A comprehensive map of genome-wide gene regulation in Mycobacterium tuberculosis

Abstract

Mycobacterium tuberculosis (MTB) is a pathogenic bacterium responsible for 12 million active cases of tuberculosis (TB) worldwide. The complexity and critical regulatory components of MTB pathogenicity are still poorly understood despite extensive research efforts. In this study, we constructed the first systems-scale map of transcription factor (TF) binding sites and their regulatory target proteins in MTB. We constructed FLAG-tagged overexpression constructs for 206 TFs in MTB, used ChIP-seq to identify genome-wide binding events and surveyed global transcriptomic changes for each overexpressed TF. Here we present data for the most comprehensive map of MTB gene regulation to date. We also define elaborate quality control measures, extensive filtering steps, and the gene-level overlap between ChIP-seq and microarray datasets. Further, we describe the use of TF overexpression datasets to validate a global gene regulatory network model of MTB and describe an online source to explore the datasets.

Figure 1. ChIP-Seq and TFOE Analysis Workflow.

All putative DNA-binding proteins in MTB genome were cloned into expression vector with FLAG-tag under the control of inducible promoter. After induction of expression, either chromatin immunoprecipitation followed by sequencing or transcriptional profiling by using high-density tiling arrays was performed for each TF. For ChIP-seq experiments, confident binding events across genome were identified after analysis and filtering of read pileups as described in Methods section. These binding events were further investigated with respect to transcription start sites and compared to expression consequences in TFOE dataset. Consensus DNA binding motifs were also identified. For TFOE experiments, differentially expressed genes were identified by microarray analysis. Differential expression signatures were used to build a transcriptional network model. Moreover, TFOE-derived regulatory influences were compared to 12 existing regulons as a validation.

Figure 2. Comparison of ChIP-Seq, TFOE data and EGRIN model.

(a) We investigated the overlap between ChIP-seq binding events, differential expression in the TFOE dataset and proximal binding in promoter window analysis in order to assess transcriptional consequences of DNA-binding events. (b) ChIP-seq and TFOE datasets were further compared to regulatory influences identified in EGRIN model to validate data-driven model predictions with experimentally identified influences.

Figure 3. Screenshot of MTB Network Portal highlighting modules, binding events and dataset tables.

MTB Network Portal provides gene- and regulatory module-centric visualizations and integrate with other TB resources such as Tuberculist and PATRIC. Only few example features are highlighted in here. (a) Expression profile of all the genes in the module together with de-novo identified motifs and motif locations are displayed for regulatory modules. (b) Table of TFs that bind with close proximity of a given gene from ChIP-seq experiments is listed on the gene page. (c) A table of ChIP-seq binding events with details for targets of a given TF is displayed together with expression consequences. (d) Detailed information for each dataset (ChIP-seq or TF overexpression) is given together with links to corresponding repositories and portal resources.

Figure 4. Screenshot of MTB Network Portal highlighting expression data, essentiality and regulatory modules.

Gene detail pages include information for available experiments associated with given a gene, essentiality graphs and link to regulatory modules that contain this gene. (a) Available gene expression datasets from TF overexpression data are listed on the gene page for each TF. (b) In vivo and in vitro essentiality data is also shown on the gene page. (c) Regulatory modules that include the gene are displayed with residual, motif logos, and motif e-values.