Universal protein-binding microarrays for the comprehensive characterization of the DNA-binding specificities of transcription factors

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Most human transcription factor (TF) genes encode multiple protein isoforms differing in DNA-binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators," both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

Recent advances in designing synthetic plant regulatory modules

Introducing novel functions in plants through synthetic multigene circuits requires strict transcriptional regulation. Currently, the use of natural regulatory modules in synthetic circuits is hindered by our limited knowledge of complex plant regulatory mechanisms, the paucity of characterized promoters, and the possibility of crosstalk with endogenous circuits. Synthetic regulatory modules can overcome these limitations. This article introduces an integrative de novo approach for designing plant synthetic promoters by utilizing the available online tools and databases. The recent achievements in designing and validating synthetic plant promoters, enhancers, transcription factors, and the challenges of establishing synthetic circuits in plants are also discussed.

Abundant clock proteins point to missing molecular regulation in the plant circadian clock

Understanding the biochemistry behind whole-organism traits such as flowering time is a longstanding challenge, where mathematical models are critical. Very few models of plant gene circuits use the absolute units required for comparison to biochemical data. We refactor two detailed models of the plant circadian clock from relative to absolute units. Using absolute RNA quantification, a simple model predicted abundant clock protein levels in Arabidopsis thaliana, up to 100,000 proteins per cell. NanoLUC reporter protein fusions validated the predicted levels of clock proteins in vivo. Recalibrating the detailed models to these protein levels estimated their DNA-binding dissociation constants (Kd). We estimate the same Kd from multiple results in vitro, extending the method to any promoter sequence. The detailed models simulated the Kd range estimated from LUX DNA-binding in vitro but departed from the data for CCA1 binding, pointing to further circadian mechanisms. Our analytical and experimental methods should transfer to understand other plant gene regulatory networks, potentially including the natural sequence variation that contributes to evolutionary adaptation.

Gene Identification for Ocular Congenital Cranial Motor Neuron Disorders Using Human Sequencing, Zebrafish Screening, and Protein Binding Microarrays

Purpose

To functionally evaluate novel human sequence-derived candidate genes and variants for unsolved ocular congenital cranial dysinnervation disorders (oCCDDs).

Methods

Through exome and genome sequencing of a genetically unsolved human oCCDD cohort, we previously reported the identification of variants in many candidate genes. Here, we describe a parallel study that prioritized a subset of these genes (43 human genes, 57 zebrafish genes) using a G0 CRISPR/Cas9-based knockout assay in zebrafish and generated F2 germline mutants for 17. We tested the functionality of variants of uncertain significance in known and novel candidate transcription factor-encoding genes through protein binding microarrays.

Results

We first demonstrated the feasibility of the G0 screen by targeting known oCCDD genes phox2a and mafba. Approximately 70% to 90% of gene-targeted G0 zebrafish embryos recapitulated germline homozygous null-equivalent phenotypes. Using this approach, we then identified three novel candidate oCCDD genes (SEMA3F, OLIG2, and FRMD4B) with putative contributions to human and zebrafish cranial motor development. In addition, protein binding microarrays demonstrated reduced or abolished DNA binding of human variants of uncertain significance in known and novel sequence-derived transcription factors PHOX2A (p.(Trp137Cys)), MAFB (p.(Glu223Lys)), and OLIG2 (p.(Arg156Leu)).

Conclusions

This study nominates three strong novel candidate oCCDD genes (SEMA3F, OLIG2, and FRMD4B) and supports the functionality and putative pathogenicity of transcription factor candidate variants PHOX2A p.(Trp137Cys), MAFB p.(Glu223Lys), and OLIG2 p.(Arg156Leu). Our findings support that G0 loss-of-function screening in zebrafish can be coupled with human sequence analysis and protein binding microarrays to aid in prioritizing oCCDD candidate genes/variants.

Experimental approaches to investigate biophysical interactions between homeodomain transcription factors and DNA

Homeodomain transcription factors (TFs) bind to specific DNA sequences to regulate the expression of target genes. Structural work has provided insight into molecular identities and aided in unraveling structural features of these TFs. However, the detailed affinity and specificity by which these TFs bind to DNA sequences is still largely unknown. Qualitative methods, such as DNA footprinting, Electrophoretic Mobility Shift Assays (EMSAs), Systematic Evolution of Ligands by Exponential Enrichment (SELEX), Bacterial One Hybrid (B1H) systems, Surface Plasmon Resonance (SPR), and Protein Binding Microarrays (PBMs) have been widely used to investigate the biochemical characteristics of TF-DNA binding events. In addition to these qualitative methods, bioinformatic approaches have also assisted in TF binding site discovery. Here we discuss the advantages and limitations of these different approaches, as well as the benefits of utilizing more quantitative approaches, such as Mechanically Induced Trapping of Molecular Interactions (MITOMI), Microscale Thermophoresis (MST) and Isothermal Titration Calorimetry (ITC), in determining the biophysical basis of binding specificity of TF-DNA complexes and improving upon existing computational approaches aimed at affinity predictions.

Protein target search diffusion-association/dissociation free energy landscape around DNA binding site with flanking sequences

In this work we present a minimal structure-based model of protein diffusional search along local DNA amid protein binding and unbinding events on the DNA, taking into account protein-DNA electrostatic interactions and hydrogen-bonding (HB) interactions or contacts at the interface. We accordingly constructed the protein diffusion-association/dissociation free energy surface and mapped it to 1D as the protein slides along DNA, maintaining the protein-DNA interfacial HB contacts that presumably dictate the DNA sequence information detection. Upon DNA helical path correction, the protein 1D diffusion rates along local DNA can be physically derived to be consistent with experimental measurements. We also show that the sequence-dependent protein sliding or stepping patterns along DNA are regulated by collective interfacial HB dynamics, which also determines the ruggedness of the protein diffusion free energy landscape on the local DNA. In comparison, protein association or binding with DNA are generically dictated by the protein-DNA electrostatic interactions, with an interaction zone of nanometers around DNA. Extra degrees of freedom (DOFs) of the protein such as rotations and conformational fluctuations can be well accommodated within the protein-DNA electrostatic interaction zone. As such we demonstrate that the protein binding or association free energy profiling along DNA smoothens over the 1D diffusion free energy landscape, which leads to population variations for an order of magnitude upon a marginal free energetic smoothening around the specific or consensus sites. We further show that the protein unbinding or dissociation from a comparatively high-binding affinity DNA site is dominated by lateral diffusion to the flanking low-affinity sites. The results predict that experimental characterizations on the relative protein-DNA binding affinities or population profiling on the DNA are systematically and physically impacted by the extra DOFs of protein motions aside from 1D translation or helical tracking, as well as from flanking DNA sequences due to protein 1D diffusion and nonspecific binding/unbinding.

