Characterization of genome-wide ordered sequence-tagged Mycobacterium mutant libraries by Cartesian Pooling-Coordinate Sequencing

Reverse genetics research approaches require the availability of methods to rapidly generate specific mutants. Alternatively, where these methods are lacking, the construction of pre-characterized libraries of mutants can be extremely valuable. However, this can be complex, expensive and time consuming. Here, we describe a robust, easy to implement parallel sequencing-based method (Cartesian Pooling-Coordinate Sequencing or CP-CSeq) that reports both on the identity as well as on the location of sequence-tagged biological entities in well-plate archived clone collections. We demonstrate this approach using a transposon insertion mutant library of the Mycobacterium bovis BCG vaccine strain, providing the largest resource of mutants in any strain of the M. tuberculosis complex. The method is applicable to any entity for which sequence-tagged identification is possible

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. In addition, these methods often limit their analysis to open-reading frames. We propose a novel method for analyzing sequence data from transposon mutant libraries using a Hidden Markov Model (HMM), along with formulas to adapt the parameters of the model to different datasets for robustness. This approach allows for the clustering of insertion sites into distinct regions of essentiality across the entire genome in a statistically rigorous manner, while also allowing for the detection of growth-defect and growth-advantage regions. RESULTS: We evaluate the performance of a 4-state HMM on a sequence dataset of M. tuberculosis transposon mutants. We also test the HMM on several synthetic datasets representing different levels of transposon insertion density and sequence coverage. We show that the HMM produces results that are highly correlated with previous assignments of essentiality for this organism. We also show that it detects growth-defect and growth-advantage genes previously shown to impair or enhance growth when disrupted. CONCLUSIONS: A 4-state HMM provides an improved way of analyzing Tn-seq data and assessing different levels of essentiality that enables not only the characterization of essential and non-essential genes, but also genes whose disruption leads to impairment (or enhancement) of growth

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. TRANSIT provides a graphical interface to three different statistical methods for analyzing TnSeq data. These methods cover a variety of approaches capable of identifying essential genes in individual datasets as well as comparative analysis between conditions. We demonstrate the utility of this software by analyzing TnSeq datasets of M. tuberculosis grown on glycerol and cholesterol. We show that TRANSIT can be used to discover genes which have been previously implicated for growth on these carbon sources. TRANSIT is written in Python, and thus can be run on Windows, OSX and Linux platforms. The source code is distributed under the GNU GPL v3 license and can be obtained from the following GitHub repository: https://github.com/mad-lab/transit

Turning the tide against TB: Remaking ineffective host defenses into mechanisms for tuberculosis control

Most antibiotics, including the drugs currently used for treating tuberculosis (TB), were first discovered as molecules that inhibit bacterial growth in laboratory culture conditions and later translated to infection models and clinical use. Mycobacterium tuberculosis (Mtb) has evolved specifically to survive in its human host, and it is in this infectious context that new drugs need to work. The host environment is characterized by a multitude of antimicrobial defenses induced by the immune system, and we can leverage these defenses to kill Mtb in vivo. Mtb employs a diverse set of responses to survive host defenses. By blocking these responses, we can make Mtb more susceptible to host immunity, turning these previously impotent defenses into effective strategies of immune control

A guide to Mycobacterium mutagenesis

The genus Mycobacterium includes several pathogens that cause severe disease in humans, like Mycobacterium tuberculosis (M. tb), the infectious agent causing tuberculosis. Genetic tools to engineer mycobacterial genomes, in a targeted or random fashion, have provided opportunities to investigate M. tb infection and pathogenesis. Furthermore, they have allowed the identification and validation of potential targets for the diagnosis, prevention, and treatment of tuberculosis. This review describes the various methods that are available for the generation of mutants in Mycobacterium species, focusing specifically on tools for altering slow-growing mycobacteria from the M. tb complex. Among others, it incorporates the recent new molecular biological technologies (e.g. ORBIT) to rapidly and/or genome-wide comprehensively obtain targeted mutants in mycobacteria. As such, this review can be used as a guide to select the appropriate genetic tools to generate mycobacterial mutants of interest, which can be used as tools to aid understanding of M. tb infection or to help developing TB intervention strategies

Statistical Analysis of Transposon Sequencing Data to Determine Essential Genes

Transposon Sequencing (TnSeq) has become a popular biological tool for assessing the phenotypes of large libraries of bacterial mutants at the same time. This allows for high-throughput identification of genes which are essential for growth, thus providing valuable information about the function of those genes and the discovery of potential drug targets that could lead to treatments. However, analysis of data obtained from TnSeq is challenging as it requires estimating unknown parameters from data that is often noisy and likely coming from a mixture of different phenotypes. In addition, the classification of essentiality is not known a priori, requiring unsupervised methods capable of identifying key features in the data to confidently determine essentiality. We present several models capable of identifying essential genes while overcoming the difficulties that are present in analyzing TnSeq data. Together, these methods provide ways to analyze TnSeq data in one or multiple conditions, confined within gene boundaries or across the entire genome, and while reducing the impact of noise and outliers that are often present in this type of data

Transposon mutagenesis in Mycobacterium kansasii links a small RNA gene to colony morphology and biofilm formation and identifies 9,885 intragenic insertions that do not compromise colony outgrowth

Mycobacterium kansasii (Mk) is a resilient opportunistic human pathogen that causes tuberculosis-like chronic pulmonary disease and mortality stemming from comorbidities and treatment failure. The standard treatment of Mk infections requires costly, long-term, multidrug courses with adverse side effects. The emergence of drug-resistant isolates further complicates the already challenging drug therapy regimens and threatens to compromise the future control of Mk infections. Despite the increasingly recognized global burden of Mk infections, the biology of this opportunistic pathogen remains essentially unexplored. In particular, studies reporting gene function or generation of defined mutants are scarce. Moreover, no transposon (Tn) mutagenesis tool has been validated for use in Mk, a situation limiting the repertoire of genetic approaches available to accelerate the dissection of gene function and the generation of gene knockout mutants in this poorly characterized pathogen. In this study, we validated the functionality of a powerful Tn mutagenesis tool in Mk and used this tool in conjunction with a forward genetic screen to establish a previously unrecognized role of a conserved mycobacterial small RNA gene of unknown function in colony morphology features and biofilm formation. We also combined Tn mutagenesis with next-generation sequencing to identify 12,071 Tn insertions that do not compromise viability in vitro. Finally, we demonstrated the susceptibility of the Galleria mellonella larva to Mk, setting the stage for further exploration of this simple and economical infection model system to the study of this pathogen