High-throughput transposon mutagenesis in the family Enterobacteriaceae reveals core essential genes and rapid turnover of essentiality.

The Enterobacteriaceae are a scientifically and medically important clade of bacteria, containing the model organism Escherichia coli, as well as major human pathogens including Salmonella enterica and Klebsiella pneumoniae. Essential gene sets have been determined for several members of the Enterobacteriaceae, with the Keio E. coli single-gene deletion library often regarded as a gold standard. However, it remains unclear how gene essentiality varies between related strains and species. To investigate this, we have assembled a collection of 13 sequenced high-density transposon mutant libraries from five genera within the Enterobacteriaceae. We first assess several gene essentiality prediction approaches, investigate the effects of transposon density on essentiality prediction, and identify biases in transposon insertion sequencing data. Based on these investigations, we develop a new classifier for gene essentiality. Using this new classifier, we define a core essential genome in the Enterobacteriaceae of 201 universally essential genes. Despite the presence of a large cohort of variably essential genes, we find an absence of evidence for genus-specific essential genes. A clear example of this sporadic essentiality is given by the set of genes regulating the σE extracytoplasmic stress response, which appears to have independently acquired essentiality multiple times in the Enterobacteriaceae. Finally, we compare our essential gene sets to the natural experiment of gene loss in obligate insect endosymbionts that have emerged from within the Enterobacteriaceae. This isolates a remarkably small set of genes absolutely required for survival and identifies several instances of essential stress responses masked by redundancy in free-living bacteria.IMPORTANCEThe essential genome, that is the set of genes absolutely required to sustain life, is a core concept in genetics. Essential genes in bacteria serve as drug targets, put constraints on the engineering of biological chassis for technological or industrial purposes, and are key to constructing synthetic life. Despite decades of study, relatively little is known about how gene essentiality varies across related bacteria. In this study, we have collected gene essentiality data for 13 bacteria related to the model organism Escherichia coli, including several human pathogens, and investigated the conservation of essentiality. We find that approximately a third of the genes essential in any particular strain are non-essential in another related strain. Surprisingly, we do not find evidence for essential genes unique to specific genera; rather it appears a substantial fraction of the essential genome rapidly gains or loses essentiality during evolution. This suggests that essentiality is not an immutable characteristic but depends crucially on the genomic context. We illustrate this through a comparison of our essential genes in free-living bacteria to genes conserved in 34 insect endosymbionts with naturally reduced genomes, finding several cases where genes generally regarded as being important for specific stress responses appear to have become essential in endosymbionts due to a loss of functional redundancy in the genome.

Network depth affects inference of gene sets from bacterial transcriptomes using denoising autoencoders.

Summary: The increasing number of publicly available bacterial gene expression data sets provides an unprecedented resource for the study of gene regulation in diverse conditions, but emphasizes the need for self-supervised methods for the automated generation of new hypotheses. One approach for inferring coordinated regulation from bacterial expression data is through neural networks known as denoising autoencoders (DAEs) which encode large datasets in a reduced bottleneck layer. We have generalized this application of DAEs to include deep networks and explore the effects of network architecture on gene set inference using deep learning. We developed a DAE-based pipeline to extract gene sets from transcriptomic data in Escherichia coli, validate our method by comparing inferred gene sets with known pathways, and have used this pipeline to explore how the choice of network architecture impacts gene set recovery. We find that increasing network depth leads the DAEs to explain gene expression in terms of fewer, more concisely defined gene sets, and that adjusting the width results in a tradeoff between generalizability and biological inference. Finally, leveraging our understanding of the impact of DAE architecture, we apply our pipeline to an independent uropathogenic E.coli dataset to identify genes uniquely induced during human colonization. Availability and implementation: https://github.com/BarquistLab/DAE_architecture_exploration.

s CIRCLE-An interactive visual exploration tool for single cell RNA-Seq data.

sCIRCLE (single-Cell Interactive Real-time Computer visualization for Low-dimensional Exploration) is a tool for exploratory analysis of single cell RNA-seq (scRNA-seq) data sets, with a focus on bacterial scRNA-seq. The software takes an information design perspective to re-envision visually and interactively exploring low dimensional representations of scRNA-Seq data. Users can project cells in various 3D and 2D spaces and interactively query and paint cells using rich metadata sets reporting on cell cluster, gene function, and gene expression. As a standalone application it contains, among other features, options for dimensionality reduction, navigation and interaction with data in 3d and 2d space, gene filtering, fold change and metacell computation as well as various capabilities for visualization, data import and export.

A cell-free transcription-translation pipeline for recreating methylation patterns boosts DNA transformation in bacteria.

The bacterial world offers diverse strains for understanding medical and environmental processes and for engineering synthetic biological chassis. However, genetically manipulating these strains has faced a long-standing bottleneck: how to efficiently transform DNA. Here, we report imitating methylation patterns rapidly in TXTL (IMPRINT), a generalized, rapid, and scalable approach based on cell-free transcription-translation (TXTL) to overcome DNA restriction, a prominent barrier to transformation. IMPRINT utilizes TXTL to express DNA methyltransferases from a bacterium's restriction-modification systems. The expressed methyltransferases then methylate DNA in vitro to match the bacterium's DNA methylation pattern, circumventing restriction and enhancing transformation. With IMPRINT, we efficiently multiplex methylation by diverse DNA methyltransferases and enhance plasmid transformation in gram-negative and gram-positive bacteria. We also develop a high-throughput pipeline that identifies the most consequential methyltransferases, and we apply IMPRINT to screen a ribosome-binding site library in a hard-to-transform Bifidobacterium. Overall, IMPRINT can enhance DNA transformation, enabling the use of sophisticated genetic manipulation tools across the bacterial world.

