What shapes eukaryotic transcriptional bursting?

Monitoring the spatio-temporal organization and dynamics of the genome

The spatio-temporal organization of chromatin in the eukaryotic cell nucleus is of vital importance for transcription, DNA replication and genome maintenance. Each of these activities is tightly regulated in both time and space. While we have a good understanding of chromatin organization in space, for example in fixed snapshots as a result of techniques like FISH and Hi-C, little is known about chromatin dynamics in living cells. The rapid development of flexible genomic loci imaging approaches can address fundamental questions on chromatin dynamics in a range of model organisms. Moreover, it is now possible to visualize not only single genomic loci but the whole genome simultaneously. These advances have opened many doors leading to insight into several nuclear processes including transcription and DNA repair. In this review, we discuss new chromatin imaging methods and how they have been applied to study transcription.

Discontinuous transcription of ribosomal DNA in human cells

Numerous studies show that various genes in all kinds of organisms are transcribed discontinuously, i.e. in short bursts or pulses with periods of inactivity between them. But it remains unclear whether ribosomal DNA (rDNA), represented by multiple copies in every cell, is also expressed in such manner. In this work, we synchronized the pol I activity in the populations of tumour derived as well as normal human cells by cold block and release. Our experiments with 5-fluorouridine (FU) and BrUTP confirmed that the nucleolar transcription can be efficiently and reversibly arrested at +4°C. Then using special software for analysis of the microscopic images, we measured the intensity of transcription signal (incorporated FU) in the nucleoli at different time points after the release. We found that the ribosomal genes in the human cells are transcribed discontinuously with periods ranging from 45 min to 75 min. Our data indicate that the dynamics of rDNA transcription follows the undulating pattern, in which the bursts are alternated by periods of rare transcription events.

Constraining the complexity of promoter dynamics using fluctuations in gene expression

Gene expression is an inherently stochastic process with transcription of mRNAs often occurring in bursts: short periods of activity followed by typically longer periods of inactivity. While a simple model involving switching between two promoter states has been widely used to analyze transcription dynamics, recent experimental observations have provided evidence for more complex kinetic schemes underlying bursting. Specifically, experiments provide evidence for complexity in promoter dynamics during the switch from the transcriptionally inactive to the transcriptionally active state. An open question in the field is: what is the minimal complexity needed to model promoter dynamics and how can we determine this? Here, we show that measurements of mRNA fluctuations can be used to set fundamental bounds on the complexity of promoter dynamics. We study models wherein the switching time distribution from transcriptionally inactive to active states is described by a general waiting-time distribution. Using approaches from renewal theory and queueing theory, we derive analytical expressions which connect the Fano factor of mRNA distributions to the waiting-time distribution for promoter switching between inactive and active states. The results derived lead to bounds on the minimal number of promoter states and thus allow us to derive bounds on the minimal complexity of promoter dynamics based on single-cell measurements of mRNA levels.

Resolving noise-control conflict by gene duplication

Gene duplication promotes adaptive evolution in two main ways: allowing one duplicate to evolve a new function and splitting ancestral functions between the duplicates. The second scenario may resolve adaptive conflicts that can rise when one gene performs different functions. In an apparent departure from both scenarios, low-expressing transcription factor (TF) duplicates commonly bind to the same DNA motifs and act in overlapping conditions. To examine for possible benefits of this apparent redundancy, we examined the Msn2 and Msn4 duplicates in budding yeast. We show that Msn2,4 function as one unit by inducing the same set of target genes in overlapping conditions. Yet, the two-factor composition allows this unit's expression to be both environmentally responsive and with low noise, resolving an adaptive conflict that limits expression of single genes. We propose that duplication can provide adaptive benefit through cooperation rather than functional divergence, allowing two-factor dynamics with beneficial properties that cannot be achieved by a single gene.

Discontinuous transcription

Numerous studies based on new single-cell and single-gene techniques show that individual genes can be transcribed in short bursts or pulses accompanied by changes in pulsing frequencies. Since so many examples of such discontinuous or fluctuating transcription have been found from prokaryotes to mammals, it now seems to be a common mode of gene expression. In this review we discuss the occurrence of the transcriptional fluctuations, the techniques used for their detection, their putative causes, kinetic characteristics, and probable physiological significance.

