Experimental and Computational Workflow for RNA Sequencing in Mycobacterium tuberculosis : From Total RNA to Differentially Expressed Genes.

RNA sequencing (RNAseq) in bacteria has become a transformative tool for many applications, including the identification of mechanisms that contribute to pathogenesis, environmental adaptation, and drug response. The kinds of analysis outputs achievable from RNA-seq depend heavily on several key technical parameters during the sample preparation, sequencing, and data processing steps. In this chapter, we will describe the process of preparing Mycobacterium tuberculosis samples into sequencing libraries, selecting the appropriate sequencing platform, and performing data processing compatible with gene expression quantification. We will also discuss how each parameter could affect outcomes. The protocols described below produce consistently high yields. This chapter should inform on the technical considerations that impact sequencing output and enable the reader to decide on the best parameters to implement based on their own experimental goals.

Transcriptional regulator-induced phenotype screen reveals drug potentiators in Mycobacterium tuberculosis.

Transposon-based strategies provide a powerful and unbiased way to study the bacterial stress response1-8, but these approaches cannot fully capture the complexities of network-based behaviour. Here, we present a network-based genetic screening approach: the transcriptional regulator-induced phenotype (TRIP) screen, which we used to identify previously uncharacterized network adaptations of Mycobacterium tuberculosis to the first-line anti-tuberculosis drug isoniazid (INH). We found regulators that alter INH susceptibility when induced, several of which could not be identified by standard gene disruption approaches. We then focused on a specific regulator, mce3R, which potentiated INH activity when induced. We compared mce3R-regulated genes with baseline INH transcriptional responses and implicated the gene ctpD (Rv1469) as a putative INH effector. Evaluating a ctpD disruption mutant demonstrated a previously unknown role for this gene in INH susceptibility. Integrating TRIP screening with network information can uncover sophisticated molecular response programs.