Transcription Factor Concentration Research Articles

Identification of mammalian transcription factors that bind to inaccessible chromatin.

Transcription factors (TFs) are proteins that affect gene expression by binding to regulatory regions of DNA in a sequence specific manner. The binding of TFs to DNA is controlled by many factors, including the DNA sequence, concentration of TF, chromatin accessibility and co-factors. Here, we systematically investigated the binding mechanism of hundreds of TFs by analysing ChIP-seq data with our explainable statistical model, ChIPanalyser. This tool uses as inputs the DNA sequence binding motif; the capacity to distinguish between strong and weak binding sites; the concentration of TF; and chromatin accessibility. We found that approximately one third of TFs are predicted to bind the genome in a DNA accessibility independent fashion, which includes TFs that can open the chromatin, their co-factors and TFs with similar motifs. Our model predicted this to be the case when the TF binds to its strongest binding regions in the genome, and only a small number of TFs have the capacity to bind dense chromatin at their weakest binding regions, such as CTCF, USF2 and CEBPB. Our study demonstrated that the binding of hundreds of human and mouse TFs is predicted by ChIPanalyser with high accuracy and showed that many TFs can bind dense chromatin.

Transcriptional kinetic synergy: A complex landscape revealed by integrating modeling and synthetic biology.

Transcription factors (TFs) control gene expression, often acting synergistically. Classical thermodynamic models offer a biophysical explanation for synergy based on binding cooperativity and regulated recruitment of RNA polymerase. Because transcription requires polymerase to transition through multiple states, recent work suggests that "kinetic synergy" can arise through TFs acting on distinct steps of the transcription cycle. These types of synergy are not mutually exclusive and are difficult to disentangle conceptually and experimentally. Here, we model and build a synthetic circuit in which TFs bind to a single shared site on DNA, such that TFs cannot synergize by simultaneous binding. We model mRNA production as a function of both TF binding and regulation of the transcription cycle, revealing a complex landscape dependent on TF concentration, DNA binding affinity, and regulatory activity. We use synthetic TFs to confirm that the transcription cycle must be integrated with recruitment for a quantitative understanding of gene regulation.

Tunable transcription factor library for robust quantification of regulatory properties in Escherichia coli.

Predicting the quantitative regulatory function of transcription factors (TFs) based on factors such as binding sequence, binding location, and promoter type is not possible. The interconnected nature of gene networks and the difficulty in tuning individual TF concentrations make the isolated study of TF function challenging. Here, we present a library of Escherichia coli strains designed to allow for precise control of the concentration of individual TFs enabling the study of the role of TF concentration on physiology and regulation. We demonstrate the usefulness of this resource by measuring the regulatory function of the zinc‐responsive TF, ZntR, and the paralogous TF pair, GalR/GalS. For ZntR, we find that zinc alters ZntR regulatory function in a way that enables activation of the regulated gene to be robust with respect to ZntR concentration. For GalR and GalS, we are able to demonstrate that these paralogous TFs have fundamentally distinct regulatory roles beyond differences in binding affinity.