Dynamics of Notch-Dependent Transcriptional Bursting in Its Native Context

Transcription is well known to be inherently stochastic and episodic, but the regulation of transcriptional dynamics is not well understood. Here, we analyze how Notch signaling modulates transcriptional bursting during animal development. Our focus is Notch regulation of transcription in germline stem cells of the nematode C. elegans. Using the MS2 system to visualize nascent transcripts and live imaging to record dynamics, we analyze bursting as a function of position within the intact animal. We find that Notch-dependent transcriptional activation is indeed "bursty"; that wild-type Notch modulates burst duration (ON-time) rather than duration of pauses between bursts (OFF-time) or mean burst intensity; and that a mutant Notch receptor, which is compromised for assembly into the Notch transcription factor complex, primarily modifies burst size (duration × intensity). These analyses thus visualize the effect of a canonical signaling pathway on metazoan transcriptional bursting in its native context.

Exact and efficient hybrid Monte Carlo algorithm for accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts

Single cells exhibit a significant amount of variability in transcript levels, which arises from slow, stochastic transitions between gene expression states. Elucidating the nature of these states and understanding how transition rates are affected by different regulatory mechanisms require state-of-the-art methods to infer underlying models of gene expression from single cell data. A Bayesian approach to statistical inference is the most suitable method for model selection and uncertainty quantification of kinetic parameters using small data sets. However, this approach is impractical because current algorithms are too slow to handle typical models of gene expression. To solve this problem, we first show that time-dependent mRNA distributions of discrete-state models of gene expression are dynamic Poisson mixtures, whose mixing kernels are characterized by a piecewise deterministic Markov process. We combined this analytical result with a kinetic Monte Carlo algorithm to create a hybrid numerical method that accelerates the calculation of time-dependent mRNA distributions by 1000-fold compared to current methods. We then integrated the hybrid algorithm into an existing Monte Carlo sampler to estimate the Bayesian posterior distribution of many different, competing models in a reasonable amount of time. We demonstrate that kinetic parameters can be reasonably constrained for modestly sampled data sets if the model is known a priori. If there are many competing models, Bayesian evidence can rigorously quantify the likelihood of a model relative to other models from the data. We demonstrate that Bayesian evidence selects the true model and outperforms approximate metrics typically used for model selection.

Quantitative single-cell live imaging links HES5 dynamics with cell-state and fate in murine neurogenesis

During embryogenesis cells make fate decisions within complex tissue environments. The levels and dynamics of transcription factor expression regulate these decisions. Here, we use single cell live imaging of an endogenous HES5 reporter and absolute protein quantification to gain a dynamic view of neurogenesis in the embryonic mammalian spinal cord. We report that dividing neural progenitors show both aperiodic and periodic HES5 protein fluctuations. Mathematical modelling suggests that in progenitor cells the HES5 oscillator operates close to its bifurcation boundary where stochastic conversions between dynamics are possible. HES5 expression becomes more frequently periodic as cells transition to differentiation which, coupled with an overall decline in HES5 expression, creates a transient period of oscillations with higher fold expression change. This increases the decoding capacity of HES5 oscillations and correlates with interneuron versus motor neuron cell fate. Thus, HES5 undergoes complex changes in gene expression dynamics as cells differentiate.

Trick or TREAT: A Scary-Good New Approach for Single-Molecule mRNA Decay Analysis

In this issue of Molecular Cell, Horvathova et al. (2017) have developed a powerful approach to single-molecule assessment of RNA decay in living cells by exploiting the ability of flavivirus RNA structural elements to trap XRN1 decay intermediates in dual-labeled reporter constructs.

Defining the Transcriptional Ecosystem

Fine tuning of the transcriptional program requires the competing action of multiple protein complexes in a well-organized environment. Genome folding creates proximity between genes, leading to accumulation of regulatory factors and formation of local microenvironments. Many roles of this complex organization controlling gene transcription remain to be explored. In this Perspective, we are proposing the existence of a transcriptional ecosystem equilibrium: a mechanism balancing transcriptional regulation between connected genes during environmental disturbances. This model is derived from chromosome architecture studies assigning genes to specific DNA structures and evidence establishing that the transcription machinery and coregulators create dynamic phase separation droplets surrounding active genes. Defining connected genes as ecosystems rather than individuals will cement that transcriptional regulation is a biochemical equilibrium and force a reassessment of direct and indirect responses to environmental disturbances.

