Michael A. DeJesus

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. A combination of high-density mutagenesis and deep-sequencing was used to characterize the composition of complex mutant libraries exposed to different conditions. This allowed the unambiguous identification of the genes that are essential for Mtb to grow in vitro, and proved to be a significant improvement over previous approaches. To further explore functions that are required for persistence in the host, we defined the pathways necessary for the utilization of cholesterol, a critical carbon source during infection. Few of the genes we identified had previously been implicated in this adaptation by transcriptional profiling, and only a fraction were encoded in the chromosomal region known to encode sterol catabolic functions. These genes comprise an unexpectedly large percentage of those previously shown to be required for bacterial growth in mouse tissue. Thus, this single nutritional change accounts for a significant fraction of the adaption to the host. This work provides the most comprehensive genetic characterization of a sterol catabolic pathway to date, suggests putative roles for uncharacterized virulence genes, and precisely maps genes encoding potential drug targets.

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. RESULTS Using a sequence dataset from a transposon library for Mycobacterium tuberculosis, we show that this Bayesian approach predicts essential genes that correspond well with genes shown to be essential in previous studies. Furthermore, we show that by using the extreme-value distribution to characterize genomic regions lacking transposon insertions, this method is capable of identifying essential domains within genes. This approach can be used for analysing transposon libraries in other organisms and augmenting essentiality predictions with statistical confidence scores.

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

ABSTRACT 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. IMPORTANCE Sequencing of transposon-insertion mutant libraries has become a widely used tool for probing the functions of genes under various conditions. The Himar1 transposon is generally believed to insert with equal probabilities at all TA dinucleotides, and therefore its absence in a mutant library is taken to indicate biological selection against the corresponding mutant. Through sequencing of a saturated Himar1 library, we found evidence that TA dinucleotides are not equally permissive for insertion. The insertion bias was observed in multiple prokaryotes and influences the statistical interpretation of transposon insertion (TnSeq) data and characterization of essential genomic regions. Using these insights, we analyzed a fully saturated TnSeq library for M. tuberculosis, enabling us to generate a comprehensive catalog of in vitro essentiality, including ORFs smaller than those found in any previous study, small (noncoding) RNAs (sRNAs), promoters, and other genomic features. Sequencing of transposon-insertion mutant libraries has become a widely used tool for probing the functions of genes under various conditions. The Himar1 transposon is generally believed to insert with equal probabilities at all TA dinucleotides, and therefore its absence in a mutant library is taken to indicate biological selection against the corresponding mutant. Through sequencing of a saturated Himar1 library, we found evidence that TA dinucleotides are not equally permissive for insertion. The insertion bias was observed in multiple prokaryotes and influences the statistical interpretation of transposon insertion (TnSeq) data and characterization of essential genomic regions. Using these insights, we analyzed a fully saturated TnSeq library for M. tuberculosis, enabling us to generate a comprehensive catalog of in vitro essentiality, including ORFs smaller than those found in any previous study, small (noncoding) RNAs (sRNAs), promoters, and other genomic features.

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

Identifying Essential Genes in Mycobacterium tuberculosis by Global Phenotypic Profiling

Transposon sequencing (TnSeq) is a next-generation deep sequencing-based method to quantitatively assess the composition of complex mutant transposon libraries after pressure from selection. Although this method can be used for any organism in which transposon mutagenesis is possible, this chapter describes its use in Mycobacterium tuberculosis. More specifically, the methods for generating complex libraries through transposon mutagenesis, design of selective pressure, extraction of genomic DNA, amplification and quantification of transposon insertions through next-generation deep sequencing are covered. Determining gene essentiality and statistical analysis on data collected are also discussed.

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. tuberculosis allowing the regulated gene silencing of the T6P phosphatase gene otsB2. We found that otsB2 is essential for growth of M. tuberculosis in vitro as well as for the acute infection phase in mice following aerosol infection. By contrast, otsB2 is not essential for the chronic infection phase in mice, highlighting the substantial remodelling of trehalose metabolism during infection by M. tuberculosis. Blocking OtsB2 resulted in the accumulation of its substrate T6P, which appears to be toxic, leading to the self-poisoning of cells. Accordingly, blocking T6P production in a ΔotsA mutant abrogated otsB2 essentiality. T6P accumulation elicited a global upregulation of more than 800 genes, which might result from an increase in RNA stability implied by the enhanced neutralization of toxins exhibiting ribonuclease activity. Surprisingly, overlap with the stress response caused by the accumulation of another toxic sugar phosphate molecule, maltose-1-phosphate, was minimal. A genome-wide screen for synthetic lethal interactions with otsA identified numerous genes, revealing additional potential drug targets synergistic with OtsB2 suitable for combination therapies that would minimize the emergence of resistance to OtsB2 inhibitors.

Behavioral and transcriptomic profiling of mice null for Lphn3, a gene implicated in ADHD and addiction.

The Latrophilin 3 (LPHN3) gene (recently renamed Adhesion G protein‐coupled receptor L3 (ADGRL3)) has been linked to susceptibility to attention deficit/hyperactivity disorder (ADHD) and vulnerability to addiction. However, its role and function are not well understood as there are no known functional variants.

Statistical analysis of genetic interactions in Tn-Seq data

Abstract Tn-Seq is an experimental method for probing the functions of genes through construction of complex random transposon insertion libraries and quantification of each mutants 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. wild-type strain versus knockout strain). Several analytical methods have been proposed for analyzing Tn-Seq data to identify genetic interactions, including estimating relative fitness ratios and fitting a generalized linear model. However, these have limitations which necessitate an improved approach. We present a hierarchical Bayesian method for identifying genetic interactions through quantifying the statistical significance of changes in enrichment. The analysis involves a four-way comparison of insertion counts across datasets to identify transposon mutants that differentially affect bacterial fitness depending on genetic background. Our approach was applied to Tn-Seq libraries made in isogenic strains of Mycobacterium tuberculosis lacking three different genes of unknown function previously shown to be necessary for optimal fitness during infection. By analyzing the libraries subjected to selection in mice, we were able to distinguish several distinct classes of genetic interactions for each target gene that shed light on their functions and roles during infection.

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

ABSTRACT 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 Mycobacterium tuberculosis, 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. This screen identified the M. tuberculosis cell envelope to be a major determinant of antibiotic susceptibility but did not yield mutants whose increase in susceptibility was due to transposon insertions in genes encoding efflux pumps. Intrinsic antibiotic resistance determinants affecting resistance to multiple antibiotics included the peptidoglycan-arabinogalactan ligase Lcp1, the mycolic acid synthase MmaA4, the protein translocase SecA2, the mannosyltransferase PimE, the cell envelope-associated protease CaeA/Hip1, and FecB, a putative iron dicitrate-binding protein. Characterization of a deletion mutant confirmed FecB to be involved in the intrinsic resistance to every antibiotic analyzed. In contrast to its predicted function, FecB was dispensable for growth in low-iron medium and instead functioned as a critical mediator of envelope integrity.

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). If two datasets that are not appropriately normalized are compared, it might cause the artifactual appearance of Differentially Essential (DE) genes in a statistical test, constituting type I errors (false positives). In this paper, we propose a novel method for normalization of TnSeq datasets that corrects for the skew of read-count distributions by fitting them to a Beta-Geometric distribution. We show that this read-count correction procedure reduces the number of false positives when comparing replicate datasets grown under the same conditions (for which no genuine differences in essentiality are expected). We compare these results to results obtained with other normalization procedures, and show that it results in greater reduction in the number of false positives. In addition we investigate the effects of normalization on the detection of DE genes.