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

Distinct Bacterial Pathways Influence the Efficacy of Antibiotics against Mycobacterium tuberculosis

Effective tuberculosis treatment requires at least 6 months of combination therapy. Alterations in the physiological state of the bacterium during infection are thought to reduce drug efficacy and prolong the necessary treatment period, but the nature of these adaptations remain incompletely defined. To identify specific bacterial functions that limit drug effects during infection, we employed a comprehensive genetic screening approach to identify mutants with altered susceptibility to the first-line antibiotics in the mouse model. We identified many mutations that increase the rate of bacterial clearance, suggesting new strategies for accelerating therapy. In addition, the drug-specific effects of these mutations suggested that different antibiotics are limited by distinct factors. Rifampin efficacy is inferred to be limited by cellular permeability, whereas isoniazid is preferentially affected by replication rate. Many mutations that altered bacterial clearance in the mouse model did not have an obvious effect on drug susceptibility using in vitro assays, indicating that these chemical-genetic interactions tend to be specific to the in vivo environment. This observation suggested that a wide variety of natural genetic variants could influence drug efficacy in vivo without altering behavior in standard drug-susceptibility tests. Indeed, mutations in a number of the genes identified in our study are enriched in drug-resistant clinical isolates, identifying genetic variants that may influence treatment outcome. Together, these observations suggest new avenues for improving therapy, as well as the mechanisms of genetic adaptations that limit it. IMPORTANCE Understanding how Mycobacterium tuberculosis survives during antibiotic treatment is necessary to rationally devise more effective tuberculosis (TB) chemotherapy regimens. Using genome-wide mutant fitness profiling and the mouse model of TB, we identified genes that alter antibiotic efficacy specifically in the infection environment and associated several of these genes with natural genetic variants found in drug-resistant clinical isolates. These data suggest strategies for synergistic therapies that accelerate bacterial clearance, and they identify mechanisms of adaptation to drug exposure that could influence treatment outcome

Multidrug-Resistant Tuberculosis in Panama Is Driven by Clonal Expansion of a Multidrug-Resistant Mycobacterium tuberculosis Strain Related to the KZN Extensively Drug-Resistant M. tuberculosis Strain from South Africa

Multidrug-resistant tuberculosis (MDR-TB) is a significant health problem in Panama. The extent to which such cases are the result of primary or acquired resistance and the strain families involved are unknown. We performed whole-genome sequencing of a collection of 66 clinical MDR isolates, along with 31 drug-susceptible isolates, that were isolated in Panama between 2001 and 2010; 78% of the MDR isolates belong to the Latin American-Mediterranean (LAM) family. Drug resistance mutations correlated well with drug susceptibility profiles. To determine the relationships among these strains and to better understand the acquisition of resistance mutations, a phylogenetic tree was constructed based on a genome-wide single-nucleotide polymorphism analysis. The phylogenetic tree shows that the isolates are highly clustered, with a single strain (LAM9-c1) accounting for nearly one-half of the MDR isolates (29/66 isolates). The LAM9-c1 strain was most prevalent among male patients of working age and was associated with high mortality rates. Members of this cluster all share identical mutations conferring resistance to isoniazid (KatG S315T mutation), rifampin (RpoB S531L mutation), and streptomycin (rrs C517T mutation). This evidence of primary resistance supports a model in which MDR-TB in Panama is driven by clonal expansion and ongoing transmission of several strains in the LAM family, including the highly successful MDR strain LAM9-c1. The phylogenetic analysis also shows that the LAM9-c1 strain is closely related to the KwaZulu-Natal (KZN) extensively drug-resistant TB strain identified in KwaZulu-Natal, South Africa. The LAM9-c1 and KZN strains likely arose from a recent common ancestor that was transmitted between Panama and South Africa and had the capacity to tolerate an accumulation of multiple resistance mutations

High-Throughput Sequencing Enhanced Phage Display Identifies Peptides That Bind Mycobacteria

Bacterial cell wall components have been previously used as infection biomarkers detectable by antibodies. However, it is possible that the surface of the Mycobacterium tuberculosis (M. tb), the causative agent of tuberculosis (TB), also possesses molecules which might be non-antigenic. This makes the probing of biomarkers on the surface of M. tb cell wall difficult using antibodies. Here we demonstrate the use of phage display technology to identify peptides that bind to mycobacteria. We identified these clones using both random clone picking and high throughput sequencing. We demonstrate that random clone picking does not necessarily identify highly enriched clones. We further showed that the clone displaying the CPLHARLPC peptide which was identified by Illumina sequencing as the most enriched, binds better to mycobacteria than three clones selected by random picking. Using surface plasmon resonance, we showed that chemically synthesised CPLHARLPC peptide binds to a 15 KDa peptide from M.tb H37Rv whole cell lysates. These observations demonstrate that phage display technology combined with high-throughput sequencing is a powerful tool to identify peptides that can be used for investigating potential non-antigenic biomarkers for TB and other bacterial infections

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