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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.

Iron is essential for nearly all bacterial pathogens, including Mycobacterium tuberculosis (Mtb), but is severely limited in the human host. To meet its iron needs, Mtb secretes siderophores, small molecules with high affinity for iron, and takes up iron-loaded mycobactins (MBT) and carboxymycobactins (cMBT), from the environment. Mtb is also capable of utilizing heme and hemoglobin which contain more than 70% of the iron in the human body. However, many components of these iron acquisition pathways are still unknown. In this study, a high-density transposon mutagenesis coupled with deep sequencing (TnSeq) showed that Mtb exhibits nearly opposite requirements for 165 genes in the presence of heme and hemoglobin versus MBT and cMBT as iron sources. The ESX-3 secretion system was assessed as essential for siderophore-mediated iron uptake and, surprisingly, also for heme utilization by Mtb. Predictions derived from the TnSeq analysis were validated by growth experiments with isogenic Mtb mutants. These results showed that (i) the efflux pump MmpL5 plays a dominant role in siderophore secretion, (ii) the Rv2047c protein is essential for growth of Mtb in the presence of mycobactin, and (iii) the transcriptional repressor Zur is required for heme utilization by Mtb. The novel genetic determinants of iron utilization revealed in this study will stimulate further experiments in this important area of Mtb physiology.

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.

Once considered a phenotypically monomorphic bacterium, there is a growing body of work demonstrating heterogeneity among Mycobacterium tuberculosis (Mtb) strains in clinically relevant characteristics, including virulence and response to antibiotics. However, the genetic and molecular basis for most phenotypic differences among Mtb strains remains unknown. To investigate the basis of strain variation in Mtb, we performed genome-wide transposon mutagenesis coupled with next-generation sequencing (TnSeq) for a panel of Mtb clinical isolates and the reference strain H37Rv to compare genetic requirements for in vitro growth across these strains. We developed an analytic approach to identify quantitative differences in genetic requirements between these genetically diverse strains, which vary in genomic structure and gene content. Using this methodology, we found differences between strains in their requirements for genes involved in fundamental cellular processes, including redox homeostasis and central carbon metabolism. Among the genes with differential requirements were katG, which encodes the activator of the first-line antitubercular agent isoniazid, and glcB, which encodes malate synthase, the target of a novel small-molecule inhibitor. Differences among strains in their requirement for katG and glcB predicted differences in their response to these antimicrobial agents. Importantly, these strain-specific differences in antibiotic response could not be predicted by genetic variants identified through whole genome sequencing or by gene expression analysis. Our results provide novel insight into the basis of variation among Mtb strains and demonstrate that TnSeq is a scalable method to predict clinically important phenotypic differences among Mtb strains.

Identification of new drug targets is vital for the advancement of drug discovery against Mycobacterium tuberculosis, especially given the increase of resistance worldwide to first- and second-line drugs. Because traditional target-based screening has largely proven unsuccessful for antibiotic discovery, we have developed a scalable platform for target identification in M. tuberculosis that is based on whole-cell screening, coupled with whole-genome sequencing of resistant mutants and recombineering to confirm. The method yields targets paired with whole-cell active compounds, which can serve as novel scaffolds for drug development, molecular tools for validation, and/or as ligands for co-crystallization. It may also reveal other information about mechanisms of action, such as activation or efflux. Using this method, we identified resistance-linked genes for eight compounds with anti-tubercular activity. Four of the genes have previously been shown to be essential: AspS, aspartyl-tRNA synthetase, Pks13, a polyketide synthase involved in mycolic acid biosynthesis, MmpL3, a membrane transporter, and EccB3, a component of the ESX-3 type VII secretion system. AspS and Pks13 represent novel targets in protein translation and cell-wall biosynthesis. Both MmpL3 and EccB3 are involved in membrane transport. Pks13, AspS, and EccB3 represent novel candidates not targeted by existing TB drugs, and the availability of whole-cell active inhibitors greatly increases their potential for drug discovery.

