SUMMARY: Tuberculosis (TB) is the leading infectious cause of mortality worldwide, due in part to a limited understanding of its clinical pathogenic spectrum of infection and disease. Historically, scientific research, diagnostic testing, and drug treatment have focused on addressing one of two disease states: latent TB infection or active TB disease. Recent research has clearly demonstrated that human TB infection, from latent infection to active disease, exists within a continuous spectrum of metabolic bacterial activity and antagonistic immunological responses. This revised understanding leads us to propose two additional clinical states: incipient and subclinical TB. The recognition of incipient and subclinical TB, which helps divide latent and active TB along the clinical disease spectrum, provides opportunities for the development of diagnostic and therapeutic interventions to prevent progression to active TB disease and transmission of TB bacilli. In this report, we review the current understanding of the pathogenesis, immunology, clinical epidemiology, diagnosis, treatment, and prevention of both incipient and subclinical TB, two emerging clinical states of an ancient bacterium.

Mycobacterium tuberculosis (MTB) infects 30% of all humans and kills someone every 20–30 s. Here we report genome-wide binding for ~80% of all predicted MTB transcription factors (TFs), and assayed global expression following induction of each TF. The MTB DNA-binding network consists of ~16,000 binding events from 154 TFs. We identify >50 TF-DNA consensus motifs and >1,150 promoter-binding events directly associated with proximal gene regulation. An additional ~4,200 binding events are in promoter windows and represent strong candidates for direct transcriptional regulation under appropriate environmental conditions. However, we also identify >10,000 ‘dormant’ DNA-binding events that cannot be linked directly with proximal transcriptional control, suggesting that widespread DNA binding may be a common feature that should be considered when developing global models of coordinated gene expression.

Approximately 5% of patients with drug-susceptible tuberculosis have a relapse after 6 months of first-line therapy, as do approximately 20% of patients after 4 months of short-course therapy. We postulated that by analyzing pretreatment isolates of Mycobacterium tuberculosis obtained from patients who subsequently had a relapse or were cured, we could determine any correlations between the minimum inhibitory concentration (MIC) of a drug below the standard resistance breakpoint and the relapse risk after treatment.

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.

The resilience of Mycobacterium tuberculosis (MTB) emerges from its ability to effectively counteract immunological, environmental and antitubercular challenges. Here, we demonstrate that MTB can tolerate drug treatment by adopting a tolerant state that can be deciphered through systems analysis of its transcriptional responses. Specifically, we demonstrate how treatment with the antitubercular drug bedaquiline activates a regulatory network that coordinates multiple resistance mechanisms to push MTB into a tolerant state. Disruption of this network, by knocking out its predicted transcription factors, Rv0324 and Rv0880, significantly increased bedaquiline killing and enabled the discovery of a second drug, pretomanid, that potentiated killing by bedaquiline. We demonstrate that the synergistic effect of this combination emerges, in part, through disruption of the tolerance network. We discuss how this network strategy also predicts drug combinations with antagonistic interactions, potentially accelerating the discovery of new effective combination drug regimens for tuberculosis.