Dynamical differential expression (DyDE) reveals the period control mechanisms of the Arabidopsis circadian oscillator

Abstract

The circadian oscillator, an internal time-keeping device found in most organisms, enables timely regulation of daily biological activities by maintaining synchrony with the external environment. The mechanistic basis underlying the adjustment of circadian rhythms to changing external conditions, however, has yet to be clearly elucidated. We explored the mechanism of action of nicotinamide in Arabidopsis thaliana , a metabolite that lengthens the period of circadian rhythms, to understand the regulation of circadian period. To identify the key mechanisms involved in the circadian response to nicotinamide, we developed a systematic and practical modeling framework based on the identification and comparison of gene regulatory dynamics. Our mathematical predictions, confirmed by experimentation, identified key transcriptional regulatory mechanisms of circadian period and uncovered the role of blue light in the response of the circadian oscillator to nicotinamide. We suggest that our methodology could be adapted to predict mechanisms of drug action in complex biological systems.

Differential effects of day-night cues and the circadian clock on the barley transcriptome

“…We used simple dynamical models to capture gene regulatory dynamics without making a priori assumptions on the structure of the network. These dynamical models have been successfully used in the past to describe circadian processes of Arabidopsis under conditions that are similar to those of our dataset (Dalchau et al, 2012; Herrero et al, 2012; Banos et al, 2015; Mombaerts et al, 2016 and Mombaerts et al, 2019). It is also important to stress that our approach could only model genes with circadian expression oscillations, while it is well known that posttranscriptional regulation and the rate of protein degradation and activity is an essential constituent of the clock mechanism in Arabidopsis (Kim et al, 2003, Más et al, 2003).…”

“…We followed an approach that searches the dynamic dependencies of Hv ELF3 and Hv LUX1 expression on other transcripts. We used Linear Time Invariant (LTI) models, for interpreting expression data without relying on a priori knowledge of the transcriptional network (Dalchau et al ., 2010; Herrero et al ., 2012; Mombaerts et al. , 2019; Supplemental Information).…”

“…We then investigated the consistency between the models obtained for Bowman and eam7 using the v-gap metric (Supplemental Data 4, Supplemental Figure S5). This approach estimates differences between models and allowed us to identify regulatory interactions that were maintained or abolished in the eam7 mutant (Mombaerts et al ., 2019). Following this approach, we identified 20 transcripts and 79 regulatory links in Bowman of which 15 transcripts and 49 regulatory links could be cross-validated in eam7 (Figure 4, Supplemental Figure S5, S6, Supplemental Data 4).…”

A multifactorial evaluation framework for gene regulatory network reconstruction

“…The network inference methods included in the comparison represent the function f at various levels of complexity. They are All-to-all (ATA) (Mombaerts et al, 2019), Gaussian Process Dynamical Models (GPDM) (Aalto et al, 2018), dynamical GEne Network Inference with Ensemble of trees (dynGENIE3) (Huynh-Thu and Geurts, 2018), Algorithm for Revealing Network Interactions (ARNI) (Casadiego et al, 2017) and Improved Chemical Model Averaging (iCheMA) (Aderhold et al, 2017). For details, we refer to publications presenting the methods.…”

“…A fitness score is computed for each pair, which is regarded as a confidence level on the existence of the corresponding regulation. Here, the methodology presented by Mombaerts et al (2019) has been extended to deal with multiexperiment datasets. The datasets are merged together so that the dynamics to be identified are identical through all experimental conditions, assuming that the signal-to-noise ratios are similar in different experiments.…”