Noncooperative Interactions between Transcription Factors and Clustered DNA Binding Sites Enable Graded Transcriptional Responses to Environmental Inputs

Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA binding profiles for Escherichia coli

The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs using ChIP-Seq. We use these data to train BoltzNet, a novel neural network that predicts TF binding energy from DNA sequence. BoltzNet mirrors a quantitative biophysical model and provides directly interpretable predictions genome-wide at nucleotide resolution. We use BoltzNet to quantitatively design novel binding sites, which we validate with biophysical experiments on purified protein. We generate models for 124 TFs that provide insight into global features of TF binding, including clustering of sites, the role of accessory bases, the relevance of weak sites, and the background affinity of the genome. Our paper provides new paradigms for studying TF-DNA binding and for the development of biophysically motivated neural networks.

SWI/SNF complex-mediated ZNF410 cooperative binding maintains chromatin accessibility and enhancer activity

The clustering of multiple transcription factor binding sites (TFBSs) for the same TF has proved to be a pervasive feature of cis-regulatory elements in the eukaryotic genome. However, the contribution of binding sites within the homotypic clusters of TFBSs (HCTs) to TF binding and target gene expression remains to be understood. Here, we characterize the CHD4 enhancers that harbor unique functional ZNF410 HCTs genome wide. We uncover that ZNF410 controls chromatin accessibility and activity of the CHD4 enhancer regions. We demonstrate that ZNF410 binds to the HCTs in a collaborative fashion, further conferring transcriptional activation. In particular, three ZNF410 motifs (sub-HCTs) located at 3' end of the distal enhancer act as "switch motifs" to control chromatin accessibility and enhancer activity. Mechanistically, the SWI/SNF complex is selectively required to mediate cooperative ZNF410 binding for CHD4 expression. Together, our findings expose a complex functional hierarchy of homotypic clustered motifs, which cooperate to fine-tune target gene expression.

From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation

Cellular processes are intricately controlled through gene regulation, which is significantly influenced by intrinsic noise due to the small number of molecules involved. The Gillespie algorithm, a widely used stochastic simulation method, is pervasively employed to model these systems. However, this algorithm typically assumes that DNA is homogeneously distributed throughout the nucleus, which is not realistic. In this study, we evaluated whether stochastic simulations based on the assumption of spatial homogeneity can accurately capture the dynamics of gene regulation. Our findings indicate that when transcription factors diffuse slowly, these simulations fail to accurately capture gene expression, highlighting the necessity to account for spatial heterogeneity. However, incorporating spatial heterogeneity considerably increases computational time. To address this, we explored various stochastic quasi-steady-state approximations (QSSAs) that simplify the model and reduce simulation time. While both the stochastic total quasi-steady state approximation (stQSSA) and the stochastic low-state quasi-steady-state approximation (slQSSA) reduced simulation time, only the slQSSA provided an accurate model reduction. Our study underscores the importance of utilizing appropriate methods for efficient and accurate stochastic simulations of gene regulatory dynamics, especially when incorporating spatial heterogeneity.

Mechanisms of synergistic Mediator recruitment in RNA polymerase II transcription activation revealed by single-molecule fluorescence

Transcription activators trigger transcript production by RNA Polymerase II (RNApII) via the Mediator coactivator complex. Here the dynamics of activator, Mediator, and RNApII binding at promoter DNA were analyzed using multi-wavelength single-molecule microscopy of fluorescently labeled proteins in budding yeast nuclear extract. Binding of Mediator and RNApII to the template required activator and an upstream activator sequence (UAS), but not a core promoter. While Mediator and RNApII sometimes bind as a pre-formed complex, more commonly Mediator binds first and subsequently recruits RNApII to form a preinitiation complex precursor (pre-PIC) tethered to activators on the UAS. Interestingly, Mediator occupancy has a highly non-linear response to activator concentration, and fluorescence intensity measurements show Mediator preferentially associates with templates having at least two activators bound. Statistical mechanical modeling suggests this "synergy" is not due to cooperative binding between activators, but instead occurs when multiple DNA-bound activator molecules simultaneously interact with a single Mediator.

Formalizing the law of diminishing returns in metabolic networks using an electrical analogy

The way biological systems respond to changes in parameter values caused by mutations is a key issue in evolution and quantitative genetics, as it affects fundamental aspects such as adaptation, selective neutrality, robustness, optimality, evolutionary equilibria, etc. We address this question using the enzyme-flux relationship in a metabolic network as a model of the genotype-phenotype relationship. The lack of a suitable mathematical tool from biochemical theory to investigate this relationship led us to use an analogy between electrical circuits and metabolic networks with uni-uni reactions. We show that a behaviour of diminishing returns, which is commonly observed at various phenotypic levels, is inevitable, irrespective of the complexity of the system. We also present a possible generalization to metabolic networks with both uni-uni and bi-bi reactions.

Negative feedback systems for modelling NF-κB transcription factor oscillatory activity

Low-dimensional negative feedback systems (NFSs) were developed within a signal flow model to describe the oscillatory activities of NF-κB caused by interactions with its inhibitor IκBα. The NFSs were established as 3rd- and 4th-order linear systems containing unperturbed and perturbed negative feedback (NF) loops with constant or time-varying NF strengths and a feed-forward loop. NF-related analytical solutions to the NFSs representing the time courses of NF-κB and IκBα were determined and their exact mathematical relationship was found. The NFS's parameters were determined to fit the experimental time courses of NF-κB in TNF-α-stimulated embryonic fibroblasts, rela-/- embryonic fibroblasts reconstituted with RelA, C9L cells, GFP-p65 knock-in embryonic fibroblasts and embryogenic fibroblasts lacking Iκβ and IκBε, LPS-stimulated IC-21 macrophages treated or not with DCPA, and anti-IgM-stimulated DT40 B-lymphocytes. The unperturbed and perturbed NFSs describing the above biosystems generated isochronous and non-isochronous solutions, depending on a constant or time-varying NF strength, respectively. The oscillation period of the NF-coupled solutions, the phase difference between them and the time delays in the appearance of cytoplasmic IκBα after stimulation of NF-κB were determined. A significant divergence between the IκBα solutions to the NFSs and the IκBα experimental courses led to a rejection of the NF coupling between NF-κB and IκBα in the above biosystems. It was shown that neither the linearity nor the low dimensionality of the NFSs altered the NF relationship and the divergence between the IκBα solutions to the NFS and IκBα experimental time courses. Although the NF relationship between IκBα and NF-κB was not confirmed in all the experimental data analyzed, delayed negative feedback was found in some cases.

Predictive Biophysical Neural Network Modeling of a Compendium of in vivo Transcription Factor DNA Binding Profiles for Escherichia coli

The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs using ChIP-Seq. We used these data to train BoltzNet, a novel neural network that predicts TF binding energy from DNA sequence. BoltzNet mirrors a quantitative biophysical model and provides directly interpretable predictions genome-wide at nucleotide resolution. We used BoltzNet to quantitatively design novel binding sites, which we validated with biophysical experiments on purified protein. We have generated models for 125 TFs that provide insight into global features of TF binding, including clustering of sites, the role of accessory bases, the relevance of weak sites, and the background affinity of the genome. Our paper provides new paradigms for studying TF-DNA binding and for the development of biophysically motivated neural networks.

