Normalization of transposon-mutant library sequencing datasets to improve identification of conditionally essential genes

Metabolic diversity of human macrophages: potential influence on Staphylococcus aureus intracellular survival

Staphylococcus aureus is a leading cause of medical device-associated biofilm infections. This is influenced by the ability of S. aureus biofilm to evade the host immune response, which is partially driven by the anti-inflammatory cytokine interleukin-10 (IL-10). Here, we show that treatment of human monocyte-derived macrophages (HMDMs) with IL-10 enhanced biofilm formation, suggesting that macrophage anti-inflammatory programming likely plays an important role during the transition from planktonic to biofilm growth. To identify S. aureus genes that were important for intracellular survival in HMDMs and how this was affected by IL-10, transposon sequencing was performed. The size of the S. aureus essential genome was similar between unstimulated HMDMs and the outgrowth control (18.5% vs 18.4%, respectively, with 54.4% overlap) but increased to 22.5% in IL-10-treated macrophages, suggesting that macrophage polarization status exerts differential pressure on S. aureus. Essential genes for S. aureus survival within IL-10-polarized HMDMs were dominated by negative regulatory pathways, including nitrogen and RNA metabolism, whereas S. aureus essential genes within untreated HMDMs were enriched in biosynthetic pathways such as purine and pyrimidine biosynthesis. To explore how IL-10 altered the macrophage intracellular metabolome, targeted metabolomics was performed on HMDMs from six individual donors. IL-10 treatment led to conserved alterations in distinct metabolites that were increased (dihydroxyacetone phosphate, glyceraldehyde-3-phosphate, and acetyl-CoA) or reduced (fructose-6-phosphate, aspartic acid, and ornithine) across donors, whereas other metabolites were variable. Collectively, these findings highlight an important aspect of population-level heterogeneity in human macrophage responsiveness that should be considered when translating results to a patient population.IMPORTANCEOne mechanism that Staphylococcus aureus biofilm elicits in the host to facilitate infection persistence is the production of the anti-inflammatory cytokine interleukin-10 (IL-10). Here, we show that exposure of human monocyte-derived macrophages (HMDMs) to IL-10 promotes S. aureus biofilm formation and programs intracellular bacteria to favor catabolic pathways. Examination of intracellular metabolites in HMDMs revealed heterogeneity between donors that may explain the observed variability in essential genes for S. aureus survival based on nutrient availability for bacteria within the intracellular compartment. Collectively, these studies provide novel insights into how IL-10 polarization affects S. aureus intracellular survival in HMDMs and the importance of considering macrophage heterogeneity between human donors as a variable when examining effector mechanisms.

A Comprehensive Overview of the Genes and Functions Required for Lettuce Infection by the Hemibiotrophic Phytopathogen Xanthomonas hortorum pv. vitians

The successful infection of a host plant by a phytopathogenic bacterium depends on a finely tuned molecular cross talk between the two partners. Thanks to transposon insertion sequencing techniques (Tn-seq), whole genomes can now be assessed to determine which genes are important for the fitness of several plant-associated bacteria in planta. Despite its agricultural relevance, the dynamic molecular interaction established between the foliar hemibiotrophic phytopathogen Xanthomonas hortorum pv. vitians and its host, lettuce (Lactuca sativa), remains completely unknown. To decipher the genes and functions mobilized by the pathogen throughout the infection process, we conducted a Tn-seq experiment in lettuce leaves to mimic the selective pressure occurring during natural infection. This genome-wide screening identified 170 genes whose disruption caused serious fitness defects in lettuce. A thorough examination of these genes using comparative genomics and gene set enrichment analyses highlighted that several functions and pathways were highly critical for the pathogen's survival. Numerous genes involved in amino acid, nucleic acid, and exopolysaccharide biosynthesis were critical. The xps type II secretion system operon, a few TonB-dependent transporters involved in carbohydrate or siderophore scavenging, and multiple genes of the carbohydrate catabolism pathways were also critical, emphasizing the importance of nutrition systems in a nutrient-limited environment. Finally, several genes implied in camouflage from the plant immune system and resistance to immunity-induced oxidative stress were strongly involved in host colonization. As a whole, these results highlight some of the central metabolic pathways and cellular functions critical for Xanthomonas host adaptation and pathogenesis. IMPORTANCE Xanthomonas hortorum was recently the subject of renewed interest, as several studies highlighted that its members were responsible for diseases in a wide range of plant species, including crops of agricultural relevance (e.g., tomato and carrot). Among X. hortorum variants, X. hortorum pv. vitians is a reemerging foliar hemibiotrophic phytopathogen responsible for severe outbreaks of bacterial leaf spot of lettuce all around the world. Despite recent findings, sustainable and practical means of disease control remain to be developed. Understanding the host-pathogen interaction from a molecular perspective is crucial to support these efforts. The genes and functions mobilized by X. hortorum pv. vitians during its interaction with lettuce had never been investigated. Our study sheds light on these processes by screening the whole pathogen genome for genes critical for its fitness during the infection process, using transposon insertion sequencing and comparative genomics.