Identifying genomic elements required for viability is central to our understanding of the basic physiology of bacterial pathogens. Recently, the combination of high-density mutagenesis and deep sequencing has allowed for the identification of required and conditionally required genes in many bacteria. Genes, however, make up only a part of the complex genomes of important bacterial pathogens. Here, we use an unbiased analysis to comprehensively identify genomic regions, including genes, domains, and intergenic elements, required for the optimal growth of Mycobacterium tuberculosis, a major global health pathogen. We found that several proteins jointly contain both domains required for optimal growth and domains that are dispensable. In addition, many non-coding regions, including regulatory elements and non-coding RNAs, are critical for mycobacterial growth. Our analysis shows that the genetic requirements for growth are more complex than can be appreciated using gene-centric analysis.

RNA-seq technologies have provided significant insight into the transcription networks of mycobacteria. However, such studies provide no definitive information on the translational landscape. Here, we use a combination of high-throughput transcriptome and proteome-profiling approaches to more rigorously understand protein expression in two mycobacterial species. RNA-seq and ribosome profiling in Mycobacterium smegmatis, and transcription start site (TSS) mapping and N-terminal peptide mass spectrometry in Mycobacterium tuberculosis, provide complementary, empirical datasets to examine the congruence of transcription and translation in the Mycobacterium genus. We find that nearly one-quarter of mycobacterial transcripts are leaderless, lacking a 5' untranslated region (UTR) and Shine-Dalgarno ribosome-binding site. Our data indicate that leaderless translation is a major feature of mycobacterial genomes and is comparably robust to leadered initiation. Using translational reporters to systematically probe the cis-sequence requirements of leaderless translation initiation in mycobacteria, we find that an ATG or GTG at the mRNA 5' end is both necessary and sufficient. This criterion, together with our ribosome occupancy data, suggests that mycobacteria encode hundreds of small, unannotated proteins at the 5' ends of transcripts. The conservation of small proteins in both mycobacterial species tested suggests that some play important roles in mycobacterial physiology. Our translational-reporter system further indicates that mycobacterial leadered translation initiation requires a Shine Dalgarno site in the 5' UTR and that ATG, GTG, TTG, and ATT codons can robustly initiate translation. Our combined approaches provide the first comprehensive view of mycobacterial gene structures and their non-canonical mechanisms of protein expression.

An important application of CRISPR interference (CRISPRi) technology is for identifying chemical-genetic interactions (CGIs). Discovery of genes that interact with exposure to antibiotics can yield insights to drug targets and mechanisms of action or resistance. The objective is to identify CRISPRi mutants whose relative abundance is suppressed (or enriched) in the presence of a drug when the target protein is depleted, reflecting synergistic behavior. Different sgRNAs for a given target can induce a wide range of protein depletion and differential effects on growth rate. The effect of sgRNA strength can be partially predicted based on sequence features. However, the actual growth phenotype depends on the sensitivity of cells to depletion of the target protein. For essential genes, sgRNA efficiency can be empirically measured by quantifying effects on growth rate. We observe that the most efficient sgRNAs are not always optimal for detecting synergies with drugs. sgRNA efficiency interacts in a non-linear way with drug sensitivity, producing an effect where the concentration-dependence is maximized for sgRNAs of intermediate strength (and less so for sgRNAs that induce too much or too little target depletion). To capture this interaction, we propose a novel statistical method called CRISPRi-DR (for Dose-Response model) that incorporates both sgRNA efficiencies and drug concentrations in a modified dose-response equation. We use CRISPRi-DR to re-analyze data from a recent CGI experiment in Mycobacterium tuberculosis to identify genes that interact with antibiotics. This approach can be generalized to non-CGI datasets, which we show via an CRISPRi dataset for E . coli growth on different carbon sources. The performance is competitive with the best of several related analytical methods. However, for noisier datasets, some of these methods generate far more significant interactions, likely including many false positives, whereas CRISPRi-DR maintains higher precision, which we observed in both empirical and simulated data.

