Circadian rhythms in plants

Delayed fluorescence

One way of profiling natural variation in circadian rhythms is by using delayed-fluorescence (DF) imaging. DF is light naturally re-emitted from photosynthetic tissues and so can be measured in any green plant without the need for genetic modification. So far, we have been able to use DF imaging to measure circadian rhythms in plants as varied as: Arabidopsis, Kalanchoe, liverworts, Brassicas, and wheat. We have adapted our DF method to optimise imaging of these plants. We recently used DF imaging to characterise circadian rhythms across 191 Arabidopsis ecotypes from Sweden and used Genome-Wide Association (GWA) to identify genes underpinning this variation. 

Machine learning

Another way we explore circadian regulation and variation is through computational approaches, such as machine learning (ML). In collaboration with researchers at IBM Research UK, we make use of publicly available transcriptional and genomic datasets to train, test, and validate ML models to answer a number of biological questions. So far, we have used ML approaches for:

• classifying rhythmic data from transcriptomic datasets
• strategic down-sampling of timepoints required to make rhythmic predictions 
• making predictions about rhythmicity based on DNA-sequence alone
• identifying biomarkers for predicting circadian time from a single transcriptomic time-point

Our current ML work is focused on defining a subset of genes to use as biomarkers to measure the internal circadian time within a plant. By comparing the relative expression of these marker genes, we can assay differences in clock behaviour as a result of natural variation, environmental stresses, or targeted genetic mutations. This work will give us the power to utilize publicly available transcriptomic datasets with just a single timepoint to look at differences in circadian phase. 

Circadian regulation in wheat

Another ongoing aspect of the project is focused on exploring how gene expression is coordinated in species with multiple copies of the same gene, as is the case in hexaploid wheat (AABBDD). By analysing circadian expression patterns in sets of three homoeologous genes (known as triads), we can identify cases where there are differential patterns in phase, period length, or rhythmicity in one of the homoeologs. Studying circadian regulation in polyploid crops is useful as it allows us to identify potential targets for manipulating clock function, which might have implications for yield and resilience. This work is in collaboration with the Antony Dodd group at the John Innes Centre.