Overlapping binding sites underlie TF genomic occupancy

Sequence-specific DNA binding by transcription factors (TFs) is a crucial step in gene regulation. However, current high-throughput in vitro approaches cannot reliably detect lower affinity TF-DNA interactions, which play key roles in gene regulation. Here, we developed PADIT-seq ( p rotein a ffinity to D NA by in vitro transcription and RNA seq uencing) to assay TF binding preferences to all 10-bp DNA sequences at far greater sensitivity than prior approaches. The expanded catalogs of low affinity DNA binding sites for the human TFs HOXD13 and EGR1 revealed that nucleotides flanking high affinity DNA binding sites create overlapping lower affinity sites that together modulate TF genomic occupancy in vivo . Formation of such extended recognition sequences stems from an inherent property of TF binding sites to interweave each other and expands the genomic sequence space for identifying noncoding variants that directly alter TF binding.

One-Sentence Summary

Overlapping DNA binding sites underlie TF genomic occupancy through their inherent propensity to interweave each other.

Integrative Conjugative Elements (ICEs) of the SXT/R391 family drive adaptation and evolution in γ-Proteobacteria

Integrative Conjugative Elements (ICEs) are mosaics containing functional modules allowing maintenance by site-specific integration and excision into and from the host genome and conjugative transfer to a specific host range. Many ICEs encode a range of adaptive functions that aid bacterial survival and evolution in a range of niches. ICEs from the SXT/R391 family are found in γ-Proteobacteria. Over 100 members have undergone epidemiological and molecular characterization allowing insight into their diversity and function. Comparative analysis of SXT/R391 elements from a wide geographic distribution has revealed conservation of key functions, and the accumulation and evolution of adaptive genes. This evolution is associated with gene acquisition in conserved hotspots and variable regions within the SXT/R391 ICEs catalysed via element-encoded recombinases. The elements can carry IS elements and transposons, and a mutagenic DNA polymerase, PolV, which are associated with their evolution. SXT/R391 ICEs isolated from different niches appear to have retained adaptive functions related to that specific niche; phage resistance determinants in ICEs carried by wastewater bacteria, antibiotic resistance determinants in clinical isolates and metal resistance determinants in bacteria recovered from polluted environments/ocean sediments. Many genes found in the element hotspots are undetermined and have few homologs in the nucleotide databases.

Haploinsufficient Transcription Factors in Myeloid Neoplasms

Many transcription factors (TFs) function as tumor suppressor genes with heterozygous phenotypes, yet haploinsufficiency generally has an underappreciated role in neoplasia. This is no less true in myeloid cells, which are normally regulated by a delicately balanced and interconnected transcriptional network. Detailed understanding of TF dose in this circuitry sheds light on the leukemic transcriptome. In this review, we discuss the emerging features of haploinsufficient transcription factors (HITFs). We posit that: (a) monoallelic and biallelic losses can have distinct cellular outcomes; (b) the activity of a TF exists in a greater range than the traditional Mendelian genetic doses; and (c) how a TF is deleted or mutated impacts the cellular phenotype. The net effect of a HITF is a myeloid differentiation block and increased intercellular heterogeneity in the course of myeloid neoplasia.

LncRNAs in melanoma phenotypic plasticity: emerging targets for promising therapies

Long non-coding RNAs (lncRNAs) have received growing attention due to their diverse regulatory roles in cancer, including in melanoma, an aggressive type of skin cancer. The plasticity and phenotypic adaptability of melanoma cells are crucial factors contributing to therapeutic resistance. The identification of molecules playing key roles in melanoma cell plasticity could unravel novel and more effective therapeutic targets. This review presents current concepts of melanoma cell plasticity, illustrating its fluidity and dismissing the outdated notion of epithelial-mesenchymal-like transition as a simplistic binary process. Emphasis is placed on the pivotal role of lncRNAs in orchestrating cell plasticity, employing various mechanisms recently elucidated and unveiling their potential as promising targets for novel therapeutic strategies. Insights into the molecular mechanisms coordinated by lncRNAs in melanoma pave the way for the development of RNA-based therapies, holding great promise for enhancing treatment outcomes and offering a glimpse into a more effective approach to melanoma treatment.

LucFlow: A method to measure Luciferase reporter expression in single cells

Reporter assays, in which the expression of an inert protein is driven by gene regulatory elements such as promoters and enhancers, are a workhorse for investigating gene regulation. Techniques for measuring reporter gene expression vary from single-cell or single-molecule approaches having low throughput to bulk Luciferase assays that have high throughput. We developed a Luciferase Reporter Assay using Flow-Cytometry (LucFlow), which measures reporter expression in single cells immunostained for Luciferase. We optimized and tested LucFlow with a murine cell line that can be differentiated into neutrophils, into which promoter-reporter and enhancer-promoter-reporter constructs have been integrated in a site-specific manner. The single-cell measurements are comparable to bulk ones but we found that dead cells have no detectable Luciferase protein, so that bulk assays underestimate reporter expression. LucFlow is able to achieve a higher accuracy than bulk methods by excluding dead cells during flow cytometry. Prior to fixation and staining, the samples are spiked with stained cells that can be discriminated during flow cytometry and control for tube-to-tube variation in experimental conditions. Computing fold change relative to control cells allows LucFlow to achieve a high level of precision. LucFlow, therefore, enables the accurate and precise measurement of reporter expression in a high throughput manner.

Dominance is common in mammals and is associated with trans-acting gene expression and alternative splicing

BACKGROUND

Dominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena play a major role in quantitative genetics. However, most genome-wide association studies (GWAS) assume alleles act additively.

RESULTS

We systematically investigate both dominance-here representing any non-additive within-locus interaction-and additivity across 574 physiological and gene expression traits in three mammalian stocks: F2 intercross pigs, rat heterogeneous stock, and mice heterogeneous stock. Dominance accounts for about one quarter of heritable variance across all physiological traits in all species. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable as additive QTLs, we identify 154, 64, and 62 novel dominance QTLs in pigs, rats, and mice respectively that are undetectable as additive QTLs. Similarly, even though most cis-acting expression QTLs are additive, gene expression exhibits a large fraction of dominance variance, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, thousands of eQTLs are associated with alternatively spliced isoforms with complex additive and dominant architectures in heterogeneous stock rats, suggesting a possible mechanism for dominance.

CONCLUSIONS

Although heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality.

Predicting the impact of sequence motifs on gene regulation using single-cell data

The binding of transcription factors at proximal promoters and distal enhancers is central to gene regulation. Identifying regulatory motifs and quantifying their impact on expression remains challenging. Using a convolutional neural network trained on single-cell data, we infer putative regulatory motifs and cell type-specific importance. Our model, scover, explains 29% of the variance in gene expression in multiple mouse tissues. Applying scover to distal enhancers identified using scATAC-seq from the developing human brain, we identify cell type-specific motif activities in distal enhancers. Scover can identify regulatory motifs and their importance from single-cell data where all parameters and outputs are easily interpretable.

