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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. Here we developed a CRISPR interference chemical-genetics platform to titrate the expression of Mtb genes and quantify bacterial fitness in the presence of different drugs. We discovered diverse mechanisms of intrinsic drug resistance, unveiling hundreds of potential targets for synergistic drug combinations. Combining chemical genetics with comparative genomics of Mtb clinical isolates, we further identified several previously unknown mechanisms of acquired drug resistance, one of which is associated with a multidrug-resistant tuberculosis outbreak in South America. Lastly, we found that the intrinsic resistance factor whiB7 was inactivated in an entire Mtb sublineage endemic to Southeast Asia, presenting an opportunity to potentially repurpose the macrolide antibiotic clarithromycin to treat tuberculosis. This chemical-genetic map provides a rich resource to understand drug efficacy in Mtb and guide future tuberculosis drug development and treatment.

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. One promising drug target in Mtb is the arabinogalactan biosynthetic enzyme DprE1, and there have been over a dozen unique chemical scaffolds identified which inhibit the activity of this protein. Among the most promising lead compounds are the benzothiazinones BTZ043 and PBTZ169, both of which are currently in or have completed phase IIa clinical trials. Due to the potential clinical utility of these drugs, we sought to identify potential synergistic interactions and new mechanisms of resistance using a genome-scale CRISPRi chemical-genetic screen with PBTZ169. We found that knockdown of rv0678, the negative regulator of the mmpS5/L5 drug efflux pump, confers resistance to PBTZ169. Mutations in rv0678 are the most common form of resistance to bedaquiline and there is already abundant evidence of these mutations emerging in bedaquiline-treated patients. We confirmed that rv0678 mutations from clinical isolates confer low level cross-resistance to BTZ043 and PBTZ169. While it is yet unclear whether rv0678 mutations would render benzothiazinones ineffective in treating TB, these results highlight the importance of monitoring for clinically prevalent rv0678 mutations during ongoing BTZ043 and PBTZ169 clinical trials.

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 govern drug efficacy could facilitate the development of more effective therapies to overcome resistance, identify new mechanisms of acquired resistance, and reveal overlooked therapeutic opportunities. To define these pathways, we developed a CRISPR interference chemical-genetics platform to titrate the expression of Mtb genes and quantify bacterial fitness in the presence of different drugs. Mining this dataset, we discovered diverse and novel mechanisms of intrinsic drug resistance, unveiling hundreds of potential targets for synergistic drug combinations. Combining chemical-genetics with comparative genomics of Mtb clinical isolates, we further identified numerous new potential mechanisms of acquired drug resistance, one of which is associated with the emergence of a multidrug-resistant tuberculosis (TB) outbreak in South America. Lastly, we make the unexpected discovery of an “acquired drug sensitivity.” We found that the intrinsic resistance factor whiB7 was inactivated in an entire Mtb sublineage endemic to Southeast Asia, presenting an opportunity to potentially repurpose the macrolide antibiotic clarithromycin to treat TB. This chemical-genetic map provides a rich resource to understand drug efficacy in Mtb and guide future TB drug development and treatment.

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.

Summary 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. We developed a CRISPR interference-based functional genomics method to systematically titrate gene expression in Mycobacterium tuberculosis (Mtb) and monitor fitness outcomes. We identified highly vulnerable genes in various processes, including novel targets unexplored for drug discovery. Equally important, we identified invulnerable essential genes, potentially explaining failed drug discovery efforts. Comparison of vulnerability between the reference and a hypervirulent Mtb isolate revealed incomplete conservation of vulnerability and that differential vulnerability can predict differential antibacterial susceptibility. Our results quantitatively redefine essential bacterial processes and identify high-value targets for drug development., Graphical abstract, Highlights • Titratable CRISPRi enables quantification of target vulnerability in mycobacteria • Essential genes and processes vary widely in their vulnerability • Differential vulnerability predicts differential antibacterial susceptibility • Generalizable approach allows prioritization of high-value targets for drug discovery, Conventional genetic approaches rely on complete gene inactivation to identify potential antimicrobial targets. In contrast to this all-or-nothing effect, small molecules rarely achieve complete target inhibition. Here, a CRISPR interference system using M. tuberculosis as a model organism is used to titrate gene expression and uncover gene vulnerability, redefining the concept of essential genes and identifying antimicrobial targets.

