Transcriptome Analysis of Capsicum Chlorosis Virus-Induced Hypersensitive Resistance Response in Bell Capsicum

Background Capsicum chlorosis virus (CaCV) is an emerging pathogen of capsicum, tomato and peanut crops in Australia and South-East Asia. Commercial capsicum cultivars with CaCV resistance are not yet available, but CaCV resistance identified in Capsicum chinense is being introgressed into commercial Bell capsicum. However, our knowledge of the molecular mechanisms leading to the resistance response to CaCV infection is limited. Therefore, transcriptome and expression profiling data provide an important resource to better understand CaCV resistance mechanisms. Methodology/Principal Findings We assembled capsicum transcriptomes and analysed gene expression using Illumina HiSeq platform combined with a tag-based digital gene expression system. Total RNA extracted from CaCV/mock inoculated CaCV resistant (R) and susceptible (S) capsicum at the time point when R line showed a strong hypersensitive response to CaCV infection was used in transcriptome assembly. Gene expression profiles of R and S capsicum in CaCV- and buffer-inoculated conditions were compared. None of the genes were differentially expressed (DE) between R and S cultivars when mock-inoculated, while 2484 genes were DE when inoculated with CaCV. Functional classification revealed that the most highly up-regulated DE genes in R capsicum included pathogenesis-related genes, cell death-associated genes, genes associated with hormone-mediated signalling pathways and genes encoding enzymes involved in synthesis of defense-related secondary metabolites. We selected 15 genes to confirm DE expression levels by real-time quantitative PCR. Conclusion/Significance DE transcript profiling data provided comprehensive gene expression information to gain an understanding of the underlying CaCV resistance mechanisms. Further, we identified candidate CaCV resistance genes in the CaCV-resistant C. annuum x C. chinense breeding line. This knowledge will be useful in future for fine mapping of the CaCV resistance locus and potential genetic engineering of resistance into CaCV-susceptible crops.

Cross-correlations between weather variables in Australia

The information on climate variations is essential for the research of many subjects, such as the performance of buildings and agricultural production. However, recorded meteorological data are often incomplete. There may be a limited number of locations recorded, while the number of recorded climatic variables and the time intervals can also be inadequate. Therefore, the hourly data of key weather parameters as required by many building simulation programmes are typically not readily available. To overcome this gap in measured information, several empirical methods and weather data generators have been developed. They generally employ statistical analysis techniques to model the variations of individual climatic variables, while the possible interactions between different weather parameters are largely ignored. Based on a statistical analysis of 10 years historical hourly climatic data over all capital cities in Australia, this paper reports on the finding of strong correlations between several specific weather variables. It is found that there are strong linear correlations between the hourly variations of global solar irradiation (GSI) and dry bulb temperature (DBT), and between the hourly variations of DBT and relative humidity (RH). With an increase in GSI, DBT would generally increase, while the RH tends to decrease. However, no such a clear correlation can be found between the DBT and atmospheric pressure (P), and between the DBT and wind speed. These findings will be useful for the research and practice in building performance simulation.