A systematic survey of TF function in E. coli suggests RNAP stabilization is a prevalent strategy for both repressors and activators

Transcription factors (TFs) are often classified as activators or repressors, yet these context-dependent labels are inadequate to predict quantitative profiles that emerge across different promoters. A mechanistic understanding of how different regulatory sequences shape TF function is challenging due to the lack of systematic genetic control in endogenous genes. To address this, we use a library of Escherichia coli strains with precise control of TF copy number, measuring the quantitative regulatory input-output function of 90 TFs on synthetic promoters that isolate the contributions of TF binding sequence, location, and basal promoter strength to gene expression. We interpret the measured regulation of these TFs using a thermodynamic model of gene expression and uncover stabilization of RNA polymerase as a pervasive regulatory mechanism, common to both activating and repressing TFs. This property suggests ways to tune the dynamic range of gene expression through the interplay of stabilizing TF function and RNA polymerase basal occupancy, a phenomenon we confirm by measuring fold change for stabilizing TFs across synthetic promoter sequences spanning over 100-fold basal expression. Our work deconstructs TF function at a mechanistic level, providing foundational principles on how gene expression is realized across different promoter contexts, with implications for decoding the relationship between sequence and gene expression.

Further Development of SAMPDI-3D: A Machine Learning Method for Predicting Binding Free Energy Changes Caused by Mutations in Either Protein or DNA

BACKGROUND/OBJECTIVES

Predicting the effects of protein and DNA mutations on the binding free energy of protein-DNA complexes is crucial for understanding how DNA variants impact wild-type cellular function. As many cellular interactions involve protein-DNA binding, accurately predicting changes in binding free energy (ΔΔG) is valuable for distinguishing pathogenic mutations from benign ones.

METHODS

This study describes the development and optimization of the SAMPDI-3Dv2 machine learning method, which is trained on an expanded database of experimentally measured ΔΔGs. This enhanced model incorporates new features, including the 3D structure of the mutant protein, features of the mutant structure, and a position-specific scoring matrix (PSSM). Benchmarking was conducted using 5-fold cross-validation.

RESULTS

The updated SAMPDI-3D model (SAMPDI-3Dv2) achieved Pearson correlation coefficients (PCCs) of 0.68 for protein and 0.80 for DNA mutations. These results represent significant improvements over existing tools. Additionally, the method's rapid execution time enables genome-scale predictions.

CONCLUSIONS

The improved SAMPDI-3Dv2 shows enhanced predictive performance for analyzing mutations in protein-DNA complexes. By leveraging structural information and an expanded training dataset, SAMPDI-3Dv2 provides researchers with a more accurate and efficient tool for mutation analysis, contributing to identifying pathogenic variants and improving our understanding of cellular function.

Divergence in a Eukaryotic Transcription Factor's co-TF Dependence Involves Multiple Intrinsically Disordered Regions Affecting Activation and Autoinhibition

Combinatorial control by multiple transcription factors (TFs) is a hallmark of eukaryotic gene regulation. Despite its prevalence and crucial roles in enhancing specificity and integrating information, the mechanisms behind why eukaryotic TFs depend on one another, and whether such interdependence evolves, are not well understood. We exploit natural variation in co-TF dependence in the yeast phosphate starvation (PHO) response to address this question. In the model yeast Saccharomyces cerevisiae, the main TF, Pho4, relies on the co-TF Pho2 to regulate ~28 genes. In a related yeast pathogen, Candida glabrata, its Pho4 exhibits significantly reduced Pho2 dependence and has an expanded target set of ~70 genes. Biochemical analyses showed C. glabrata Pho4 (CgPho4) binds to the same consensus motif with 3-4-fold higher affinity than ScPho4 does. A machine-learning-based prediction and yeast one-hybrid assay identified two Intrinsically Disordered Regions (IDRs) in CgPho4 that boost the activity of the main activation domain but showed little to no activity on their own. We also found evidence for autoinhibition behind the co-TF dependence in ScPho4. An IDR in ScPho4 next to its DNA binding domain was found to act as a double-edged sword: it both allows for enhanced activity with Pho2, and inhibits Pho4's activity without Pho2. This study provides a detailed molecular picture of how co-TF dependence is mediated and how its evolution, mainly driven by IDR divergence, can lead to significant rewiring of the regulatory network.

Evolution of the basic helix-loop-helix transcription factor SPATULA and its role in gynoecium development

BACKGROUND AND AIMS

SPATULA (SPT) encodes a basic helix-loop-helix transcription factor in Arabidopsis thaliana that functions in the development of the style, stigma and replum tissues, all of which arise from the carpel margin meristem of the gynoecium. Here we use a comparative approach to investigate the evolutionary history of SPT and identify changes that potentially contributed to its role in gynoecium development.

METHODS

We investigate SPT's molecular and functional evolution using phylogenetic reconstruction, yeast two-hybrid analyses of protein-protein interactions, microarray-based analyses of protein-DNA interactions, plant transformation assays, RNA in situ hybridization, and in silico analyses of promoter sequences.

KEY RESULTS

We demonstrate the SPT lineage to have arisen at the base of euphyllophytes from a clade of potentially light-regulated transcription factors through gene duplication followed by the loss of an active phytochrome binding (APB) domain. We also clarify the more recent evolutionary history of SPT and its paralogue ALCATRAZ (ALC), which appear to have arisen through a large-scale duplication within Brassicales. We find that SPT orthologues from diverse groups of seed plants share strikingly similar capacities for protein-protein and protein-DNA interactions, and that SPT coding regions from a wide taxonomic range of plants are able to complement loss-of-function spt mutations in transgenic Arabidopsis. However, the expression pattern of SPT appears to have evolved significantly within angiosperms, and we identify structural changes in SPT's promoter region that correlate with the acquisition of high expression levels in tissues arising from the carpel margin meristem in Brassicaceae.

CONCLUSIONS

We conclude that changes in SPT's expression pattern made a major contribution to the evolution of its developmental role in the gynoecium of Brassicaceae. By contrast, the main biochemical capacities of SPT, as well as many of its immediate transcriptional targets, appear to have been conserved at least since the base of living angiosperms.

Multiomic profiling of chronically activated CD4+ T cells identifies drivers of exhaustion and metabolic reprogramming

Repeated antigen exposure leads to T-cell exhaustion, a transcriptionally and epigenetically distinct cellular state marked by loss of effector functions (e.g., cytotoxicity, cytokine production/release), up-regulation of inhibitory receptors (e.g., PD-1), and reduced proliferative capacity. Molecular pathways underlying T-cell exhaustion have been defined for CD8+ cytotoxic T cells, but which factors drive exhaustion in CD4+ T cells, that are also required for an effective immune response against a tumor or infection, remains unclear. Here, we utilize quantitative proteomic, phosphoproteomic, and metabolomic analyses to characterize the molecular basis of the dysfunctional cell state induced by chronic stimulation of CD4+ memory T cells. We identified a dynamic response encompassing both known and novel up-regulated cell surface receptors, as well as dozens of unexpected transcriptional regulators. Integrated causal network analysis of our combined data predicts the histone acetyltransferase p300 as a driver of aspects of this phenotype following chronic stimulation, which we confirmed via targeted small molecule inhibition. While our integrative analysis also revealed large-scale metabolic reprogramming, our independent investigation confirmed a global remodeling away from glycolysis to a dysfunctional fatty acid oxidation-based metabolism coincident with oxidative stress. Overall, these data provide both insights into the mechanistic basis of CD4+ T-cell exhaustion and serve as a valuable resource for future interventional studies aimed at modulating T-cell dysfunction.

