Stability of the pH-Dependent Parallel-Stranded d(CGA) Motif

ABSTRACTNon-canonical DNA structures that retain programmability and structural predictability are increasingly being used in DNA nanotechnology applications, where they offer versatility beyond traditional Watson-Crick interactions. The d(CGA) triplet repeat motif is structurally dynamic and can transition between parallel-stranded homo-base paired duplex and anti-parallel unimolecular hairpin in a pH-dependent manner. Here, we evaluate the thermodynamic stability and nuclease sensitivity of oligonucleotides composed of the d(CGA) motif and several structurally related sequence variants. These results show that the structural transition resulting from decreasing the pH is accompanied by both a significant energetic stabilization and decreased nuclease sensitivity as unimolecular hairpin structures are converted to parallel-stranded homo-base paired duplexes. Furthermore, the stability of the parallel-stranded duplex form can be altered by changing the 5′-nucleobase of the d(CGA) triplet and the frequency and position of the altered triplets within long stretches of d(CGA) triplets. This work offers insight into the stability and versatility of the d(CGA) triplet repeat motif and provides constraints for using this pH-adaptive structural motif for creating DNA-based nanomaterials.STATEMENT OF SIGNIFICANCEThis article addresses the stability of the d(CGA) triplet motif and variants in solution. Our study reveals changes in thermodynamic stability and nuclease resistance in response to pH. The identity of the 5′-nucleobase within each triplet and the position and frequency of different triplets within stretches of d(CGA) triplets can tune parallel-stranded duplex stability. This tunability can be used for nanotechnological applications where the specificity of the 5′-nucleobase pairing interaction is used to order of long stretches of d(CGA) triplets. These results can inform the rational design of pH-sensitive structurally switchable DNA-based nanomaterials.

Transcription promotes the interaction of the FAcilitates Chromatin Transactions (FACT) complex with nucleosomes in S. cerevisiae

ABSTRACTThe FACT (FAcilitates Chromatin Transactions) complex is enriched on highly expressed genes, where it facilitates transcription while maintaining chromatin structure. How it is targeted to these regions is unknown. In vitro, FACT binds destabilized nucleosomes, supporting the hypothesis that FACT is targeted to transcribed chromatin through recognition of RNA polymerase-disrupted nucleosomes. In this study, we used high resolution analysis of FACT occupancy in S. cerevisiae to test this hypothesis. We demonstrate that FACT interacts with unstable nucleosomes in vivo and its interaction with chromatin is dependent on transcription by any of the three RNA polymerases. Deep sequencing of micrococcal nuclease-resistant fragments shows that FACT-bound nucleosomes exhibit differences in micrococcal nuclease sensitivity compared to bulk chromatin, consistent with a modified nucleosome structure being the preferred ligand for this complex. While the presence of altered nucleosomes associated with FACT can also be explained by the known ability of this complex to modulate nucleosome structure, transcription inhibition alleviates this effect indicating that it is not due to FACT interaction alone. Collectively these results suggest that FACT is targeted to transcribed genes through preferential interaction with RNA polymerase disrupted nucleosomes.

iSeg: an efficient algorithm for segmentation of genomic and epigenomic data

BackgroundIdentification of functional elements of a genome often requires dividing a sequence of measurements along a genome into segments where adjacent segments have different properties, such as different mean values. This problem is often called the segmentation problem in the field of genomics, and the change-point problem in other scientific disciplines. Despite dozens of algorithms developed to address this problem in genomics research, methods with improved accuracy and speed are still needed to effectively tackle both existing and emerging genomic and epigenomic segmentation problems.ResultsWe designed an efficient algorithm, called iSeg, for segmentation of genomic and epigenomic profiles. iSeg first utilizes dynamic programming to identify candidate segments and test for significance. It then uses a novel data structure based on two coupled balanced binary trees to detect overlapping significant segments and update them simultaneously during searching and refinement stages. Refinement and merging of significant segments are performed at the end to generate the final set of segments. By using an objective function based on the p-values of the segments, the algorithm can serve as a general computational framework to be combined with different assumptions on the distributions of the data. As a general segmentation method, it can segment different types of genomic and epigenomic data, such as DNA copy number variation, nucleosome occupancy, nuclease sensitivity, and differential nuclease sensitivity data. Using simple t-tests to compute p-values across multiple datasets of different types, we evaluate iSeg using both simulated and experimental datasets and show that it performs satisfactorily when compared with some other popular methods, which often employ more sophisticated statistical models. Implemented in C++, iSeg is also very computationally efficient, well suited for large numbers of input profiles and data with very long sequences.ConclusionsWe have developed an effective and efficient general-purpose segmentation tool for sequential data and illustrated its use in segmentation of genomic and epigenomic profiles.

Tracking chromatin states using controlled DNase I treatment and real-time PCR

A novel approach to DNase I-sensitivity analysis was applied to examining genes of the spermatogenic pathway, reflective of the substantial morphological and genomic changes that occur during this program of differentiation. A new real-time PCR-based strategy that considers the nuances of response to nuclease treatment was used to assess the nuclease susceptibility through differentiation. Data analysis was automated with the K-Lab PCR algorithm, facilitating the rapid analysis of multiple samples while eliminating the subjectivity usually associated with Ct analyses. The utility of this assay and analytical paradigm as applied to nuclease-sensitivity mapping is presented.