Precise modulation of transcription factor levels identifies features underlying dosage sensitivity

Transcriptional regulation exhibits extensive robustness, but human genetics indicates sensitivity to transcription factor (TF) dosage. Reconciling such observations requires quantitative studies of TF dosage effects at trait-relevant ranges, largely lacking so far. TFs play central roles in both normal-range and disease-associated variation in craniofacial morphology; we therefore developed an approach to precisely modulate TF levels in human facial progenitor cells and applied it to SOX9, a TF associated with craniofacial variation and disease (Pierre Robin sequence (PRS)). Most SOX9-dependent regulatory elements (REs) are buffered against small decreases in SOX9 dosage, but REs directly and primarily regulated by SOX9 show heightened sensitivity to SOX9 dosage; these RE responses partially predict gene expression responses. Sensitive REs and genes preferentially affect functional chondrogenesis and PRS-like craniofacial shape variation. We propose that such REs and genes underlie the sensitivity of specific phenotypes to TF dosage, while buffering of other genes leads to robust, nonlinear dosage-to-phenotype relationships.

Molecular encoding of stimulus features in a single sensory neuron type enables neuronal and behavioral plasticity

Neurons modify their transcriptomes in response to an animal's experience. How specific experiences are transduced to modulate gene expression and precisely tune neuronal functions are not fully defined. Here, we describe the molecular profile of a thermosensory neuron pair in C. elegans experiencing different temperature stimuli. We find that distinct salient features of the temperature stimulus, including its duration, magnitude of change, and absolute value, are encoded in the gene expression program in this single neuron type, and we identify a novel transmembrane protein and a transcription factor whose specific transcriptional dynamics are essential to drive neuronal, behavioral, and developmental plasticity. Expression changes are driven by broadly expressed activity-dependent transcription factors and corresponding cis-regulatory elements that nevertheless direct neuron- and stimulus-specific gene expression programs. Our results indicate that coupling of defined stimulus characteristics to the gene regulatory logic in individual specialized neuron types can customize neuronal properties to drive precise behavioral adaptation.

Modulation of transcription factor dynamics allows versatile information transmission

Cells detect changes in their environment and generate responses, often involving changes in gene expression. In this paper we use information theory and a simple transcription model to analyze whether the resulting gene expression serves to identify extracellular stimuli and assess their intensity when they are encoded in the amplitude, duration or frequency of pulses of a transcription factor's nuclear concentration (or activation state). We find, for all cases, that about three ranges of input strengths can be distinguished and that maximum information transmission occurs for fast and high activation threshold promoters. The three input modulation modes differ in the sensitivity to changes in the promoters parameters. Frequency modulation is the most sensitive and duration modulation, the least. This is key for signal identification: there are promoter parameters that yield a relatively high information transmission for duration or amplitude modulation and a much smaller value for frequency modulation. The reverse situation cannot be found with a single promoter transcription model. Thus, pulses of transcription factors can selectively activate the "frequency-tuned" promoter while prolonged nuclear accumulation would activate promoters of all three modes simultaneously. Frequency modulation is therefore highly selective and better suited than the other encoding modes for signal identification without requiring other mediators of the transduction process.

Enhanced Transcriptional Strength of HIV-1 Subtype C Minimizes Gene Expression Noise and Confers Stability to the Viral Latent State

Stochastic fluctuations in gene expression emanating from the HIV-1 long terminal repeat (LTR), amplified by the Tat positive feedback circuit, determine the choice between viral infection fates: active transcription (ON) or transcriptional silence (OFF). The emergence of several transcription factor binding site (TFBS) variant strains in HIV-1 subtype C (HIV-1C), especially those containing the duplication of the NF-κB motif, mandates the evaluation of the effect of enhanced transcriptional strength on gene expression noise and its influence on viral fate selection switch. Using a panel of subgenomic LTR-variant strains containing different copy numbers of the NF-κB motif (ranging from 0 to 4), we used flow cytometry, mRNA quantification, and pharmacological perturbations to demonstrate an inverse correlation between promoter strength and gene expression noise in Jurkat T cells and primary CD4⁺ T cells. The inverse correlation is consistent in clonal cell populations at constant intracellular concentrations of Tat and when NF-κB levels were regulated pharmacologically. Further, we show that strong LTRs containing at least two copies of the NF-κB motif in the enhancer establish a more stable latent state and demonstrate more rapid latency reversal than weak LTRs containing fewer motifs. We also demonstrate a cooperative binding of NF-κB to the motif cluster in HIV-1C LTRs containing two, three, or four NF-κB motifs (Hill coefficient [H] = 2.61, 3.56, and 3.75, respectively). The present work alludes to a possible evolution of the HIV-1C LTR toward gaining transcriptional strength associated with attenuated gene expression noise with implications for viral latency. IMPORTANCE Over the past two consecutive decades, HIV-1 subtype C (HIV-1C) has been undergoing directional evolution toward augmenting the transcriptional strength of the long terminal repeat (LTR) by adding more copies of the existing transcription factor binding site (TFBS) by sequence duplication. Additionally, the duplicated elements are genetically diverse, suggesting broader-range signal receptivity by variant LTRs. The HIV-1 promoter is inherently noisy, and the stochastic fluctuations in gene expression of variant LTRs may influence the active transcription (ON)/transcriptional silence (OFF) latency decisions. The evolving NF-κB motif variations of HIV-1C offer a powerful opportunity to examine how the transcriptional strength of the LTR might influence gene expression noise. Our work here shows that the augmented transcriptional strength of the HIV-1C LTR leads to concomitantly reduced gene expression noise, consequently leading to stabler latency maintenance and rapid latency reversal. The present work offers a novel lead toward appreciating the molecular mechanisms governing HIV-1 latency.

Molecular encoding of stimulus features in a single sensory neuron type enables neuronal and behavioral plasticity

Neurons modify their transcriptomes in response to an animal’s experience. How specific experiences are transduced to modulate gene expression and precisely tune neuronal functions are not fully defined. Here, we describe the molecular profile of a thermosensory neuron pair in C. elegans experiencing different temperature stimuli. We find that distinct salient features of the temperature stimulus including its duration, magnitude of change, and absolute value are encoded in the gene expression program in this single neuron, and identify a novel transmembrane protein and a transcription factor whose specific transcriptional dynamics are essential to drive neuronal, behavioral, and developmental plasticity. Expression changes are driven by broadly expressed activity-dependent transcription factors and corresponding cis -regulatory elements that nevertheless direct neuron- and stimulus-specific gene expression programs. Our results indicate that coupling of defined stimulus characteristics to the gene regulatory logic in individual specialized neuron types can customize neuronal properties to drive precise behavioral adaptation.

