Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function

Circadian regulation of the transcriptome in a complex polyploid crop

The circadian clock is a finely balanced timekeeping mechanism that coordinates programmes of gene expression. It is currently unknown how the clock regulates expression of homoeologous genes in polyploids. Here, we generate a high-resolution time-course dataset to investigate the circadian balance between sets of 3 homoeologous genes (triads) from hexaploid bread wheat. We find a large proportion of circadian triads exhibit imbalanced rhythmic expression patterns, with no specific subgenome favoured. In wheat, period lengths of rhythmic transcripts are found to be longer and have a higher level of variance than in other plant species. Expression of transcripts associated with circadian controlled biological processes is largely conserved between wheat and Arabidopsis; however, striking differences are seen in agriculturally critical processes such as starch metabolism. Together, this work highlights the ongoing selection for balance versus diversification in circadian homoeologs and identifies clock-controlled pathways that might provide important targets for future wheat breeding.

Spatially specific mechanisms and functions of the plant circadian clock

Like many organisms, plants have evolved a genetic network, the circadian clock, to coordinate processes with day/night cycles. In plants, the clock is a pervasive regulator of development and modulates many aspects of physiology. Clock-regulated processes range from the correct timing of growth and cell division to interactions with the root microbiome. Recently developed techniques, such as single-cell time-lapse microscopy and single-cell RNA-seq, are beginning to revolutionize our understanding of this clock regulation, revealing a surprising degree of organ, tissue, and cell-type specificity. In this review, we highlight recent advances in our spatial view of the clock across the plant, both in terms of how it is regulated and how it regulates a diversity of output processes. We outline how understanding these spatially specific functions will help reveal the range of ways that the clock provides a fitness benefit for the plant.

Nutrient-Mediated Perception and Signalling in Human Metabolism: A Perspective of Nutrigenomics

The interaction between selective nutrients and linked genes involving a specific organ reveals the genetic make-up of an individual in response to a particular nutrient. The interaction of genes with food opens opportunities for the addition of bioactive compounds for specific populations comprising identical genotypes. The slight difference in the genetic blueprints of humans is advantageous in determining the effect of nutrients and their metabolism in the body. The basic knowledge of emerging nutrigenomics and nutrigenetics can be applied to optimize health, prevention, and treatment of diseases. In addition, nutrient-mediated pathways detecting the cellular concentration of nutrients such as sugars, amino acids, lipids, and metabolites are integrated and coordinated at the organismal level via hormone signals. This review deals with the interaction of nutrients with various aspects of nutrigenetics and nutrigenomics along with pathways involved in nutrient sensing and regulation, which can provide a detailed understanding of this new leading edge in nutrition research and its potential application to dietetic practice.

Bluster or Lustre: Can AI Improve Crops and Plant Health?

In a changing climate where future food security is a growing concern, researchers are exploring new methods and technologies in the effort to meet ambitious crop yield targets. The application of Artificial Intelligence (AI) including Machine Learning (ML) methods in this area has been proposed as a potential mechanism to support this. This review explores current research in the area to convey the state-of-the-art as to how AI/ML have been used to advance research, gain insights, and generally enable progress in this area. We address the question-Can AI improve crops and plant health? We further discriminate the bluster from the lustre by identifying the key challenges that AI has been shown to address, balanced with the potential issues with its usage, and the key requisites for its success. Overall, we hope to raise awareness and, as a result, promote usage, of AI related approaches where they can have appropriate impact to improve practices in agricultural and plant sciences.