Cooperative Binding Of Transcription Factors Research Articles

High-throughput data and modeling reveal insights into the mechanisms of cooperative DNA-binding by transcription factor proteins.

Cooperative DNA-binding by transcription factor (TF) proteins is critical for eukaryotic gene regulation. In the human genome, many regulatory regions contain TF-binding sites in close proximity to each other, which can facilitate cooperative interactions. However, binding site proximity does not necessarily imply cooperative binding, as TFs can also bind independently to each of their neighboring target sites. Currently, the rules that drive cooperative TF binding are not well understood. In addition, it is oftentimes difficult to infer direct TF-TF cooperativity from existing DNA-binding data. Here, we show that in vitro binding assays using DNA libraries of a few thousand genomic sequences with putative cooperative TF-binding events can be used to develop accurate models of cooperativity and to gain insights into cooperative binding mechanisms. Using factors ETS1 and RUNX1 as our case study, we show that the distance and orientation between ETS1 sites are critical determinants of cooperative ETS1-ETS1 binding, while cooperative ETS1-RUNX1 interactions show more flexibility in distance and orientation and can be accurately predicted based on the affinity and sequence/shape features of the binding sites. The approach described here, combining custom experimental design with machine-learning modeling, can be easily applied to study the cooperative DNA-binding patterns of any TFs.

Transcription factor competition facilitates self-sustained oscillations in single gene genetic circuits.

Genetic feedback loops can be used by cells to regulate internal processes or to keep track of time. It is often thought that, for a genetic circuit to display self-sustained oscillations, a degree of cooperativity is needed in the binding and unbinding of actor species. This cooperativity is usually modeled using a Hill function, regardless of the actual promoter architecture. Furthermore, genetic circuits do not operate in isolation and often transcription factors are shared between different promoters. In this work we show how mathematical modelling of genetic feedback loops can be facilitated with a mechanistic fold-change function that takes into account the titration effect caused by competing binding sites for transcription factors. The model shows how the titration effect facilitates self-sustained oscillations in a minimal genetic feedback loop: a gene that produces its own repressor directly without cooperative transcription factor binding. The use of delay-differential equations leads to a stability contour that predicts whether a genetic feedback loop will show self-sustained oscillations, even when taking the bursty nature of transcription into account.

Detecting clusters of transcription factors based on a nonhomogeneous poisson process model

Rapidly growing genome-wide ChIP-seq data have provided unprecedented opportunities to explore transcription factor (TF) binding under various cellular conditions. Despite the rich resources, development of analytical methods for studying the interaction among TFs in gene regulation still lags behind. In order to address cooperative TF binding and detect TF clusters with coordinative functions, we have developed novel computational methods based on clustering the sample paths of nonhomogeneous Poisson processes. Simulation studies demonstrated the capability of these methods to accurately detect TF clusters and uncover the hierarchy of TF interactions. A further application to the multiple-TF ChIP-seq data in mouse embryonic stem cells(ESCs) showed that our methods identified the cluster of core ESC regulators reported in the literature and provided new insights on functional implications of transcrisptional regulatory modules. Effective analytical tools are essential for studying protein-DNA relations. Information derived from this research will help us better understand the orchestration of transcription factors in gene regulation processes.

OR07-3 Hormone-Controlled Cooperative Binding of Transcription Factors Within Enhancer Clusters Drives Synergistic Induction of Fasting-Regulated Genes