Cross-platform DNA motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors

A DNA sequence pattern, or "motif", is an essential representation of DNA-binding specificity of a transcription factor (TF). Any particular motif model has potential flaws due to shortcomings of the underlying experimental data and computational motif discovery algorithm. As a part of the Codebook/GRECO-BIT initiative, here we evaluated at large scale the cross-platform recognition performance of positional weight matrices (PWMs), which remain popular motif models in many practical applications. We applied ten different DNA motif discovery tools to generate PWMs from the "Codebook" data comprised of 4,237 experiments from five different platforms profiling the DNA-binding specificity of 394 human proteins, focusing on understudied transcription factors of different structural families. For many of the proteins, there was no prior knowledge of a genuine motif. By benchmarking-supported human curation, we constructed an approved subset of experiments comprising about 30% of all experiments and 50% of tested TFs which displayed consistent motifs across platforms and replicates. We present the Codebook Motif Explorer (https://mex.autosome.org), a detailed online catalog of DNA motifs, including the top-ranked PWMs, and the underlying source and benchmarking data. We demonstrate that in the case of high-quality experimental data, most of the popular motif discovery tools detect valid motifs and generate PWMs, which perform well both on genomic and synthetic data. Yet, for each of the algorithms, there were problematic combinations of proteins and platforms, and the basic motif properties such as nucleotide composition and information content offered little help in detecting such pitfalls. By combining multiple PMWs in decision trees, we demonstrate how our setup can be readily adapted to train and test binding specificity models more complex than PWMs. Overall, our study provides a rich motif catalog as a solid baseline for advanced models and highlights the power of the multi-platform multi-tool approach for reliable mapping of DNA binding specificities.

TFAM as a sensor of UVC-induced mitochondrial DNA damage

Mitochondria lack nucleotide excision DNA repair; however, mitochondrial DNA (mtDNA) is resistant to mutation accumulation following DNA damage. These observations suggest additional damage sensing or protection mechanisms. Transcription Factor A, Mitochondrial (TFAM) compacts mtDNA into nucleoids. As such, TFAM has emerged as a candidate for protecting DNA or sensing damage. To examine these possibilities, we used live-cell imaging, cell-based assays, atomic force microscopy, and high-throughput protein-DNA binding assays to characterize the binding properties of TFAM to UVC-irradiated DNA and cellular consequences of UVC irradiation. Our data indicate an increase in mtDNA degradation and turnover, without a loss in mitochondrial membrane potential that might trigger mitophagy. We identified a reduction in sequence specificity of TFAM associated with UVC irradiation and a redistribution of TFAM binding throughout the mitochondrial genome. Our AFM data show increased compaction of DNA by TFAM in the presence of damage. Despite the TFAM-mediated compaction of mtDNA, we do not observe any protective effect on DNA damage accumulation in cells or in vitro. Taken together, these studies indicate that UVC-induced DNA damage promotes compaction by TFAM, suggesting that TFAM may act as a damage sensor, sequestering damaged genomes to prevent mutagenesis by direct removal or suppression of replication.

Genome-wide mapping of the binding sites of myocyte enhancer factor 2A in chicken primary myoblasts

Myocyte enhancer factor 2A (MEF2A) is a transcription factor that plays a critical role in cell proliferation, differentiation and apoptosis. In contrast to the wide characterization of its regulation mechanism in mammalian skeletal muscle, its role in chickens is limited. Especially, its wide target genes remain to be identified. Therefore, we utilized Cleavage Under Targets and Tagmentation (CUT&Tag) technology to reveal the genome-wide binding profile of MEF2A in chicken primary myoblasts thus gaining insights into its potential role in muscle development. Our results revealed that MEF2A binding sites were primarily distributed in intergenic and intronic regions. Within the promoter region, although only 8.87% of MEF2A binding sites were found, these binding sites were concentrated around the transcription start site (TSS). Following peak annotation, a total of 1903 genes were identified as potential targets of MEF2A. Gene Ontology (GO) enrichment analysis further revealed that MEF2A target genes may be involved in the regulation of embryonic development in multiple organ systems, including muscle development, gland development, and visual system development. Moreover, a comparison of the MEF2A target genes identified in chicken primary myoblasts with those in mouse C2C12 cells revealed 388 target genes are conserved across species, 1515 target genes are chicken specific. Among these conserved genes, ankyrin repeat and SOCS box containing 5 (ASB5), transmembrane protein 182 (TMEM182), myomesin 2 (MYOM2), leucyl and cystinyl aminopeptidase (LNPEP), actinin alpha 2 (ACTN2), sorbin and SH3 domain containing 1 (SORBS1), ankyrin 3 (ANK3), sarcoglycan delta (SGCD), and ORAI calcium release-activated calcium modulator 1 (ORAI1) exhibited consistent expression patterns with MEF2A during embryonic muscle development. Finally, TMEM182, as an important negative regulator of muscle development, has been validated to be regulated by MEF2A by dual-luciferase and quantitative real-time PCR (qPCR) assays. In summary, our study for the first time provides a wide landscape of MEF2A target genes in chicken primary myoblasts, which supports the active role of MEF2A in chicken muscle development.

DNA sequence and chromatin differentiate sequence-specific transcription factor binding in the human malaria parasite Plasmodium falciparum

Development of the malaria parasite, Plasmodium falciparum, is regulated by a limited number of sequence-specific transcription factors (TFs). However, the mechanisms by which these TFs recognize genome-wide binding sites is largely unknown. To address TF specificity, we investigated the binding of two TF subsets that either bind CACACA or GTGCAC DNA sequence motifs and further characterized two additional ApiAP2 TFs, PfAP2-G and PfAP2-EXP, which bind unique DNA motifs (GTAC and TGCATGCA). We also interrogated the impact of DNA sequence and chromatin context on P. falciparum TF binding by integrating high-throughput in vitro and in vivo binding assays, DNA shape predictions, epigenetic post-translational modifications, and chromatin accessibility. We found that DNA sequence context minimally impacts binding site selection for paralogous CACACA-binding TFs, while chromatin accessibility, epigenetic patterns, co-factor recruitment, and dimerization correlate with differential binding. In contrast, GTGCAC-binding TFs prefer different DNA sequence context in addition to chromatin dynamics. Finally, we determined that TFs that preferentially bind divergent DNA motifs may bind overlapping genomic regions due to low-affinity binding to other sequence motifs. Our results demonstrate that TF binding site selection relies on a combination of DNA sequence and chromatin features, thereby contributing to the complexity of P. falciparum gene regulatory mechanisms.

Rapid profiling of transcription factor-cofactor interaction networks reveals principles of epigenetic regulation

Transcription factor (TF)-cofactor (COF) interactions define dynamic, cell-specific networks that govern gene expression; however, these networks are understudied due to a lack of methods for high-throughput profiling of DNA-bound TF-COF complexes. Here, we describe the Cofactor Recruitment (CoRec) method for rapid profiling of cell-specific TF-COF complexes. We define a lysine acetyltransferase (KAT)-TF network in resting and stimulated T cells. We find promiscuous recruitment of KATs for many TFs and that 35% of KAT-TF interactions are condition specific. KAT-TF interactions identify NF-κB as a primary regulator of acutely induced histone 3 lysine 27 acetylation (H3K27ac). Finally, we find that heterotypic clustering of CBP/P300-recruiting TFs is a strong predictor of total promoter H3K27ac. Our data support clustering of TF sites that broadly recruit KATs as a mechanism for widespread co-occurring histone acetylation marks. CoRec can be readily applied to different cell systems and provides a powerful approach to define TF-COF networks impacting chromatin state and gene regulation.

