Expansion of the circadian transcriptome in Brassica rapa and genome-wide diversification of paralog expression patterns

An important challenge of crop improvement strategies is assigning function to paralogs in polyploid crops. Gene expression is one method for determining the activity of paralogs; however, the majority of transcript abundance data represents a static point that does not consider the spatial and temporal dynamics of the transcriptome. Studies in Arabidopsis have estimated up to 90% of the transcriptome to be under diel or circadian control depending on the condition. As a result, time of day effects on the transcriptome have major implications on how we characterize gene activity. In this study, we aimed to resolve the circadian transcriptome in the polyploid crop Brassica rapa and explore the fate of multicopy orthologs of Arabidopsis circadian regulated genes. We performed a high-resolution time course study with 2 h sampling density to capture the genes under circadian control. Strikingly, more than two-thirds of expressed genes exhibited rhythmicity indicative of circadian regulation. To compare the expression patterns of paralogous genes, we developed a program in R called DiPALM (Differential Pattern Analysis by Linear Models) that analyzes time course data to identify transcripts with significant pattern differences. Using DiPALM, we identified genome-wide divergence of expression patterns among retained paralogs. Cross-comparison with a previously generated diel drought experiment in B. rapa revealed evidence for differential drought response for these diverging paralog pairs. Using gene regulatory network models we compared transcription factor targets between B. rapa and Arabidopsis circadian networks to reveal additional evidence for divergence in expression between B. rapa paralogs that may be driven in part by variation in conserved non coding sequences. These findings provide new insight into the rapid expansion and divergence of the transcriptional network in a polyploid crop and offer a new method for assessing paralog activity at the transcript level.