Abstract During fasting, hepatocytes produce glucose in response to hormonal signals. Glucagon and glucocorticoids are principal hormones secreted during fasting that cooperate in regulating glucose production via increasing hepatic gluconeogenesis. However, how these hormone signals are integrated in hepatocytes and translated to a biological output is unknown. We used genome-wide profiling of gene expression, enhancer dynamics and transcription factor binding in primary mouse hepatocytes to uncover the mode of cooperation between glucagon and corticosterone (the major glucocorticoid in mice). We found that compared to a single treatment with either hormone alone, a dual treatment initiates a pro-gluconeogenic gene program in hepatocytes by synergistically inducing gluconeogenic genes. We show that the mechanism driving synergistic gene expression is based on glucagon-mediated enhancer activation, leading to increased binding of the glucocorticoid receptor (GR) upon corticosterone stimulation. This was shown by glucagon-dependent increases in the active enhancer mark H3K27ac at GR binding sites (both signals were measured genome-wide via chromatin immunoprecipitation sequencing). Thus, glucagon-dependent enhancer activation, mediated by the glucagon-activated transcription factor CREB, assists GR loading to specific enhancers located near synergistic gluconeogenic genes. This 'assisted loading' mechanism is enhancer-specific as GR was able to efficiently bind other enhancers without assistance by CREB. Interestingly, we found that the glucagon-CREB axis does not only activate single enhancers but is also able to activate entire enhancer clusters. Indeed, CREB binding in one enhancer unit within a cluster, led to cluster-wide enhancer activation, thereby assisting the loading of GR in other enhancer units within the cluster that are unbound by CREB directly. These chromatin and gene expression changes collectively lead to synergistic glucose production from hepatocytes in the presence of glucagon and corticosterone. In summary, we show that hepatocytes integrate extracellular signals by an enhancer-specific mechanism: glucagon activates enhancers, thereby assisting the loading of GR upon stimulation by corticosterone. This chromatin-bound integration of endocrine signals leads to synergistic gene induction and a tailored response to fasting. Presentation: Saturday, June 11, 2022 12:00 p.m. - 12:15 p.m.

Identification of Robust and Key Differentially Expressed Genes during C2C12 Cell Myogenesis Based on Multiomics Data.

Myogenesis is a central step in prenatal myofiber formation, postnatal myofiber hypertrophy, and muscle damage repair in adulthood. RNA-Seq technology has greatly helped reveal the molecular mechanism of myogenesis, but batch effects in different experiments inevitably lead to misinterpretation of differentially expressed genes (DEGs). We previously applied the robust rank aggregation (RRA) method to effectively circumvent batch effects across multiple RNA-Seq datasets from 3T3-L1 cells. Here, we also used the RRA method to integrate nine RNA-Seq datasets from C2C12 cells and obtained 3140 robust DEGs between myoblasts and myotubes, which were then validated with array expression profiles and H3K27ac signals. The upregulated robust DEGs were highly enriched in gene ontology (GO) terms related to muscle cell differentiation and development. Considering that the cooperative binding of transcription factors (TFs) to enhancers to regulate downstream gene expression is a classical epigenetic mechanism, differentially expressed TFs (DETFs) were screened, and potential novel myogenic factors (MAF, BCL6, and ESR1) with high connection degree in protein–protein interaction (PPI) network were presented. Moreover, KLF5 cooperatively binds with the three key myogenic factors (MYOD, MYOG, and MEF2D) in C2C12 cells. Motif analysis speculates that the binding of MYOD and MYOG is KLF5-independent, while MEF2D is KLF5-dependent. It was revealed that KLF5-binding sites could be exploited to filter redundant MYOD-, MYOG-, and MEF2D-binding sites to focus on key enhancers for myogenesis. Further functional annotation of KLF5-binding sites suggested that KLF5 may regulate myogenesis through the PI3K-AKt signaling pathway, Rap1 signaling pathway, and the Hippo signaling pathway. In general, our study provides a wealth of untapped candidate targets for myogenesis and contributes new insights into the core regulatory mechanisms of myogenesis relying on KLF5-binding signal.

Hormone-controlled cooperative binding of transcription factors drives synergistic induction of fasting-regulated genes.