Gene identification for ocular congenital cranial motor neuron disorders using human sequencing, zebrafish screening, and protein binding microarrays

Purpose

To functionally evaluate novel human sequence-derived candidate genes and variants for unsolved ocular congenital cranial dysinnervation disorders (oCCDDs).

Methods

Through exome and genome sequencing of a genetically unsolved human oCCDD cohort, we previously identified variants in 80 strong candidate genes. Here, we further prioritized a subset of these (43 human genes, 57 zebrafish genes) using a G0 CRISPR/Cas9-based knockout assay in zebrafish and generated F2 germline mutants for seventeen. We tested the functionality of variants of uncertain significance in known and novel candidate transcription factor-encoding genes through protein binding microarrays.

Results

We first demonstrated the feasibility of the G0 screen by targeting known oCCDD genes phox2a and mafba. 70-90% of gene-targeted G0 zebrafish embryos recapitulated germline homozygous null-equivalent phenotypes. Using this approach, we then identified three novel candidate oCCDD genes (SEMA3F, OLIG2, and FRMD4B) with putative contributions to human and zebrafish cranial motor development. In addition, protein binding microarrays demonstrated reduced or abolished DNA binding of human variants of uncertain significance in known and novel sequence-derived transcription factors PHOX2A (p.(Trp137Cys)), MAFB (p.(Glu223Lys)), and OLIG2 (p.(Arg156Leu)).

Conclusions

This study nominates three strong novel candidate oCCDD genes (SEMA3F, OLIG2, and FRMD4B) and supports the functionality and putative pathogenicity of transcription factor candidate variants PHOX2A p.(Trp137Cys), MAFB p.(Glu223Lys), and OLIG2 p.(Arg156Leu). Our findings support that G0 loss-of-function screening in zebrafish can be coupled with human sequence analysis and protein binding microarrays to aid in prioritizing oCCDD candidate genes/variants.

Geometric deep learning of protein-DNA binding specificity

Predicting protein-DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protein-DNA complexes usually exhibit binding to a selected DNA target site, whereas a protein binds, with varying degrees of binding specificity, to a wide range of DNA sequences. This information is not directly accessible in a single structure. Here, to access this information, we present Deep Predictor of Binding Specificity (DeepPBS), a geometric deep-learning model designed to predict binding specificity from protein-DNA structure. DeepPBS can be applied to experimental or predicted structures. Interpretable protein heavy atom importance scores for interface residues can be extracted. When aggregated at the protein residue level, these scores are validated through mutagenesis experiments. Applied to designed proteins targeting specific DNA sequences, DeepPBS was demonstrated to predict experimentally measured binding specificity. DeepPBS offers a foundation for machine-aided studies that advance our understanding of molecular interactions and guide experimental designs and synthetic biology.

Transcription factor binding specificities of the oomycete Phytophthora infestans reflect conserved and divergent evolutionary patterns and predict function

BACKGROUND

Identifying the DNA-binding specificities of transcription factors (TF) is central to understanding gene networks that regulate growth and development. Such knowledge is lacking in oomycetes, a microbial eukaryotic lineage within the stramenopile group. Oomycetes include many important plant and animal pathogens such as the potato and tomato blight agent Phytophthora infestans, which is a tractable model for studying life-stage differentiation within the group.

RESULTS

Mining of the P. infestans genome identified 197 genes encoding proteins belonging to 22 TF families. Their chromosomal distribution was consistent with family expansions through unequal crossing-over, which were likely ancient since each family had similar sizes in most oomycetes. Most TFs exhibited dynamic changes in RNA levels through the P. infestans life cycle. The DNA-binding preferences of 123 proteins were assayed using protein-binding oligonucleotide microarrays, which succeeded with 73 proteins from 14 families. Binding sites predicted for representatives of the families were validated by electrophoretic mobility shift or chromatin immunoprecipitation assays. Consistent with the substantial evolutionary distance of oomycetes from traditional model organisms, only a subset of the DNA-binding preferences resembled those of human or plant orthologs. Phylogenetic analyses of the TF families within P. infestans often discriminated clades with canonical and novel DNA targets. Paralogs with similar binding preferences frequently had distinct patterns of expression suggestive of functional divergence. TFs were predicted to either drive life stage-specific expression or serve as general activators based on the representation of their binding sites within total or developmentally-regulated promoters. This projection was confirmed for one TF using synthetic and mutated promoters fused to reporter genes in vivo.

CONCLUSIONS

We established a large dataset of binding specificities for P. infestans TFs, representing the first in the stramenopile group. This resource provides a basis for understanding transcriptional regulation by linking TFs with their targets, which should help delineate the molecular components of processes such as sporulation and host infection. Our work also yielded insight into TF evolution during the eukaryotic radiation, revealing both functional conservation as well as diversification across kingdoms.

Site-Specific Acetylation of the Transcription Factor Protein Max Modulates Its DNA Binding Activity

Chemical protein synthesis provides a powerful means to prepare novel modified proteins with precision down to the atomic level, enabling an unprecedented opportunity to understand fundamental biological processes. Of particular interest is the process of gene expression, orchestrated through the interactions between transcription factors (TFs) and DNA. Here, we combined chemical protein synthesis and high-throughput screening technology to decipher the role of post-translational modifications (PTMs), e.g., Lys-acetylation on the DNA binding activity of Max TF. We synthesized a focused library of singly, doubly, and triply modified Max variants including site-specifically acetylated and fluorescently tagged analogs. The resulting synthetic analogs were employed to decipher the molecular role of Lys-acetylation on the DNA binding activity and sequence specificity of Max. We provide evidence that the acetylation sites at Lys-31 and Lys-57 significantly inhibit the DNA binding activity of Max. Furthermore, by utilizing high-throughput binding measurements, we assessed the binding activities of the modified Max variants across diverse DNA sequences. Our results indicate that acetylation marks can alter the binding specificities of Max toward certain sequences flanking its consensus binding sites. Our work provides insight into the hidden molecular code of PTM-TFs and DNA interactions, paving the way to interpret gene expression regulation programs.

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Most human Transcription factors (TFs) genes encode multiple protein isoforms differing in DNA binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators", both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues

Homeodomains (HDs) are the second largest class of DNA binding domains (DBDs) among eukaryotic sequence-specific transcription factors (TFs) and are the TF structural class with the largest number of disease-associated mutations in the Human Gene Mutation Database (HGMD). Despite numerous structural studies and large-scale analyses of HD DNA binding specificity, HD-DNA recognition is still not fully understood. Here, we analyze 92 human HD mutants, including disease-associated variants and variants of uncertain significance (VUS), for their effects on DNA binding activity. Many of the variants alter DNA binding affinity and/or specificity. Detailed biochemical analysis and structural modeling identifies 14 previously unknown specificity-determining positions, 5 of which do not contact DNA. The same missense substitution at analogous positions within different HDs often exhibits different effects on DNA binding activity. Variant effect prediction tools perform moderately well in distinguishing variants with altered DNA binding affinity, but poorly in identifying those with altered binding specificity. Our results highlight the need for biochemical assays of TF coding variants and prioritize dozens of variants for further investigations into their pathogenicity and the development of clinical diagnostics and precision therapies.

