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

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. RESULTS: In this paper, we introduce a novel statistical method for identifying genes with significant variability of insertion counts across multiple conditions based on Zero-Inflated Negative Binomial (ZINB) regression. Using likelihood ratio tests, we show that the ZINB distribution fits TnSeq data better than either ANOVA or a Negative Binomial (in a generalized linear model). We use ZINB regression to identify genes required for infection of M. tuberculosis H37Rv in C57BL/6 mice. We also use ZINB to perform a analysis of genes conditionally essential in H37Rv cultures exposed to multiple antibiotics. CONCLUSIONS: Our results show that, not only does ZINB generally identify most of the genes found by pairwise resampling (and vastly out-performs ANOVA), but it also identifies additional genes where variability is detectable only when the magnitudes of insertion counts are treated separately from local differences in saturation, as in the ZINB model

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

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. We sought to overcome this limitation by creating a completely saturated transposon library in Mycobacterium tuberculosis. In assessing the composition of this highly saturated library by deep sequencing, we discovered that a previously unknown sequence bias of the Himar1 element rendered approximately 9% of potential TA dinucleotide insertion sites less permissible for insertion. We used a hidden Markov model of essentiality that accounted for this unanticipated bias, allowing us to confidently evaluate the essentiality of features that contained as few as 2 TA sites, including open reading frames (ORF), experimentally identified noncoding RNAs, methylation sites, and promoters. In addition, several essential regions that did not correspond to known features were identified, suggesting uncharacterized functions that are necessary for growth. This work provides an authoritative catalog of essential regions of the M. tuberculosis genome and a statistical framework for applying saturating mutagenesis to other bacteria

The Mycobacterium tuberculosis transposon sequencing database (MtbTnDB): a large-scale guide to genetic conditional essentiality [preprint]

Characterization of gene essentiality across different conditions is a useful approach for predicting gene function. Transposon sequencing (TnSeq) is a powerful means of generating genome-wide profiles of essentiality and has been used extensively in Mycobacterium tuberculosis (Mtb) genetic research. Over the past two decades, dozens of TnSeq screens have been published, yielding valuable insights into the biology of Mtb in vitro, inside macrophages, and in model host organisms. However, these Mtb TnSeq profiles are distributed across dozens of research papers within supplementary materials, which makes querying them cumbersome and assembling a complete and consistent synthesis of existing data challenging. Here, we address this problem by building a central repository of publicly available TnSeq screens performed in M. tuberculosis, which we call the Mtb transposon sequencing database (MtbTnDB). The MtbTnDB encompasses 64 published and unpublished TnSeq screens, and is standardized, open-access, and allows users easy access to data, visualizations, and functional predictions through an interactive web-app (www.mtbtndb.app). We also present evidence that (i) genes in the same genomic neighborhood tend to have similar TnSeq profiles, and (ii) clusters of genes with similar TnSeq profiles tend to be enriched for genes belonging to the same functional categories. Finally, we test and evaluate machine learning models trained on TnSeq profiles to guide functional annotation of orphan genes in Mtb. In addition to facilitating the exploration of conditional genetic essentiality in this important human pathogen via a centralized TnSeq data repository, the MtbTnDB will enable hypothesis generation and the extraction of meaningful patterns by facilitating the comparison of datasets across conditions. This will provide a basis for insights into the functional organization of Mtb genes as well as gene function prediction

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

Probing Differences in Gene Essentiality Between the Human and Animal Adapted Lineages of the Mycobacterium tuberculosis Complex Using TnSeq.

Members of the Mycobacterium tuberculosis complex (MTBC) show distinct host adaptations, preferences and phenotypes despite being >99% identical at the nucleic acid level. Previous studies have explored gene expression changes between the members, however few studies have probed differences in gene essentiality. To better understand the functional impacts of the nucleic acid differences between Mycobacterium bovis and Mycobacterium tuberculosis, we used the Mycomar T7 phagemid delivery system to generate whole genome transposon libraries in laboratory strains of both species and compared the essentiality status of genes during growth under identical in vitro conditions. Libraries contained insertions in 54% of possible TA sites in M. bovis and 40% of those present in M. tuberculosis, achieving similar saturation levels to those previously reported for the MTBC. The distributions of essentiality across the functional categories were similar in both species. 527 genes were found to be essential in M. bovis whereas 477 genes were essential in M. tuberculosis and 370 essential genes were common in both species. CRISPRi was successfully utilised in both species to determine the impacts of silencing genes including wag31, a gene involved in peptidoglycan synthesis and Rv2182c/Mb2204c, a gene involved in glycerophospholipid metabolism. We observed species specific differences in the response to gene silencing, with the inhibition of expression of Mb2204c in M. bovis showing significantly less growth impact than silencing its orthologue (Rv2182c) in M. tuberculosis. Given that glycerophospholipid metabolism is a validated pathway for antimicrobials, our observations suggest that target vulnerability in the animal adapted lineages cannot be assumed to be the same as the human counterpart. This is of relevance for zoonotic tuberculosis as it implies that the development of antimicrobials targeting the human adapted lineage might not necessarily be effective against the animal adapted lineage. The generation of a transposon library and the first reported utilisation of CRISPRi in M. bovis will enable the use of these tools to further probe the genetic basis of survival under disease relevant conditions