(CTG)n repeat-mediated dysregulation of MBNL1 and MBNL2 expression during myogenesis in DM1 occurs already at the myoblast stage

Myotonic dystrophy type 1 (DM1) is a severe neuromuscular disorder caused by the expression of trinucleotide repeat-containing DMPK transcripts. Abnormally expanded (CUG)n repeats in these transcripts form hairpin-like structures that cause the RNA to accumulate in the cell nucleus by sequestering isoforms of the Muscleblind (MBNL) family, tissue-specific regulators of developmentally programmed, post-transcriptional processes in RNA metabolism. Through this mechanism, the function of MBNL in RNA processing becomes dominantly perturbed, which eventually leads to aberrant alternative splicing and the expression of foetal splice variants of a wide variety of proteins, including the MBNL isoforms themselves. Here, we employ a patient-derived muscle cell model for DM1 to examine in detail the expression of MBNL RNA and protein variants during myogenic differentiation. This DM1 model consists of a panel of isogenic myoblast cell lines that either contain a pathogenic DMPK allele with a congenital mutation of 2600 triplets, or lack this expanded repeat through CRISPR/Cas9-mediated gene editing. We found that the temporal expression levels of MBNL1, MBNL2 and MBNL3 RNAs are not influenced by presence of the (CTG)2600 repeat during myogenesis in vitro. However, throughout myoblast proliferation and differentiation to myotubes a disproportionate inclusion of MBNL1 exon 5 and MBNL2 exons 5 and 8 occurs in cells with the (CTG)2600 repeat. As a consequence, a reduced quantity and imbalanced collection of splice variants of MBNL1 and MBNL2 accumulates in both the cytoplasm and the nucleus of DM1 myoblasts and myotubes. We thus propose that both the quantitative and qualitative changes in the intracellular partitioning of MBNL proteins are a pivotal cause of skeletal muscle problems in DM1, starting already in muscle progenitor cells.

A single-cell transcriptomic atlas of the developing chicken limb

BACKGROUND

Through precise implementation of distinct cell type specification programs, differentially regulated in both space and time, complex patterns emerge during organogenesis. Thanks to its easy experimental accessibility, the developing chicken limb has long served as a paradigm to study vertebrate pattern formation. Through decades' worth of research, we now have a firm grasp on the molecular mechanisms driving limb formation at the tissue-level. However, to elucidate the dynamic interplay between transcriptional cell type specification programs and pattern formation at its relevant cellular scale, we lack appropriately resolved molecular data at the genome-wide level. Here, making use of droplet-based single-cell RNA-sequencing, we catalogue the developmental emergence of distinct tissue types and their transcriptome dynamics in the distal chicken limb, the so-called autopod, at cellular resolution.

RESULTS

Using single-cell RNA-sequencing technology, we sequenced a total of 17,628 cells coming from three key developmental stages of chicken autopod patterning. Overall, we identified 23 cell populations with distinct transcriptional profiles. Amongst them were small, albeit essential populations like the apical ectodermal ridge, demonstrating the ability to detect even rare cell types. Moreover, we uncovered the existence of molecularly distinct sub-populations within previously defined compartments of the developing limb, some of which have important signaling functions during autopod pattern formation. Finally, we inferred gene co-expression modules that coincide with distinct tissue types across developmental time, and used them to track patterning-relevant cell populations of the forming digits.

CONCLUSIONS

We provide a comprehensive functional genomics resource to study the molecular effectors of chicken limb patterning at cellular resolution. Our single-cell transcriptomic atlas captures all major cell populations of the developing autopod, and highlights the transcriptional complexity in many of its components. Finally, integrating our data-set with other single-cell transcriptomics resources will enable researchers to assess molecular similarities in orthologous cell types across the major tetrapod clades, and provide an extensive candidate gene list to functionally test cell-type-specific drivers of limb morphological diversification.