An intrinsically disordered transcription activation domain increases the DNA binding affinity and reduces the specificity of NFκB p50/RelA

Many transcription factors contain intrinsically disordered transcription activation domains (TADs), which mediate interactions with coactivators to activate transcription. Historically, DNA-binding domains and TADs have been considered as modular units, but recent studies have shown that TADs can influence DNA binding. Whether these results can be generalized to more TADs is not clear. Here, we biophysically characterized the NFκB p50/RelA heterodimer including the RelA TAD and investigated the TAD's influence on NFκB-DNA interactions. In solution, we show the RelA TAD is disordered but compact, with helical tendency in two regions that interact with coactivators. We determined that the presence of the TAD increased the stoichiometry of NFκB-DNA complexes containing promoter DNA sequences with tandem κB recognition motifs by promoting the binding of NFκB dimers in excess of the number of κB sites. In addition, we measured the binding affinity of p50/RelA for DNA containing tandem κB sites and single κB sites. While the presence of the TAD enhanced the binding affinity of p50/RelA for all κB sequences tested, it also increased the affinity for nonspecific DNA sequences by over 10-fold, leading to an overall decrease in specificity for κB DNA sequences. In contrast, previous studies have generally reported that TADs decrease DNA-binding affinity and increase sequence specificity. Our results reveal a novel function of the RelA TAD in promoting binding to nonconsensus DNA, which sheds light on previous observations of extensive nonconsensus DNA binding by NFκB in vivo in response to strong inflammatory signals.

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.

Notch-dependent DNA cis-regulatory elements and their dose-dependent control of C. elegans stem cell self-renewal

A long-standing biological question is how DNA cis-regulatory elements shape transcriptional patterns during metazoan development. Reporter constructs, cell culture assays and computational modeling have made major contributions to answering this question, but analysis of elements in their natural context is an important complement. Here, we mutate Notch-dependent LAG-1 binding sites (LBSs) in the endogenous Caenorhabditis elegans sygl-1 gene, which encodes a key stem cell regulator, and analyze the consequences on sygl-1 expression (nascent transcripts, mRNA, protein) and stem cell maintenance. Mutation of one LBS in a three-element cluster approximately halved both expression and stem cell pool size, whereas mutation of two LBSs essentially abolished them. Heterozygous LBS mutant clusters provided intermediate values. Our results lead to two major conclusions. First, both LBS number and configuration impact cluster activity: LBSs act additively in trans and synergistically in cis. Second, the SYGL-1 gradient promotes self-renewal above its functional threshold and triggers differentiation below the threshold. Our approach of coupling CRISPR/Cas9 LBS mutations with effects on both molecular and biological readouts establishes a powerful model for in vivo analyses of DNA cis-regulatory elements.

Developmental disorders caused by haploinsufficiency of transcriptional regulators: a perspective based on cell fate determination

Many human birth defects and neurodevelopmental disorders are caused by loss-of-function mutations in a single copy of transcription factor (TF) and chromatin regulator genes. Although this dosage sensitivity has long been known, how and why haploinsufficiency (HI) of transcriptional regulators leads to developmental disorders (DDs) is unclear. Here I propose the hypothesis that such DDs result from defects in cell fate determination that are based on disrupted bistability in the underlying gene regulatory network (GRN). Bistability, a crucial systems biology concept to model binary choices such as cell fate decisions, requires both positive feedback and ultrasensitivity, the latter often achieved through TF cooperativity. The hypothesis explains why dosage sensitivity of transcriptional regulators is an inherent property of fate decisions, and why disruption of either positive feedback or cooperativity in the underlying GRN is sufficient to cause disease. I present empirical and theoretical evidence in support of this hypothesis and discuss several issues for which it increases our understanding of disease, such as incomplete penetrance. The proposed framework provides a mechanistic, systems-level explanation of HI of transcriptional regulators, thus unifying existing theories, and offers new insights into outstanding issues of human disease. This article has an associated Future Leader to Watch interview with the author of the paper.

Identifying the combinatorial control of signal-dependent transcription factors

The effectiveness of immune responses depends on the precision of stimulus-responsive gene expression programs. Cells specify which genes to express by activating stimulus-specific combinations of stimulus-induced transcription factors (TFs). Their activities are decoded by a gene regulatory strategy (GRS) associated with each response gene. Here, we examined whether the GRSs of target genes may be inferred from stimulus-response (input-output) datasets, which remains an unresolved model-identifiability challenge. We developed a mechanistic modeling framework and computational workflow to determine the identifiability of all possible combinations of synergistic (AND) or non-synergistic (OR) GRSs involving three transcription factors. Considering different sets of perturbations for stimulus-response studies, we found that two thirds of GRSs are easily distinguishable but that substantially more quantitative data is required to distinguish the remaining third. To enhance the accuracy of the inference with timecourse experimental data, we developed an advanced error model that avoids error overestimates by distinguishing between value and temporal error. Incorporating this error model into a Bayesian framework, we show that GRS models can be identified for individual genes by considering multiple datasets. Our analysis rationalizes the allocation of experimental resources by identifying most informative TF stimulation conditions. Applying this computational workflow to experimental data of immune response genes in macrophages, we found that a much greater fraction of genes are combinatorially controlled than previously reported by considering compensation among transcription factors. Specifically, we revealed that a group of known NFκB target genes may also be regulated by IRF3, which is supported by chromatin immuno-precipitation analysis. Our study provides a computational workflow for designing and interpreting stimulus-response gene expression studies to identify underlying gene regulatory strategies and further a mechanistic understanding.

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.

An Improved CRISPR Interference Tool to Engineer Rhodococcus opacus

Rhodococcus opacus is a nonmodel bacterium that is well suited for valorizing lignin. Despite recent advances in our systems-level understanding of its versatile metabolism, studies of its gene functions at a single gene level are still lagging. Elucidating gene functions in nonmodel organisms is challenging due to limited genetic engineering tools that are convenient to use. To address this issue, we developed a simple gene repression system based on CRISPR interference (CRISPRi). This gene repression system uses a T7 RNA polymerase system to express a small guide RNA, demonstrating improved repression compared to the previously demonstrated CRISPRi system (i.e., the maximum repression efficiency improved from 58% to 85%). Additionally, our cloning strategy allows for building multiple CRISPRi plasmids in parallel without any PCR step, facilitating the engineering of this GC-rich organism. Using the improved CRISPRi system, we confirmed the annotated roles of four metabolic pathway genes, which had been identified by our previous transcriptomic analysis to be related to the consumption of benzoate, vanillate, catechol, and acetate. Furthermore, we showed our tool's utility by demonstrating the inducible accumulation of muconate that is a precursor of adipic acid, an important monomer for nylon production. While the maximum muconate yield obtained using our tool was 30% of the yield obtained using gene knockout, our tool showed its inducibility and partial repressibility. Our CRISPRi tool will be useful to facilitate functional studies of this nonmodel organism and engineer this promising microbial chassis for lignin valorization.

