The Role of Genome Accessibility in Transcription Factor Binding in Bacteria

ChIP-seq enables genome-scale identification of regulatory regions that govern gene expression. However, the biological insights generated from ChIP-seq analysis have been limited to predictions of binding sites and cooperative interactions. Furthermore, ChIP-seq data often poorly correlate with in vitro measurements or predicted motifs, highlighting that binding affinity alone is insufficient to explain transcription factor (TF)-binding in vivo. One possibility is that binding sites are not equally accessible across the genome. A more comprehensive biophysical representation of TF-binding is required to improve our ability to understand, predict, and alter gene expression. Here, we show that genome accessibility is a key parameter that impacts TF-binding in bacteria. We developed a thermodynamic model that parameterizes ChIP-seq coverage in terms of genome accessibility and binding affinity. The role of genome accessibility is validated using a large-scale ChIP-seq dataset of the M. tuberculosis regulatory network. We find that accounting for genome accessibility led to a model that explains 63% of the ChIP-seq profile variance, while a model based in motif score alone explains only 35% of the variance. Moreover, our framework enables de novo ChIP-seq peak prediction and is useful for inferring TF-binding peaks in new experimental conditions by reducing the need for additional experiments. We observe that the genome is more accessible in intergenic regions, and that increased accessibility is positively correlated with gene expression and anti-correlated with distance to the origin of replication. Our biophysically motivated model provides a more comprehensive description of TF-binding in vivo from first principles towards a better representation of gene regulation in silico, with promising applications in systems biology.
Author Summary A quantitative description of transcription factor (TF) binding in vivo is critical for our understanding of gene regulation. Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) provides a genome-scale map of TF-binding. However, a quantitative characterization of the impact of genome accessibility on TF-binding in bacteria remains elusive. In order to help recruit or block gene expression, TFs must have physical access to regulatory regions. This paper presents a thermodynamics model that describes TF-binding in terms of genome accessibility and binding site affinity. We apply this model in a ChIP-seq dataset for Mycobacterium tuberculosis and observed that genome accessibility is critical to our understanding of TF-binding in vivo. This new model provides practical applications, such as de novo prediction of TF-binding peaks and a framework to measure DNA accessibility from ChIP-seq data. Our model enables us to quantify the relationship of genome accessibility with genomic features and suggest mechanisms that influence genome accessibility in vivo (e.g. distance to oriC). The model proposed in this study gives new perspective for ChIP-seq analysis in bacteria towards an improved description of gene regulation in silico.