A Systematic View Exploring the Role of Chloroplasts in Plant Abiotic Stress Responses

Figure 1

Workflow diagram summarizing analysis process about rice plastid-related genes. The workflow illustrates the entire analysis process in this study. First of all, we retrieved 4,707 plastid transcripts from GO slim annotation at the RGAP database. Then we removed unannotated and duplicated information to collect 3,314 plastid genes. By querying these genes to our meta-expression data source, we obtained 2,839 genes that only have the most highly expressed probe. We clustered these intensity values with KMC algorithm as 20 clusters. As a result, we identified 1,695 leaf-preferred or ubiquitously expressed genes for further analysis. With these two sets of genes, we performed functional characterization like GO enrichment, KEGG enrichment, and MapMan analysis to characterize their functions. In addition to these analyses, we queried the 1,695 genes to an abiotic stress expression database (DB) to identify stress-responsive plastid genes. As a result, we clustered 264 cold or heat stress-responsive plastid genes and conducted a literature search. Altogether, we constructed a hypothetical protein–protein interaction model of stress-related plastid genes.