DNA-binding Factors Research Articles

Identifying the “stripe” transcription factors and cooperative binding related to DNA methylation

DNA methylation plays a critical role in gene regulation by modulating the DNA binding of transcription factors (TFs). This study integrates TFs’ ChIP-seq profiles with WGBS profiles to investigate how DNA methylation affects protein interactions. Statistical methods and a 5-letter DNA motif calling model have been developed to characterize DNA sequences bound by proteins, while considering the effects of DNA modifications. By employing these methods, 79 significant universal “stripe” TFs and cofactors (USFs), 2360 co-binding protein pairs, and distinct protein modules associated with various DNA methylation states have been identified. The USFs hint a regulatory hierarchy within these protein interactions. Proteins preferentially bind to non-CpG sites in methylated regions, indicating binding affinity is not solely CpG-dependent. Proteins involved in methylation-specific USFs and cobinding pairs play essential roles in promoting and sustaining DNA methylation through interacting with DNMTs or inhibiting TET binding. These findings underscore the interplay between protein binding and methylation, offering insights into epigenetic regulation in cellular biology.

Renin-angiotensin blockade ameliorates the progression of glomerular injury in podocyte-specific Ctcf knockout mice.

Several studies have shown that the progression of proteinuria and renal tissue injury is associated with activation of the intrarenal renin-angiotensin system (RAS). CCCTC-binding factor (CTCF) is a DNA-binding factor that plays an essential role in the regulation of gene expression. In the present study, we aimed to investigate the phenotypic effects of CTCF deficiency in podocytes. Angiotensin II type 1 receptor blockers (ARBs) were administered to the podocyte-specific Ctcf knockout mice, and histological and biochemical analyzes were performed. We also investigated the changes in the expression of podocin in podocyte cell cultures with or without stimulation with angiotensin II from glomeruli isolated using magnetic beads from podocyte-specific Ctcf knockout mice. Mice in which Ctcf was deleted from podocytes developed glomerulopathy and mice developed severe progressive proteinuria, and impaired renal function. Moreover, ARBs suppressed the development of glomerulopathy in podocyte-specific Ctcf knockout mice. Both real-time polymerase chain reaction and western blotting showed that podocin expression was decreased in cell cultures stimulated with angiotensin II. Furthermore, RAS components gene expressions in podocyte cell cultures isolated from podocyte-specific Ctcf knockout mice were significantly increased. These results suggest that RAS is involved in the development of glomerulopathy in podocyte-specific Ctcf knockout mice. Elucidation of the pathophysiology of podocyte-specific Ctcf knockout mice may provide new insights into the relationship between podocyte injury and chronic glomerulonephritis.

Discovery of a novel mutation F184S (c.551T>C) in GATA4 gene causing congenital heart disease in a consanguineous Saudi family

Background & aimCongenital heart disease (CHD) is the most common cause of non-infectious deaths in infants worldwide. However, the molecular mechanisms underlying CHD remain unclear. Approximately 30 % of the causes are believed to be genetic mutations and chromosomal abnormalities. In this study, we aimed to identify the genetic causes of CHD in consanguineous families. MethodsFourth-generation pedigrees with CHD were recruited. The main cardiac features of the patient included absence of the right pulmonary artery and a large dilated left pulmonary artery. To determine the underlying genetic cause, whole-exome sequencing was performed and subsequently confirmed using Sanger sequencing and different online databases to study the pathogenesis of the identified gene mutation. An in-silico homology model was created using the Alphafold homology model structure of GATA4 (AF-P43694-F1). The missense3D online program was used to evaluate the structural alterations. ResultsWe identified a deleterious mutation c.551T > C (p.Phe184Ser) in GATA4. GATA4 is a highly conserved zinc-finger transcription factor, and its continuous expression is essential for cardiogenesis during embryogenesis. The in-silico model suggested a compromised binding efficiency with other proteins. Several variant interpretation algorithms indicated that the F184S missense variant in GATA4 is damaging, whereas HOPE analysis indicated the functional impairment of DNA binding of transcription factors and zinc-ion binding activities of GATA4. ConclusionThe variant identified in GATA4 appears to cause recessive CHD in the family. In silico analysis suggested that this variant was damaging and caused multiple structural and functional aberrations. This study may support prenatal screening of the fetus in this family to prevent diseases in new generations.