Target Search Dynamics of Sox Transcription Factors

Here we investigate the impact of regions outside of the DNA‐binding domain (DBD) on target search efficiency of transcription factors (TFs). In living organisms, TFs are key for gene regulation by binding sequence DNA‐binding sites. The facilitated diffusion theory proposes a search process combining TF 1D sliding on DNA and 3D diffusion in the nucleus. Moreover, the local non‐specific TF‐DNA interactions mediated by electrostatic forces enhance the target search efficiency to reach on‐rates faster than allowed by free diffusion. While such theoretical models fit TF behaviors in prokaryotes, how TFs search the chromatinized eukaryotic genome is poorly understood. More importantly, the relevance of regions outside of the DNA‐binding domain for the search process remains to be clarified.We hypothesize that positive charges flanking the DBD accelerate TF target search by interacting non‐specifically with negatively charged DNA. We tackle this hypothesis with a comparative study between Sox2 and Sox17. While recognizing the same DNA motif, Sox2 displays higher non‐specific binding affinity, which we think is caused by a higher content in positive charges close to the DBD.To probe the kinetics of in vivo Sox‐DNA interactions in living mouse embryonic stem cells, we used highly inclined and laminated optical sheet (HiLo) microscopy. We determined that Sox2 and Sox17 have similar profiles of residence time with specific binding of ~ 12 s and non‐specific binding of ~ 0.7 s. We then quantified the search efficiency of Sox2 and Sox17 by determining their pseudo on‐rate, which we define as the time‐averaged number of specifically bound particles per time interval and per TF concentration. Since Sox2 and Sox17 bind to the same DNA motifs, they thus have the same number of targets in the mouse genome. Therefore, the pseudo on‐rate scales linearly with association rate at a given TF concentration. We found that Sox2 has a two‐fold higher pseudo on‐rate than Sox17. We also generated a Sox2 mutant in which its DBD flanking region was swapped with the one from Sox17. This led to a two‐fold decrease in pseudo on‐rate as compared to the wild‐type Sox2. These results thus directly support the hypothesis that non‐specific electrostatic interactions of positively charged residues in regions flanking the DBD enhance their DNA association rates.One limitation of in vivo experiments is that the chromatin context in which TF‐DNA interactions are observed is unknown. To dissect the differential interactions of Sox2 and Sox17 with naked versus nucleosomal DNA, we are using chemically‐synthesized in vitro chromatin combined with single‐molecule tracking via total internal reflection (TIRF) microscopy. Since Sox2 but not Sox17 is known as a pioneer factor, quantifying their interactions with chromatinized DNA will shed light on search mechanisms of pioneer TFs in compact chromatin. In that sense, we foresee that more positively‐charged DBD flanking regions may stabilize specific binding of TF to nucleosomal DNA and help TFs to invade and remodel chromatin.Taken together, our combination of in vivo and in vitro single‐molecule imaging techniques allows dissecting how the charge distribution of DBD flanking regions modulates TF target search on chromatin.

Ratiometric detection of transcription factor based on Europium(III) complex-doped silicon nanoparticles and a G-quadruplex-selective Iridium(III) complex

To sensitively detect transcription factor (TF) in complicated biological systems, a ratiometric luminescent detection system based on europium(III) complex-doped silicon nanoparticles (SiNPs) and a G-quadruplex (G4)-selective iridium(III) complex has been successfully constructed. During the biosensor fabrication, the G-rich hairpin DNA H1 was immobilized on the surface of SiNPs which was doped with the europium(III) complex Eu(III)(EDTA)(DPA). The dsDNA (S1/S2) contains TF recognition site, and one of its ssDNA S2 is protected by TF from the digestion of exonuclease III (Exo III). The survived S2 starts a signal amplification process at the aid of Exo III, then a lot of G4 DNA was generated which can be recognized by the G4-selective iridium(III) complex (Ir(III)-2G). So, the emission intensity of Ir(III)-2G is enhanced with the increase of TF concentration, while the emission of Eu(III)(EDTA)(DPA) keeps stable. Ascribed to the long lifetimes of both metal complexes, the constructed detection platform can perform time-resolved emission spectroscopy (TRES) measurement in complicated biological systems. The ratiometric TF detection platform not only can detect NF-κB P50 in buffered system in the linear range of 0.05–5 nM with the limit of detection (LOD) 0.014 nM, but also is able to monitor NF-κB P50 in 10% human serum in the linear range of 0.05–5 nM with the LOD of 0.010 nM through TRES measurement. The two-metal complex-based ratiometric TF biosensor provides new strategy for low concentrations of biomarker detection in the complicated biological systems.

Thermodynamics-based modeling reveals regulatory effects of indirect transcription factor-DNA binding in Drosophila

Sequence-to-expression relationship is one of the major research topics in the field of regulatory genomics. Prediction of epigenomic states such as DNA accessibility or transcription factor(TF)-DNA binding has been studied extensively, but predicting a gene's expression in different cell types based on its regulatory enhancer sequences remains largely unsolved. Here, we present a thermodynamics-based sequence-to-expression model that combines DNA accessibility, TF concentrations and enhancer sequence information to classify enhancers into their spatio-temporal activity classes in Drosophila mesoderm.

