Deciphering the Quantitative Effects of Cooperativity and Mutations on Transcription Factor Binding

Transcription factor (TF) proteins bind to DNA in a sequence specific manner to regulate gene expression. The binding affinity of TFs for individual sites is well characterized and can be represented using DNA motif models such as position weight matrices. However, there are many factors influencing TF-DNA recognition in the cell, leading to complexities than cannot be captured by motif models alone. Here, we present our studies on two factors: cooperative TF binding and alterations in TF binding due to DNA mutations. Both factors require quantitative and rigorous approaches to distinguish real effects from random noise.

First, we present a new method for characterizing cooperative binding of TFs to DNA. This method addresses the issue that TF binding sites located in close proximity, which occurs frequently across the human genome, are not necessarily bound cooperatively. To distinguish between cooperative and independent binding, we developed a high-throughput on-chip binding assay designed specifically to measure TF binding to neighboring sites. Using the experimental data from our assay, we trained machine learning models to differentiate between cooperative and independent binding of TFs. This method enabled us to reveal molecular mechanisms used by TFs to bind DNA cooperatively.

Second, we introduce QBiC-Pred (Quantitative Predictions of TF Binding Changes Due to Sequence Variants), an ordinary least squares based method to predict the magnitude of the effect of DNA mutations on TF-DNA recognition. We implemented QBiC-Pred as a web service: qbic.genome.duke.edu, which allows users to run our models through a user-friendly web interface. We used this method to identify non-recurring putative regulatory driver mutations in cancer. Our approach is novel because we prioritize mutations based on their effects on transcription factor (TF) binding, instead of relying on the recurrence of the mutations among tumor samples---which is often difficult to perform as individual non-coding mutations are rarely seen in more than one donor. Focusing on the functional effects of non-coding mutations across regulatory regions, we identified dozens of genes whose regulation in tumor cells is likely to be significantly perturbed by non-coding mutations.