Conserved regulatory logic at accessible and inaccessible chromatin during the acute inflammatory response in mammals

The regulatory elements controlling gene expression during acute inflammation are not fully elucidated. Here we report the identification of a set of NF-κB-bound elements and common chromatin landscapes underlying the acute inflammatory response across cell-types and mammalian species. Using primary vascular endothelial cells (human/mouse/bovine) treated with the pro-inflammatory cytokine, Tumor Necrosis Factor-α, we identify extensive (~30%) conserved orthologous binding of NF-κB to accessible, as well as nucleosome-occluded chromatin. Regions with the highest NF-κB occupancy pre-stimulation show dramatic increases in NF-κB binding and chromatin accessibility post-stimulation. These 'pre-bound' regions are typically conserved (~56%), contain multiple NF-κB motifs, are utilized by diverse cell types, and overlap rare non-coding mutations and common genetic variation associated with both inflammatory and cardiovascular phenotypes. Genetic ablation of conserved, 'pre-bound' NF-κB regions within the super-enhancer associated with the chemokine-encoding CCL2 gene and elsewhere supports the functional relevance of these elements.

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.

Gene Regulation in and out of Equilibrium

Determining whether and how a gene is transcribed are two of the central processes of life. The conceptual basis for understanding such gene regulation arose from pioneering biophysical studies in eubacteria. However, eukaryotic genomes exhibit vastly greater complexity, which raises questions not addressed by this bacterial paradigm. First, how is information integrated from many widely separated binding sites to determine how a gene is transcribed? Second, does the presence of multiple energy-expending mechanisms, which are absent from eubacterial genomes, indicate that eukaryotes are capable of improved forms of genetic information processing? An updated biophysical foundation is needed to answer such questions. We describe the linear framework, a graph-based approach to Markov processes, and show that it can accommodate many previous studies in the field. Under the assumption of thermodynamic equilibrium, we introduce a language of higher-order cooperativities and show how it can rigorously quantify gene regulatory properties suggested by experiment. We point out that fundamental limits to information processing arise at thermodynamic equilibrium and can only be bypassed through energy expenditure. Finally, we outline some of the mathematical challenges that must be overcome to construct an improved biophysical understanding of gene regulation.

Structural basis for DNA recognition and allosteric control of the retinoic acid receptors RAR-RXR

Retinoic acid receptors (RARs) as a functional heterodimer with retinoid X receptors (RXRs), bind a diverse series of RA-response elements (RAREs) in regulated genes. Among them, the non-canonical DR0 elements are bound by RXR–RAR with comparable affinities to DR5 elements but DR0 elements do not act transcriptionally as independent RAREs. In this work, we present structural insights for the recognition of DR5 and DR0 elements by RXR–RAR heterodimer using x-ray crystallography, small angle x-ray scattering, and hydrogen/deuterium exchange coupled to mass spectrometry. We solved the crystal structures of RXR–RAR DNA-binding domain in complex with the Rarb2 DR5 and RXR–RXR DNA-binding domain in complex with Hoxb13 DR0. While cooperative binding was observed on DR5, the two molecules bound non-cooperatively on DR0 on opposite sides of the DNA. In addition, our data unveil the structural organization and dynamics of the multi-domain RXR–RAR DNA complexes providing evidence for DNA-dependent allosteric communication between domains. Differential binding modes between DR0 and DR5 were observed leading to differences in conformation and structural dynamics of the multi-domain RXR–RAR DNA complexes. These results reveal that the topological organization of the RAR binding element confer regulatory information by modulating the overall topology and structural dynamics of the RXR–RAR heterodimers.

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

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.

Deep learning of immune cell differentiation

Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular atlas of chromatin accessibility across 81 immune cell types, we asked if a convolutional neural network (CNN) could learn to infer cell type-specific chromatin accessibility solely from regulatory DNA sequences. With a tailored architecture and an ensemble approach to CNN parameter interpretation, we show that our trained network ("AI-TAC") does so by rediscovering ab initio the binding motifs for known regulators and some unknown ones. Motifs whose importance is learned virtually as functionally important overlap strikingly well with positions determined by chromatin immunoprecipitation for several TFs. AI-TAC establishes a hierarchy of TFs and their interactions that drives lineage specification and also identifies stage-specific interactions, like Pax5/Ebf1 vs. Pax5/Prdm1, or the role of different NF-κB dimers in different cell types. AI-TAC assigns Spi1/Cebp and Pax5/Ebf1 as the drivers necessary for myeloid and B lineage fates, respectively, but no factors seemed as dominantly required for T cell differentiation, which may represent a fall-back pathway. Mouse-trained AI-TAC can parse human DNA, revealing a strikingly similar ranking of influential TFs and providing additional support that AI-TAC is a generalizable regulatory sequence decoder. Thus, deep learning can reveal the regulatory syntax predictive of the full differentiative complexity of the immune system.

Transcription Factors in Cartilage Homeostasis and Osteoarthritis

Osteoarthritis (OA) is the most common degenerative joint disease, and it is characterized by articular cartilage loss. In part, OA is caused by aberrant anabolic and catabolic activities of the chondrocyte, the only cell type present in cartilage. These chondrocyte activities depend on the intra- and extracellular signals that the cell receives and integrates into gene expression. The key proteins for this integration are transcription factors. A large number of transcription factors exist, and a better understanding of the transcription factors activated by the various signaling pathways active during OA can help us to better understand the complex etiology of OA. In addition, establishing such a profile can help to stratify patients in different subtypes, which can be a very useful approach towards personalized therapy. In this review, we discuss crucial transcription factors for extracellular matrix metabolism, chondrocyte hypertrophy, chondrocyte senescence, and autophagy in chondrocytes. In addition, we discuss how insight into these factors can be used for treatment purposes.

First Responders Shape a Prompt and Sharp NF-κB-Mediated Transcriptional Response to TNF-α

Nuclear factor (NF)-κB controls the transcriptional response to inflammatory signals by translocating into the nucleus, but we lack a single-cell characterization of the resulting transcription dynamics. Here we show that upon tumor necrosis factor (TNF)-α transcription of NF-κB target genes is heterogeneous in individual cells but results in an average nascent transcription profile that is prompt (i.e., occurs almost immediately) and sharp (i.e., increases and decreases rapidly) compared with NF-κB nuclear localization. Using an NF-κB-controlled MS2 reporter we show that the single-cell nascent transcription is more heterogeneous than NF-κB translocation dynamics, with a fraction of synchronized “first responders” that shape the average transcriptional profile and are more prone to respond to multiple TNF-α stimulations. A mathematical model combining NF-κB-mediated gene activation and a gene refractory state is able to reproduce these features. Our work shows how the expression of target genes induced by transcriptional activators can be heterogeneous across single cells and yet time resolved on average.