During fasting, hepatocytes produce glucose in response to hormonal signals. Glucagon and glucocorticoids are principal fasting hormones that cooperate in regulating glucose production via gluconeogenesis. However, how these hormone signals are integrated and interpreted to a biological output is unknown. Here, we use genome-wide profiling of gene expression, enhancer dynamics and transcription factor (TF) binding in primary mouse hepatocytes to uncover the mode of cooperation between glucagon and glucocorticoids. We found that compared to a single treatment with each hormone, a dual treatment directs hepatocytes to a pro-gluconeogenic gene program by synergistically inducing gluconeogenic genes. The cooperative mechanism driving synergistic gene expression is based on ‘assisted loading’ whereby a glucagon-activated TF (cAMP responsive element binding protein; CREB) leads to enhancer activation which facilitates binding of the glucocorticoid receptor (GR) upon glucocorticoid stimulation. Glucagon does not only activate single enhancers but also activates enhancer clusters, thereby assisting the loading of GR also across enhancer units within the cluster. In summary, we show that cells integrate extracellular signals by an enhancer-specific mechanism: one hormone-activated TF activates enhancers, thereby assisting the loading of a TF stimulated by a second hormone, leading to synergistic gene induction and a tailored transcriptional response to fasting.

A deep learning model to identify gene expression level using cobinding transcription factor signals.

Gene expression is directly controlled by transcription factors (TFs) in a complex combination manner. It remains a challenging task to systematically infer how the cooperative binding of TFs drives gene activity. Here, we quantitatively analyzed the correlation between TFs and surveyed the TF interaction networks associated with gene expression in GM12878 and K562 cell lines. We identified six TF modules associated with gene expression in each cell line. Furthermore, according to the enrichment characteristics of TFs in these TF modules around a target gene, a convolutional neural network model, called TFCNN, was constructed to identify gene expression level. Results showed that the TFCNN model achieved a good prediction performance for gene expression. The average of the area under receiver operating characteristics curve (AUC) can reach up to 0.975 and 0.976, respectively in GM12878 and K562 cell lines. By comparison, we found that the TFCNN model outperformed the prediction models based on SVM and LDA. This is due to the TFCNN model could better extract the combinatorial interaction among TFs. Further analysis indicated that the abundant binding of regulatory TFs dominates expression of target genes, while the cooperative interaction between TFs has a subtle regulatory effects. And gene expression could be regulated by different TF combinations in a nonlinear way. These results are helpful for deciphering the mechanism of TF combination regulating gene expression.

Cooperative binding between distant transcription factors is a hallmark of active enhancers

Enhancers harbor binding motifs that recruit transcription factors (TFs) for gene activation. While cooperative binding of TFs at enhancers is known to be critical for transcriptional activation of a handful of developmental enhancers, the extent of TF cooperativity genome-wide is unknown. Here, we couple high-resolution nuclease footprinting with single-molecule methylation profiling to characterize TF cooperativity at active enhancers in the Drosophila genome. Enrichment of short micrococcal nuclease (MNase)-protected DNA segments indicates that the majority of enhancers harbor two or more TF-binding sites, and we uncover protected fragments that correspond to co-bound sites in thousands of enhancers. From the analysis of co-binding, we find that cooperativity dominates TF binding invivo at the majority of active enhancers. Cooperativity is highest between sites spaced 50bp apart, indicating that cooperativity occurs without apparent protein-protein interactions. Our findings suggest nucleosomes promoting cooperativity because co-binding may effectively clear nucleosomes and promote enhancer function.

Base-resolution models of transcription-factor binding reveal soft motif syntax.

The arrangement of transcription factor (TF) binding motifs (syntax) is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution ChIP-nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using CRISPR-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data.

Nonreciprocal and Conditional Cooperativity Directs the Pioneer Activity of Pluripotency Transcription Factors

Cooperative binding of transcription factors (TFs) to chromatin orchestrates gene expression programming and cell fate specification. However, the biophysical principles of TF cooperativity remain incompletely understood. Here we use single-molecule fluorescence microscopy to study the partnership between Sox2 and Oct4, two core members of the pluripotency gene regulatory network. We find that the ability of Sox2 to target DNA inside nucleosomes is strongly affected by the translational and rotational positioning of its binding motif.

