Transcriptional profiling of Mycobacterium tuberculosis strains exposed to antibiotic treatment.
We profiled the transcriptional response of different, phylogenetically distinct Mycobacterium tuberculosis (Mtb) strains to isoniazid (INH) and rifampicin (RIF) using RNA-seq. We also hypothesized that an improved Mtb TRN could be inferred using a large, diverse compendium of RNA-seq data and therefore sought to supplement the existing repository of Mtb expression data available on SRA with additional conditions that could inform on Mtb's underlying regulatory programs. We went on to infer Mtb's TRN using a collection of inference methods in combination with this compendium, and employed it to build an interpretable machine learning model of Mtb fitness under stress. Overall design: RNA-seq analysis was performed on samples taken from Mtb cultures subjected to various stresses.