H-NS is a bacterial transposon capture protein

The histone-like nucleoid structuring (H-NS) protein is a DNA binding factor, found in gammaproteobacteria, with functional equivalents in diverse microbes. Universally, such proteins are understood to silence transcription of horizontally acquired genes. Here, we identify transposon capture as a major overlooked function of H-NS. Using genome-scale approaches, we show that H-NS bound regions are transposition “hotspots”. Since H-NS often interacts with pathogenicity islands, such targeting creates clinically relevant phenotypic diversity. For example, in Acinetobacter baumannii, we identify altered motility, biofilm formation, and interactions with the human immune system. Transposon capture is mediated by the DNA bridging activity of H-NS and, if absent, more ubiquitous transposition results. Consequently, transcribed and essential genes are disrupted. Hence, H-NS directs transposition to favour evolutionary outcomes useful for the host cell.

DNA-binding factor footprints and enhancer RNAs identify functional non-coding genetic variants

BackgroundGenome-wide association studies (GWAS) have revealed a multitude of candidate genetic variants affecting the risk of developing complex traits and diseases. However, the highlighted regions are typically in the non-coding genome, and uncovering the functional causative single nucleotide variants (SNVs) is challenging. Prioritization of variants is commonly based on genomic annotation with markers of active regulatory elements, but current approaches still poorly predict functional variants. To address this, we systematically analyze six markers of active regulatory elements for their ability to identify functional variants.ResultsWe benchmark against molecular quantitative trait loci (molQTL) from assays of regulatory element activity that identify allelic effects on DNA-binding factor occupancy, reporter assay expression, and chromatin accessibility. We identify the combination of DNase footprints and divergent enhancer RNA (eRNA) as markers for functional variants. This signature provides high precision, but with a trade-off of low recall, thus substantially reducing candidate variant sets to prioritize variants for functional validation. We present this as a framework called FINDER—Functional SNV IdeNtification using DNase footprints and eRNA.ConclusionsWe demonstrate the utility to prioritize variants using leukocyte count trait and analyze variants in linkage disequilibrium with a lead variant to predict a functional variant in asthma. Our findings have implications for prioritizing variants from GWAS, in development of predictive scoring algorithms, and for functionally informed fine mapping approaches.

Scalable assessment of genome editing off-targets associated with genetic variants.

Genome editing with RNA-guided DNA binding factors carries risk of off-target editing at homologous sequences. Genetic variants may introduce sequence changes that increase homology to a genome editing target, thereby increasing risk of off-target editing. Conventional methods to verify candidate off-targets rely on access to cells with genomic DNA carrying these sequences. However, for candidate off-targets associated with genetic variants, appropriate cells for experimental verification may not be available. Here we develop a method, Assessment By Stand-in Off-target LentiViral Ensemble with sequencing (ABSOLVE-seq), to integrate a set of candidate off-target sequences along with unique molecular identifiers (UMIs) in genomes of primary cells followed by clinically relevant gene editor delivery. Gene editing of dozens of candidate off-target sequences may be evaluated in a single experiment with high sensitivity, precision, and power. We provide an open-source pipeline to analyze sequencing data. This approach enables experimental assessment of the influence of human genetic diversity on specificity evaluation during gene editing therapy development.

Deep DNAshape webserver: prediction and real-time visualization of DNA shape considering extended k-mers.

Sequence-dependent DNA shape plays an important role in understanding protein-DNA binding mechanisms. High-throughput prediction of DNA shape features has become a valuable tool in the field of protein-DNA recognition, transcription factor-DNA binding specificity, and gene regulation. However, our widely used webserver, DNAshape, relies on statistically summarized pentamer query tables to query DNA shape features. These query tables do not consider flanking regions longer than two base pairs, and acquiring a query table for hexamers or higher-order k-mers is currently still unrealistic due to limitations in achieving sufficient statistical coverage in molecular simulations or structural biology experiments. A recent deep-learning method, Deep DNAshape, can predict DNA shape features at the core of a DNA fragment considering flanking regions of up to seven base pairs, trained on limited simulation data. However, Deep DNAshape is rather complicated to install, and it must run locally compared to the pentamer-based DNAshape webserver, creating a barrier for users. Here, we present the Deep DNAshape webserver, which has the benefits of both methods while being accurate, fast, and accessible to all users. Additional improvements of the webserver include the detection of user input in real time, the ability of interactive visualization tools and different modes of analyses. URL: https://deepdnashape.usc.edu.

Gauge fixing for sequence-function relationships.

