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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

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Project description:Chickpea (Cicer arietinum L.) is an important cool season food legume, however, its production is severely constrained by the foliar disease Ascochyta blight caused by the fungus Ascochyta rabiei (syn. Phoma rabiei). Several disease management options have been developed to control the pathogen, including breeding for host plant resistance. However, the pathogen population is evolving to produce more aggressive isolates. For host resistance to be effective, the plant must quickly recognize the pathogen and instigate initial defense mechanisms, optimally at the point of contact. Given that the most resistant host genotypes display rapid pathogen recognition and response, the approach taken was to assess the type, speed and pattern of recognition via Resistance Gene Analog (RGA) transcription among resistant and susceptible cultivated chickpea varieties. RGAs are key factors in the recognition of plant pathogens and the signaling of inducible defenses. In this study, a suite of RGA loci were chosen for further investigation from both published literature and from newly mined homologous sequences within the National Center for Biotechnology Information (NCBI) database. Following their validation in the chickpea genome, 10 target RGAs were selected for differential expression analysis in response to A. rabiei infection. This was performed in a set of four chickpea varieties including two resistant cultivars (ICC3996 and PBA Seamer), one moderately resistant cultivar (PBA HatTrick) and one susceptible cultivar (Kyabra). Gene expression at each RGA locus was assessed via qPCR at 2, 6, and 24 h after A. rabiei inoculation with a previously characterized highly aggressive isolate. As a result, all loci were differentially transcribed in response to pathogen infection in at least one genotype and at least one time point after inoculation. Among these, the differential expression of four RGAs was significant and consistently increased in the most resistant genotype ICC3996 immediately following inoculation, when spore germination began and ahead of penetration into the plant's epidermal tissues. Further in silico analyses indicated that the differentially transcribed RGAs function through ADP-binding within the pathogen recognition pathway. These represent clear targets for future functional validation and potential for selective resistance breeding for introgression into elite cultivars.

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

Project description:Ascochyta blight (AB), caused by a necrotrophic fungus, Ascochyta rabiei (syn. Phoma rabiei) has the potential to destroy the chickpea industry worldwide, due to limited sources of genetic resistance in the cultivated gene pool, high evolutionary potential of the pathogen and challenges with integrated disease management. Therefore, the deployment of stable genetic resistance in new cultivars could provide an effective disease control strategy. To investigate the genetic basis of AB resistance, genotyping-by-sequencing based DArTseq-single nucleotide polymorphism (SNP) marker data along with phenotypic data of 251 advanced breeding lines and chickpea cultivars were used to perform genome-wide association (GWAS) analysis. Host resistance was evaluated seven weeks after sowing using two highly aggressive single spore isolates (F17191-1 and TR9571) of A. rabiei. GWAS analyses based on single-locus and multi-locus mixed models and haplotyping trend regression identified twenty-six genomic regions on Ca1, Ca4, and Ca6 that showed significant association with resistance to AB. Two haplotype blocks (HB) on chromosome Ca1; HB5 (992178-1108145 bp), and HB8 (1886221-1976301 bp) were associated with resistance against both isolates. Nine HB on the chromosome, Ca4, spanning a large genomic region (14.9-56.6 Mbp) were also associated with resistance, confirming the role of this chromosome in providing resistance to AB. Furthermore, trait-marker associations in two F3 derived populations for resistance to TR9571 isolate at the seedling stage under glasshouse conditions were also validated. Eighty-nine significantly associated SNPs were located within candidate genes, including genes encoding for serine/threonine-protein kinase, Myb protein, quinone oxidoreductase, and calmodulin-binding protein all of which are implicated in disease resistance. Taken together, this study identifies valuable sources of genetic resistance, SNP markers and candidate genes underlying genomic regions associated with AB resistance which may enable chickpea breeding programs to make genetic gains via marker-assisted/genomic selection strategies.

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

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