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Nascent transcription upon TNF-α is heterogeneous, with a subset of “first responders”

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The average nascent transcription is prompt and sharper than NF-κB response

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First responders do not depend on NF-κB dynamics and respond more to pulsed stimuli

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A model including NF-κB and a gene refractory state reproduces these observations

Poly-Enrich: count-based methods for gene set enrichment testing with genomic regions

Gene set enrichment (GSE) testing enhances the biological interpretation of ChIP-seq data and other large sets of genomic regions. Our group has previously introduced two GSE methods for genomic regions: ChIP-Enrich for narrow regions and Broad-Enrich for broad regions. Here, we introduce Poly-Enrich, which has wider applicability, additional capabilities and models the number of peaks assigned to a gene using a generalized additive model with a negative binomial family to determine gene set enrichment, while adjusting for gene locus length. As opposed to ChIP-Enrich, Poly-Enrich works well even when nearly all genes have a peak, illustrated by using Poly-Enrich to characterize pathways and types of genic regions enriched with different families of repetitive elements. By comparing Poly-Enrich and ChIP-Enrich results with ENCODE ChIP-seq data, we found that the optimal test depends more on the pathway being regulated than on properties of the transcription factors. Using known transcription factor functions, we discovered clusters of related biological processes consistently better modeled with Poly-Enrich. This suggests that the regulation of certain processes may be modified by multiple binding events, better modeled by a count-based method. Our new hybrid method automatically uses the optimal method for each gene set, with correct FDR-adjustment.

Synthetic and genomic regulatory elements reveal aspects of cis-regulatory grammar in mouse embryonic stem cells

In embryonic stem cells (ESCs), a core transcription factor (TF) network establishes the gene expression program necessary for pluripotency. To address how interactions between four key TFs contribute to cis-regulation in mouse ESCs, we assayed two massively parallel reporter assay (MPRA) libraries composed of binding sites for SOX2, POU5F1 (OCT4), KLF4, and ESRRB. Comparisons between synthetic cis-regulatory elements and genomic sequences with comparable binding site configurations revealed some aspects of a regulatory grammar. The expression of synthetic elements is influenced by both the number and arrangement of binding sites. This grammar plays only a small role for genomic sequences, as the relative activities of genomic sequences are best explained by the predicted occupancy of binding sites, regardless of binding site identity and positioning. Our results suggest that the effects of transcription factor binding sites (TFBS) are influenced by the order and orientation of sites, but that in the genome the overall occupancy of TFs is the primary determinant of activity.

Transcription factors are proteins that flip genetic switches; their role is to control when and where genes are active. They do this by binding to short stretches of DNA called cis-regulatory sequences. Each sequence can have several binding sites for different transcription factors, but it is largely unclear whether the transcription factors binding to the same regulatory sequence actually work together.

It is possible that each transcription factor may work independently and there only needs to be critical mass of transcription factors bound to throw the genetic switch. If this is the case, the most important features of a cis-regulatory sequence should be the number of binding sites it contains, and how tightly the transcription factors bind to those sites. The more transcription factors and the more strongly they bind, the more active the gene should be. An alternative option is that certain transcription factors may work better together, enhancing each other's effects such that the total effect is more than the sum of its parts. If this is true, the order, orientation and spacing of the binding sites within a sequence should matter more than the number.

One way to investigate to distinguish between these possibilities is to study mouse embryonic stem cells, which have a core set of four transcription factors. Looking directly at a real genome, however, can be confusing and it is difficult to measure the effects of different cis-regulatory sequences because genes differ in so many other ways. To tackle this problem, King et al. created a synthetic set of cis-regulatory sequences based on the four core transcription factors found in mouse stem cells.

The synthetic set had every combination of two, three or four of the binding sites, with each site either facing forwards or backwards along the DNA strand. King et al. attached each of the synthetic cis-regulatory sequences to a reporter gene to find out how well each sequence performed. This revealed that the cis-regulatory sequences with the most binding sites and the tightest binding affinities work best, suggesting that transcription factors mainly work independently.

There was evidence of some interaction between some transcription factors, because, of the synthetic sequences with four binding sites, some worked better than others, and there were patterns in the most effective binding site combinations. However, these effects were small and when King et al. went on to test sequences from the real mouse genome, the most important factor by far was the number of binding sites.

Synthetic libraries of DNA sequences allow researchers to examine gene regulation more clearly than is possible in real genomes. Yet this approach does have its limitations and it is impossible to capture every type of cis-regulatory sequence in one library. The next step to extend this work is to combine the two approaches, taking sequences from the real genome and manipulating them one by one. This could help to unravel the rules that govern how cis-regulatory sequences work in real cells.

Genome reading by the NF-κB transcription factors

The NF-κB family of dimeric transcription factors regulates transcription by selectively binding to DNA response elements present within promoters or enhancers of target genes. The DNA response elements, collectively known as κB sites or κB DNA, share the consensus 5'-GGGRNNNYCC-3' (where R, Y and N are purine, pyrimidine and any nucleotide base, respectively). In addition, several DNA sequences that deviate significantly from the consensus have been shown to accommodate binding by NF-κB dimers. X-ray crystal structures of NF-κB in complex with diverse κB DNA have helped elucidate the chemical principles that underlie target selection in vitro. However, NF-κB dimers encounter additional impediments to selective DNA binding in vivo. Work carried out during the past decades has identified some of the barriers to sequence selective DNA target binding within the context of chromatin and suggests possible mechanisms by which NF-κB might overcome these obstacles. In this review, we first highlight structural features of NF-κB:DNA complexes and how distinctive features of NF-κB proteins and DNA sequences contribute to specific complex formation. We then discuss how native NF-κB dimers identify DNA binding targets in the nucleus with support from additional factors and how post-translational modifications enable NF-κB to selectively bind κB sites in vivo.

Melanoma plasticity and phenotypic diversity: therapeutic barriers and opportunities

An incomplete view of the mechanisms that drive metastasis, the primary cause of cancer-related death, has been a major barrier to development of effective therapeutics and prognostic diagnostics. Increasing evidence indicates that the interplay between microenvironment, genetic lesions, and cellular plasticity drives the metastatic cascade and resistance to therapies. Here, using melanoma as a model, we outline the diversity and trajectories of cell states during metastatic dissemination and therapy exposure, and highlight how understanding the magnitude and dynamics of nongenetic reprogramming in space and time at single-cell resolution can be exploited to develop therapeutic strategies that capitalize on nongenetic tumor evolution.

Multi-input chemical control of protein dimerization for programming graded cellular responses

Chemical and optogenetic methods for post-translationally controlling protein function have enabled modulation and engineering of cellular functions. However, most of these methods only confer single-input, single-output control. To increase the diversity of post-translational behaviors that can be programmed, we built a system based on a single protein receiver that can integrate multiple drug inputs, including approved therapeutics. Our system translates drug inputs into diverse outputs using a suite of engineered reader proteins to provide variable dimerization states of the receiver protein. We show that our single receiver protein architecture can be used to program a variety of cellular responses, including graded and proportional dual-output control of transcription and mammalian cell signaling. We apply our tools to titrate the competing activities of the Rac and Rho GTPases to control cell morphology. Our versatile tool set will enable researchers to post-translationally program mammalian cellular processes and to engineer cell therapies.