Quantitative models of sequence-function relationships are ubiquitous in computational biology, e.g., for modeling the DNA binding of transcription factors or the fitness landscapes of proteins. Interpreting these models, however, is complicated by the fact that the values of model parameters can often be changed without affecting model predictions. Before the values of model parameters can be meaningfully interpreted, one must remove these degrees of freedom (called "gauge freedoms" in physics) by imposing additional constraints (a process called "fixing the gauge"). However, strategies for fixing the gauge of sequence-function relationships have received little attention. Here we derive an analytically tractable family of gauges for a large class of sequence-function relationships. These gauges are derived in the context of models with all-order interactions, but an important subset of these gauges can be applied to diverse types of models, including additive models, pairwise-interaction models, and models with higher-order interactions. Many commonly used gauges are special cases of gauges within this family. We demonstrate the utility of this family of gauges by showing how different choices of gauge can be used both to explore complex activity landscapes and to reveal simplified models that are approximately correct within localized regions of sequence space. The results provide practical gauge-fixing strategies and demonstrate the utility of gauge-fixing for model exploration and interpretation.

Predicting regulatory mutations and their target genes by new computational integrative analysis: A study of follicular lymphoma

Mutations in DNA regulatory regions are increasingly being recognized as important drivers of cancer and other complex diseases. These mutations can regulate gene expression by affecting DNA-protein binding and epigenetic profiles, such as DNA methylation in genome regulatory elements. However, identifying mutation hotspots associated with expression regulation and disease progression in non-coding DNA remains a challenge. Unlike most existing approaches that assign a mutation score to individual single nucleotide polymorphisms (SNP), a mutation block (MB)-based approach was introduced in this study to assess the collective impact of a cluster of SNPs on transcription factor-DNA binding affinity, differential gene expression (DEG), and nearby DNA methylation. Moreover, the long-distance target genes of functional MBs were identified using a new permutation-based algorithm that assessed the significance of correlations between DNA methylation at regulatory regions and target gene expression. Two new Python packages were developed. The Differential Methylation Region (DMR-analysis) analysis tool was used to detect DMR and map them to regulatory elements. The second tool, an integrated DMR, DEG, and SNP analysis tool (DDS-analysis), was used to combine the omics data to identify functional MBs and long-distance target genes. Both tools were validated in follicular lymphoma (FL) cohorts, where not only known functional MBs and their target genes (BCL2 and BCL6) were recovered, but also novel genes were found, including CDCA4 and JAG2, which may be associated with FL development. These genes are linked to target gene expression and are significantly correlated with the methylation of nearby DNA sequences in FL. The proposed computational integrative analysis of multiomics data holds promise for identifying regulatory mutations in cancer and other complex diseases.

SOSTDC1 Nuclear Translocation Facilitates BTIC Maintenance and CHD1-Mediated HR Repair to Promote Tumor Progression and Olaparib Resistance in TNBC.

Breast tumor-initiating cells (BTICs) of triple-negative breast cancer (TNBC) tissues actively repair DNA and are resistant to treatments including chemotherapy, radiotherapy, and targeted therapy. Herein, it is found that a previously reported secreted protein, sclerostin domain containing 1 (SOSTDC1), is abundantly expressed in BTICs of TNBC cells and positively correlated with a poor patient prognosis. SOSTDC1 knockdown impairs homologous recombination (HR) repair, BTIC maintenance, and sensitized bulk cells and BTICs to Olaparib. Mechanistically, following Olaparib treatment, SOSTDC1 translocates to the nucleus in an importin-α dependent manner. Nuclear SOSTDC1 interacts with the N-terminus of the nucleoprotein, chromatin helicase DNA-binding factor (CHD1), to promote HR repair and BTIC maintenance. Furthermore, nuclear SOSTDC1 bound to β-transducin repeat-containing protein (β-TrCP) binding motifs of CHD1 is found, thereby blocking the β-TrCP-CHD1 interaction and inhibiting β-TrCP-mediated CHD1 ubiquitination and degradation. Collectively, these findings identify a novel nuclear SOSTDC1 pathway in regulating HR repair and BTIC maintenance, providing insight into the TNBC therapeutic strategies.

