High UV damage and low repair, but not cytosine deamination, stimulate mutation hotspots at ETS binding sites in melanoma.

Noncoding mutation hotspots have been identified in melanoma and many of them occur at the binding sites of E26 transformation-specific (ETS) proteins; however, their formation mechanism and functional impacts are not fully understood. Here, we used UV (Ultraviolet) damage sequencing data and analyzed cyclobutane pyrimidine dimer (CPD) formation, DNA repair, and CPD deamination in human cells at single-nucleotide resolution. Our data show prominent CPD hotspots immediately after UV irradiation at ETS binding sites, particularly at sites with a conserved TTCCGG motif, which correlate with mutation hotspots identified in cutaneous melanoma. Additionally, CPDs are repaired slower at ETS binding sites than in flanking DNA. Cytosine deamination in CPDs to uracil is suggested as an important step for UV mutagenesis. However, we found that CPD deamination is significantly suppressed at ETS binding sites, particularly for the CPD hotspot on the 5' side of the ETS motif, arguing against a role for CPD deamination in promoting ETS-associated UV mutations. Finally, we analyzed a subset of frequently mutated promoters, including the ribosomal protein genes RPL13A and RPS20, and found that mutations in the ETS motif can significantly reduce the promoter activity. Thus, our data identify high UV damage and low repair, but not CPD deamination, as the main mechanism for ETS-associated mutations in melanoma and uncover important roles of often-overlooked mutation hotspots in perturbing gene transcription.

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.

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.

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.

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.

Transcription Factor-Centric Approach to Identify Non-Recurring Putative Regulatory Drivers in Cancer.

Recent efforts to sequence the genomes of thousands of matched normal-tumor samples have led to the identification of millions of somatic mutations, the majority of which are non-coding. Most of these mutations are believed to be passengers, but a small number of non-coding mutations could contribute to tumor initiation or progression, e.g. by leading to dysregulation of gene expression. Efforts to identify putative regulatory drivers rely primarily on information about the recurrence of mutations across tumor samples. However, in regulatory regions of the genome, individual mutations are rarely seen in more than one donor. Instead of using recurrence information, here we present a method to identify putative regulatory driver mutations based on the magnitude of their effects on transcription factor-DNA binding. For each gene, we integrate the effects of mutations across all its regulatory regions, and we ask whether these effects are larger than expected by chance, given the mutation spectra observed in regulatory DNA in the cohort of interest. We applied our approach to analyze mutations in a liver cancer data set with ample somatic mutation and gene expression data available. By combining the effects of mutations across all regulatory regions of each gene, we identified dozens of genes whose regulation in tumor cells is likely to be significantly perturbed by non-coding mutations. Overall, our results show that focusing on the functional effects of non-coding mutations, rather than their recurrence, has the potential to identify putative regulatory drivers and the genes they dysregulate in tumor cells.

MESH1 knockdown triggers proliferation arrest through TAZ repression.

All organisms are constantly exposed to various stresses, necessitating adaptive strategies for survival. In bacteria, the main stress-coping mechanism is the stringent response triggered by the accumulation of "alarmone" (p)ppGpp to arrest proliferation and reprogram transcriptome. While mammalian genomes encode MESH1-the homolog of the (p)ppGpp hydrolase SpoT, current knowledge about its function remains limited. We found MESH1 expression tended to be higher in tumors and associated with poor patient outcomes. Consistently, MESH1 knockdown robustly inhibited proliferation, depleted dNTPs, reduced tumor sphere formation, and retarded xenograft growth. These antitumor phenotypes associated with MESH1 knockdown were accompanied by a significantly altered transcriptome, including the repressed expression of TAZ, a HIPPO coactivator, and proliferative gene. Importantly, TAZ restoration mitigated many anti-growth phenotypes of MESH1 knockdown, including proliferation arrest, reduced sphere formation, tumor growth inhibition, dNTP depletion, and transcriptional changes. Furthermore, TAZ repression was associated with the histone hypo-acetylation at TAZ regulatory loci due to the induction of epigenetic repressors HDAC5 and AHRR. Together, MESH1 knockdown in human cells altered the genome-wide transcriptional patterns and arrested proliferation that mimicked the bacterial stringent response through the epigenetic repression of TAZ expression.

