Gene regulation: when analog beats digital

Developing a Temperature-Inducible Transcriptional Rheostat in Neurospora crassa

Heat shock protein (HSP)-encoding genes (hsp), part of the highly conserved heat shock response (HSR), are known to be induced by thermal stress in several organisms. In Neurospora crassa, three hsp genes, hsp30, hsp70, and hsp80, have been characterized; however, the role of defined cis elements in their responses to discrete changes in temperature remains largely unexplored. To fill this gap, while also aiming to obtain a reliable fungal heat shock-inducible system, we analyzed different sections of each hsp promoter by assessing the expression of real-time transcriptional reporters. Whereas all three promoters and their resected versions were acutely induced by high temperatures, only hsp30 displayed a broad range of expression and high tunability, amply exceeding other inducible promoter systems existing in Neurospora, such as quinic acid- or light-inducible ones. As proof of concept, we employed one of these promoters to control the expression of clr-2, which encodes the master regulator of Neurospora cellulolytic capabilities. The resulting strain fails to grow on cellulose at 25°C, whereas it grows robustly if heat shock pulses are delivered daily. Additionally, we designed two hsp30 synthetic promoters and characterized them, as well as the native promoters, using a gradient of high temperatures, yielding a wide range of responses to thermal stimuli. Thus, Neurospora hsp30-based promoters represent a new set of modular elements that can be used as transcriptional rheostats to adjust the expression of a gene of interest or for the implementation of regulated circuitries for synthetic biology and biotechnological strategies. IMPORTANCE A timely and dynamic response to strong temperature fluctuations is paramount for organismal biology. At the same time, inducible promoters are a powerful tool for fungal biotechnological and synthetic biology endeavors. In this work, we analyzed the activity of several N. crassa heat shock protein (hsp) promoters at a wide range of temperatures, observing that hsp30 exhibits remarkable sensitivity and a dynamic range of expression as we charted the response of this promoter to subtle increases in temperature, and also as we built and analyzed synthetic promoters based on hsp30 cis elements. As proof of concept, we tested the ability of hsp30 to provide tight control of a central process, cellulose degradation. While this study provides an unprecedented description of the regulation of the N. crassa hsp genes, it also contributes a noteworthy addition to the molecular toolset of transcriptional controllers in filamentous fungi.

Signal amplification in the KEAP1-NRF2-ARE antioxidant response pathway

The KEAP1-NRF2-ARE signaling pathway plays a central role in mediating the adaptive cellular stress response to oxidative and electrophilic chemicals. This canonical pathway has been extensively studied and reviewed in the past two decades, but rarely was it looked at from a quantitative signaling perspective. Signal amplification, i.e., ultrasensitivity, is crucially important for robust induction of antioxidant genes to appropriate levels that can adequately counteract the stresses. In this review article, we examined a number of well-known molecular events in the KEAP1-NRF2-ARE pathway from a quantitative perspective with a focus on how signal amplification can be achieved. We illustrated, by using a series of mathematical models, that redox-regulated protein sequestration, stabilization, translation, nuclear trafficking, DNA promoter binding, and transcriptional induction - which are embedded in the molecular network comprising KEAP1, NRF2, sMaf, p62, and BACH1 - may generate highly ultrasensitive NRF2 activation and antioxidant gene induction. The emergence and degree of ultrasensitivity depend on the strengths of protein-protein and protein-DNA interaction and protein abundances. A unique, quantitative understanding of signal amplification in the KEAP1-NRF2-ARE pathway will help to identify sensitive targets for the prevention and therapeutics of oxidative stress-related diseases and develop quantitative adverse outcome pathway models to facilitate the health risk assessment of oxidative chemicals.

CRISPR-Based Synthetic Transcription Factors In Vivo: The Future of Therapeutic Cellular Programming

Pinpoint control over endogenous gene expression in vivo has long been a fevered dream for clinicians and researchers alike. With the recent repurposing of programmable, RNA-guided DNA endonucleases from the CRISPR bacterial immune system, this dream is becoming a powerful reality. Engineered CRISPR/Cas9-based transcriptional regulators and epigenome editors have enabled researchers to perturb endogenous gene expression in vivo, allowing for the therapeutic reprogramming of cell and tissue behavior. For this technology to be of maximal use, a variety of technological hurdles still need to be addressed. Better understanding of the design principle controlling gene expression together with technologies that enable spatiotemporal control of transcriptional engineering are fundamental for rational design, improved efficacy, and ultimately safe translation to humans. In this review, we will discuss recent advances and integrative strategies that can help pave the path toward a new class of transcriptional therapeutics.

