Quantitative dissection of transcription in development yields evidence for transcription-factor-driven chromatin accessibility

Abstract

Thermodynamic models of gene regulation can predict transcriptional regulation in bacteria, but in eukaryotes chromatin accessibility and energy expenditure may call for a different framework. Here we systematically tested the predictive power of models of DNA accessibility based on the Monod-Wyman-Changeux (MWC) model of allostery, which posits that chromatin fluctuates between accessible and inaccessible states. We dissected the regulatory dynamics of hunchback by the activator Bicoid and the pioneer-like transcription factor Zelda in living Drosophila embryos and showed that no thermodynamic or non-equilibrium MWC model can recapitulate hunchback transcription. Therefore, we explored a model where DNA accessibility is not the result of thermal fluctuations but is catalyzed by Bicoid and Zelda, possibly through histone acetylation, and found that this model can predict hunchback dynamics. Thus, our theory-experiment dialogue uncovered potential molecular mechanisms of transcriptional regulatory dynamics, a key step toward reaching a predictive understanding of developmental decision-making.

Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer

“…While we chose the initial rate of transcription as the experimental measurable to confront against our model predictions, the MS2 technique can also report on other dynamical features of transcription such as the time window over which transcription occurs and the fraction of loci that engage in transcription at any point over the nuclear cycle. While these two quantities have been shown to be relevant in shaping gene expression patterns in other regulatory contexts [Garcia et al, 2013, Lammers et al, 2020, Eck et al, 2020, Dufourt et al, 2018, Reimer et al, 2021, we found that the transcription time window was not significantly regulated in the presence of Runt. As described in Section S8, we did find some modulation of the fraction of transcriptionally engaged loci for a subset of our synthetic enhancer constructs but, as we could not detect a clear trend in how this fraction of active loci was modulated, we did not pursue a theoretical dissection of the control of this quantity by Runt.…”

“…Doing so with high temporal resolution using FISH is challenging, although it can be accomplished to some degree by synchronizing embryo deposition before fixation [Park et al, 2019]. Second, while most transcription factors directly dictate the rate of RNAP loading, and hence the rate of mRNA production [Spitz and Furlong, 2012, Garcia et al, 2013, Eck et al, 2020, typical FISH measurements report on the accumulated mRNA in the cytoplasm, which is a convolution of all processes of the transcription cycle-initiation, elongation, and termination [Liu et al, 2021, Alberts, 2015]-as well as mRNA nuclear export dynamics, diffusion, and degradation. These processes could be modulated in space and time, potentially confounding measurements.…”

“…The transcriptional input-output function in Figure 2B indicates that, in order to predict the rate of RNAP loading and to test our theoretical model, we need to first measure the concentration of the input Bicoid and Runt transcription factors. In order to quantify the concentration profile of Bicoid, we used an established eGFP-Bicoid line [Gregor et al, 2007] and measured mean Bicoid nuclear concentration dynamics along the anterior-posterior axis of the embryo over nuclear cycles 13 and 14 (nc13 and nc14, respectively) as shown in Movie S1 [Eck et al, 2020]. An example snapshot and time trace of Bicoid nuclear concentration dynamics at 40% of the embryo length appear in Figure 3A and B.…”

“…In order to quantify the transcriptional activity reported by MS2, we measured the mean MS2 spot fluorescence over nuclei in a narrow spatial window (Fig. 3I [Garcia et al, 2013, Eck et al, 2020. To measure the initial rate of RNAP loading, we obtained the slope of the initial rise in the number of actively transcribing RNAP molecules over the same time window used to average input transcription factor concentration (Fig.…”

Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage

“…We parameterize this R(t) as the sum of a constant term hRi that represents the mean, or time-averaged, rate of initiation, and a small temporal fluctuation term given by δR(t) such that R(t) = hRi + δR(t). This mean-field parameterization is motivated by the fact that many genes are well approximated by constant rates of initiation [25,31,35,61]. The fluctuation term δR(t) allows for slight time-dependent deviations from the mean initiation rate.…”