Calculation of Free Energy Consumption in Gene Transcription with Complex Promoter Structure

From the viewpoint of thermodynamics, gene transcription necessarily consumes free energy due to nonequilibrium processes. On the other hand, regulatory molecules present on the core promoter of a gene interact often in a dynamic, highly combinatorial, and possibly energy-dependent manner, leading to a complex promoter structure. This raises the question of how gene transcription with general promoter topology consumes free energy. We propose a biophysically intuitive approach to calculate energy consumption (quantified by the production rate of entropy) of a gene transcription process. Then, we show that the numbers of the ON and OFF states of a promoter can reduce energy consumption of the gene system and the Fano factor of mRNA, and in contrast to other regulatory ways, the cooperative binding of transcription factors to DNA sites always reduces energy consumption but amplifies the mRNA noise. While our proposed approach is general, our obtained qualitative results can in turn be used to the inference of complex promoter structure.

Cooperative Binding between Distant Transcription Factors is a Hallmark of Active Enhancers

Enhancers harbor binding motifs that recruit transcription factors (TFs) for gene activation. While cooperative binding of TFs at enhancers is known to be critical for transcriptional activation of a handful of developmental enhancers, the extent TF cooperativity genome-wide is unknown. Here, we couple high-resolution nuclease footprinting with single-molecule methylation profiling to characterize TF cooperativity at active enhancers in the Drosophila genome. Enrichment of short MNase-protected DNA segments indicates that the majority of enhancers harbor two or more TF binding sites, and we uncover protected fragments that correspond to co-bound sites in thousands of enhancers. We integrate MNase-seq, methylation accessibility profiling, and CUT&RUN chromatin profiling as a comprehensive strategy to characterize co-binding of the Trithorax-like (TRL) DNA binding protein and multiple other TFs. We identify states where an enhancer is bound by no TF, by either single factor, by multiple factors, or where binding sites are occluded by nucleosomes. From the analysis of co-binding, we find that cooperativity dominates TF binding in vivo at a majority of active enhancers. Factor cooperativity occurs predominantly between sites spaced 50 bp apart in active enhancers, indicating that most TF cooperativity in cells occurs without apparent protein-protein interactions. Our findings suggest a mechanism for nucleosomes to promote cooperativity by requiring co-binding to effectively clear nucleosomes and promote enhancer function.

Cooperative binding of transcription factors in the human genome

Transcription factors (TFs) cooperatively bind to specific DNA sequences to control chromatin and gene transcription in eukaryotes. Here, we searched canonical binding, co-binding and tethered binding regions of a TF within ChIP-seq peaks, and investigated the effect of TF-TF cooperation in human GM12878 and K562 cells.We found that TFs except for CTCF and SPI1 showed a large proportion of co-binding and tethered binding regions, and TFs frequently co-binding with other TFs would also frequently tether other TFs to their binding positions. We further observed lower in vivo nucleosome occupancy, higher in vitro nucleosome occupancy and higher levels of H2A.Z, H3K27ac, H3K9ac, H3K4me1, H3K4me2 and H3K4me3 within distal co-binding regions where other TFs were recruited. In addition, target genes for proximal co-binding regions where other TFs were recruited showed significantly higher expression levels. These results indicated that TF-TF cooperation directly associates with the chromatin structure and gene transcription.

Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions

Recent high-throughput transcription factor (TF) binding assays revealed that TF cooperativity is a widespread phenomenon. However, a global mechanistic and functional understanding of TF cooperativity is still lacking. To address this, here we introduce a statistical learning framework that provides structural insight into TF cooperativity and its functional consequences based on next generation sequencing data. We identify DNA shape as driver for cooperativity, with a particularly strong effect for Forkhead-Ets pairs. Follow-up experiments reveal a local shape preference at the Ets-DNA-Forkhead interface and decreased cooperativity upon loss of the interaction. Additionally, we discover many functional associations for cooperatively bound TFs. Examination of the link between FOXO1:ETV6 and lymphomas reveals that their joint expression levels improve patient clinical outcome stratification. Altogether, our results demonstrate that inter-family cooperative TF binding is driven by position-specific DNA readout mechanisms, which provides an additional regulatory layer for downstream biological functions.

BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization

BackgroundVariation in chromatin organization across single cells can help shed important light on the mechanisms controlling gene expression, but scale, noise, and sparsity pose significant challenges for interpretation of single cell chromatin data. Here, we develop BROCKMAN (Brockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides), an approach to infer variation in transcription factor (TF) activity across samples through unsupervised analysis of the variation in DNA sequences associated with an epigenomic mark.ResultsBROCKMAN represents each sample as a vector of epigenomic-mark-associated DNA word frequencies, and decomposes the resulting matrix to find hidden structure in the data, followed by unsupervised grouping of samples and identification of the TFs that distinguish groups. Applied to single cell ATAC-seq, BROCKMAN readily distinguished cell types, treatments, batch effects, experimental artifacts, and cycling cells. We show that each variable component in the k-mer landscape reflects a set of co-varying TFs, which are often known to physically interact. For example, in K562 cells, AP-1 TFs were central determinant of variability in chromatin accessibility through their variable expression levels and diverse interactions with other TFs. We provide a theoretical basis for why cooperative TF binding – and any associated epigenomic mark – is inherently more variable than non-cooperative binding.ConclusionsBROCKMAN and related approaches will help gain a mechanistic understanding of the trans determinants of chromatin variability between cells, treatments, and individuals.

A novel, intergenic enhancer contributes to Il2 gene expression via chromosomal looping.

Abstract IL-2 is a key immunoregulatory cytokine with pleotropic effects on many immune cell types. Its expression is highly regulated in T cells through multiple signaling pathways, chromatin remodeling, DNA methylation, and cooperative transcription factor binding. Recently, we identified an evolutionarily conserved intergenic enhancer 83 kb upstream of the Il2 gene that augments transcription from the Il2 promoter and upstream regulatory region (URR) by >50-fold in recombinant reporter assays. Upon TCR/CD28 co-stimulation of primary CD4+ T cells, a long-range chromosomal loop is established between the −83 kb CNS and the endogenous Il2 promoter, and both regions become epigenetically marked by histone acetylation and methylation. To determine the relative contribution of the URR vs. this new distal element to inducible transcription of the Il2 gene, we deleted the URR or the −83 kb CNS in mice using CRISPR/CAS genome editing. Deletion of the URR (~500 bp beyond the TATA element) completely abolished the Il2 transcription by CD4+ T cells, while a ~500 bp deletion of the −83 kb CNS resulted in a 2- to 4-fold decrease in IL-2 production. Young mice deficient for the −83 kb enhancer exhibited no major defects in lymphoid development, however, mutant CD4+ T cells showed reduced proliferative capacity in vitro. Furthermore, TGFB-induced iTreg differentiation from mutant conventional CD4+ precursors was strongly impaired, but could be fully restored by addition of exogenous IL-2. These results show that induction of Il2 depends upon cooperation between the promoter-URR and at least one intergenic enhancer, and suggest mechanisms by which graded expression of Il2 might be achieved through ‘modular’ recruitment of distinct enhancer elements.

Molecular mechanisms of cooperative binding of transcription factors Runx1-CBFβ-Ets1 on the TCRα gene enhancer.

Ets1 is an essential transcription factor (TF) for several important physiological processes, including cell proliferation and differentiation. Its recognition of the enhancer region of the TCRα gene is enhanced by the cooperative binding of the Runx1–CBFβ heterodimer, with the cancelation of phosphorylation-dependent autoinhibition. The detailed mechanism of this interesting cooperativity between Ets1 and the Runx1–CBFβ heterodimer is still largely unclear. Here, we investigated the molecular mechanisms of this cooperativity, by using molecular dynamics simulations. Consequently, we detected high flexibility of the loop region between the HI2 and H1 helices of Ets1. Upon Runx1–CBFβ heterodimer binding, this loop transiently adopts various sub-stable conformations in its interactions with the DNA. In addition, a network analysis suggested an allosteric pathway in the molecular assembly and identified some key residues that coincide with previous experimental studies. Our simulations suggest that the cooperative binding of Ets1 and the Runx1–CBFβ heterodimer alters the DNA conformation and induces sub-stable conformations of the HI2–H1 loop of Ets1. This phenomenon increases the flexibility of the regulatory module, including the HI2 helix, and destabilizes the inhibitory form of this module. Thus, we hypothesize that this effect facilitates Ets1–DNA binding and prevents the phosphorylation-dependent DNA binding autoinhibition.

