Bacterial transcription factors (TFs) affect gene regulation by recognizing and binding regulatory DNA sequences and subsequent recruitment of transcriptional machinery. The emergence of new genomic techniques has made global mapping of TF-DNA binding sites more feasible. The advent of high-throughput, next-generation sequencing (NGS) methods such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) provides high-resolution genome-wide binding data that helps increase our understanding of bacterial gene regulatory networks. This technology has so far been applied on a handful of bacterial TFs, leading to more transcription factor binding sites (TFBS) open for discovery. Despite being one of the most studied microorganisms in scientific research, only a select few Escherichia coli TFs have been studied on a genome-wide scale.
In order to generate a more accurate map of E.coli TF binding locations, this work uses a combination of ChIP-based protocols both done in vivo and in vitro to properly identify all possible binding locations for TFs being studied. Results show that our methods not only find strong, well-known reported binding sites but also discover many more novel, biologically relevant ones. Our ability to properly evaluate and characterize TFBS based on the analysis of large-scale data sets generated from all the ChIP experiments improves how binding sites are being called in high-throughput data, constructing a comprehensive framework to build and model the regulatory network on.
The successful implementation of the developed in vitro ChIP method shows good reproducibility with in vivo ChIP data, and also captures more actual binding occurrences, fairly complementing data generated from motif-based computationally predicted binding sites. Assessment of the differential binding patterns observed highlight the role that binding motif sequence and DNA accessibility play in determining TF binding in the cell.
This work also demonstrates the unprecedented ability of using both in vivo and in vitro binding methods in parallel to generate a more complete model of TF binding. Over-all, the results present the potential to further our understanding of bacterial regulatory networks through an integrated analysis and characterization of TF–DNA binding behavior across the genome.2021-09-28T00:00:00