Systematic assessment of ISWI subunits shows that NURF creates local accessibility for CTCF

Catalytic activity of the imitation switch (ISWI) family of remodelers is critical for nucleosomal organization and DNA binding of certain transcription factors, including the insulator protein CTCF. Here we define the contribution of individual subcomplexes by deriving a panel of isogenic mouse stem cell lines, each lacking one of six ISWI accessory subunits. Individual deletions of subunits of either CERF, RSF, ACF, WICH or NoRC subcomplexes only moderately affect the chromatin landscape, while removal of the NURF-specific subunit BPTF leads to a strong reduction in chromatin accessibility and SNF2H ATPase localization around CTCF sites. This affects adjacent nucleosome occupancy and CTCF binding. At a group of sites with reduced chromatin accessibility, CTCF binding persists but cohesin occupancy is reduced, resulting in decreased insulation. These results suggest that CTCF binding can be separated from its function as an insulator in nuclear organization and identify a specific role for NURF in mediating SNF2H localization and chromatin opening at bound CTCF sites.

Novel putative causal mutations associated with fat traits in Nellore cattle uncovered by eQTLs located in open chromatin regions

Intramuscular fat (IMF) and backfat thickness (BFT) are critical economic traits impacting meat quality. However, the genetic variants controlling these traits need to be better understood. To advance knowledge in this area, we integrated RNA-seq and single nucleotide polymorphisms (SNPs) identified in genomic and transcriptomic data to generate a linkage disequilibrium filtered panel of 553,581 variants. Expression quantitative trait loci (eQTL) analysis revealed 36,916 cis-eQTLs and 14,408 trans-eQTLs. Association analysis resulted in three eQTLs associated with BFT and 24 with IMF. Functional enrichment analysis of genes regulated by these 27 eQTLs revealed noteworthy pathways that can play a fundamental role in lipid metabolism and fat deposition, such as immune response, cytoskeleton remodeling, iron transport, and phospholipid metabolism. We next used ATAC-Seq assay to identify and overlap eQTL and open chromatin regions. Six eQTLs were in regulatory regions, four in predicted insulators and possible CCCTC-binding factor DNA binding sites, one in an active enhancer region, and the last in a low signal region. Our results provided novel insights into the transcriptional regulation of IMF and BFT, unraveling putative regulatory variants.

RUNX1 regulates promoter activity in the absence of cognate DNA binding motifs.

Runt-related transcription factor 1 (RUNX1) plays an important role in normal haematopoietic cell development and function, and its function is frequently disrupted in leukaemia. RUNX1 is widely recognised as a sequence-specific DNA binding factor that recognises the motif 5'-TG(T/C)GGT-3' in promoter and enhancer regions of its target genes. Moreover, RUNX1 fusion proteins, such as RUNX1-ETO formed by the t(8;21) translocation, retain the ability to recognise and bind to this sequence to elicit atypical gene regulatory effects on bona fide RUNX1 targets. However, our analysis of publicly available RUNX1 chromatin immunoprecipitation sequencing (ChIP-Seq) data has provided evidence challenging this dogma, revealing that this motif-specific model of RUNX1 recruitment and function is incomplete. Our analyses revealed that the majority of RUNX1 genomic localisation occurs outside of promoters, that 20% of RUNX1 binding sites lack consensus RUNX motifs, and that binding in the absence of a cognate binding site is more common in promoter regions compared to distal sites. Reporter assays demonstrate that RUNX1 can drive promoter activity in the absence of a recognised DNA binding motif, in contrast to RUNX1-ETO. RUNX1-ETO supresses activity when it is recruited to promoters containing a sequence specific motif, while interestingly, it binds but does not repress promoters devoid of a RUNX1 recognition site. These data suggest that RUNX1 regulation of target genes occurs through multiple mechanisms depending on genomic location, the type of regulatory element and mode of recruitment.

TBCA: Prediction of transcription factor binding sites using a deep neural network with lightweight attention mechanism.

The identification of transcription factor binding sites (TFBSs) is crucial for understanding the regulatory mechanisms of gene expression, which contributes to unraveling cellular functions and disease development. Currently, the most common approach involves the use of deep learning techniques to predict TFBSs by combining sequence and shape features. Although significant progress has been made with these methods, the integration of local features extracted from DNA sequences and shapes with global features has not yet reached a sufficient level, and there is still significant room for improvement in the accuracy of prediction results. In this paper, we propose a novel framework based on convolution and attention mechanisms, referred to as TBCA, which combines DNA sequence information and shape information for predicting transcription factor binding sites. In this work, we employ a two-layer convolutional neural network (CNNs) and self-attention mechanism to extract complex sequence features from DNA. What's more, we utilize a Fourier-transform-enhanced multi-head attention along with channel attention to extract high-order shape features of DNA. Finally, these high-order sequence and shape features are integrated into the channel dimension to achieve accurate TFBSs prediction. Our research results demonstrate that TBCA exhibits superior predictive performance in 165 validated ChIP-seq datasets. Furthermore, the employed attention mechanisms can automatically learn important features at different positions and scales, enhancing the accuracy and robustness of feature representation. We also conduct an in-depth analysis of the contributions of five different shapes to site prediction, revealing that shape features can enhance the prediction of transcription factor DNA binding.