Of numbers and movement - understanding transcription factor pathogenesis by advanced microscopy

Transcription factors (TFs) are life-sustaining and, therefore, the subject of intensive research. By regulating gene expression, TFs control a plethora of developmental and physiological processes, and their abnormal function commonly leads to various developmental defects and diseases in humans. Normal TF function often depends on gene dosage, which can be altered by copy-number variation or loss-of-function mutations. This explains why TF haploinsufficiency (HI) can lead to disease. Since aberrant TF numbers frequently result in pathogenic abnormalities of gene expression, quantitative analyses of TFs are a priority in the field. In vitro single-molecule methodologies have significantly aided the identification of links between TF gene dosage and transcriptional outcomes. Additionally, advances in quantitative microscopy have contributed mechanistic insights into normal and aberrant TF function. However, to understand TF biology, TF-chromatin interactions must be characterised in vivo, in a tissue-specific manner and in the context of both normal and altered TF numbers. Here, we summarise the advanced microscopy methodologies most frequently used to link TF abundance to function and dissect the molecular mechanisms underlying TF HIs. Increased application of advanced single-molecule and super-resolution microscopy modalities will improve our understanding of how TF HIs drive disease.

Haematopoietic stem cells: entropic landscapes of differentiation

The metaphor of a potential epigenetic differentiation landscape broadly suggests that during differentiation a stem cell approaches a stable equilibrium state from a higher free energy towards a stable equilibrium state which represents the final cell type. It has been conjectured that there is an analogy to the concept of entropy in statistical mechanics. In this context, in the undifferentiated state, the entropy would be large since fewer constraints exist on the gene expression programmes of the cell. As differentiation progresses, gene expression programmes become more and more constrained and thus the entropy would be expected to decrease. In order to assess these predictions, we compute the Shannon entropy for time-resolved single-cell gene expression data in two different experimental set-ups of haematopoietic differentiation. We find that the behaviour of this entropy measure is in contrast to these predictions. In particular, we find that the Shannon entropy is not a decreasing function of developmental pseudo-time but instead it increases towards the time point of commitment before decreasing again. This behaviour is consistent with an increase in gene expression disorder observed in populations sampled at the time point of commitment. Single cells in these populations exhibit different combinations of regulator activity that suggest the presence of multiple configurations of a potential differentiation network as a result of multiple entry points into the committed state.

The haploinsufficient tumor suppressor, CUX1, acts as an analog transcriptional regulator that controls target genes through distal enhancers that loop to target promoters

One third of tumor suppressors are haploinsufficient transcriptional regulators, yet it remains unknown how a 50% reduction of a transcription factor is translated at the cis-regulatory level into a malignant transcriptional program. We studied CUX1, a haploinsufficient transcription factor that is recurrently mutated in hematopoietic and solid tumors. We determined CUX1 DNA-binding and target gene regulation in the wildtype and haploinsufficient states. CUX1 binds with transcriptional activators and cohesin at distal enhancers across three different human cell types. Haploinsufficiency of CUX1 altered the expression of a large number of genes, including cell cycle regulators, with concomitant increased cellular proliferation. Surprisingly, CUX1 occupancy decreased genome-wide in the haploinsufficient state, and binding site affinity did not correlate with differential gene expression. Instead, differentially expressed genes had multiple, low-affinity CUX1 binding sites, features of analog gene regulation. A machine-learning algorithm determined that chromatin accessibility, enhancer activity, and distance to the transcription start site are features of dose-sensitive CUX1 transcriptional regulation. Moreover, CUX1 is enriched at sites of DNA looping, as determined by Hi-C analysis, and these loops connect CUX1 to the promoters of regulated genes. We propose an analog model for haploinsufficient transcriptional deregulation mediated by higher order genome architecture.

Computational Framework for Modeling Multiple Noncooperative Transcription Factor Binding and Its Application to the Analysis of Nuclear Factor Kappa B Oscillatory Response

Recent studies have shown that regulation of many important genes is achieved with multiple transcription factor (TF) binding sites with low or no cooperativity. Additionally, noncooperative binding sites are gaining more and more importance in the field of synthetic biology. Here, we introduce a computational framework that can be applied to dynamical modeling and analysis of gene regulatory networks with multiple noncooperative TF binding sites. We propose two computational methods to be used within the framework, that is, average promoter state approximation and expression profiles based modeling. We demonstrate the application of the proposed framework on the analysis of nuclear factor kappa B (NF-κB) oscillatory response. We show that different promoter expression hypotheses in a combination with the number of TF binding sites drastically affect the dynamics of the observed system and should not be ignored in the process of quantitative dynamical modeling, as is usually the case in existent state-of-the-art computational analyses.

Absence of a simple code: how transcription factors read the genome

Transcription factors (TFs) influence cell fate by interpreting the regulatory DNA within a genome. TFs recognize DNA in a specific manner; the mechanisms underlying this specificity have been identified for many TFs based on 3D structures of protein-DNA complexes. More recently, structural views have been complemented with data from high-throughput in vitro and in vivo explorations of the DNA-binding preferences of many TFs. Together, these approaches have greatly expanded our understanding of TF-DNA interactions. However, the mechanisms by which TFs select in vivo binding sites and alter gene expression remain unclear. Recent work has highlighted the many variables that influence TF-DNA binding, while demonstrating that a biophysical understanding of these many factors will be central to understanding TF function.