Signal Integration of IFN-I and IFN-II With TLR4 Involves Sequential Recruitment of STAT1-Complexes and NFκB to Enhance Pro-inflammatory Transcription

Atherosclerosis is a chronic inflammatory disease of the blood vessels, characterized by atherosclerotic lesion formation. Vascular Smooth Muscle Cells (VSMC), macrophages (MΦ), and dendritic cells (DC) play a crucial role in vascular inflammation and atherosclerosis. Interferon (IFN)α, IFNγ, and Toll-like receptor (TLR)4 activate pro-inflammatory gene expression and are pro-atherogenic. Gene expression regulation of many pro-inflammatory genes has shown to rely on Signal Integration (SI) between IFNs and TLR4 through combinatorial actions of the Signal Transducer and Activator of Transcription (STAT)1 complexes ISGF3 and γ-activated factor (GAF), and Nuclear Factor-κB (NFκB). Thus, IFN pre-treatment (“priming”) followed by LPS stimulation leads to enhanced transcriptional responses as compared to the individual stimuli. To characterize the mechanism of priming-induced IFNα + LPS- and IFNγ + LPS-dependent SI in vascular cells as compared to immune cells, we performed a comprehensive genome-wide analysis of mouse VSMC, MΦ, and DC in response to IFNα, IFNγ, and/or LPS. Thus, we identified IFNα + LPS or IFNγ + LPS induced genes commonly expressed in these cell types that bound STAT1 and p65 at comparable γ-activated sequence (GAS), Interferon-stimulated response element (ISRE), or NFκB sites in promoter proximal and distal regions. Comparison of the relatively high number of overlapping ISRE sites in these genes unraveled a novel role of ISGF3 and possibly STAT1/IRF9 in IFNγ responses. In addition, similar STAT1-p65 co-binding modes were detected for IFNα + LPS and IFNγ + LPS up-regulated genes, which involved recruitment of STAT1 complexes preceding p65 to closely located GAS/NFκB or ISRE/NFκB composite sites already upon IFNα or IFNγ treatment. This STAT1-p65 co-binding significantly increased after subsequent LPS exposure and correlated with histone acetylation, PolII recruitment, and amplified target gene transcription in a STAT1-p65 co-bound dependent manner. Thus, co-binding of STAT1-containing transcription factor complexes and NFκB, activated by IFN-I or IFN-II together with LPS, provides a platform for robust transcriptional activation of pro-inflammatory genes. Moreover, our data offer an explanation for the comparable effects of IFNα or IFNγ priming on TLR4-induced activation in vascular and immune cells, with important implications in atherosclerosis.

Considering Abundance, Affinity, and Binding Site Availability in the NF-κB Target Selection Puzzle

The NF-κB transcription regulation system governs a diverse set of responses to various cytokine stimuli. With tools from in vitro biochemical characterizations, to omics-based whole genome investigations, great strides have been made in understanding how NF-κB transcription factors control the expression of specific sets of genes. Nonetheless, these efforts have also revealed a very large number of potential binding sites for NF-κB in the human genome, and a puzzle emerges when trying to explain how NF-κB selects from these many binding sites to direct cell-type- and stimulus-specific gene expression patterns. In this review, we surmise that target gene transcription can broadly be thought of as a function of the nuclear abundance of the various NF-κB dimers, the affinity of NF-κB dimers for the regulatory sequence and the availability of this regulatory site. We use this framework to place quantitative information that has been gathered about the NF-κB transcription regulation system into context and thus consider questions it answers, and questions it raises. We end with a brief discussion of some of the future prospects that new approaches could bring to our understanding of how NF-κB transcription factors orchestrate diverse responses in different biological contexts.

An enriched network motif family regulates multistep cell fate transitions with restricted reversibility

Multistep cell fate transitions with stepwise changes of transcriptional profiles are common to many developmental, regenerative and pathological processes. The multiple intermediate cell lineage states can serve as differentiation checkpoints or branching points for channeling cells to more than one lineages. However, mechanisms underlying these transitions remain elusive. Here, we explored gene regulatory circuits that can generate multiple intermediate cellular states with stepwise modulations of transcription factors. With unbiased searching in the network topology space, we found a motif family containing a large set of networks can give rise to four attractors with the stepwise regulations of transcription factors, which limit the reversibility of three consecutive steps of the lineage transition. We found that there is an enrichment of these motifs in a transcriptional network controlling the early T cell development, and a mathematical model based on this network recapitulates multistep transitions in the early T cell lineage commitment. By calculating the energy landscape and minimum action paths for the T cell model, we quantified the stochastic dynamics of the critical factors in response to the differentiation signal with fluctuations. These results are in good agreement with experimental observations and they suggest the stable characteristics of the intermediate states in the T cell differentiation. These dynamical features may help to direct the cells to correct lineages during development. Our findings provide general design principles for multistep cell linage transitions and new insights into the early T cell development. The network motifs containing a large family of topologies can be useful for analyzing diverse biological systems with multistep transitions.

The functions of cells are dynamically controlled in many biological processes including development, regeneration and disease progression. Cell fate transition, or the switch of cellular functions, often involves multiple steps. The intermediate stages of the transition provide the biological systems with the opportunities to regulate the transitions in a precise manner. These transitions are controlled by key regulatory genes of which the expression shows stepwise patterns, but how the interactions of these genes can determine the multistep processes was unclear. Here, we present a comprehensive analysis on the design principles of gene circuits that govern multistep cell fate transition. We found a large network family with common structural features that can generate systems with the ability to control three consecutive steps of the transition. We found that this type of networks is enriched in a gene circuit controlling the development of T lymphocyte, a crucial type of immune cells. We performed mathematical modeling using this gene circuit and we recapitulated the stepwise and irreversible loss of stem cell properties of the developing T lymphocytes. Our findings can be useful to analyze a wide range of gene regulatory networks controlling multistep cell fate transitions.

Evolution and Diversification of FRUITFULL Genes in Solanaceae

Ecologically and economically important fleshy edible fruits have evolved from dry fruit numerous times during angiosperm diversification. However, the molecular mechanisms that underlie these shifts are unknown. In the Solanaceae there has been a major shift to fleshy fruits in the subfamily Solanoideae. Evidence suggests that an ortholog of FRUITFULL (FUL), a transcription factor that regulates cell proliferation and limits the dehiscence zone in the silique of Arabidopsis, plays a similar role in dry-fruited Solanaceae. However, studies have shown that FUL orthologs have taken on new functions in fleshy fruit development, including regulating elements of tomato ripening such as pigment accumulation. FUL belongs to the core eudicot euFUL clade of the angiosperm AP1/FUL gene lineage. The euFUL genes fall into two paralogous clades, euFULI and euFULII. While most core eudicots have one gene in each clade, Solanaceae have two: FUL1 and FUL2 in the former, and MBP10 and MBP20 in the latter. We characterized the evolution of the euFUL genes to identify changes that might be correlated with the origin of fleshy fruit in Solanaceae. Our analyses revealed that the Solanaceae FUL1 and FUL2 clades probably originated through an early whole genome multiplication event. By contrast, the data suggest that the MBP10 and MBP20 clades are the result of a later tandem duplication event. MBP10 is expressed at weak to moderate levels, and its atypical short first intron lacks putative transcription factor binding sites, indicating possible pseudogenization. Consistent with this, our analyses show that MBP10 is evolving at a faster rate compared to MBP20. Our analyses found that Solanaceae euFUL gene duplications, evolutionary rates, and changes in protein residues and expression patterns are not correlated with the shift in fruit type. This suggests deeper analyses are needed to identify the mechanism underlying the change in FUL ortholog function.

