Locke, J. C. | bioRxiv Science

bioRxiv2020

A spatial model of the plant circadian clock reveals design principles for coordinated timing under noisy environments

Individual plant cells possess a genetic network, the circadian clock, that times internal processes to the day-night cycle. Mathematical models of the clock network have driven a mechanistic understanding of the clock in plants. However, these models are typically either whole plant models that ignore tissue or cell type specific clock behavior, or phase only models that do not include clock network components explicitly. It is increasingly clear that in order to reveal the design principles of the plant circadian clock, clock network models must address spatial differences. This is because complex spatial behaviours have been observed in tissues and cells in plants, including period and phase differences between cells and spatial waves of gene expression between organs. Here, we implement an up to date clock network model on a spatial template of the plant. In our model, the sensitivity to light inputs varies across the plant, and cells communicate their clock timing locally via the levels of core clock mRNA levels by cell-to-cell coupling. We found that differences in sensitivities to environmental input in the model can explain the experimentally observed differences in clock periods in different organs, and we show using the model that a plausible coupling mechanism can generate the experimentally observed waves in clock gene expression across the plant. We then examined what features of the plant circadian system allow it to keep time under noisy light-dark (LD) cycles. We found that differences in sensitivity to light can allow regional flexibility in phase even under LD cycles, whilst local cell-to-cell coupling minimized variability in clock rhythms in neighboring cells. Thus, local sensitivity to environmental inputs combined with cell-to-cell coupling allows for flexible yet robust circadian timing under noisy environments.

plant biology↗

bioRxiv2020

Co-expression networks from gene expression variability between genetically identical seedlings can reveal novel regulatory relationships

Co-expression networks are a powerful tool to understand gene regulation. They have been used to identify new regulation and function of genes involved in plant development and their response to the environment. Up to now, co-expression networks have been inferred using transcriptomes generated on plants experiencing genetic or environmental perturbation, or from expression time series. We propose a new approach by showing that co-expression networks can be constructed in the absence of genetic and environmental perturbation, for plants at the same developmental stage. For this we used transcriptomes that were generated from genetically identical individual plants that were grown in the same conditions and for the same amount of time. Twelve time points were used to cover the 24h light/dark cycle. We used variability in gene expression between individual plants of the same time point to infer a co-expression network. We show that this network is biologically relevant and use it to suggest new gene functions and to identify new targets for the transcription factors GI, PIF4 and PRR5. Moreover, we find different co-regulation in this network based on changes in expression between individual plants, compared to the usual approach requiring environmental perturbation. Our work shows that gene co-expression networks can be identified using variability in gene expression between individual plants, without the need for genetic or environmental perturbations. It will allow further exploration of gene regulation in contexts with subtle differences between plants, which could be closer to what individual plants in a population might face in the wild. Author summaryPlant development and response to changes in the environment are strongly regulated at the level of gene expression. That is why understanding how gene expression is regulated is key, and transcriptome approaches have allowed the analysis of transcription for all genes of the genome. Extracting useful information from the high amount of data generated by transcriptomes is a challenge, and gene co-expression networks are a powerful tool to do this. The principle is to find genes that co-vary in expression in different conditions and to pair them together. Communities of genes that are more closely linked are then identified and this is the starting point to look for their implication in the same pathway. Co-expression networks have been used to identify new regulation and function of genes involved in plant development and their response to the environment. They were constructed using transcriptomes generated on plants experiencing genetic or environmental perturbation. We show that co-expression networks can in fact be constructed in the absence of genetic and environmental perturbation. Our work will allow further exploration of gene co-regulation in contexts with subtle differences between plants, which could be closer to what individual plants in a population might face in the wild.

plant biology↗