Chemical-genetics (C-G) experiments can be used to identify interactions between inhibitory compounds and bacterial genes, potentially revealing the targets of drugs, or other functionally interacting genes and pathways. C-G experiments involve constructing a library of hypomorphic strains with essential genes that can be knocked-down, treating it with an inhibitory compound, and using high-throughput sequencing to quantify changes in relative abundance of individual mutants. The hypothesis is that, if the target of a drug or other genes in the same pathway are present in the library, such genes will display an excessive fitness defect due to the synergy between the dual stresses of protein depletion and antibiotic exposure. While assays at a single drug concentration are susceptible to noise and can yield false-positive interactions, improved detection can be achieved by requiring that the synergy between gene and drug be concentration-dependent. We present a novel statistical method based on Linear Mixed Models, called CGA-LMM, for analyzing C-G data. The approach is designed to capture the dependence of the abundance of each gene in the hypomorph library on increasing concentrations of drug through slope coefficients. To determine which genes represent candidate interactions, CGA-LMM uses a conservative population-based approach in which genes with negative slopes are considered significant only if they are outliers with respect to the rest of the population (assuming that most genes in the library do not interact with a given inhibitor). We applied the method to analyze 3 independent hypomorph libraries of M. tuberculosis for interactions with antibiotics with anti-tubercular activity, and we identify known target genes or expected interactions for 7 out of 9 drugs where relevant interacting genes are known.

BackgroundDeep 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.ResultsIn 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 ofM. tuberculosisH37Rv in C57BL/6 mice. We also use ZINB to perform a analysis of genes conditionally essential in H37Rv cultures exposed to multiple antibiotics.ConclusionsOur 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.

Background 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. Methods To characterize the function of this little known gene, we phenotyped Lphn3 null mice. We assessed motivation for food reward and working memory via instrumental responding tasks, motor coordination via rotarod, and depressive-like behavior via forced swim. We also measured neurite outgrowth of primary hippocampal and cortical neuron cultures. Standard blood chemistries and blood counts were performed. Finally, we also evaluated the transcriptome in several brain regions. Results Behaviorally, loss of Lphn3 increases both reward motivation and activity levels. Lphn3 null mice display significantly greater instrumental responding for food than wild-type mice, particularly under high response ratios, and swim incessantly during a forced swim assay. However, loss of Lphn3 does not interfere with working memory or motor coordination. Primary hippocampal and cortical neuron cultures demonstrate that null neurons display comparatively enhanced neurite outgrowth after 2 and 3 days in vitro. Standard blood chemistry panels reveal that nulls have low serum calcium levels. Finally, analysis of the transcriptome from prefrontal cortical, striatal, and hippocampal tissue at different developmental time points shows that loss of Lphn3 results in genotype-dependent differential gene expression (DGE), particularly for cell adhesion molecules and calcium signaling proteins. Much of the DGE is attenuated with age, and is consistent with the idea that ADHD is associated with delayed cortical maturation. Conclusions Transcriptome changes likely affect neuron structure and function, leading to behavioral anomalies consistent with both ADHD and addiction phenotypes. The data should further motivate analyses of Lphn3 function in the developmental timing of altered gene expression and calcium signaling, and their effects on neuronal structure/function during development.

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. This assumption, however, is inconsistent with the observed insertion frequencies from transposon mutant libraries of M. tuberculosis . We propose a modified Binomial model of essentiality that can characterize the insertion probability of individual genes in which we allow local variation in the background insertion frequency in different non-essential regions of the genome. Using the Metropolis-Hastings algorithm, samples of the posterior insertion probabilities were obtained for each gene, and the probability of each gene being essential is estimated. We compared our predictions to those of previous methods and show that, by taking into consideration local insertion frequencies, our method is capable of making more conservative predictions that better match what is experimentally known about essential and non-essential genes.

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.

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. In this paper, we present a comprehensive bioinformatic analysis of all 3989 ORFs (open reading frames) in the M. tuberculosis H37Rv genome. Our method combines information from comparative analysis (alignment to start sites of orthologs in other Actinobacteria), sequence conservation, "protein likeness", putative ribosome binding sites, and other data to identify translational start sites. The features are combined in a linear model that is trained on dataset of known start sites verified by mass spectrometry, with a cross-validated accuracy of 94%. The method can be viewed as an augmentation of Hidden Markov Model-based tools such as Glimmer and GeneMark by incorporating more information than just the raw genomic sequence to decide which position is the legitimate translational start site for each ORF. Using this analysis, we identify 269 genes that most likely need to be re-annotated, and identify the best alterative translational start site for each. These revised ORF definitions could be used in the reannotation of the H37Rv genome, as well as to prioritize genes for experimental start-site validation.