Rapid profiling of transcription factor-cofactor interaction networks reveals principles of epigenetic regulation

Transcription factor (TF)-cofactor (COF) interactions define dynamic, cell-specific networks that govern gene expression; however, these networks are understudied due to a lack of methods for high-throughput profiling of DNA-bound TF-COF complexes. Here we describe the Cofactor Recruitment (CoRec) method for rapid profiling of cell-specific TF-COF complexes. We define a lysine acetyltransferase (KAT)-TF network in resting and stimulated T cells. We find promiscuous recruitment of KATs for many TFs and that 35% of KAT-TF interactions are condition specific. KAT-TF interactions identify NF-κB as a primary regulator of acutely induced H3K27ac. Finally, we find that heterotypic clustering of CBP/P300-recruiting TFs is a strong predictor of total promoter H3K27ac. Our data supports clustering of TF sites that broadly recruit KATs as a mechanism for widespread co-occurring histone acetylation marks. CoRec can be readily applied to different cell systems and provides a powerful approach to define TF-COF networks impacting chromatin state and gene regulation.

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.

Less-is-more: selecting transcription factor binding regions informative for motif inference

Numerous statistical methods have emerged for inferring DNA motifs for transcription factors (TFs) from genomic regions. However, the process of selecting informative regions for motif inference remains understudied. Current approaches select regions with strong ChIP-seq signal for a given TF, assuming that such strong signal primarily results from specific interactions between the TF and its motif. Additionally, these selection approaches do not account for non-target motifs, i.e. motifs of other TFs; they presume the occurrence of these non-target motifs infrequent compared to that of the target motif, and thus assume these have minimal interference with the identification of the target. Leveraging extensive ChIP-seq datasets, we introduced the concept of TF signal 'crowdedness', referred to as C-score, for each genomic region. The C-score helps in highlighting TF signals arising from non-specific interactions. Moreover, by considering the C-score (and adjusting for the length of genomic regions), we can effectively mitigate interference of non-target motifs. Using these tools, we find that in many instances, strong ChIP-seq signal stems mainly from non-specific interactions, and the occurrence of non-target motifs significantly impacts the accurate inference of the target motif. Prioritizing genomic regions with reduced crowdedness and short length markedly improves motif inference. This 'less-is-more' effect suggests that ChIP-seq region selection warrants more attention.

Predicting DNA structure using a deep learning method

Understanding the mechanisms of protein-DNA binding is critical in comprehending gene regulation. Three-dimensional DNA structure, also described as DNA shape, plays a key role in these mechanisms. In this study, we present a deep learning-based method, Deep DNAshape, that fundamentally changes the current k-mer based high-throughput prediction of DNA shape features by accurately accounting for the influence of extended flanking regions, without the need for extensive molecular simulations or structural biology experiments. By using the Deep DNAshape method, DNA structural features can be predicted for any length and number of DNA sequences in a high-throughput manner, providing an understanding of the effects of flanking regions on DNA structure in a target region of a sequence. The Deep DNAshape method provides access to the influence of distant flanking regions on a region of interest. Our findings reveal that DNA shape readout mechanisms of a core target are quantitatively affected by flanking regions, including extended flanking regions, providing valuable insights into the detailed structural readout mechanisms of protein-DNA binding. Furthermore, when incorporated in machine learning models, the features generated by Deep DNAshape improve the model prediction accuracy. Collectively, Deep DNAshape can serve as versatile and powerful tool for diverse DNA structure-related studies.

Position Weight Matrix or Acyclic Probabilistic Finite Automaton: Which model to use? A decision rule inferred for the prediction of transcription factor binding sites

Prediction of transcription factor binding sites (TFBS) is an example of application of Bioinformatics where DNA molecules are represented as sequences of A, C, G and T symbols. The most used model in this problem is Position Weight Matrix (PWM). Notwithstanding the advantage of being simple, PWMs cannot capture dependency between nucleotide positions, which may affect prediction performance. Acyclic Probabilistic Finite Automata (APFA) is an alternative model able to accommodate position dependencies. However, APFA is a more complex model, which means more parameters have to be learned. In this paper, we propose an innovative method to identify when position dependencies influence preference for PWMs or APFAs. This implied using position dependency features extracted from 1106 sets of TFBS to infer a decision tree able to predict which is the best model - PWM or APFA - for a given set of TFBSs. According to our results, as few as three pinpointed features are able to choose the best model, providing a balance of performance (average precision) and model simplicity.

JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles

JASPAR (https://jaspar.elixir.no/) is a widely-used open-access database presenting manually curated high-quality and non-redundant DNA-binding profiles for transcription factors (TFs) across taxa. In this 10th release and 20th-anniversary update, the CORE collection has expanded with 329 new profiles. We updated three existing profiles and provided orthogonal support for 72 profiles from the previous release's UNVALIDATED collection. Altogether, the JASPAR 2024 update provides a 20% increase in CORE profiles from the previous release. A trimming algorithm enhanced profiles by removing low information content flanking base pairs, which were likely uninformative (within the capacity of the PFM models) for TFBS predictions and modelling TF-DNA interactions. This release includes enhanced metadata, featuring a refined classification for plant TFs' structural DNA-binding domains. The new JASPAR collections prompt updates to the genomic tracks of predicted TF binding sites (TFBSs) in 8 organisms, with human and mouse tracks available as native tracks in the UCSC Genome browser. All data are available through the JASPAR web interface and programmatically through its API and the updated Bioconductor and pyJASPAR packages. Finally, a new TFBS extraction tool enables users to retrieve predicted JASPAR TFBSs intersecting their genomic regions of interest.

DeepPBS: Geometric deep learning for interpretable prediction of protein-DNA binding specificity

Predicting specificity in protein-DNA interactions is a challenging yet essential task for understanding gene regulation. Here, we present Deep Predictor of Binding Specificity (DeepPBS), a geometric deep-learning model designed to predict binding specificity across protein families based on protein-DNA structures. The DeepPBS architecture allows investigation of different family-specific recognition patterns. DeepPBS can be applied to predicted structures, and can aid in the modeling of protein-DNA complexes. DeepPBS is interpretable and can be used to calculate protein heavy atom-level importance scores, demonstrated as a case-study on p53-DNA interface. When aggregated at the protein residue level, these scores conform well with alanine scanning mutagenesis experimental data. The inference time for DeepPBS is sufficiently fast for analyzing simulation trajectories, as demonstrated on a molecular-dynamics simulation of a Drosophila Hox-DNA tertiary complex with its cofactor. DeepPBS and its corresponding data resources offer a foundation for machine-aided protein-DNA interaction studies, guiding experimental choices and complex design, as well as advancing our understanding of molecular interactions.

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.