Hypersensitive detection of transcription factors by multiple amplification strategy based on molecular beacon

Detection and quantification of specific targets at ultralow concentrations are crucial in biotechnological applications and biomedical diagnostics. However, traditional detection methods are time-consuming and complex. In this study, a simple, sensitive, and specific fluorescence-based strategy to detect transcription factors (TFs) was developed by combining molecular beacon (MB) with exonuclease III (Exo III). The strategy was validated using nuclear factor-kappa B (NF-κB) p65 as a model case. In this assay, the TF concentration is determined by estimating the concentration of the nucleic acid released through the reactions of TFs with DNA-1/DNA-2 duplex probe and Exo III; thus, this method can overcome the limitations of the traditional methods. The nucleic acid is then cyclically amplified in the presence of MB and Exo III, and the sequence fragments containing the fluorescent group are released. This method is sensitive, expandable, and suitable for the direct detection of TFs in crude nuclear extracts of cancer cells. Furthermore, it can provide key test data for studies of coronavirus disease 2019-induced acute respiratory distress syndrome and related research.

Altering transcription factor binding reveals comprehensive transcriptional kinetics of a basic gene.

Transcription is a vital process activated by transcription factor (TF) binding. The active gene releases a burst of transcripts before turning inactive again. While the basic course of transcription is well understood, it is unclear how binding of a TF affects the frequency, duration and size of a transcriptional burst. We systematically varied the residence time and concentration of a synthetic TF and characterized the transcription of a synthetic reporter gene by combining single molecule imaging, single molecule RNA-FISH, live transcript visualisation and analysis with a novel algorithm, Burst Inference from mRNA Distributions (BIRD). For this well-defined system, we found that TF binding solely affected burst frequency and variations in TF residence time had a stronger influence than variations in concentration. This enabled us to device a model of gene transcription, in which TF binding triggers multiple successive steps before the gene transits to the active state and actual mRNA synthesis is decoupled from TF presence. We quantified all transition times of the TF and the gene, including the TF search time and the delay between TF binding and the onset of transcription. Our quantitative measurements and analysis revealed detailed kinetic insight, which may serve as basis for a bottom-up understanding of gene regulation.

An intrinsically disordered region-mediated confinement state contributes to the dynamics and function of transcription factors

Transcription factors (TFs) regulate gene expression by binding to specific consensus motifs within the local chromatin context. The mechanisms by which TFs navigate the nuclear environment as they search for binding sites remain unclear. Here, we used single-molecule tracking and machine-learning-based classification to directly measure the nuclear mobility of the glucocorticoid receptor (GR) in live cells. We revealed two distinct and dynamic low-mobility populations. One accounts for specific binding to chromatin, while the other represents a confinement state that requires an intrinsically disordered region (IDR), implicated in liquid-liquid condensate subdomains. Further analysis showed that the dwell times of both subpopulations follow a power-law distribution, consistent with a broad distribution of affinities on the GR cistrome and interactome. Together, our data link IDRs with a confinement state that is functionally distinct from specific chromatin binding and modulates the transcriptional output by increasing the local concentration of TFs at specific sites.

A Quantitative Optogenetic Tool to Control Gene Expression during Embryonic Development

During embryonic development, cell specification is tightly regulated in space and time by specific proteins called transcription factors (TFs). To directly interfere with this process, we use a combination of optogenetic light perturbation, real-time imaging of gene expression, and Drosophila genetics to build synthetic spatial and temporal gene activity patterns in living fly embryos. Upon light illumination, TFs translocate from cell nuclei into the cytosolic compartment, which allows control over nuclear TF concentration and thus modulate gene expression.

Optogenetic Control Reveals Differential Promoter Interpretation of Transcription Factor Nuclear Translocation Dynamics.

SUMMARYGene expression is thought to be affected not only by the concentration of transcription factors (TFs) but also the dynamics of their nuclear translocation. Testing this hypothesis requires direct control of TF dynamics. Here, we engineer CLASP, an optogenetic tool for rapid and tunable translocation of a TF of interest. Using CLASP fused to Crz1, we observe that, for the same integrated concentration of nuclear TF over time, changing input dynamics changes target gene expression: pulsatile inputs yield higher expression than continuous inputs, or vice versa, depending on the target gene. Computational modeling reveals that a dose-response saturating at low TF input can yield higher gene expression for pulsatile versus continuous input, and that multi-state promoter activation can yield the opposite behavior. Our integrated tool development and modeling approach characterize promoter responses to Crz1 nuclear translocation dynamics, extracting quantitative features that may help explain the differential expression of target genes.

Transcription Factors and DNA Play Hide and Seek.