Sex dependent glial-specific changes in the chromatin accessibility landscape in late-onset Alzheimer's disease brains.

BACKGROUND: In the post-GWAS era, there is an unmet need to decode the underpinning genetic etiologies of late-onset Alzheimer's disease (LOAD) and translate the associations to causation. METHODS: We conducted ATAC-seq profiling using NeuN sorted-nuclei from 40 frozen brain tissues to determine LOAD-specific changes in chromatin accessibility landscape in a cell-type specific manner. RESULTS: We identified 211 LOAD-specific differential chromatin accessibility sites in neuronal-nuclei, four of which overlapped with LOAD-GWAS regions (±100 kb of SNP). While the non-neuronal nuclei did not show LOAD-specific differences, stratification by sex identified 842 LOAD-specific chromatin accessibility sites in females. Seven of these sex-dependent sites in the non-neuronal samples overlapped LOAD-GWAS regions including APOE. LOAD loci were functionally validated using single-nuclei RNA-seq datasets. CONCLUSIONS: Using brain sorted-nuclei enabled the identification of sex-dependent cell type-specific LOAD alterations in chromatin structure. These findings enhance the interpretation of LOAD-GWAS discoveries, provide potential pathomechanisms, and suggest novel LOAD-loci.

Competition for DNA binding between paralogous transcription factors determines their genomic occupancy and regulatory functions.

Most eukaryotic transcription factors (TFs) are part of large protein families, with members of the same family (i.e., paralogous TFs) recognizing similar DNA-binding motifs but performing different regulatory functions. Many TF paralogs are coexpressed in the cell and thus can compete for target sites across the genome. However, this competition is rarely taken into account when studying the in vivo binding patterns of eukaryotic TFs. Here, we show that direct competition for DNA binding between TF paralogs is a major determinant of their genomic binding patterns. Using yeast proteins Cbf1 and Pho4 as our model system, we designed a high-throughput quantitative assay to capture the genomic binding profiles of competing TFs in a cell-free system. Our data show that Cbf1 and Pho4 greatly influence each other's occupancy by competing for their common putative genomic binding sites. The competition is different at different genomic sites, as dictated by the TFs' expression levels and their divergence in DNA-binding specificity and affinity. Analyses of ChIP-seq data show that the biophysical rules that dictate the competitive TF binding patterns in vitro are also followed in vivo, in the complex cellular environment. Furthermore, the Cbf1-Pho4 competition for genomic sites, as characterized in vitro using our new assay, plays a critical role in the specific activation of their target genes in the cell. Overall, our study highlights the importance of direct TF-TF competition for genomic binding and gene regulation by TF paralogs, and proposes an approach for studying this competition in a quantitative and high-throughput manner.

Mutational processes in cancer preferentially affect binding of particular transcription factors.

Protein binding microarrays provide comprehensive information about the DNA binding specificities of transcription factors (TFs), and can be used to quantitatively predict the effects of DNA sequence variation on TF binding. There has also been substantial progress in dissecting the patterns of mutations, i.e., the "mutational signatures", generated by different mutational processes. By combining these two layers of information we can investigate whether certain mutational processes tend to preferentially affect binding of particular classes of TFs. Such preferential alterations of binding might predispose to particular oncogenic pathways. We developed and implemented a method, termed "Signature-QBiC", that integrates protein binding microarray data with the signatures of mutational processes, with the aim of predicting which TFs' binding profiles are preferentially perturbed by particular mutational processes. We used Signature-QBiC to predict the effects of 47 signatures of mutational processes on 582 human TFs. Pathway analysis showed that binding of TFs involved in NOTCH1 signaling is strongly affected by the signatures of several mutational processes, including exposure to ultraviolet radiation. Additionally, toll-like-receptor signaling pathways are also vulnerable to disruption by this exposure. This study provides a novel overview of the effects of mutational processes on TF binding and the potential of these processes to activate oncogenic pathways through mutating TF binding sites.

DNA mismatches reveal conformational penalties in protein-DNA recognition.

Transcription factors recognize specific genomic sequences to regulate complex gene-expression programs. Although it is well-established that transcription factors bind to specific DNA sequences using a combination of base readout and shape recognition, some fundamental aspects of protein-DNA binding remain poorly understood1,2. Many DNA-binding proteins induce changes in the structure of the DNA outside the intrinsic B-DNA envelope. However, how the energetic cost that is associated with distorting the DNA contributes to recognition has proven difficult to study, because the distorted DNA exists in low abundance in the unbound ensemble3-9. Here we use a high-throughput assay that we term SaMBA (saturation mismatch-binding assay) to investigate the role of DNA conformational penalties in transcription factor-DNA recognition. In SaMBA, mismatched base pairs are introduced to pre-induce structural distortions in the DNA that are much larger than those induced by changes in the Watson-Crick sequence. Notably, approximately 10% of mismatches increased transcription factor binding, and for each of the 22 transcription factors that were examined, at least one mismatch was found that increased the binding affinity. Mismatches also converted non-specific sites into high-affinity sites, and high-affinity sites into 'super sites' that exhibit stronger affinity than any known canonical binding site. Determination of high-resolution X-ray structures, combined with nuclear magnetic resonance measurements and structural analyses, showed that many of the DNA mismatches that increase binding induce distortions that are similar to those induced by protein binding-thus prepaying some of the energetic cost incurred from deforming the DNA. Our work indicates that conformational penalties are a major determinant of protein-DNA recognition, and reveals mechanisms by which mismatches can recruit transcription factors and thus modulate replication and repair activities in the cell10,11.

DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling.

DNA primase synthesizes short RNA primers that initiate DNA synthesis of Okazaki fragments on the lagging strand by DNA polymerase during DNA replication. The binding of prokaryotic DnaG-like primases to DNA occurs at a specific trinucleotide recognition sequence. It is a pivotal step in the formation of Okazaki fragments. Conventional biochemical tools that are used to determine the DNA recognition sequence of DNA primase provide only limited information. Using a high-throughput microarray-based binding assay and consecutive biochemical analyses, it has been shown that 1) the specific binding context (flanking sequences of the recognition site) influences the binding strength of the DNA primase to its template DNA, and 2) stronger binding of primase to the DNA yields longer RNA primers, indicating higher processivity of the enzyme. This method combines PBM and primase activity assay and is designated as high-throughput primase profiling (HTPP), and it allows characterization of specific sequence recognition by DNA primase in unprecedented time and scalability.

Unexpected implications of STAT3 acetylation revealed by genetic encoding of acetyl-lysine.

The signal transducer and activator of transcription 3 (STAT3) protein is activated by phosphorylation of a specific tyrosine residue (Tyr705) in response to various extracellular signals. STAT3 activity was also found to be regulated by acetylation of Lys685. However, the molecular mechanism by which Lys685 acetylation affects the transcriptional activity of STAT3 remains elusive. By genetically encoding the co-translational incorporation of acetyl-lysine into position Lys685 and co-expression of STAT3 with the Elk receptor tyrosine kinase, we were able to characterize site-specifically acetylated, and simultaneously acetylated and phosphorylated STAT3. We measured the effect of acetylation on the crystal structure, and DNA binding affinity and specificity of Tyr705-phosphorylated and non-phosphorylated STAT3. In addition, we monitored the deacetylation of acetylated Lys685 by reconstituting the mammalian enzymatic deacetylation reaction in live bacteria. Surprisingly, we found that acetylation, per se, had no effect on the crystal structure, and DNA binding affinity or specificity of STAT3, implying that the previously observed acetylation-dependent transcriptional activity of STAT3 involves an additional cellular component. In addition, we discovered that Tyr705-phosphorylation protects Lys685 from deacetylation in bacteria, providing a new possible explanation for the observed correlation between STAT3 activity and Lys685 acetylation.

QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants.

Non-coding genetic variants/mutations can play functional roles in the cell by disrupting regulatory interactions between transcription factors (TFs) and their genomic target sites. For most human TFs, a myriad of DNA-binding models are available and could be used to predict the effects of DNA mutations on TF binding. However, information on the quality of these models is scarce, making it hard to evaluate the statistical significance of predicted binding changes. Here, we present QBiC-Pred, a web server for predicting quantitative TF binding changes due to nucleotide variants. QBiC-Pred uses regression models of TF binding specificity trained on high-throughput in vitro data. The training is done using ordinary least squares (OLS), and we leverage distributional results associated with OLS estimation to compute, for each predicted change in TF binding, a P-value reflecting our confidence in the predicted effect. We show that OLS models are accurate in predicting the effects of mutations on TF binding in vitro and in vivo, outperforming widely-used PWM models as well as recently developed deep learning models of specificity. QBiC-Pred takes as input mutation datasets in several formats, and it allows post-processing of the results through a user-friendly web interface. QBiC-Pred is freely available at http://qbic.genome.duke.edu.

Genetic variation determines VEGF-A plasma levels in cancer patients.

Angiogenesis is essential in tumor biology and is regulated by vascular endothelial growth factor (VEGF) ligands and receptors. Here we aimed to discover genetic variants associated with levels of circulating angiogenic proteins in cancer patients. Plasma was collected at baseline in 216 pancreatic and 114 colorectal cancer patients. Thirty-one angiogenic proteins were measured by ELISA. 484,523 Single Nucleotide Polymorphisms (SNP) were tested for association with plasma levels for each protein in pancreatic cancer patients. Three top-ranked hits were then genotyped in colorectal cancer patients, where associations with the same proteins were measured. The results demonstrated rs2284284 and MCP1 (P-value = 6.7e-08), rs7504372 and VEGF-C (P-value = 9.8e-09), and rs7767396 and VEGF-A (P-value = 5.8e-09) were SNP-protein pairs identified in pancreatic cancer patients. In colorectal cancer patients, only rs7767396 (A > G) and VEGF-A was validated (P-value = 5.18e-05). The AA genotype of rs7767396 exhibited 2.04-2.3 and 2.7-3.4-fold higher VEGF-A levels than those with AG and GG genotypes. The G allele of rs7767396 reduces binding of the NF-AT1 transcription factor. In conclusion, a common genetic variant predicts the plasma levels of VEGF-A in cancer patients through altered binding of NF-AT1.

DNA Sequence Context Controls the Binding and Processivity of the T7 DNA Primase.

Primases are key enzymes involved in DNA replication. They act on single-stranded DNA and catalyze the synthesis of short RNA primers used by DNA polymerases. Here, we investigate the DNA binding and activity of the bacteriophage T7 primase using a new workflow called high-throughput primase profiling (HTPP). Using a unique combination of high-throughput binding assays and biochemical analyses, HTPP reveals a complex landscape of binding specificity and functional activity for the T7 primase, determined by sequences flanking the primase recognition site. We identified specific features, such as G/T-rich flanks, which increase primase-DNA binding up to 10-fold and, surprisingly, also increase the length of newly formed RNA (up to 3-fold). To our knowledge, variability in primer length has not been reported for this primase. We expect that applying HTPP to additional enzymes will reveal new insights into the effects of DNA sequence composition on the DNA recognition and functional activity of primases.

The vitamin D receptor gene as a determinant of survival in pancreatic cancer patients: Genomic analysis and experimental validation.

PURPOSE: Advanced pancreatic cancer is a highly refractory disease almost always associated with survival of little more than a year. New interventions based on novel targets are needed. We aim to identify new genetic determinants of overall survival (OS) in patients after treatment with gemcitabine using genome-wide screens of germline DNA. We aim also to support these findings with in vitro functional analysis. PATIENTS AND METHODS: Genome-wide screens of germline DNA in two independent cohorts of pancreatic cancer patients (from the Cancer and Leukemia Group B (CALGB) 80303 and the Mayo Clinic) were used to select new genes associated with OS. The vitamin D receptor gene (VDR) was selected, and the interactions of genetic variation in VDR with circulating vitamin D levels and gemcitabine treatment were evaluated. Functional effects of common VDR variants were also evaluated in experimental assays in human cell lines. RESULTS: The rs2853564 variant in VDR was associated with OS in patients from both the Mayo Clinic (HR 0.81, 95% CI 0.70-0.94, p = 0.0059) and CALGB 80303 (HR 0.74, 0.63-0.87, p = 0.0002). rs2853564 interacted with high pre-treatment levels of 25-hydroxyvitamin D (25(OH)D, a measure of endogenous vitamin D) (p = 0.0079 for interaction) and with gemcitabine treatment (p = 0.024 for interaction) to confer increased OS. rs2853564 increased transcriptional activity in luciferase assays and reduced the binding of the IRF4 transcription factor. CONCLUSION: Our findings propose VDR as a novel determinant of survival in advanced pancreatic cancer patients. Common functional variation in this gene might interact with endogenous vitamin D and gemcitabine treatment to determine improved patient survival. These results support evidence for a modulatory role of the vitamin D pathway for the survival of advanced pancreatic cancer patients.

Divergence in DNA Specificity among Paralogous Transcription Factors Contributes to Their Differential In Vivo Binding.

Paralogous transcription factors (TFs) are oftentimes reported to have identical DNA-binding motifs, despite the fact that they perform distinct regulatory functions. Differential genomic targeting by paralogous TFs is generally assumed to be due to interactions with protein co-factors or the chromatin environment. Using a computational-experimental framework called iMADS (integrative modeling and analysis of differential specificity), we show that, contrary to previous assumptions, paralogous TFs bind differently to genomic target sites even in vitro. We used iMADS to quantify, model, and analyze specificity differences between 11 TFs from 4 protein families. We found that paralogous TFs have diverged mainly at medium- and low-affinity sites, which are poorly captured by current motif models. We identify sequence and shape features differentially preferred by paralogous TFs, and we show that the intrinsic differences in specificity among paralogous TFs contribute to their differential in vivo binding. Thus, our study represents a step forward in deciphering the molecular mechanisms of differential specificity in TF families.

Incomplete MyoD-induced transdifferentiation is associated with chromatin remodeling deficiencies.

Our current understanding of cellular transdifferentiation systems is limited. It is oftentimes unknown, at a genome-wide scale, how much transdifferentiated cells differ quantitatively from both the starting cells and the target cells. Focusing on transdifferentiation of primary human skin fibroblasts by forced expression of myogenic transcription factor MyoD, we performed quantitative analyses of gene expression and chromatin accessibility profiles of transdifferentiated cells compared to fibroblasts and myoblasts. In this system, we find that while many of the early muscle marker genes are reprogrammed, global gene expression and accessibility changes are still incomplete when compared to myoblasts. In addition, we find evidence of epigenetic memory in the transdifferentiated cells, with reminiscent features of fibroblasts being visible both in chromatin accessibility and gene expression. Quantitative analyses revealed a continuum of changes in chromatin accessibility induced by MyoD, and a strong correlation between chromatin-remodeling deficiencies and incomplete gene expression reprogramming. Classification analyses identified genetic and epigenetic features that distinguish reprogrammed from non-reprogrammed sites, and suggested ways to potentially improve transdifferentiation efficiency. Our approach for combining gene expression, DNA accessibility, and protein-DNA binding data to quantify and characterize the efficiency of cellular transdifferentiation on a genome-wide scale can be applied to any transdifferentiation system.

Inkjet-printed point-of-care immunoassay on a nanoscale polymer brush enables subpicomolar detection of analytes in blood.

The ELISA is the mainstay for sensitive and quantitative detection of protein analytes. Despite its utility, ELISA is time-consuming, resource-intensive, and infrastructure-dependent, limiting its availability in resource-limited regions. Here, we describe a self-contained immunoassay platform (the "D4 assay") that converts the sandwich immunoassay into a point-of-care test (POCT). The D4 assay is fabricated by inkjet printing assay reagents as microarrays on nanoscale polymer brushes on glass chips, so that all reagents are "on-chip," and these chips show durable storage stability without cold storage. The D4 assay can interrogate multiple analytes from a drop of blood, is compatible with a smartphone detector, and displays analytical figures of merit that are comparable to standard laboratory-based ELISA in whole blood. These attributes of the D4 POCT have the potential to democratize access to high-performance immunoassays in resource-limited settings without sacrificing their performance.