Weak links: Advancing target-based drug discovery by identifying the most vulnerable targets.

Mycobacterium tuberculosis remains the most common infectious killer worldwide despite decades of antitubercular drug development. Effectively controlling the tuberculosis (TB) pandemic will require innovation in drug discovery. In this review, we provide a brief overview of the two main approaches to discovering new TB drugs-phenotypic screens and target-based drug discovery-and outline some of the limitations of each method.

Compensatory evolution in NusG improves fitness of drug-resistant M. tuberculosis.

Drug-resistant bacteria are emerging as a global threat, despite frequently being less fit than their drug-susceptible ancestors. Here we sought to define the mechanisms that drive or buffer the fitness cost of rifampicin resistance (RifR) in the bacterial pathogen Mycobacterium tuberculosis (Mtb). Rifampicin inhibits RNA polymerase (RNAP) and is a cornerstone of modern short-course tuberculosis therapy.

Beyond antibiotic resistance: The whiB7 transcription factor coordinates an adaptive response to alanine starvation in mycobacteria.

Pathogenic mycobacteria are a significant cause of morbidity and mortality worldwide. The conserved whiB7 stress response reduces the effectiveness of antibiotic therapy by activating several intrinsic antibiotic resistance mechanisms. Despite our comprehensive biochemical understanding of WhiB7, the complex set of signals that induce whiB7 expression remain less clear.

Beyond antibiotic resistance: the transcription factor coordinates an adaptive response to alanine starvation in mycobacteria.

Pathogenic mycobacteria are a significant cause of morbidity and mortality worldwide. These bacteria are highly intrinsically drug resistant, making infections challenging to treat. The conserved stress response is a key contributor to mycobacterial intrinsic drug resistance.

Mutations in Confer Low-Level Resistance to Benzothiazinone DprE1 Inhibitors in Mycobacterium tuberculosis.

Tuberculosis (TB) is the leading cause of death from any bacterial infection, causing 1.5 million deaths worldwide each year. Due to the emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb) there have been significant efforts aimed at developing novel drugs to treat TB.

CRISPRi chemical genetics and comparative genomics identify genes mediating drug potency in Mycobacterium tuberculosis.

Mycobacterium tuberculosis (Mtb) infection is notoriously difficult to treat. Treatment efficacy is limited by Mtb's intrinsic drug resistance, as well as its ability to evolve acquired resistance to all antituberculars in clinical use. A deeper understanding of the bacterial pathways that influence drug efficacy could facilitate the development of more effective therapies, identify new mechanisms of acquired resistance, and reveal overlooked therapeutic opportunities.

CRISPR Interference Reveals That All--Retinoic Acid Promotes Macrophage Control of Mycobacterium tuberculosis by Limiting Bacterial Access to Cholesterol and Propionyl Coenzyme A.

Macrophages are a protective replicative niche for Mycobacterium tuberculosis (Mtb) but can kill the infecting bacterium when appropriately activated. To identify mechanisms of clearance, we compared levels of bacterial restriction by human macrophages after treatment with 26 compounds, including some currently in clinical trials for tuberculosis. All--retinoic acid (ATRA), an active metabolite of vitamin A, drove the greatest increase in Mtb control.

Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis.

Antibacterial agents target the products of essential genes but rarely achieve complete target inhibition. Thus, the all-or-none definition of essentiality afforded by traditional genetic approaches fails to discern the most attractive bacterial targets: those whose incomplete inhibition results in major fitness costs. In contrast, gene "vulnerability" is a continuous, quantifiable trait that relates the magnitude of gene inhibition to the effect on bacterial fitness.

Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression.

Background: Deep sequencing of transposon mutant libraries (or TnSeq) is a powerful method for probing essentiality of genomic loci under different environmental conditions. Various analytical methods have been described for identifying conditionally essential genes whose tolerance for insertions varies between two conditions. However, for large-scale experiments involving many conditions, a method is needed for identifying genes that exhibit significant variability in insertions across multiple conditions.

Chemical Genetic Interaction Profiling Reveals Determinants of Intrinsic Antibiotic Resistance in Mycobacterium tuberculosis.

Chemotherapy for tuberculosis (TB) is lengthy and could benefit from synergistic adjuvant therapeutics that enhance current and novel drug regimens. To identify genetic determinants of intrinsic antibiotic susceptibility in , we applied a chemical genetic interaction (CGI) profiling approach. We screened a saturated transposon mutant library and identified mutants that exhibit altered fitness in the presence of partially inhibitory concentrations of rifampin, ethambutol, isoniazid, vancomycin, and meropenem, antibiotics with diverse mechanisms of action.

Statistical analysis of genetic interactions in Tn-Seq data.