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., Author Summary Trehalose biosynthesis is considered an attractive target for the development of new drugs against various microbial pathogens including Mycobacterium tuberculosis. In this human pathogen, two partially redundant pathways mediate trehalose biosynthesis. The OtsA-OtsB2 pathway, which dominates in culture, involves trehalose-6-phosphate (T6P) synthase OtsA and T6P phosphatase OtsB2. While OtsA is dispensable, OtsB2 is strictly essential for growth of M. tuberculosis. Using conditional gene silencing, we here show that essentiality of OtsB2 is linked to accumulation of its substrate T6P, which exhibits direct or indirect toxic effects. Regulated gene expression in vivo revealed that OtsB2 is required to establish an acute infection of M. tuberculosis in a mouse infection model, but is surprisingly fully dispensable during the chronic infection phase. This highlights that trehalose metabolism of M. tuberculosis is substantially remodelled during infection.

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.

Elucidating the genetic determinants of radiation response is crucial to optimizing and individualizing radiotherapy for cancer patients. In order to identify genes that are involved in enhanced sensitivity or resistance to radiation, a library of stable mutant murine embryonic stem cells (ESCs), each with a defined mutation, was screened for cell viability and gene expression in response to radiation exposure. We focused on a cancer-relevant subset of over 500 mutant ESC lines. We identified 13 genes; 7 genes that have been previously implicated in radiation response and 6 other genes that have never been implicated in radiation response. After screening, proteomic analysis showed enrichment for genes involved in cellular component disassembly (e.g. Dstn and Pex14) and regulation of growth (e.g. Adnp2, Epc1, and Ing4). Overall, the best targets with the highest potential for sensitizing cancer cells to radiation were Dstn and Map2k6, and the best targets for enhancing resistance to radiation were Iqgap and Vcan. Hence, we provide compelling evidence that screening mutant ESCs is a powerful approach to identify genes that alter radiation response. Ultimately, this knowledge can be used to define genetic variants or therapeutic targets that will enhance clinical therapy.

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. 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.

We used a genomic library of mutant murine embryonic stem cells (ESCs) and report the methodology required to simultaneously culture, differentiate, and screen more than 3,200 heterozygous mutant clones to identify host-based genes involved in both sensitivity and resistance to rabies virus infection. Established neuronal differentiation protocols were miniaturized such that many clones could be handled simultaneously, and molecular markers were used to show that the resultant cultures were pan-neuronal. Next, we used a green fluorescent protein (GFP) labeled rabies virus to develop, validate, and implement one of the first host-based, high-content, high-throughput screens for rabies virus. Undifferentiated cell and neuron cultures were infected with GFP-rabies and live imaging was used to evaluate GFP intensity at time points corresponding to initial infection/uptake and early and late replication. Furthermore, supernatants were used to evaluate viral shedding potential. After repeated testing, 63 genes involved in either sensitivity or resistance to rabies infection were identified. To further explore hits, we used a completely independent system (siRNA) to show that reduction in target gene expression leads to the observed phenotype. We validated the immune modulatory gene Unc13d and the dynein adapter gene Bbs4 by treating wild-type ESCs and primary neurons with siRNA; treated cultures were resistant to rabies infection/replication. Overall, the potential of such in vitro functional genomics screens in stem cells adds additional value to other libraries of stem cells. This technique is applicable to any bacterial or virus interactome and any cell or tissue types that can be differentiated from ESCs. Stem Cells 2015;33:2509–2522

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., Author Summary The adaptation of a bacterial pathogen to the environments encountered during infection requires the wholesale remodeling of cellular physiology. One of the major changes encountered upon infection is nutritional, as the bacterium is forced to utilize compounds scavenged from the host. Mycobacterium tuberculosis has the unusual ability to use host cholesterol as a source of carbon and energy, and this capacity is required for persistence in animal models. However, our ignorance of the biochemical pathways involved in sterol degradation has limited our ability to assess the importance of this carbon shift in shaping the metabolic state of the bacterium. In this work, we developed a new method to quantitatively profile the fitness of thousands of mutants simultaneously. This allowed the identification of each bacterial gene that is required for the bacterium to grow using cholesterol as a carbon source. Reconstruction of the pathways comprised by these essential gene products revealed that adaptation to cholesterol required widespread metabolic changes. These genes account for a significant fraction of the bacterial functions important for growth in animal tissues, suggesting that the physiology of this intracellular pathogen is shaped by carbon sources available in this environment.