Transcription factors (TFs) bind to specific DNA motifs to regulate the expression of target genes. To reach their binding sites, TFs diffuse in 3D and perform local motions such as 1D sliding, hopping, or intersegmental transfer. TF-DNA interactions depend on multiple parameters, such as the chromatin environment, TF partitioning into distinct subcellular regions, and cooperativity with other DNA-binding proteins. In this review, how current understanding of the search process has initially been shaped by prokaryotic studies is discussed, as well as what is known about the parameters regulating TF search efficiency in the context of the complex eukaryotic chromatin landscape.

Dissecting the binding mechanisms of transcription factors to DNA using a statistical thermodynamics framework

Transcription Factors (TFs) bind to DNA and control activity of target genes. Here, we present ChIPanalyser, a user-friendly, versatile and powerful R/Bioconductor package predicting and modelling the binding of TFs to DNA. ChIPanalyser performs similarly to state-of-the-art tools, but is an explainable model and provides biological insights into binding mechanisms of TFs. We focused on investigating the binding mechanisms of three TFs that are known architectural proteins CTCF, BEAF-32 and su(Hw) in three Drosophila cell lines (BG3, Kc167 and S2). While CTCF preferentially binds only to a subset of high affinity sites located mainly in open chromatin, BEAF-32 binds to most of its high affinity binding sites available in open chromatin. In contrast, su(Hw) binds to both open chromatin and also partially closed chromatin. Most importantly, differences in TF binding profiles between cell lines for these TFs are mainly driven by differences in DNA accessibility and not by differences in TF concentrations between cell lines. Finally, we investigated binding of Hox TFs in Drosophila and found that Ubx binds only in open chromatin, while Abd-B and Dfd are capable to bind in both open and partially closed chromatin. Overall, our results show that TFs display different binding mechanisms and that our model is able to recapitulate their specific binding behaviour.

Design Principle for Decoding Calcium Signals to Generate Specific Gene Expression Via Transcription.

The second messenger calcium plays a key role in conveying specificity of signaling pathways in plant cells. Specific calcium signatures are decoded to generate correct gene expression responses and amplification of calcium signatures is vital to this process. (1) It is not known if this amplification is an intrinsic property of all calcium-regulated gene expression responses and whether all calcium signatures have the potential to be amplified, or (2) how a given calcium signature maintains specificity in cells containing a great number of transcription factors (TFs) and other proteins with the potential to be calcium-regulated. The work presented here uncovers the design principle by which it is possible to decode calcium signals into specific changes in gene transcription in plant cells. Regarding the first question, we found that the binding mechanism between protein components possesses an intrinsic property that will nonlinearly amplify any calcium signal. This nonlinear amplification allows plant cells to effectively distinguish the kinetics of different calcium signatures to produce specific and appropriate changes in gene expression. Regarding the second question, we found that the large number of calmodulin (CaM)-binding TFs or proteins in plant cells form a buffering system such that the concentration of an active CaM-binding TF is insensitive to the concentration of any other CaM-binding protein, thus maintaining specificity. The design principle revealed by this work can be used to explain how any CaM-binding TF decodes calcium signals to generate specific gene expression responses in plant cells via transcription.

Low-Affinity Binding Sites and the Transcription Factor Specificity Paradox in Eukaryotes.

Eukaryotic transcription factors (TFs) from the same structural family tend to bind similar DNA sequences, despite the ability of these TFs to execute distinct functions in vivo. The cell partly resolves this specificity paradox through combinatorial strategies and the use of low-affinity binding sites, which are better able to distinguish between similar TFs. However, because these sites have low affinity, it is challenging to understand how TFs recognize them in vivo. Here, we summarize recent findings and technological advancements that allow for the quantification and mechanistic interpretation of TF recognition across a wide range of affinities. We propose a model that integrates insights from the fields of genetics and cell biology to provide further conceptual understanding of TF binding specificity. We argue that in eukaryotes, target specificity is driven by an inhomogeneous 3D nuclear distribution of TFs and by variation in DNA binding affinity such that locally elevated TF concentration allows low-affinity binding sites to be functional.

Stochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory network.

