Abstract
Differential gene expression profiles and metabolic networks are valuable tools for the genetic characterization of agronomic traits. In this study, we used large-scale expression analyses to identify modified biological processes in caffeine-free coffee plants. The first step was the large-scale sequencing of RNA from young and developing tissues of caffeine-free plants (AC1) and plants with normal concentrations of the compound (MN). The resulting 65,000 sequences were analyzed in silico for identification of 171 genes with differential expression between treatments, and establishment of metabolic networks associated with levels of caffeine. Few genes were mapped onto metabolic pathways, indicating that low caffeine has no major effects on physiological processes. The differential expression observed in silico was validated for 12 selected genes in field experiments using qPCR. The expression profile of 5 genes differed on the analyses, and the rest confirmed the in silico profile. Among the validated genes two of them, FIG and LSM-l, may control other agronomic traits associated with low caffeine content in coffee tissues. These genes are potential markers for use in association with other current markers for assisted selection of low-caffeine coffee. Therefore, they may improve the efficiency and effectiveness of coffee breeding programs.
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