| Cucumber (Cucumis sativa Linn) belongs to the Cucurbitaceous family and is one of the world’s most economically valuable and nutritional vegetable crops. It has been widely cultivated in the world. Botrytis cinerea can also infect more than 200 types of plants besides cucumber, resulting in serious economic losses. For this reason, we carried out the following research:1.Firstly, B. cinerea P729 gene was selected as a target gene for quantitative detection, then this gene was used to construct a clone vector. We inserted the vector into Escherichia coli cells. Then the Escherichia coli cells were cultured until the vector had a lot of copies. We used serial dilution to establish a standard curve for the qPCR method. Finally we applied the PMA-qPCR method to detect the sick leaves. This study showed that PMA-qPCR method could identify and quantify the number of live pathogen in actual samples quickly and provided a technical support for the prevention of cucumber gray mold.2.With the advent of Hi-seq technology, it is possible to study the plant-pathogen interaction at the transcription level. To the best of our knowledge, this is the first application of transcriptome to identify differentially expressed genes (DEGs) of cucumber and B. cinerea before and after the plant-pathogen interaction. In total,248,908,688 raw reads were generated. After removing low-quality reads and those containing adapter and poly-N,238,341,648 clean reads remained to map the reference genome. There were 3,512 cucumber DEGs and 1,735 B. cinerea DEGs. GO enrichment and KEGG enrichment analysis were performed on these DEGs to study the interaction between cucumber and B. cinerea. To verify the reliability and accuracy of our transcriptome data,5 DEGs of cucumber and 5 DEGs of B. cinerea were chosen for qPCR verification. This is the first systematic transcriptome analysis of components related to the B. cinerea-cucumber interaction. Functional genes and putative pathways identified herein will increase our understanding of the mechanism of the pathogen-host interaction.3.Despite our expanding knowledge about the biochemistry of gene regulation involved in host-pathogen interactions, a quantitative understanding of this process at a transcriptional level is still limited. We devised and assessed a computational framework that could address this question. This framework was founded on a mixture model-based likelihood, equipped with functionality to cluster genes per dynamic and functional changes of gene expression within an interconnected system composed of the host and pathogen. If genes from the host and pathogen were clustered in the same group due to a similar pattern of dynamic profiles, they were likely to be reciprocally co-evolving. If genes from the two organisms were clustered in different groups, this meant that they experienced strong host-pathogen interactions. The framework can test the rates of change for individual gene clusters during pathogenic infection and quantify their impacts on host-pathogen interactions. The framework was validated by a pathological study of cucumber leaves infected by fungal B. cinerea in which co-evolving and interactive genes that determine cucumber-B. cinerea interactions are identified. The new framework should find its wide application to studying host-pathogen interactions for any other interconnected systems. |
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