Common Variants in the Glycerol Kinase Gene Reduce Tuberculosis Drug Efficacy

Despite the administration of multiple drugs that are highly effective in vitro, tuberculosis (TB) treatment requires prolonged drug administration and is confounded by the emergence of drug-resistant strains. To understand the mechanisms that limit antibiotic efficacy, we performed a comprehensive genetic study to identify Mycobacterium tuberculosis genes that alter the rate of bacterial clearance in drug-treated mice. Several functionally distinct bacterial genes were found to alter bacterial clearance, and prominent among these was the glpK gene that encodes the glycerol-3-kinase enzyme that is necessary for glycerol catabolism. Growth on glycerol generally increased the sensitivity of M. tuberculosis to antibiotics in vitro, and glpK-deficient bacteria persisted during antibiotic treatment in vivo, particularly during exposure to pyrazinamide-containing regimens. Frameshift mutations in a hypervariable homopolymeric region of the glpK gene were found to be a specific marker of multidrug resistance in clinical M. tuberculosis isolates, and these loss-of-function alleles were also enriched in extensively drug-resistant clones. These data indicate that frequently observed variation in the glpK coding sequence produces a drug-tolerant phenotype that can reduce antibiotic efficacy and may contribute to the evolution of resistance. IMPORTANCE: TB control is limited in part by the length of antibiotic treatment needed to prevent recurrent disease. To probe mechanisms underlying survival under antibiotic pressure, we performed a genetic screen for M. tuberculosis mutants with altered susceptibility to treatment using the mouse model of TB. We identified multiple genes involved in a range of functions which alter sensitivity to antibiotics. In particular, we found glycerol catabolism mutants were less susceptible to treatment and that common variation in a homopolymeric region in the glpK gene was associated with drug resistance in clinical isolates. These studies indicate that reversible high-frequency variation in carbon metabolic pathways can produce phenotypically drug-tolerant clones and have a role in the development of resistance

Common Variants in the Glycerol Kinase Gene Reduce Tuberculosis Drug Efficacy

Despite the administration of multiple drugs that are highly effective in vitro, tuberculosis (TB) treatment requires prolonged drug administration and is confounded by the emergence of drug-resistant strains. To understand the mechanisms that limit antibiotic efficacy, we performed a comprehensive genetic study to identify Mycobacterium tuberculosis genes that alter the rate of bacterial clearance in drug-treated mice. Several functionally distinct bacterial genes were found to alter bacterial clearance, and prominent among these was the glpK gene that encodes the glycerol-3-kinase enzyme that is necessary for glycerol catabolism. Growth on glycerol generally increased the sensitivity of M. tuberculosis to antibiotics in vitro, and glpK-deficient bacteria persisted during antibiotic treatment in vivo, particularly during exposure to pyrazinamide-containing regimens. Frameshift mutations in a hypervariable homopolymeric region of the glpK gene were found to be a specific marker of multidrug resistance in clinical M. tuberculosis isolates, and these loss-of-function alleles were also enriched in extensively drug-resistant clones. These data indicate that frequently observed variation in the glpK coding sequence produces a drug-tolerant phenotype that can reduce antibiotic efficacy and may contribute to the evolution of resistance. IMPORTANCE TB control is limited in part by the length of antibiotic treatment needed to prevent recurrent disease. To probe mechanisms underlying survival under antibiotic pressure, we performed a genetic screen for M. tuberculosis mutants with altered susceptibility to treatment using the mouse model of TB. We identified multiple genes involved in a range of functions which alter sensitivity to antibiotics. In particular, we found glycerol catabolism mutants were less susceptible to treatment and that common variation in a homopolymeric region in the glpK gene was associated with drug resistance in clinical isolates. These studies indicate that reversible high-frequency variation in carbon metabolic pathways can produce phenotypically drug-tolerant clones and have a role in the development of resistance.ope

TnseqDiff: identification of conditionally essential genes in transposon sequencing studies

Abstract Background Tn-Seq is a high throughput technique for analysis of transposon mutant libraries to determine conditional essentiality of a gene under an experimental condition. A special feature of the Tn-seq data is that multiple mutants in a gene provides independent evidence to prioritize that gene as being essential. The existing methods do not account for this feature or rely on a high-density transposon library. Moreover, these methods are unable to accommodate complex designs. Results The method proposed here is specifically designed for the analysis of Tn-Seq data. It utilizes two steps to estimate the conditional essentiality for each gene in the genome. First, it collects evidence of conditional essentiality for each insertion by comparing read counts of that insertion between conditions. Second, it combines insertion-level evidence for the corresponding gene. It deals with data from both low- and high-density transposon libraries and accommodates complex designs. Moreover, it is very fast to implement. The performance of the proposed method was tested on simulated data and experimental Tn-Seq data from Serratia marcescens transposon mutant library used to identify genes that contribute to fitness in a murine model of infection. Conclusion We describe a new, efficient method for identifying conditionally essential genes in Tn-Seq experiments with high detection sensitivity and specificity. It is implemented as TnseqDiff function in R package Tnseq and can be installed from the Comprehensive R Archive Network, CRAN.https://deepblue.lib.umich.edu/bitstream/2027.42/137673/1/12859_2017_Article_1745.pd

Host-pathogen genetic interactions underlie tuberculosis susceptibility in genetically diverse mice [preprint]

The outcome of an encounter with Mycobacterium tuberculosis (Mtb) depends on the pathogen’s ability to adapt to the heterogeneous immune response of the host. Understanding this interplay has proven difficult, largely because experimentally tractable small animal models do not recapitulate the heterogenous disease observed in natural infections. We leveraged the genetically diverse Collaborative Cross (CC) mouse panel in conjunction with a library of Mtb mutants to associate bacterial genetic requirements with host genetics and immunity. We report that CC strains vary dramatically in their susceptibility to infection and represent reproducible models of qualitatively distinct immune states. Global analysis of Mtb mutant fitness across the CC panel revealed that a large fraction of the pathogen’s genome is necessary for adaptation to specific host microenvironments. Both immunological and bacterial traits were associated with genetic variants distributed across the mouse genome, elucidating the complex genetic landscape that underlies host-pathogen interactions in a diverse population