Fig 1.
Treatments effects in transcriptional networks.
Treatment effects can be perceived as perturbations in molecular networks [18,19]. In transcriptional networks, such perturbations usually only affect a very small number of regulatory links directly. For example, only the red links have been directly affected by the treatment. All other links are unchanged, although all nodes (concentrations) in the Figure have been (indirectly) affected due to cascading and feedback effects. Hence, Differential Expression (DE) might not distinguish between direct and indirect effects of a treatment. Dynamical Differential Expression (DyDE), therefore, investigates how and why changes occur, instead of simply measuring what and how much is produced by those changes.
Fig 2.
The effects of NAM on the circadian regulation of the transcriptome.
(A) Illustration of the shape of S. The first panel shows two period of a perfect sinusoidal shape, whereas the second panel displays the segmentation of the period P into p1 and p2, where p1 is greater than p2. p1 and p2 follows the formula: P = (p1 + p2)/2. The last panel displays the case were p1 is smaller than p2. (B) Number of periodic transcripts that have been identified in untreated and NAM-treated plants, as well as the intersection. (C) Circadian period of untreated and NAM-treated transcripts plus minus standard deviation. The mean increase of period following the NAM treatment is of 3.3h. (D) Amplitude analysis (normalized) for the same transcripts.
Fig 3.
Network inference and analysis by dynamical differential expression (DyDE).
(A) Ordinary Differential Equations (ODEs) capture the dependence of the rate of the concentration of a transcript on the concentration of another transcript. First order linear models are used to represent the dynamics between two genes. Here, a good agreement (plain line) with the data (dotted line) was found (57% goodness of fit). (B) The inverse regulation is considered. In this case, it is not possible to find a combination of parameters so that a first order linear model captures the dynamics involved. For this inverse regulation the model that best described the data obtained a goodness of fit of only 16%. (C) A threshold by which each model is (in)validated is applied on the goodness of fit of the models. As an example, a threshold of 46% would consider a link from TOC1 to PRR9 but not the other way around. The same threshold is applied to all models. (D) A first order linear model is evaluated in the presence of nicotinamide between the same species. The nu gap is then applied to compare models (A) and (D) to quantify whether the models are similar, or significantly affected by NAM.
Fig 4.
DyDE applied to the Arabidopsis circadian oscillator genes.
(A) Common network and nu-gap analysis. The common network displays the models that have been validated in both untreated and NAM-treated plants. A directed arrow from gene a to gene b (blue circles), therefore, represents a dynamical model that captures the dependency of b on a. Red arrows represent the models associated with the top five highest nu-gap values. (B) Bar plot comparing the connectivity loss (%) associated to each gene. For a particular gene, the connectivity loss corresponds to the total amount of incoming and outgoing links that were validated in untreated plants but not in NAM-treated plants. Error bars represent the standard deviation of connectivity loss for ± 5% change in fitness threshold selection.
Fig 5.
Reverse genetic analysis validates the prediction that TOC1 and PRR7 are associated with the effect of nicotinamide on circadian period.
(A) The change in circadian period caused by 20 mM NAM (period difference) in circadian mutants measured using CCA1:LUC, TOC1:LUC, and CAB2:LUC. (B) CCA1:LUC activity for Col-0 and prr7-3 in the presence (yellow) and absence (white) of 20 mM NAM. (C) Dose response of circadian period to NAM for prr7-11, prr9-10, prr7-3prr9-10, prr3-1, prr5-1 and toc1-2. Bars ±SD. N >16 from > 2 technical replicates. Open symbols indicate minus NAM. Mean time courses for these data are shown in S5 Fig. Statistical analysis is detailed in S6 Table.
Fig 6.
The effect of nicotinamide on circadian period requires blue light.
(A) CCA1:LUC rhythms in monochromatic blue or red light ± 20 mM NAM. (B) The change in circadian period ± 20 mM NAM in constant white (grey) and monochromatic red or blue light for a range of reporters. (C) 20 mM NAM abolishes circadian rhythms of [Ca2+]cyt in both Col-0, prr7-11 and prr7-prr9. (D) Robust circadian rhythms of [Ca2+]cyt in monochromatic blue light are abolished by 20 mM NAM. (E) Red light induced elevations of [Ca2+]cyt early in the photoperiod are not abolished by nicotinamide. (F) PHYB-ox enhances circadian rhythms of [Ca2+]cyt in monochromatic red light. Light conditions are indicated by the colored boxes on the X axes. White is red/blue mix, black is dark, monochromatic light is represented by the appropriate color with subjective night shaded darker than subjective day. NAM indicated by yellow. Bars are SD. n > 7.
Fig 7.
A blue light dependent module regulates the response of the circadian oscillator to NAM.
NAM might regulate the circadian oscillator through regulation of cADPR dependent circadian oscillations of [Ca2+]. CCA1 is a repressor of ADPRc. ADPRc generation of cADPR and [Ca2+] oscillations is inhibited by NAM. Both the effects of NAM on the circadian oscillator and circadian oscillations of [Ca2+]i are blue-light dependent. The regulation of [Ca2+] on the circadian oscillator is indicated by a dotted line. NAM could also regulate the circadian oscillator by Ca2+-independent events. We determined that the NAM-induced changes in circadian period are mediated principally by the interaction between PRR7 and PRR9, as well as TOC1.These interactions are shown in red in the model.