Rfx2 Stabilizes Foxj1 Binding at Chromatin Loops to Enable Multiciliated Cell Gene Expression.

Cooperative transcription factor binding at cis-regulatory sites in the genome drives robust eukaryotic gene expression, and many such sites must be coordinated to produce coherent transcriptional programs. The transcriptional program leading to motile cilia formation requires members of the DNA-binding forkhead (Fox) and Rfx transcription factor families and these factors co-localize to cilia gene promoters, but it is not clear how many cilia genes are regulated by these two factors, whether these factors act directly or indirectly, or how these factors act with specificity in the context of a 3-dimensional genome. Here, we use genome-wide approaches to show that cilia genes reside at the boundaries of topological domains and that these areas have low enhancer density. We show that the transcription factors Foxj1 and Rfx2 binding occurs in the promoters of more cilia genes than other known cilia transcription factors and that while Rfx2 binds directly to promoters and enhancers equally, Foxj1 prefers direct binding to enhancers and is stabilized at promoters by Rfx2. Finally, we show that Rfx2 and Foxj1 lie at the anchor endpoints of chromatin loops, suggesting that target genes are activated when Foxj1 bound at distal sites is recruited via a loop created by Rfx2 binding at both sites. We speculate that the primary function of Rfx2 is to stabilize distal enhancers with proximal promoters by operating as a scaffolding factor, bringing key regulatory domains bound by Foxj1 into close physical proximity and enabling coordinated cilia gene expression.

Cooperative Binding of Transcription Factors Orchestrates Reprogramming

Oct4, Sox2, Klf4, and cMyc (OSKM) reprogram somatic cells to pluripotency. To gain a mechanistic understanding of their function, we mapped OSKM-binding, stage-specific transcription factors (TFs), and chromatin states in discrete reprogramming stages and performed loss- and gain-of-function experiments. We found that OSK predominantly bind active somatic enhancers early in reprogramming and immediately initiate their inactivation genome-wide by inducing the redistribution of somatic TFs away from somatic enhancers to sites elsewhere engaged by OSK, recruiting Hdac1, and repressing the somatic TF Fra1. Pluripotency enhancer selection is a stepwise process that also begins early in reprogramming through collaborative binding of OSK at sites with high OSK-motif density. Most pluripotency enhancers are selected later in the process and require OS and other pluripotency TFs. Somatic and pluripotency TFs modulate reprogramming efficiency when overexpressed by altering OSK targeting, somatic-enhancer inactivation, and pluripotency enhancer selection. Together, our data indicate that collaborative interactions among OSK andwith stage-specific TFs direct both somatic-enhancer inactivation and pluripotency-enhancer selection to drive reprogramming.

Quantitative profiling of selective Sox/POU pairing on hundreds of sequences in parallel by Coop-seq.

Cooperative binding of transcription factors is known to be important in the regulation of gene expression programs conferring cellular identities. However, current methods to measure cooperativity parameters have been laborious and therefore limited to studying only a few sequence variants at a time. We developed Coop-seq (cooperativity by sequencing) that is capable of efficiently and accurately determining the cooperativity parameters for hundreds of different DNA sequences in a single experiment. We apply Coop-seq to 12 dimer pairs from the Sox and POU families of transcription factors using 324 unique sequences with changed half-site orientation, altered spacing and discrete randomization within the binding elements. The study reveals specific dimerization profiles of different Sox factors with Oct4. By contrast, Oct4 and the three neural class III POU factors Brn2, Brn4 and Oct6 assemble with Sox2 in a surprisingly indistinguishable manner. Two novel half-site configurations can support functional Sox/Oct dimerization in addition to known composite motifs. Moreover, Coop-seq uncovers a nucleotide switch within the POU half-site when spacing is altered, which is mirrored in genomic loci bound by Sox2/Oct4 complexes.