Cellular reprogramming is traditionally accomplished through an open loop (OL) control approach, wherein key transcription factors (TFs) are injected in cells to steer the state of the pluripotency (PL) gene regulatory network (GRN), as encoded by TFs concentrations, to the pluripotent state. Due to the OL nature of this approach, the concentration of TFs cannot be accurately controlled. Recently, a closed loop (CL) feedback control strategy was proposed to overcome this problem with promising theoretical results. However, previous analyses of the controller were based on deterministic models. It is well known that cellular systems are characterized by substantial stochasticity, especially when molecules are in low copy number as it is the case in reprogramming problems wherein the gene copy number is usually one or two. Hence, in this paper, we analyze the Chemical Master Equation (CME) for the reaction model of the PL GRN with and without the feedback controller. We computationally and analytically investigate the performance of the controller in biologically relevant parameter regimes where stochastic effects dictate system dynamics. Our results indicate that the feedback control approach still ensures reprogramming even when both the PL GRN and the controller are stochastic.

Ordering up gene expression by slowing down transcription factor binding kinetics.

Efficient regulation of a complex genetic response requires that the gene products, which catalyze the response, be synthesized in a temporally ordered manner to match the sequential nature of the reaction pathway they act upon. Transcription regulation networks coordinate this aspect of cellular control by modulating transcription factor (TF) concentrations through time. The effect a TF has on the timing of gene expression is often modeled assuming that the TF-promoter binding reaction is in thermodynamic equilibrium with changes in TF concentration over time; however, non-equilibrium dynamics resulting from relatively slow TF-binding kinetics can result in different network behavior. Here, I highlight a recent study of the bacterial SOS response, where a single TF regulates multiple target promoters, to show how a disequilibrium of TF binding at promoters results in a more complex behavior, enabling a larger temporal separation of promoter activities that depends not only upon slow TF binding kinetics at promoters, but also on the magnitude of the response stimulus. I also discuss the dependence of network behavior on specific TF regulatory mechanisms and the implications non-equilibrium dynamics have for stochastic gene expression.

Highlights from Recent Cancer Literature

Highlights from Recent Cancer Literature

Building a minimal and generalizable model of transcription factor-based biosensors: Showcasing flavonoids.

Progress in synthetic biology tools has transformed the way we engineer living cells. Applications of circuit design have reached a new level, offering solutions for metabolic engineering challenges that include developing screening approaches for libraries of pathway variants. The use of transcription‐factor‐based biosensors for screening has shown promising results, but the quantitative relationship between the sensors and the sensed molecules still needs more rational understanding. Herein, we have successfully developed a novel biosensor to detect pinocembrin based on a transcriptional regulator. The FdeR transcription factor (TF), known to respond to naringenin, was combined with a fluorescent reporter protein. By varying the copy number of its plasmid and the concentration of the biosensor TF through a combinatorial library, different responses have been recorded and modeled. The fitted model provides a tool to understand the impact of these parameters on the biosensor behavior in terms of dose–response and time curves and offers guidelines to build constructs oriented to increased sensitivity and or ability of linear detection at higher titers. Our model, the first to explicitly take into account the impact of plasmid copy number on biosensor sensitivity using Hill‐based formalism, is able to explain uncharacterized systems without extensive knowledge of the properties of the TF. Moreover, it can be used to model the response of the biosensor to different compounds (here naringenin and pinocembrin) with minimal parameter refitting.

Co-regulation analysis of co-expressed modules under cold and pathogen stress conditions in tomato.

A primary mechanism for controlling the development of multicellular organisms is transcriptional regulation, which carried out by transcription factors (TFs) that recognize and bind to their binding sites on promoter region. The distance from translation start site, order, orientation, and spacing between cis elements are key factors in the concentration of active nuclear TFs and transcriptional regulation of target genes. In this study, overrepresented motifs in cold and pathogenesis responsive genes were scanned via Gibbs sampling method, this method is based on detection of overrepresented motifs by means of a stochastic optimization strategy that searches for all possible sets of short DNA segments. Then, identified motifs were checked by TRANSFAC, PLACE and Soft Berry databases in order to identify putative TFs which, interact to the motifs. Several cis/trans regulatory elements were found using these databases. Moreover, cross-talk between cold and pathogenesis responsive genes were confirmed. Statistical analysis was used to determine distribution of identified motifs on promoter region. In addition, co-regulation analysis results, illustrated genes in pathogenesis responsive module are divided into two main groups. Also, promoter region was crunched to six subareas in order to draw the pattern of distribution of motifs in promoter subareas. The result showed the majority of motifs are concentrated on 700 nucleotides upstream of the translational start site (ATG). In contrast, this result isn't true in another group. In other words, there was no difference between total and compartmentalized regions in cold responsive genes.