, and comparisons between A. rabiei-resistant and susceptible genotypes revealed potential gene 'signatures' predictive of effective A. rabiei resistance. These genes included several pathogenesis-related proteins, SNAKIN2 antimicrobial peptide, proline-rich protein, disease resistance response protein DRRG49-C, environmental stress-inducible protein, leucine-zipper protein, polymorphic antigen membrane protein, as well as several unknown proteins. The potential involvement of these genes and their pathways of induction are discussed. This study represents the first large-scale gene expression profiling in chickpea, and future work will focus on functional validation of the genes of interest. Keywords: time course disease state analysis

Dataset Information

A Mechanistic Weather-Driven Model for Ascochyta rabiei Infection and Disease Development in Chickpea.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure