dna-sequence-recognition-dna-primase-using-high-throughput-primase

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. ...

Duke University

Until now, it has been reasonably assumed that specific base-pair recognition is the only mechanism controlling the specificity of transcription factor (TF)-DNA binding. Contrary to this assumption, here we show that nonspecific DNA sequences possessing certain repeat symmetries, when present outside of specific TF binding sites (TFBSs), statistically control TF-DNA binding preferences. We used high-throughput protein-DNA binding assays to measure the binding levels and free energies of binding for several human TFs to tens of thousands of short DNA sequences with varying repeat symmetries. Based on statistical mechanics modeling, we identify a new protein-DNA binding mechanism induced by DNA sequence symmetry in the absence of specific base-pair recognition, and experimentally demonstrate that this mechanism indeed governs protein-DNA binding preferences. 

Protein-DNA binding in the absence of specific base-pair recognition.

Recent genome-wide experiments in different eukaryotic genomes provide an unprecedented view of transcription factor (TF) binding locations and of nucleosome occupancy. These experiments revealed that a large fraction of TF binding events occur in regions where only a small number of specific TF binding sites (TFBSs) have been detected. Furthermore, in vitro protein-DNA binding measurements performed for hundreds of TFs indicate that TFs are bound with wide range of affinities to different DNA sequences that lack known consensus motifs. These observations have thus challenged the classical picture of specific protein-DNA binding and strongly suggest the existence of additional recognition mechanisms that affect protein-DNA binding preferences. We have previously demonstrated that repetitive DNA sequence elements characterized by certain symmetries statistically affect protein-DNA binding preferences. We call this binding mechanism nonconsensus protein-DNA binding in order to emphasize the point that specific consensus TFBSs do not contribute to this effect. In this paper, using the simple statistical mechanics model developed previously, we calculate the nonconsensus protein-DNA binding free energy for the entire C. elegans and D. melanogaster genomes. Using the available chromatin immunoprecipitation followed by sequencing (ChIP-seq) results on TF-DNA binding preferences for ~100 TFs, we show that DNA sequences characterized by low predicted free energy of nonconsensus binding have statistically higher experimental TF occupancy and lower nucleosome occupancy than sequences characterized by high free energy of nonconsensus binding. This is in agreement with our previous analysis performed for the yeast genome. We suggest therefore that nonconsensus protein-DNA binding assists the formation of nucleosome-free regions, as TFs outcompete nucleosomes at genomic locations with enhanced nonconsensus binding. In addition, here we perform a new, large-scale analysis using in vitro TF-DNA preferences obtained from the universal protein binding microarrays (PBM) for ~90 eukaryotic TFs belonging to 22 different DNA-binding domain types. As a result of this new analysis, we conclude that nonconsensus protein-DNA binding is a widespread phenomenon that significantly affects protein-DNA binding preferences and need not require the presence of consensus (specific) TFBSs in order to achieve genome-wide TF-DNA binding specificity. 

Nonconsensus Protein Binding to Repetitive DNA Sequence Elements Significantly Affects Eukaryotic Genomes.

In the process of transcription elongation, RNA polymerase (RNAP) pauses at highly nonrandom positions across genomic DNA, broadly regulating transcription; however, molecular mechanisms responsible for the recognition of such pausing positions remain poorly understood. Here, using a combination of statistical mechanical modeling and high-throughput sequencing and biochemical data, we evaluate the effect of thermal fluctuations on the regulation of RNAP pausing. We demonstrate that diffusive backtracking of RNAP, which is biased by repetitive DNA sequence elements, causes transcriptional pausing. This effect stems from the increased microscopic heterogeneity of an elongation complex, and thus is entropy-dominated. This report shows a linkage between repetitive sequence elements encoded in the genome and regulation of RNAP pausing driven by thermal fluctuations.

Control of transcriptional pausing by biased thermal fluctuations on repetitive genomic sequences.

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.

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

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.

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

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.

Ariel Afek

Center for Genomic and Computational Biology,
Department of Biostatistics and Bioinformatics,
Center for Genomic and Computational Biology, Department of Biostatistics and Bioinformatics

Duke University

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

Ariel Afek

Center for Genomic and Computational Biology,Department of Biostatistics and Bioinformatics,Center for Genomic and Computational Biology, Department of Biostatistics and Bioinformatics

Duke University

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

Center for Genomic and Computational Biology,
Department of Biostatistics and Bioinformatics,
Center for Genomic and Computational Biology, Department of Biostatistics and Bioinformatics