Biology subjects

Bishop, A. R.

Publications and source records attributed to Bishop, A. R..

4 recordsLinked to original sources

bioRxiv2025

Contribution of DNA breathing to physical interactions with transcription factors

Interaction between transcription factors (TFs) and DNA plays a key role in regulating gene expression. It is generally believed that these interactions are controlled through recognition of DNA core motifs by TFs. Nevertheless, several studies pointed out the limitation of this view, in particular, DNA sequence variants influencing TF binding are often located outside of core motifs. One possible explanation is that the physical properties of DNA may play a role in TF-DNA interactions. Recent studies have supported the importance of DNA shape features, especially in flanking regions of core motifs. Another important physical property of DNA is DNA breathing, the spontaneous opening of double-stranded DNA through thermal motions. But there have been few genomic studies of the role of DNA breathing in TF-DNA interactions. In this work, we analyzed in vitro TF-DNA binding data of three TFs and found that DNA breathing features inside or near core motifs are correlated with binding affinity. This suggests that these TFs may prefer locally and temporally melted DNA formed through breathing. We extended the analysis to 44 TFs with in vivo ChIP-seq binding data. We found that for a large proportion of TFs, their breathing features in or near core motifs are associated with binding, but the sign and magnitude of these associations vary substantially across TF families. Altogether, our study supports the hypothesis that DNA breathing features near binding motifs contribute to TF-DNA interactions. Author SummaryProper regulation of when and where genes are expressed is crucial to biological development and function. This process is largely controlled by interaction of transcription factors (TFs) with DNA sequences. The recognition of specific DNA sequences by TFs is important to ensure that only the correct genes are activated. Extensive work has shown that TFs prefer to bind certain DNA sequence patterns of 6-20 bp, known as motifs. However, the structure of DNA molecules may also play a role. In this work, we explored the role of DNA breathing, which refers to spontaneous opening of double strand DNA due to thermal motions. This process creates transient, single-strand "bubbles" in DNA. Through examining TF-DNA binding data of >60 TFs, we found that the propensity of DNA forming bubbles near motifs is often associated with binding affinity of DNA sequence. Interestingly the patterns of these associations seem to vary with TFs. Altogether, our results highlighted the potential of DNA breathing in influencing TF-DNA interactions.

genetics↗

bioRxiv2024

Advancing Transcription Factor Binding Site Prediction Using DNA Breathing Dynamics and Sequence Transformers via Cross Attention

Understanding the impact of genomic variants on transcription factor binding and gene regulation remains a key area of research, with implications for unraveling the complex mechanisms underlying various functional effects. Our study delves into the role of DNAs biophysical properties, including thermodynamic stability, shape, and flexibility in transcription factor (TF) binding. We developed a multi-modal deep learning model integrating these properties with DNA sequence data. Trained on ChIP-Seq (chromatin immunoprecipitation sequencing) data in vivo involving 690 TF-DNA binding events in human genome, our model significantly improves prediction performance in over 660 binding events, with up to 9.6% increase in AUROC metric compared to the baseline model when using no DNA biophysical properties explicitly. Further, we expanded our analysis to in vitro high-throughput Systematic Evolution of Ligands by Exponential enrichment (SELEX) and Protein Binding Microarray (PBM) datasets, comparing our model with established frameworks. The inclusion of DNA breathing features consistently improved TF binding predictions across different cell lines in these datasets. Notably, for complex ChIP-Seq datasets, integrating DNABERT2 with a cross-attention mechanism provided greater predictive capabilities and insights into the mechanisms of disease-related non-coding variants found in genome-wide association studies. This work highlights the importance of DNA biophysical characteristics in TF binding and the effectiveness of multi-modal deep learning models in gene regulation studies.

bioinformatics↗

bioRxiv2023

Examining DNA Breathing with pyDNA-EPBD

MotivationThe two strands of the DNA double helix locally and spontaneously separate and recombine in living cells due to the inherent thermal DNA motion.This dynamics results in transient openings in the double helix and is referred to as "DNA breathing" or "DNA bubbles." The propensity to form local transient openings is important in a wide range of biological processes, such as transcription, replication, and transcription factors binding. However, the modeling and computer simulation of these phenomena, have remained a challenge due to the complex interplay of numerous factors, such as, temperature, salt content, DNA sequence, hydrogen bonding, base stacking, and others. ResultsWe present pyDNA-EPBD, a parallel software implementation of the Extended Peyrard-Bishop-Dauxois (EPBD) nonlinear DNA model that allows us to describe some features of DNA dynamics in detail. The pyDNA-EPBD generates genomic scale profiles of average base-pair openings, base flipping probability,DNA bubble probability, and calculations of the characteristically dynamic length indicating the number of base pairs statistically significantly affected by a single point mutation using the Markov Chain Monte Carlo (MCMC) algorithm.

genomics↗

bioRxiv2020

Assessing the heterogeneity of cardiac non-myocytes and the effect of cell culture with integrative single cell analysis

Cardiac non-myocytes comprise a diverse and crucial cell population in the heart that plays dynamic roles in cardiac wound healing and growth. Non-myocytes broadly fall into four cell types: endothelium, fibroblasts, leukocytes, and pericytes. Here we characterize the diversity of the non-myocytes in vivo and in vitro using mass cytometry. By leveraging single-cell RNA sequencing we inform the design of a mass cytometry panel. To aid in annotation of the mass cytometry datasets, we utilize data integration with a neural network. We introduce approximately 460,000[~] single cell proteomes of non-myocytes as well as 5,000[~] CD31 negative single cell transcriptomes. Using our data, as well as previously reported datasets, we characterize cardiac non-myocytes with high depth in six mice, characterizing novel surface markers (CD9, CD200, Notch3, and FolR2). Further, we find that extended cell culture promotes the proliferation of CD45+CD11b+FolR2+IAIE- myeloid cells in addition to fibroblasts.

cell biology↗