Drosophila melanogaster ( Drosophila ) is one of the most extensively studied animal models we have, with a broad, advanced, and organized research community. Yet, Drosophila has barely been exploited to understand the underlying mechanisms of mycobacterial infections, which cause some of the deadliest infectious diseases humans are currently battling. Here, we identified mycobacterial genes required for the pathogen’s growth during Drosophila infection. Using Mycobacterium marinum ( Mmar ) to model mycobacterial pathogens, we first validated that an established mycobacterial virulence factor, EccB1 of the ESX-1 Type VII secretion system, is required for Mmar growth within the flies. Subsequently, we identified Mmar virulence genes in Drosophila in a high-throughput genome-wide phenotypic manner using transposon insertion sequencing. Of the 181 identified virulence genes, the vast majority (91%) had orthologs in the tuberculosis-causing M. tuberculosis ( Mtb ), suggesting that the encoded virulence mechanisms may be conserved across Mmar and Mtb species. By studying one of the identified genes in more depth, the putative ATP-binding protein ABC transporter encoded by mmar_1660 , we found that both the Mmar gene and its Mtb ortholog ( rv3041c ) were required for virulence in human macrophages as well. We pinpointed the probable virulence mechanism of the genes to their requirements for growth during iron limitation, a condition met by mycobacteria during host infection. Together, our results bring forward Drosophila as a promising host model to study and identify mycobacterial virulence factors, providing insights that may transfer to Mtb human infection.

In most well-studied rod-shaped bacteria, peptidoglycan is primarily crosslinked by penicillin-binding proteins (PBPs). However, in mycobacteria, crosslinks formed by L,D-transpeptidases (LDTs) are highly abundant. To elucidate the role of these unusual crosslinks, we characterized Mycobacterium smegmatis cells lacking all LDTs. We find that crosslinks generate by LDTs are required for rod shape maintenance specifically at sites of aging cell wall, a byproduct of polar elongation. Asymmetric polar growth leads to a non-uniform distribution of these two types of crosslinks in a single cell. Consequently, in the absence of LDT-mediated crosslinks, PBP-catalyzed crosslinks become more important. Because of this, Mycobacterium tuberculosis (Mtb) is more rapidly killed using a combination of drugs capable of PBP- and LDT- inhibition. Thus, knowledge about the spatial and genetic relationship between drug targets can be exploited to more effectively treat this pathogen. Most bacteria have a cell wall that protects them and maintains their shape. Many of these organisms make their cell walls from fibers of proteins and sugars, called peptidoglycan. As bacteria grow, peptidoglycan is constantly broken down and reassembled, and in many species, new units of peptidoglycan are added into the sidewall. However, in a group of bacteria called mycobacteria, which cause tuberculosis and other diseases, the units are added at the tips. The peptidoglycan layer is often a successful target for antibiotic treatments. But, drugs that treat tuberculosis do not attack this layer, partly because we know very little about the cell walls of mycobacteria. Here, Baranowski et al. used genetic manipulation and microscopy to study how mycobacteria build their cell wall. The results showed that these bacteria link peptidoglycan units together in an unusual way. In most bacteria, peptidoglycan units are connected by chemical links known as 4-3 crosslinks. This is initially the same in mycobacteria, but as the cell grows and the cell wall expands, these bonds break and so-called 3-3 crosslinks form. In genetically modified bacteria that could not form these 3-3 bonds, the cell wall became brittle and weak, and the bacteria eventually died. These findings could be important for developing new drugs that treat infections caused by mycobacteria. Baranowski et al. demonstrate that a combination of drugs blocking both 4-3 and 3-3 crosslinks is particularly effective at killing the bacterium that causes tuberculosis.