Deciphering the Role of the Ser-Phosphorylation Pattern on the DNA-Binding Activity of Max Transcription Factor Using Chemical Protein Synthesis

The chemical synthesis of site-specifically modified transcription factors (TFs) is a powerful method to investigate how post-translational modifications (PTMs) influence TF-DNA interactions and impact gene expression. Among these TFs, Max plays a pivotal role in controlling the expression of 15 % of the genome. The activity of Max is regulated by PTMs; Ser-phosphorylation at the N-terminus is considered one of the key regulatory mechanisms. In this study, we developed a practical synthetic strategy to prepare homogeneous full-length Max for the first time, to explore the impact of Max phosphorylation. We prepared a focused library of eight Max variants, with distinct modification patterns, including mono-phosphorylated, and doubly phosphorylated analogues at Ser2/Ser11 as well as fluorescently labeled variants through native chemical ligation. Through comprehensive DNA binding analyses, we discovered that the phosphorylation position plays a crucial role in the DNA-binding activity of Max. Furthermore, in vitro high-throughput analysis using DNA microarrays revealed that the N-terminus phosphorylation pattern does not interfere with the DNA sequence specificity of Max. Our work provides insights into the regulatory role of Max's phosphorylation on the DNA interactions and sequence specificity, shedding light on how PTMs influence TF function.

Single-nucleotide variants within heart enhancers increase binding affinity and disrupt heart development

Transcriptional enhancers direct precise gene expression patterns during development and harbor the majority of variants associated with phenotypic diversity, evolutionary adaptations, and disease. Pinpointing which enhancer variants contribute to changes in gene expression and phenotypes is a major challenge. Here, we find that suboptimal or low-affinity binding sites are necessary for precise gene expression during heart development. Single-nucleotide variants (SNVs) can optimize the affinity of ETS binding sites, causing gain-of-function (GOF) gene expression, cell migration defects, and phenotypes as severe as extra beating hearts in the marine chordate Ciona robusta. In human induced pluripotent stem cell (iPSC)-derived cardiomyocytes, a SNV within a human GATA4 enhancer increases ETS binding affinity and causes GOF enhancer activity. The prevalence of suboptimal-affinity sites within enhancers creates a vulnerability whereby affinity-optimizing SNVs can lead to GOF gene expression, changes in cellular identity, and organismal-level phenotypes that could contribute to the evolution of novel traits or diseases.

Predictive analyses of regulatory sequences with EUGENe

Deep learning has become a popular tool to study cis-regulatory function. Yet efforts to design software for deep-learning analyses in regulatory genomics that are findable, accessible, interoperable and reusable (FAIR) have fallen short of fully meeting these criteria. Here we present elucidating the utility of genomic elements with neural nets (EUGENe), a FAIR toolkit for the analysis of genomic sequences with deep learning. EUGENe consists of a set of modules and subpackages for executing the key functionality of a genomics deep learning workflow: (1) extracting, transforming and loading sequence data from many common file formats; (2) instantiating, initializing and training diverse model architectures; and (3) evaluating and interpreting model behavior. We designed EUGENe as a simple, flexible and extensible interface for streamlining and customizing end-to-end deep-learning sequence analyses, and illustrate these principles through application of the toolkit to three predictive modeling tasks. We hope that EUGENe represents a springboard towards a collaborative ecosystem for deep-learning applications in genomics research.

Altered binding affinity of SIX1-Q177R correlates with enhanced WNT5A and WNT pathway effector expression in Wilms tumor

Wilms tumors present as an amalgam of varying proportions of tissues located within the developing kidney, one being the nephrogenic blastema comprising multipotent nephron progenitor cells (NPCs). The recurring missense mutation Q177R in NPC transcription factors SIX1 and SIX2 is most correlated with tumors of blastemal histology and is significantly associated with relapse. Yet, the transcriptional regulatory consequences of SIX1/2-Q177R that might promote tumor progression and recurrence have not been investigated extensively. Utilizing multiple Wilms tumor transcriptomic datasets, we identified upregulation of the gene encoding non-canonical WNT ligand WNT5A in addition to other WNT pathway effectors in SIX1/2-Q177R mutant tumors. SIX1 ChIP-seq datasets from Wilms tumors revealed shared binding sites for SIX1/SIX1-Q177R within a promoter of WNT5A and at putative distal cis-regulatory elements (CREs). We demonstrate colocalization of SIX1 and WNT5A in Wilms tumor tissue and utilize in vitro assays that support SIX1 and SIX1-Q177R activation of expression from the WNT5A CREs, as well as enhanced binding affinity within the WNT5A promoter that may promote the differential expression of WNT5A and other WNT pathway effectors associated with SIX1-Q177R tumors.

Deep DNAshape: Predicting DNA shape considering extended flanking regions using a deep learning method

Understanding the mechanisms of protein-DNA binding is critical in comprehending gene regulation. Three-dimensional DNA shape plays a key role in these mechanisms. In this study, we present a deep learning-based method, Deep DNAshape, that fundamentally changes the current k -mer based high-throughput prediction of DNA shape features by accurately accounting for the influence of extended flanking regions, without the need for extensive molecular simulations or structural biology experiments. By using the Deep DNAshape method, refined DNA shape features can be predicted for any length and number of DNA sequences in a high-throughput manner, providing a deeper understanding of the effects of flanking regions on DNA shape in a target region of a sequence. Deep DNAshape method provides access to the influence of distant flanking regions on a region of interest. Our findings reveal that DNA shape readout mechanisms of a core target are quantitatively affected by flanking regions, including extended flanking regions, providing valuable insights into the detailed structural readout mechanisms of protein-DNA binding. Furthermore, when incorporated in machine learning models, the features generated by Deep DNAshape improve the model prediction accuracy. Collectively, Deep DNAshape can serve as a versatile and powerful tool for diverse DNA structure-related studies.

Short tandem repeats bind transcription factors to tune eukaryotic gene expression

Short tandem repeats (STRs) are enriched in eukaryotic cis-regulatory elements and alter gene expression, yet how they regulate transcription remains unknown. We found that STRs modulate transcription factor (TF)-DNA affinities and apparent on-rates by about 70-fold by directly binding TF DNA-binding domains, with energetic impacts exceeding many consensus motif mutations. STRs maximize the number of weakly preferred microstates near target sites, thereby increasing TF density, with impacts well predicted by statistical mechanics. Confirming that STRs also affect TF binding in cells, neural networks trained only on in vivo occupancies predicted effects identical to those observed in vitro. Approximately 90% of TFs preferentially bound STRs that need not resemble known motifs, providing a cis-regulatory mechanism to target TFs to genomic sites.

Computational design of sequence-specific DNA-binding proteins

Sequence-specific DNA-binding proteins (DBPs) play critical roles in biology and biotechnology, and there has been considerable interest in the engineering of DBPs with new or altered specificities for genome editing and other applications. While there has been some success in reprogramming naturally occurring DBPs using selection methods, the computational design of new DBPs that recognize arbitrary target sites remains an outstanding challenge. We describe a computational method for the design of small DBPs that recognize specific target sequences through interactions with bases in the major groove, and employ this method in conjunction with experimental screening to generate binders for 5 distinct DNA targets. These binders exhibit specificity closely matching the computational models for the target DNA sequences at as many as 6 base positions and affinities as low as 30-100 nM. The crystal structure of a designed DBP-target site complex is in close agreement with the design model, highlighting the accuracy of the design method. The designed DBPs function in both Escherichia coli and mammalian cells to repress and activate transcription of neighboring genes. Our method is a substantial step towards a general route to small and hence readily deliverable sequence-specific DBPs for gene regulation and editing.

