Deciphering and Prediction of Transcriptome Dynamics under Fluctuating Field Conditions

Determining the drivers of gene expression patterns is more straightforward in laboratory conditions than in the complex fluctuating environments where organisms typically live. We gathered transcriptome data from the leaves of rice plants in a paddy field along with the corresponding meteorological data and used them to develop statistical models for the endogenous and external influences on gene expression. Our results indicate that the transcriptome dynamics are predominantly governed by endogenous diurnal rhythms, ambient temperature, plant age, and solar radiation. The data revealed diurnal gates for environmental stimuli to influence transcription and pointed to relative influences exerted by circadian and environmental factors on different metabolic genes. The model also generated predictions for the influence of changing temperatures on transcriptome dynamics. We anticipate that our models will help translate the knowledge amassed in laboratories to problems in agriculture and that our approach to deciphering the transcriptome fluctuations in complex environments will be applicable to other organisms. [Display omitted] ► Deciphering rice leaf transcriptome dynamics under complex field conditions ► Field transcriptome is primarily influenced by ambient temperature and circadian clock ► Response thresholds to light were very low for many light-responsive genes ► Modeling transcriptome fluctuations in changing environments yields predictive data A massive integration of transcriptome and meteorological data organized by statistical modeling reveals the endogenous and environment factors that drive gene expression changes in rice plants in a paddy field. The model reveals insights into the dominant influences on specific pathways and enables predictions for how rising temperatures may affect plants at the transcriptional level.