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Review
. 2015 Sep 15:86:80-8.
doi: 10.1016/j.ymeth.2015.05.022. Epub 2015 May 29.

Defining bacterial regulons using ChIP-seq

Kevin S Myers  1 , Dan M Park  2 , Nicole A Beauchene  3 , Patricia J Kiley  4
Affiliations
Review

Defining bacterial regulons using ChIP-seq

Kevin S Myers et al. Methods. .

Abstract

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is a powerful method that identifies protein-DNA binding sites in vivo. Recent studies have illustrated the value of ChIP-seq in studying transcription factor binding in various bacterial species under a variety of growth conditions. These results show that in addition to identifying binding sites, correlation of ChIP-seq data with expression data can reveal important information about bacterial regulons and regulatory networks. In this chapter, we provide an overview of the current state of knowledge about ChIP-seq methodology in bacteria, from sample preparation to raw data analysis. We also describe visualization and various bioinformatic analyses of processed ChIP-seq data.

Keywords: Bacterial regulons; Bioinformatics analysis of genomic data; ChIP-seq; Genome-wide analysis; Systems biology; Transcription factor binding sites; Transcriptional regulation.

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Figures

Figure 1
Figure 1. ChIP-seq Sample Preparation and Analysis
ChIP-seq begins with cell cultures grown in defined conditions. When cultures reach the desired growth stage, they are treated with formaldehyde to crosslink proteins and DNA. The cells are lysed and sheared so the average DNA length is ~500 bp. The DNA-protein samples are split into two populations, the Immunoprecipitate (IP) fraction, which is enriched for a particular protein of interest using an antibody, and the Input fraction that is not enriched for any particular protein of interest. The crosslinks are reversed by heat treatment and the DNA fragments are subjected to high-throughput sequencing. After sequencing, the resulting sequencing reads are examined for quality and trimmed based on read quality. The trimmed reads are then aligned to a reference genome and algorithms and/or visual inspection identify ChIP-seq peaks. After peaks are associated with genes downstream, several bioinformatic analyses can be performed including motif identification and analysis, differential occupancy analysis, and correlation with expression data for in depth understanding of bacterial regulons.
Figure 2
Figure 2. Comparing ChIP-seq data between growth conditions
Shown are RNAP (σ70) ChIP-seq data traces collected from cultures grown under aerobic (red) or anaerobic (blue) conditions [2]. ChIP-seq IP/INPUT ratio is shown on the y-axis and genomic position is shown on the x-axis. The asterisk indicates an example of differential binding between growth conditions. This figure was generated in the MochiView browser [53].
Figure 3
Figure 3. Example of combining bacterial regulons to better understand gene regulation in response to oxygen
Shown is the proposed regulatory role of FNR (red ovals) [2], ArcA (purple square) [3], IscR (blue ovals) (Unpublished Data), and Fur (green rectangle) (Unpublished Data) on operons (blue, yellow, and gray circles) with a significant change in expression based on the presence or absence of O2. Lines indicate a regulatory role of a TF on expression from ChIP-chip/ChIP-seq and RNA expression data, either direct (blue circles) or indirect (yellow circles). Approximately 60% of the operons with an O2-dependent change in expression are regulated by FNR, ArcA, IscR, or Fur. The remaining 40% (gray circles) are regulated by other mechanisms not currently understood.
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