A survey on algorithms to characterize transcription factor binding sites

Transcription factors (TFs) are key regulatory proteins that control the transcriptional rate of cells by binding short DNA sequences called transcription factor binding sites (TFBS) or motifs. Identifying and characterizing TFBS is fundamental to understanding the regulatory mechanisms governing the transcriptional state of cells. During the last decades, several experimental methods have been developed to recover DNA sequences containing TFBS. In parallel, computational methods have been proposed to discover and identify TFBS motifs based on these DNA sequences. This is one of the most widely investigated problems in bioinformatics and is referred to as the motif discovery problem. In this manuscript, we review classical and novel experimental and computational methods developed to discover and characterize TFBS motifs in DNA sequences, highlighting their advantages and drawbacks. We also discuss open challenges and future perspectives that could fill the remaining gaps in the field.

De novo distillation of thermodynamic affinity from deep learning regulatory sequence models of in vivo protein-DNA binding

Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences, in vivo genomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the sequence determinants of TF binding, both intrinsic and context-specific, is essential to understand gene regulation and the impact of regulatory, non-coding genetic variation. Biophysical models trained on in vitro TF binding assays can estimate intrinsic affinity landscapes and predict occupancy based on TF concentration and affinity. However, these models cannot adequately explain context-specific, in vivo binding profiles. Conversely, deep learning models, trained on in vivo TF binding assays, effectively predict and explain genomic occupancy profiles as a function of complex regulatory sequence syntax, albeit without a clear biophysical interpretation. To reconcile these complementary models of in vitro and in vivo TF binding, we developed Affinity Distillation (AD), a method that extracts thermodynamic affinities de-novo from deep learning models of TF chromatin immunoprecipitation (ChIP) experiments by marginalizing away the influence of genomic sequence context. Applied to neural networks modeling diverse classes of yeast and mammalian TFs, AD predicts energetic impacts of sequence variation within and surrounding motifs on TF binding as measured by diverse in vitro assays with superior dynamic range and accuracy compared to motif-based methods. Furthermore, AD can accurately discern affinities of TF paralogs. Our results highlight thermodynamic affinity as a key determinant of in vivo binding, suggest that deep learning models of in vivo binding implicitly learn high-resolution affinity landscapes, and show that these affinities can be successfully distilled using AD. This new biophysical interpretation of deep learning models enables high-throughput in silico experiments to explore the influence of sequence context and variation on both intrinsic affinity and in vivo occupancy.

TSPTFBS 2.0: trans-species prediction of transcription factor binding sites and identification of their core motifs in plants

Introduction

An emerging approach using promoter tiling deletion via genome editing is beginning to become popular in plants. Identifying the precise positions of core motifs within plant gene promoter is of great demand but they are still largely unknown. We previously developed TSPTFBS of 265 Arabidopsis transcription factor binding sites (TFBSs) prediction models, which now cannot meet the above demand of identifying the core motif.

Methods

Here, we additionally introduced 104 maize and 20 rice TFBS datasets and utilized DenseNet for model construction on a large-scale dataset of a total of 389 plant TFs. More importantly, we combined three biological interpretability methods including DeepLIFT, in-silico tiling deletion, and in-silico mutagenesis to identify the potential core motifs of any given genomic region.

Results

For the results, DenseNet not only has achieved greater predictability than baseline methods such as LS-GKM and MEME for above 389 TFs from Arabidopsis, maize and rice, but also has greater performance on trans-species prediction of a total of 15 TFs from other six plant species. A motif analysis based on TF-MoDISco and global importance analysis (GIA) further provide the biological implication of the core motif identified by three interpretability methods. Finally, we developed a pipeline of TSPTFBS 2.0, which integrates 389 DenseNet-based models of TF binding and the above three interpretability methods.

Discussion

TSPTFBS 2.0 was implemented as a user-friendly web-server (http://www.hzau-hulab.com/TSPTFBS/), which can support important references for editing targets of any given plant promoters and it has great potentials to provide reliable editing target of genetic screen experiments in plants.

Double DAP-seq uncovered synergistic DNA binding of interacting bZIP transcription factors

Many eukaryotic transcription factors (TF) form homodimer or heterodimer complexes to regulate gene expression. Dimerization of BASIC LEUCINE ZIPPER (bZIP) TFs are critical for their functions, but the molecular mechanism underlying the DNA binding and functional specificity of homo- versus heterodimers remains elusive. To address this gap, we present the double DNA Affinity Purification-sequencing (dDAP-seq) technique that maps heterodimer binding sites on endogenous genomic DNA. Using dDAP-seq we profile twenty pairs of C/S1 bZIP heterodimers and S1 homodimers in Arabidopsis and show that heterodimerization significantly expands the DNA binding preferences of these TFs. Analysis of dDAP-seq binding sites reveals the function of bZIP9 in abscisic acid response and the role of bZIP53 heterodimer-specific binding in seed maturation. The C/S1 heterodimers show distinct preferences for the ACGT elements recognized by plant bZIPs and motifs resembling the yeast GCN4 cis-elements. This study demonstrates the potential of dDAP-seq in deciphering the DNA binding specificities of interacting TFs that are key for combinatorial gene regulation.

UV irradiation remodels the specificity landscape of transcription factors

Somatic mutations are highly enriched at transcription factor (TF) binding sites, with the strongest trend being observed for ultraviolet light (UV)-induced mutations in melanomas. One of the main mechanisms proposed for this hypermutation pattern is the inefficient repair of UV lesions within TF-binding sites, caused by competition between TFs bound to these lesions and the DNA repair proteins that must recognize the lesions to initiate repair. However, TF binding to UV-irradiated DNA is poorly characterized, and it is unclear whether TFs maintain specificity for their DNA sites after UV exposure. We developed UV-Bind, a high-throughput approach to investigate the impact of UV irradiation on protein-DNA binding specificity. We applied UV-Bind to ten TFs from eight structural families, and found that UV lesions significantly altered the DNA-binding preferences of all the TFs tested. The main effect was a decrease in binding specificity, but the precise effects and their magnitude differ across factors. Importantly, we found that despite the overall reduction in DNA-binding specificity in the presence of UV lesions, TFs can still compete with repair proteins for lesion recognition, in a manner consistent with their specificity for UV-irradiated DNA. In addition, for a subset of TFs, we identified a surprising but reproducible effect at certain nonconsensus DNA sequences, where UV irradiation leads to a high increase in the level of TF binding. These changes in DNA-binding specificity after UV irradiation, at both consensus and nonconsensus sites, have important implications for the regulatory and mutagenic roles of TFs in the cell.

Widespread perturbation of ETS factor binding sites in cancer

Although >90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted.