Analysis of Gene Essentiality from TnSeq Data Using Transit

TnSeq, or sequencing of transposon insertion libraries, has proven to be a valuable method for probing the functions of genes in a wide range of bacteria. TnSeq has found many applications for studying genes involved in core functions (such as cell division or metabolism), stress response, virulence, etc., as well as to identify potential drug targets. Two of the most commonly used transposons in practice are Himar1, which inserts randomly at TA dinucleotides, and Tn5, which can insert more broadly throughout the genome. These insertions cause putative gene function disruption, and clones with insertions in genes that cannot tolerate disruption (in a given condition) are eliminated from the population. Deep sequencing can be used to efficiently profile the surviving members, with insertions in genes that can be inferred to be non-essential. Data from TnSeq experiments (i.e. transposon insertion counts at specific genomic locations) is inherently noisy, making rigorous statistical analysis (e.g. quantifying significance) challenging. In this chapter, we describe Transit, a Python-based software package for analyzing TnSeq data that combines a variety of data processing tools, quality assessment methods, and analytical algorithms for identifying essential (or conditionally essential) genes.

A large-scale whole-genome sequencing analysis reveals false positives of bacterial essential genes

Essential genes are crucial for bacterial viability and represent attractive targets for novel anti-pathogen drug discovery. However, essential genes determined by the transposon insertion sequencing (Tn-seq) approach often contain many false positives. We hypothesized that some of those false positives are genes that are actually deleted from the genome, so they do not present any transposon insertion in the course of Tn-seq analysis. Based on this assumption, we performed a large-scale whole-genome sequencing analysis for the bacterium of interest. Our analysis revealed that some "essential genes" are indeed removed from the analyzed bacterial genomes. Since these genes were kicked out by bacteria, they should not be defined as essential. Our work showed that gene deletion is one of the false positive sources of essentiality determination, which is apparently underestimated in previous studies. We suggest subtracting the genome backgrounds before the evaluation of Tn-seq, and created a list of false positive gene essentiality as a reference for the downstream application. KEY POINTS: • Discovery of false positives of essential genes defined previously through the analyses of a large scale of whole-genome sequencing data • These false positives are the results of gene deletions in the studied genomes • Sequencing the target genome before Tn-seq analysis is of importance while some studies neglected it.

FASTQINS and ANUBIS: two bioinformatic tools to explore facts and artifacts in transposon sequencing and essentiality studies

Transposon sequencing is commonly applied for identifying the minimal set of genes required for cellular life; a major challenge in fields such as evolutionary or synthetic biology. However, the scientific community has no standards at the level of processing, treatment, curation and analysis of this kind data. In addition, we lack knowledge about artifactual signals and the requirements a dataset has to satisfy to allow accurate prediction. Here, we have developed FASTQINS, a pipeline for the detection of transposon insertions, and ANUBIS, a library of functions to evaluate and correct deviating factors known and uncharacterized until now. ANUBIS implements previously defined essentiality estimate models in addition to new approaches with advantages like not requiring a training set of genes to predict general essentiality. To highlight the applicability of these tools, and provide a set of recommendations on how to analyze transposon sequencing data, we performed a comprehensive study on artifacts corrections and essentiality estimation at a 1.5-bp resolution, in the genome-reduced bacterium Mycoplasma pneumoniae. We envision FASTQINS and ANUBIS to aid in the analysis of Tn-seq procedures and lead to the development of accurate genome essentiality estimates to guide applications such as designing live vaccines or growth optimization.

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.

Genome-wide assessment of antimicrobial tolerance in Yersinia pseudotuberculosis under ciprofloxacin stress

Yersinia pseudotuberculosis is a Gram-negative bacterium capable of causing gastrointestinal infection and is closely related to the highly virulent plague bacillus Yersinia pestis. Infections by both species are currently treatable with antibiotics such as ciprofloxacin, a quinolone-class drug of major clinical importance in the treatment of many other infections. Our current understanding of the mechanism of action of ciprofloxacin is that it inhibits DNA replication by targeting DNA gyrase, and that resistance is primarily due to mutation of this target site, along with generic efflux and detoxification strategies. We utilized transposon-directed insertion site sequencing (TraDIS or TnSeq) to identify the non-essential chromosomal genes in Y. pseudotuberculosis that are required to tolerate sub-lethal concentrations of ciprofloxacin in vitro. As well as highlighting recognized antibiotic resistance genes, we provide evidence that multiple genes involved in regulating DNA replication and repair are central in enabling Y. pseudotuberculosis to tolerate the antibiotic, including DksA (yptb0734), a regulator of RNA polymerase, and Hda (yptb2792), an inhibitor of DNA replication initiation. We furthermore demonstrate that even at sub-lethal concentrations, ciprofloxacin causes severe cell-wall stress, requiring lipopolysaccharide lipid A, O-antigen and core biosynthesis genes to resist the sub-lethal effects of the antibiotic. It is evident that coping with the consequence(s) of antibiotic-induced stress requires the contribution of scores of genes that are not exclusively engaged in drug resistance.

TnSeq of Mycobacterium tuberculosis clinical isolates reveals strain-specific antibiotic liabilities

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.

Statistical analysis of genetic interactions in Tn-Seq data

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.