Tn-Seq is an experimental method for probing the functions of genes through construction of complex random transposon insertion libraries and quantification of each mutant's abundance using next-generation sequencing. An important emerging application of Tn-Seq is for identifying genetic interactions, which involves comparing Tn mutant libraries generated in different genetic backgrounds (e.g.

Comprehensive Essentiality Analysis of the Mycobacterium tuberculosis Genome via Saturating Transposon Mutagenesis.

Unlabelled: For decades, identifying the regions of a bacterial chromosome that are necessary for viability has relied on mapping integration sites in libraries of random transposon mutants to find loci that are unable to sustain insertion. To date, these studies have analyzed subsaturated libraries, necessitating the application of statistical methods to estimate the likelihood that a gap in transposon coverage is the result of biological selection and not the stochasticity of insertion. As a result, the essentiality of many genomic features, particularly small ones, could not be reliably assessed.

Trehalose-6-Phosphate-Mediated Toxicity Determines Essentiality of OtsB2 in Mycobacterium tuberculosis In Vitro and in Mice.

Trehalose biosynthesis is considered an attractive target for the development of antimicrobials against fungal, helminthic and bacterial pathogens including Mycobacterium tuberculosis. The most common biosynthetic route involves trehalose-6-phosphate (T6P) synthase OtsA and T6P phosphatase OtsB that generate trehalose from ADP/UDP-glucose and glucose-6-phosphate. In order to assess the drug target potential of T6P phosphatase, we generated a conditional mutant of M.

Normalization of transposon-mutant library sequencing datasets to improve identification of conditionally essential genes.

Sequencing of transposon-mutant libraries using next-generation sequencing (TnSeq) has become a popular method for determining which genes and non-coding regions are essential for growth under various conditions in bacteria. For methods that rely on quantitative comparison of counts of reads at transposon insertion sites, proper normalization of TnSeq datasets is vitally important. Real TnSeq datasets are often noisy and exhibit a significant skew that can be dominated by high counts at a small number of sites (often for non-biological reasons).

TRANSIT--A Software Tool for Himar1 TnSeq Analysis.

TnSeq has become a popular technique for determining the essentiality of genomic regions in bacterial organisms. Several methods have been developed to analyze the wealth of data that has been obtained through TnSeq experiments. We developed a tool for analyzing Himar1 TnSeq data called TRANSIT.

Capturing Uncertainty by Modeling Local Transposon Insertion Frequencies Improves Discrimination of Essential Genes.

Transposon mutagenesis experiments enable the identification of essential genes in bacteria. Deep-sequencing of mutant libraries provides a large amount of high-resolution data on essentiality. Statistical methods developed to analyze this data have traditionally assumed that the probability of observing a transposon insertion is the same across the genome.

A Hidden Markov Model for identifying essential and growth-defect regions in bacterial genomes from transposon insertion sequencing data.

Background: Knowledge of which genes are essential to the survival of an organism is critical to understanding the function of genes, and for the identification of potential drug targets for antimicrobial treatment. Previous statistical methods for assessing essentiality based on sequencing of tranposon libraries have usually limited their assessment to strict 'essential' or 'non-essential' categories. However, this binary view of essentiality does not accurately represent the more nuanced ways the growth of an organism might be affected by the disruption of its genes.

Bayesian analysis of gene essentiality based on sequencing of transposon insertion libraries.

Motivation: Next-generation sequencing affords an efficient analysis of transposon insertion libraries, which can be used to identify essential genes in bacteria. To analyse this high-resolution data, we present a formal Bayesian framework for estimating the posterior probability of essentiality for each gene, using the extreme-value distribution to characterize the statistical significance of the longest region lacking insertions within a gene. We describe a sampling procedure based on the Metropolis-Hastings algorithm to calculate posterior probabilities of essentiality while simultaneously integrating over unknown internal parameters.

Reannotation of translational start sites in the genome of Mycobacterium tuberculosis.

Identification and correction of incorrect ORF start sites is important for a variety of experimental and analytical purposes, ranging from cloning to inference of operon structure. The genome of the H37Rv reference strain of Mycobacterium tuberculosis (Mtb) was originally annotated when it was first sequenced nearly 15 years ago. While this annotation has served the TB research community well as a standard of reference for over a decade, it has been demonstrated experimentally that the actual start sites for an estimated 5-10% of open reading frames differ from the annotation.

High-resolution phenotypic profiling defines genes essential for mycobacterial growth and cholesterol catabolism.

The pathways that comprise cellular metabolism are highly interconnected, and alterations in individual enzymes can have far-reaching effects. As a result, global profiling methods that measure gene expression are of limited value in predicting how the loss of an individual function will affect the cell. In this work, we employed a new method of global phenotypic profiling to directly define the genes required for the growth of Mycobacterium tuberculosis.