Using Restriction Endonuclease, Protection, Selection, and Amplification to Identify Preferred DNA-Binding Sequences of Microbial Transcription Factors

Regulation of gene expression is a vital component of cellular biology. Transcription factor proteins often bind regulatory DNA sequences upstream of transcription start sites to facilitate the activation or repression of RNA polymerase. Research laboratories have devoted many projects to understanding the transcription regulatory networks for transcription factors, as these regulated genes provide critical insight into the biology of the host organism. Various in vivo and in vitro assays have been developed to elucidate transcription regulatory networks. Several assays, including SELEX-seq and ChIP-seq, capture DNA-bound transcription factors to determine the preferred DNA-binding sequences, which can then be mapped to the host organism's genome to identify candidate regulatory genes. In this protocol, we describe an alternative in vitro, iterative selection approach to ascertaining DNA-binding sequences of a transcription factor of interest using restriction endonuclease, protection, selection, and amplification (REPSA). Contrary to traditional antibody-based capture methods, REPSA selects for transcription factor-bound DNA sequences by challenging binding reactions with a type IIS restriction endonuclease. Cleavage-resistant DNA species are amplified by PCR and then used as inputs for the next round of REPSA. This process is repeated until a protected DNA species is observed by gel electrophoresis, which is an indication of a successful REPSA experiment. Subsequent high-throughput sequencing of REPSA-selected DNAs accompanied by motif discovery and scanning analyses can be used for determining transcription factor consensus binding sequences and potential regulated genes, providing critical first steps in determining organisms' transcription regulatory networks. IMPORTANCE Transcription regulatory proteins are an essential class of proteins that help maintain cellular homeostasis by adapting the transcriptome based on environmental cues. Dysregulation of transcription factors can lead to diseases such as cancer, and many eukaryotic and prokaryotic transcription factors have become enticing therapeutic targets. Additionally, in many understudied organisms, the transcription regulatory networks for uncharacterized transcription factors remain unknown. As such, the need for experimental techniques to establish transcription regulatory networks is paramount. Here, we describe a step-by-step protocol for REPSA, an inexpensive, iterative selection technique to identify transcription factor-binding sequences without the need for antibody-based capture methods.

MORF and MOZ acetyltransferases target unmethylated CpG islands through the winged helix domain

Human acetyltransferases MOZ and MORF are implicated in chromosomal translocations associated with aggressive leukemias. Oncogenic translocations involve the far amino terminus of MOZ/MORF, the function of which remains unclear. Here, we identified and characterized two structured winged helix (WH) domains, WH1 and WH2, in MORF and MOZ. WHs bind DNA in a cooperative manner, with WH1 specifically recognizing unmethylated CpG sequences. Structural and genomic analyses show that the DNA binding function of WHs targets MORF/MOZ to gene promoters, stimulating transcription and H3K23 acetylation, and WH1 recruits oncogenic fusions to HOXA genes that trigger leukemogenesis. Cryo-EM, NMR, mass spectrometry and mutagenesis studies provide mechanistic insight into the DNA-binding mechanism, which includes the association of WH1 with the CpG-containing linker DNA and binding of WH2 to the dyad of the nucleosome. The discovery of WHs in MORF and MOZ and their DNA binding functions could open an avenue in developing therapeutics to treat diseases associated with aberrant MOZ/MORF acetyltransferase activities.

Parallelized multidimensional analytic framework applied to mammary epithelial cells uncovers regulatory principles in EMT

A proper understanding of disease etiology will require longitudinal systems-scale reconstruction of the multitiered architecture of eukaryotic signaling. Here we combine state-of-the-art data acquisition platforms and bioinformatics tools to devise PAMAF, a workflow that simultaneously examines twelve omics modalities, i.e., protein abundance from whole-cells, nucleus, exosomes, secretome and membrane; N-glycosylation, phosphorylation; metabolites; mRNA, miRNA; and, in parallel, single-cell transcriptomes. We apply PAMAF in an established in vitro model of TGFβ-induced epithelial to mesenchymal transition (EMT) to quantify >61,000 molecules from 12 omics and 10 timepoints over 12 days. Bioinformatics analysis of this EMT-ExMap resource allowed us to identify; -topological coupling between omics, -four distinct cell states during EMT, -omics-specific kinetic paths, -stage-specific multi-omics characteristics, -distinct regulatory classes of genes, -ligand-receptor mediated intercellular crosstalk by integrating scRNAseq and subcellular proteomics, and -combinatorial drug targets (e.g., Hedgehog signaling and CAMK-II) to inhibit EMT, which we validate using a 3D mammary duct-on-a-chip platform. Overall, this study provides a resource on TGFβ signaling and EMT.

Enzymatic Synthesis of High-Density RNA Microarrays

Oligonucleotide microarrays are used to investigate the interactome of nucleic acids. DNA microarrays are commercially available, whereas equivalent RNA microarrays are not. This protocol describes a method to convert DNA microarrays of any density and complexity into RNA microarrays using only readily available materials and reagents. This simple conversion protocol will facilitate the accessibility of RNA microarrays to a wide range of researchers. In addition to general considerations for the design of a template DNA microarray, this procedure describes the experimental steps of hybridization of an RNA primer to the immobilized DNA, followed by its covalent attachment via psoralen-mediated photocrosslinking. The subsequent enzymatic processing steps comprise the extension of the primer with T7 RNA polymerase to generate complementary RNA, and finally the removal of the DNA template with TURBO DNase. Beyond the conversion process, we also describe approaches to detect the RNA product either by internal labeling with fluorescently labeled NTPs or via hybridization to the product strand, a step that can then be complemented by an RNase H assay to confirm the nature of the product. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Conversion of a DNA microarray to an RNA microarray Alternate Protocol: Detection of RNA via incorporation of Cy3-UTP Support Protocol 1: Detection of RNA via hybridization Support Protocol 2: RNase H assay.

Enhanced Yeast One-Hybrid Assays to Study Protein-DNA Interactions

The specificity in gene regulation is controlled by interactions between transcription factors (TFs) and genomic DNA regions such as promoters and enhancers. Enhanced yeast one-hybrid (eY1H) assays are among the methods used for high-throughput detection of transcription factor-DNA interactions. Here, we describe the procedure for screening interactions between DNA regions of interest ("DNA-baits") and an array of transcription factors ("TF-preys"), after DNA-bait and TF-prey yeast strains have been generated. Using a high-density array robotic platform, this method can be used to screen interactions between multiple DNA regions and >1000 TFs within a single experiment.

Learning probabilistic protein-DNA recognition codes from DNA-binding specificities using structural mappings

Knowledge of how proteins interact with DNA is essential for understanding gene regulation. Although DNA-binding specificities for thousands of transcription factors (TFs) have been determined, the specific amino acid-base interactions comprising their structural interfaces are largely unknown. This lack of resolution hampers attempts to leverage these data in order to predict specificities for uncharacterized TFs or TFs mutated in disease. Here we introduce recognition code learning via automated mapping of protein-DNA structural interfaces (rCLAMPS), a probabilistic approach that uses DNA-binding specificities for TFs from the same structural family to simultaneously infer both which nucleotide positions are contacted by particular amino acids within the TF as well as a recognition code that relates each base-contacting amino acid to nucleotide preferences at the DNA positions it contacts. We apply rCLAMPS to homeodomains, the second largest family of TFs in metazoans and show that it learns a highly effective recognition code that can predict de novo DNA-binding specificities for TFs. Furthermore, we show that the inferred amino acid-nucleotide contacts reveal whether and how nucleotide preferences at individual binding site positions are altered by mutations within TFs. Our approach is an important step toward automatically uncovering the determinants of protein-DNA specificity from large compendia of DNA-binding specificities and inferring the altered functionalities of TFs mutated in disease.