Combinatorial processing of bacterial and host-derived innate immune stimuli at the single-cell level

During the course of a bacterial infection, cells are exposed simultaneously to a range of bacterial and host factors, which converge on the central transcription factor nuclear factor (NF)-κB. How do single cells integrate and process these converging stimuli? Here we tackle the question of how cells process combinatorial signals by making quantitative single-cell measurements of the NF-κB response to combinations of bacterial lipopolysaccharide and the stress cytokine tumor necrosis factor. We found that cells encode the presence of both stimuli via the dynamics of NF-κB nuclear translocation in individual cells, suggesting the integration of NF-κB activity for these stimuli occurs at the molecular and pathway level. However, the gene expression and cytokine secretion response to combinatorial stimuli were more complex, suggesting that other factors in addition to NF-κB contribute to signal integration at downstream layers of the response. Taken together, our results support the theory that during innate immune threat assessment, a pathogen recognized as both foreign and harmful will recruit an enhanced immune response. Our work highlights the remarkable capacity of individual cells to process multiple input signals and suggests that a deeper understanding of signal integration mechanisms will facilitate efforts to control dysregulated immune responses.

Heterosis Is a Systemic Property Emerging From Non-linear Genotype-Phenotype Relationships: Evidence From in Vitro Genetics and Computer Simulations

Heterosis, the superiority of hybrids over their parents for quantitative traits, represents a crucial issue in plant and animal breeding as well as evolutionary biology. Heterosis has given rise to countless genetic, genomic and molecular studies, but has rarely been investigated from the point of view of systems biology. We hypothesized that heterosis is an emergent property of living systems resulting from frequent concave relationships between genotypic variables and phenotypes, or between different phenotypic levels. We chose the enzyme-flux relationship as a model of the concave genotype-phenotype (GP) relationship, and showed that heterosis can be easily created in the laboratory. First, we reconstituted in vitro the upper part of glycolysis. We simulated genetic variability of enzyme activity by varying enzyme concentrations in test tubes. Mixing the content of "parental" tubes resulted in "hybrids," whose fluxes were compared to the parental fluxes. Frequent heterotic fluxes were observed, under conditions that were determined analytically and confirmed by computer simulation. Second, to test this model in a more realistic situation, we modeled the glycolysis/fermentation network in yeast by considering one input flux, glucose, and two output fluxes, glycerol and acetaldehyde. We simulated genetic variability by randomly drawing parental enzyme concentrations under various conditions, and computed the parental and hybrid fluxes using a system of differential equations. Again we found that a majority of hybrids exhibited positive heterosis for metabolic fluxes. Cases of negative heterosis were due to local convexity between certain enzyme concentrations and fluxes. In both approaches, heterosis was maximized when the parents were phenotypically close and when the distributions of parental enzyme concentrations were contrasted and constrained. These conclusions are not restricted to metabolic systems: they only depend on the concavity of the GP relationship, which is commonly observed at various levels of the phenotypic hierarchy, and could account for the pervasiveness of heterosis.

NF-κB, the Importance of Being Dynamic: Role and Insights in Cancer

In this review, we aim at describing the results obtained in the past years on dynamics features defining NF-κB regulatory functions, as we believe that these developments might have a transformative effect on the way in which NF-κB involvement in cancer is studied. We will also describe technical aspects of the studies performed in this context, including the use of different cellular models, culture conditions, microscopy approaches and quantification of the imaging data, balancing their strengths and limitations and pointing out to common features and to some open questions. Our emphasis in the methodology will allow a critical overview of literature and will show how these cutting-edge approaches can contribute to shed light on the involvement of NF-κB deregulation in tumour onset and progression. We hypothesize that this “dynamic point of view” can be fruitfully applied to untangle the complex relationship between NF-κB and cancer and to find new targets to restrain cancer growth.

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.

Asymmetry between Activation and Deactivation during a Transcriptional Pulse

Transcription in eukaryotic cells occurs in gene-specific bursts or pulses of activity. Recent studies identified a spectrum of transcriptionally active “on-states,” interspersed with periods of inactivity, but these “off-states” and the process of transcriptional deactivation are poorly understood. To examine what occurs during deactivation, we investigate the dynamics of switching between variable rates. We measured live single-cell expression of luciferase reporters from human growth hormone or human prolactin promoters in a pituitary cell line. Subsequently, we applied a statistical variable-rate model of transcription, validated by single-molecule FISH, to estimate switching between transcriptional rates. Under the assumption that transcription can switch to any rate at any time, we found that transcriptional activation occurs predominantly as a single switch, whereas deactivation occurs with graded, stepwise decreases in transcription rate. Experimentally altering cAMP signalling with forskolin or chromatin remodelling with histone deacetylase inhibitor modifies the duration of defined transcriptional states. Our findings reveal transcriptional activation and deactivation as mechanistically independent, asymmetrical processes.

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Gene transcription switches between variable rates

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Single-cell microscopy and mathematical modeling quantifies switch dynamics

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We observe an asymmetry in the activation/deactivation of transcriptional bursts

DNA-binding affinity and transcriptional activity of the RelA homodimer of nuclear factor κB are not correlated

The nuclear factor κB (NF-κB) transcription factor family regulates genes involved in cell proliferation and inflammation. The promoters of these genes often contain NF-κB-binding sites (κB sites) arranged in tandem. How NF-κB activates transcription through these multiple sites is incompletely understood. We report here an X-ray crystal structure of homodimers comprising the RelA DNA-binding domain containing the Rel homology region (RHR) in NF-κB bound to an E-selectin promoter fragment with tandem κB sites. This structure revealed that two dimers bind asymmetrically to the symmetrically arranged κB sites at which multiple cognate contacts between one dimer to the corresponding DNA are broken. Because simultaneous RelA-RHR dimer binding to tandem sites in solution was anti-cooperative, we inferred that asymmetric RelA-RHR binding with fewer contacts likely indicates a dissociative binding mode. We found that both κB sites are essential for reporter gene activation by full-length RelA homodimer, suggesting that dimers facilitate DNA binding to each other even though their stable co-occupation is not promoted. Promoter variants with altered spacing and orientation of tandem κB sites displayed unexpected reporter activities that were not explained by the solution-binding pattern of RelA-RHR. Remarkably, full-length RelA bound all DNAs with a weaker affinity and specificity. Moreover, the transactivation domain played a negative role in DNA binding. These observations suggest that other nuclear factors influence full-length RelA binding to DNA by neutralizing the transactivation domain negative effect. We propose that DNA binding by NF-κB dimers is highly complex and modulated by facilitated association-dissociation processes.