Intratumoral Heterogeneity: More Than Just Mutations

Most human tumors are composed of genetically and phenotypically heterogeneous cancer cell populations, which poses a major challenge for the clinical management of cancer patients. Advances of single-cell technologies have allowed the profiling of tumors at unprecedented depth, which, in combination with newly developed computational tools, enable the dissection of tumor evolution with increasing precision. However, our understanding of mechanisms that regulate intratumoral heterogeneity and our ability to modulate it has been lagging behind. Recent data demonstrate that epigenetic regulators, including histone demethylases, may control the cell-to-cell variability of transcriptomes and chromatin profiles and they may modulate therapeutic responses via this function. Thus, the therapeutic targeting of epigenetic enzymes may be used to decrease intratumoral cellular heterogeneity and treatment resistance, when used in combination with other types of agents.

NITPicker: selecting time points for follow-up experiments

Background

The design of an experiment influences both what a researcher can measure, as well as how much confidence can be placed in the results. As such, it is vitally important that experimental design decisions do not systematically bias research outcomes. At the same time, making optimal design decisions can produce results leading to statistically stronger conclusions. Deciding where and when to sample are among the most critical aspects of many experimental designs; for example, we might have to choose the time points at which to measure some quantity in a time series experiment. Choosing times which are too far apart could result in missing short bursts of activity. On the other hand, there may be time points which provide very little information regarding the overall behaviour of the quantity in question.

Results

In this study, we develop a tool called NITPicker (Next Iteration Time-point Picker) for selecting optimal time points (or spatial points along a single axis), that eliminates some of the biases caused by human decision-making, while maximising information about the shape of the underlying curves. NITPicker uses ideas from the field of functional data analysis. NITPicker is available on the Comprehensive R Archive Network (CRAN) and code for drawing figures is available on Github (https://github.com/ezer/NITPicker).

Conclusions

NITPicker performs well on diverse real-world datasets that would be relevant for varied biological applications, including designing follow-up experiments for longitudinal gene expression data, weather pattern changes over time, and growth curves.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2717-5) contains supplementary material, which is available to authorized users.

Memory and relatedness of transcriptional activity in mammalian cell lineages

Phenotypically identical mammalian cells often display considerable variability in transcript levels of individual genes. How transcriptional activity propagates in cell lineages, and how this varies across genes is poorly understood. Here we combine live-cell imaging of short-lived transcriptional reporters in mouse embryonic stem cells with mathematical modelling to quantify the propagation of transcriptional activity over time and across cell generations in phenotypically homogenous cells. In sister cells we find mean transcriptional activity to be strongly correlated and transcriptional dynamics tend to be synchronous; both features control how quickly transcriptional levels in sister cells diverge in a gene-specific manner. Moreover, mean transcriptional activity is transmitted from mother to daughter cells, leading to multi-generational transcriptional memory and causing inter-family heterogeneity in gene expression.

An Optogenetic Platform for Real-Time, Single-Cell Interrogation of Stochastic Transcriptional Regulation

Transcription is a highly regulated and inherently stochastic process. The complexity of signal transduction and gene regulation makes it challenging to analyze how the dynamic activity of transcriptional regulators affects stochastic transcription. By combining a fast-acting, photo-regulatable transcription factor with nascent RNA quantification in live cells and an experimental setup for precise spatiotemporal delivery of light inputs, we constructed a platform for the real-time, single-cell interrogation of transcription in Saccharomyces cerevisiae. We show that transcriptional activation and deactivation are fast and memoryless. By analyzing the temporal activity of individual cells, we found that transcription occurs in bursts, whose duration and timing are modulated by transcription factor activity. Using our platform, we regulated transcription via light-driven feedback loops at the single-cell level. Feedback markedly reduced cell-to-cell variability and led to qualitative differences in cellular transcriptional dynamics. Our platform establishes a flexible method for studying transcriptional dynamics in single cells.

