The inner 24-hour cycles — or circadian rhythms — are key to maintaining human, plant, and animal health, which could provide valuable insight into how broken clocks impact health.
Circadian rhythms, such as the sleep-wake cycle, are innate to most living organisms and critical to life on Earth. The word circadian originates from the Latin phrase ‘circa diem’ which means ‘around a day’.
Biologically, the circadian clock temporally orchestrates physiology, biochemistry, and metabolism across the 24-hour day-night cycle. This is why being out of kilter can affect our fitness levels, our health, or our ability to survive. For example, experiencing jet lag is a chronobiological problem — our body clocks are out of sync because the normal external cues such as light or temperature have changed.
For this latest research, the team applied ML to predict complex temporal circadian gene expression patterns in the model plant Arabidopsis thaliana. Taking newly generated datasets, published temporal datasets, and Arabidopsis genomes, the team of scientists trained ML models to make predictions about circadian gene regulation and expression patterns.
Featured in the journal PNAS, the work demonstrates the power of AI and ML-based approaches to enable more cost-effective analysis and deeper insight into the function of the circadian clock and its regulation. These approaches are redefining how scientists use public data and generate testable hypotheses to understand gene expression control in plants and humans.
Detecting circadian rhythms
Prof Anthony Hall, Group Leader at the Earlham Institute, said: “Genes involved in the circadian clock typically show an oscillation between off-on state rhythmic patterns throughout 24 hours. This pattern is called circadian rhythmicity. “Detecting circadian rhythmicity with existing methods is challenging as it requires using sequencing technologies to generate long, high-resolution, time-series transcriptome datasetes to measure gene expression throughout the day. Not only is this expensive, but it is also time-consuming for laboratory scientists. Consequently, our knowledge to date of how genes are controlled and regulated in a circadian clock is limited.”
The development of AI and ML-based technology was initially applied to the model plant Arabidopsis, progressing to testing other complex or temporal gene expression patterns as well as other species across Arabidopsis ecotypes. Furthermore, the team has adapted the ML approach for wheat to show that the methods used to allow accurate analysis of key food crops.
“We re-defined the field by developing ML models to distinguish circadian transcripts that don’t use transcriptomic timepoint information, but instead DNA sequence features generated from public genomic resources. Therefore, allowing us to predict the circadian regulation of genes simply by analysing the genome DNA sequence.” The researchers based their study on the theory that a major mechanism of gene expression control, be it circadian or other mechanisms, is through transcription factors (and other factors) that bind to a regulatory DNA sequence. Transcription factors are vital molecules that can control gene expression — directing when, where, and to what degree genes are expressed. They bind to specific sequences of DNA and control the transcription of DNA into mRNA.
Dr. Gardiner adds: “Our ML models and their application in crops, where circadian rhythms are critical to maintaining healthy growth and development, could lead to increased yields as agricultural scientists and farmers begin to use the model to understand the inner rhythms of the plants they grow and harvest.
“However, the technology we developed goes beyond the scope of plants. We are now looking at different species to investigate the circadian clock and its link to disease in humans, for example, where the dysregulation of the circadian clock has been associated with a range of diseases from depression to cancer.” Dr. Gardiner is clear about the value of ML and AI in gaining deeper insights into circadian regulation: “What makes our models more informative is our usage of explainable AI algorithms,” she explains. “We wanted to use the interpretation of our ML models to illuminate what’s inside the ‘black box so that we can better understand the predictions they make.
“We used local model explanations that are transcript specific to rank DNA sequence features, which provide a detailed profile of the potential circadian regulatory mechanisms for each transcript. Using the local explanation derived from ranked DNA sequence features allows us to distinguish the temporal phase of transcript expression and, in doing so, reveal hidden sub-classes within the circadian class. E.g., whether a transcript is likely to show its peak expression in the morning, afternoon, evening, or night.”