The androgen receptor interacts with GATA3 to transcriptionally regulate a luminal epithelial cell phenotype in breast cancer

BackgroundThe androgen receptor (AR) is a tumor suppressor in estrogen receptor (ER) positive breast cancer, a role sustained in some ER negative breast cancers. Key factors dictating AR genomic activity in a breast context are largely unknown. Herein, we employ an unbiased chromatin immunoprecipitation-based proteomic technique to identify endogenous AR interacting co-regulatory proteins in ER positive and negative models of breast cancer to gain new insight into mechanisms of AR signaling in this disease.ResultsThe DNA-binding factor GATA3 is identified and validated as a novel AR interacting protein in breast cancer cells irrespective of ER status. AR activation by the natural ligand 5α-dihydrotestosterone (DHT) increases nuclear AR-GATA3 interactions, resulting in AR-dependent enrichment of GATA3 chromatin binding at a sub-set of genomic loci. Silencing GATA3 reduces but does not prevent AR DNA binding and transactivation of genes associated with AR/GATA3 co-occupied loci, indicating a co-regulatory role for GATA3 in AR signaling. DHT-induced AR/GATA3 binding coincides with upregulation of luminal differentiation genes, including EHF and KDM4B, established master regulators of a breast epithelial cell lineage. These findings are validated in a patient-derived xenograft model of breast cancer. Interaction between AR and GATA3 is also associated with AR-mediated growth inhibition in ER positive and ER negative breast cancer.ConclusionsAR and GATA3 interact to transcriptionally regulate luminal epithelial cell differentiation in breast cancer regardless of ER status. This interaction facilitates the tumor suppressor function of AR and mechanistically explains why AR expression is associated with less proliferative, more differentiated breast tumors and better overall survival in breast cancer.

Selective Occupation by E2F and RB of Loci Expressed by RNA Polymerase III

In all cases tested, TFIIIB is responsible for recruiting pol III to its genetic templates. In mammalian cells, RB binds TFIIIB and prevents its interactions with both promoter DNA and pol III, thereby suppressing transcription. As TFIIIB is not recruited to its target genes when bound by RB, the mechanism predicts that pol III-dependent templates will not be occupied by RB; this contrasts with the situation at most genes controlled by RB, where it can be tethered by promoter-bound sequence-specific DNA-binding factors such as E2F. Contrary to this prediction, however, ChIP-seq data reveal the presence of RB in multiple cell types and the related protein p130 at many loci that rely on pol III for their expression, including RMRP, RN7SL, and a variety of tRNA genes. The sets of genes targeted varies according to cell type and growth state. In such cases, recruitment of RB and p130 can be explained by binding of E2F1, E2F4 and/or E2F5. Genes transcribed by pol III had not previously been identified as common targets of E2F family members. The data provide evidence that E2F may allow for the selective regulation of specific non-coding RNAs by RB, in addition to its influence on overall pol III output through its interaction with TFIIIB.

Chromatin Immunoprecipitation in Adipose Tissue and Adipocytes: How to Proceed and Optimize the Protocol for Transcription Factor DNA Binding.

Chromatin immunoprecipitation (ChIP) coupled to qPCR or sequencing is a crucial experiment to determine direct transcriptional regulation under the control of specific transcriptional factors or co-regulators at loci-specific or pan-genomic levels.Here we provide a reliable method for processing ChIP from adipocytes or frozen adipose tissue collection, isolation of nuclei, cross-linking of protein-DNA complexes, chromatin shearing, immunoprecipitation, and DNA purification. We also discuss critical steps for optimizing the experiment to perform a successful ChIP in lipid-rich cells/tissues.

Investigation of transcription factor–DNA binding with electrolyte-gated organic transistors

The first electrolyte-gated organic transistor biosensor for the detection of a transcription factor (NF-Y) in buffer and cell lysate.

A bioinformatics approach to identify DNA-binding factors at a specific genomic locus

A bioinformatics approach to identify DNA-binding factors at a specific genomic locus

Short, but matters: short tandem repeats confer variation in transcription factor-DNA binding

Short, but matters: short tandem repeats confer variation in transcription factor-DNA binding