Gene transcription in bursting: a unified mode for realizing accuracy and stochasticity

There is accumulating evidence that, from bacteria to mammalian cells, messenger RNAs (mRNAs) are produced in intermittent bursts - a much 'noisier' process than traditionally thought. Based on quantitative measurements at individual promoters, diverse phenomenological models have been proposed for transcriptional bursting. Nevertheless, the underlying molecular mechanisms and significance for cellular signalling remain elusive. Here, we review recent progress, address the above issues and illuminate our viewpoints with simulation results. Despite being widely used in modelling and in interpreting experimental data, the traditional two-state model is far from adequate to describe or infer the molecular basis and stochastic principles of transcription. In bacteria, DNA supercoiling contributes to the bursting of those genes that express at high levels and are topologically constrained in short loops; moreover, low-affinity cis-regulatory elements and unstable protein complexes can play a key role in transcriptional regulation. Integrating data on the architecture, kinetics, and transcriptional input-output function is a promising approach to uncovering the underlying dynamic mechanism. For eukaryotes, distinct bursting features described by the multi-scale and continuum models coincide with those predicted by four theoretically derived principles that govern how the transcription apparatus operates dynamically. This consistency suggests a unified framework for comprehending bursting dynamics at the level of the structural and kinetic basis of transcription. Moreover, the existing models can be unified by a generic model. Remarkably, transcriptional bursting enables regulatory information to be transmitted in a digital manner, with the burst frequency representing the strength of regulatory signals. Such a mode guarantees high fidelity for precise transcriptional regulation and also provides sufficient randomness for realizing cellular heterogeneity.

Allele-specific RNA imaging shows that allelic imbalances can arise in tissues through transcriptional bursting

Extensive cell-to-cell variation exists even among putatively identical cells, and there is great interest in understanding how the properties of transcription relate to this heterogeneity. Differential expression from the two gene copies in diploid cells could potentially contribute, yet our ability to measure from which gene copy individual RNAs originated remains limited, particularly in the context of tissues. Here, we demonstrate quantitative, single molecule allele-specific RNA FISH adapted for use on tissue sections, allowing us to determine the chromosome of origin of individual RNA molecules in formaldehyde-fixed tissues. We used this method to visualize the allele-specific expression of Xist and multiple autosomal genes in mouse kidney. By combining these data with mathematical modeling, we evaluated models for allele-specific heterogeneity, in particular demonstrating that apparent expression from only one of the alleles in single cells can arise as a consequence of low-level mRNA abundance and transcriptional bursting.

In mammals, most cells of the body contain two genetic datasets: one from the mother and one from the father, and—in theory—these two sets of information could contribute equally to produce the molecules in a given cell. In practice, however, this is not always the case, which can have dramatic implications for many traits, including visible features (such as fur color) and even disease outcomes. However, it remains difficult to measure the parental origin of individual molecules in a given cell and thus to assess what processes lead to an imbalance of the maternal and paternal contribution. We adapted a microscopy technique—called allele-specific single molecule RNA FISH—that uses a combination of fluorescent tags to specifically label one type of molecule, RNA, depending on its origin, for use in mouse kidney sections. Focusing on RNAs that were previously reported to show imbalance, we performed measurements and combined these with mathematical modeling to quantify imbalance in tissues and explain how these can arise. We found that we could recapitulate the observed imbalances using models that only take into account the random processes that produce RNA, without the need to invoke special regulatory mechanisms to create unequal contributions.

A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics

Single-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intra-cellular variability, a key step for understanding heterogeneity in cell populations. However, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple, flexible, and scalable method for estimating cell-specific and population-average parameters of non-linear mixed-effects models of cellular networks, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

Genomic encoding of transcriptional burst kinetics

Mammalian gene expression is inherently stochastic^1,2resulting in discrete bursts of RNA molecules synthesised from each allele^3–7. Although known to be regulated by promoters and enhancers, it is unclear how cis-regulatory sequences encode transcriptional burst kinetics. Characterization of transcriptional bursting, including the burst size and frequency, have mainly relied on live-cell^4,6,8 or single-molecule RNA-FISH^3,5,8,9 recordings of selected loci. Here, we inferred transcriptome-wide burst frequencies and sizes for endogenous genes using allele-sensitive single-cell RNA-sequencing (scRNA-seq). We show that core promoter elements affect burst size and uncover synergistic effects between TATA and Initiator elements, which were masked at mean expression levels. Importantly, we provide transcriptome-wide support for enhancers controlling burst frequencies and we additionally demonstrate that cell-type-specific gene expression is primarily shaped by changes in burst frequencies. Altogether, our data show that burst frequency is primarily encoded in enhancers and burst size in core promoters, and that allelic scRNA-seq is a powerful paradigm for investigating transcriptional kinetics.

Modulation of transcriptional burst frequency by histone acetylation

Many mammalian genes are transcribed during short bursts of variable frequencies and sizes that substantially contribute to cell-to-cell variability. However, which molecular mechanisms determine bursting properties remains unclear. To probe putative mechanisms, we combined temporal analysis of transcription along the circadian cycle with multiple genomic reporter integrations, using both short-lived luciferase live microscopy and single-molecule RNA-FISH. Using the Bmal1 circadian promoter as our model, we observed that rhythmic transcription resulted predominantly from variations in burst frequency, while the genomic position changed the burst size. Thus, burst frequency and size independently modulated Bmal1 transcription. We then found that promoter histone-acetylation level covaried with burst frequency, being greatest at peak expression and lowest at trough expression, while remaining unaffected by the genomic location. In addition, specific deletions of ROR-responsive elements led to constitutively elevated histone acetylation and burst frequency. We then investigated the suggested link between histone acetylation and burst frequency by dCas9p300-targeted modulation of histone acetylation, revealing that acetylation levels influence burst frequency more than burst size. The correlation between acetylation levels at the promoter and burst frequency was also observed in endogenous circadian genes and in embryonic stem cell fate genes. Thus, our data suggest that histone acetylation-mediated control of transcription burst frequency is a common mechanism to control mammalian gene expression.

Cholesterol signaling in single cells: lessons from STAR and sm-FISH

Cholesterol is an important regulator of cell signaling, both through direct impacts on cell membranes and through oxy-metabolites that activate specific receptors (steroids, hydroxy-cholesterols, bile acids). Cholesterol moves slowly through and between cell membranes with the assistance of specific binding proteins and transfer processes. The prototype cholesterol regulator is the Steroidogenesis Acute Regulatory (STAR), which moves cholesterol into mitochondria, where steroid synthesis is initiated by cytochrome P450 11A1 in multiple endocrine cell types. CYP27A1 generates hydroxyl cholesterol metabolites that activate LXR nuclear receptors to control cholesterol homeostatic and transport mechanisms. LXR regulation of cholesterol transport and storage as cholesterol ester droplets is shared by both steroid-producing cells and macrophage. This cholesterol signaling is crucial to brain neuron regulation by astrocytes and microglial macrophage, mediated by ApoE and sensitive to disruption by β-amyloid plaques. sm-FISH delivers appreciable insights into signaling in single cells, by resolving single RNA molecules as mRNA and by quantifying pre-mRNA at gene loci. sm-FISH has been applied to problems in physiology, embryo development and cancer biology, where single cell features have critical impacts. sm-FISH identifies novel features of STAR transcription in adrenal and testis cells, including asymmetric expression at individual gene loci, delayed splicing and 1:1 association of mRNA with mitochondria. This may represent a functional unit for the translation-dependent cholesterol transfer directed by STAR, which integrates into mitochondrial fusion dynamics. Similar cholesterol dynamics repeat with different players in the cycling of cholesterol between astrocytes and neurons in the brain, which may be abnormal in neurodegenerative diseases.

Frequency Modulation of Transcriptional Bursting Enables Sensitive and Rapid Gene Regulation

Gene regulation is a complex non-equilibrium process. Here, we show that quantitating the temporal regulation of key gene states (transcriptionally inactive, active, and refractory) provides a parsimonious framework for analyzing gene regulation. Our theory makes two non-intuitive predictions. First, for transcription factors (TFs) that regulate transcription burst frequency, as opposed to amplitude or duration, weak TF binding is sufficient to elicit strong transcriptional responses. Second, refractoriness of a gene after a transcription burst enables rapid responses to stimuli. We validate both predictions experimentally by exploiting the natural, optogenetic-like responsiveness of the Neurospora GATA-type TF White Collar Complex (WCC) to blue light. Further, we demonstrate that differential regulation of WCC target genes is caused by different gene activation rates, not different TF occupancy, and that these rates are tuned by both the core promoter and the distance between TF-binding site and core promoter. In total, our work demonstrates the relevance of a kinetic, non-equilibrium framework for understanding transcriptional regulation.