Systematic interrogation of CRISPR antimicrobials in Klebsiella pneumoniae reveals nuclease-, guide- and strain-dependent features influencing antimicrobial activity.

CRISPR-Cas systems can be utilized as programmable-spectrum antimicrobials to combat bacterial infections. However, how CRISPR nucleases perform as antimicrobials across target sites and strains remains poorly explored. Here, we address this knowledge gap by systematically interrogating the use of CRISPR antimicrobials using multidrug-resistant and hypervirulent strains of Klebsiella pneumoniae as models. Comparing different Cas nucleases, DNA-targeting nucleases outperformed RNA-targeting nucleases based on the tested targets. Focusing on AsCas12a that exhibited robust targeting across different strains, we found that the elucidated modes of escape varied widely, restraining opportunities to enhance killing. We also encountered individual guide RNAs yielding different extents of clearance across strains, which were linked to an interplay between improper gRNA folding and strain-specific DNA repair and survival. To explore features that could improve targeting across strains, we performed a genome-wide screen in different K. pneumoniae strains that yielded guide design rules and trained an algorithm for predicting guide efficiency. Finally, we showed that Cas12a antimicrobials can be exploited to eliminate K. pneumoniae when encoded in phagemids delivered by T7-like phages. Altogether, our results highlight the importance of evaluating antimicrobial activity of CRISPR antimicrobials across relevant strains and define critical parameters for efficient CRISPR-based targeting.

A method to correct for local alterations in DNA copy number that bias functional genomics assays applied to antibiotic-treated bacteria.

Functional genomics techniques, such as transposon insertion sequencing and RNA-sequencing, are key to studying relative differences in bacterial mutant fitness or gene expression under selective conditions. However, certain stress conditions, mutations, or antibiotics can directly interfere with DNA synthesis, resulting in systematic changes in local DNA copy numbers along the chromosome. This can lead to artifacts in sequencing-based functional genomics data when comparing antibiotic treatment to an unstressed control. Further, relative differences in gene-wise read counts may result from alterations in chromosomal replication dynamics, rather than selection or direct gene regulation. We term this artifact "chromosomal location bias" and implement a principled statistical approach to correct it by calculating local normalization factors along the chromosome. These normalization factors are then directly incorporated into statistical analyses using standard RNA-sequencing analysis methods without modifying the read counts themselves, preserving important information about the mean-variance relationship in the data. We illustrate the utility of this approach by generating and analyzing a ciprofloxacin-treated transposon insertion sequencing data set in Escherichia coli as a case study. We show that ciprofloxacin treatment generates chromosomal location bias in the resulting data, and we further demonstrate that failing to correct for this bias leads to false predictions of mutant drug sensitivity as measured by minimum inhibitory concentrations. We have developed an R package and user-friendly graphical Shiny application, ChromoCorrect, that detects and corrects for chromosomal bias in read count data, enabling the application of functional genomics technologies to the study of antibiotic stress.IMPORTANCEAltered gene dosage due to changes in DNA replication has been observed under a variety of stresses with a variety of experimental techniques. However, the implications of changes in gene dosage for sequencing-based functional genomics assays are rarely considered. We present a statistically principled approach to correcting for the effect of changes in gene dosage, enabling testing for differences in the fitness effects or regulation of individual genes in the presence of confounding differences in DNA copy number. We show that failing to correct for these effects can lead to incorrect predictions of resistance phenotype when applying functional genomics assays to investigate antibiotic stress, and we provide a user-friendly application to detect and correct for changes in DNA copy number.

Improved RNA stability estimation through Bayesian modeling reveals most Salmonella transcripts have subminute half-lives.

RNA decay is a crucial mechanism for regulating gene expression in response to environmental stresses. In bacteria, RNA-binding proteins (RBPs) are known to be involved in posttranscriptional regulation, but their global impact on RNA half-lives has not been extensively studied. To shed light on the role of the major RBPs ProQ and CspC/E in maintaining RNA stability, we performed RNA sequencing of Salmonella enterica over a time course following treatment with the transcription initiation inhibitor rifampicin (RIF-seq) in the presence and absence of these RBPs. We developed a hierarchical Bayesian model that corrects for confounding factors in rifampicin RNA stability assays and enables us to identify differentially decaying transcripts transcriptome-wide. Our analysis revealed that the median RNA half-life in Salmonella in early stationary phase is less than 1 min, a third of previous estimates. We found that over half of the 500 most long-lived transcripts are bound by at least one major RBP, suggesting a general role for RBPs in shaping the transcriptome. Integrating differential stability estimates with cross-linking and immunoprecipitation followed by RNA sequencing (CLIP-seq) revealed that approximately 30% of transcripts with ProQ binding sites and more than 40% with CspC/E binding sites in coding or 3' untranslated regions decay differentially in the absence of the respective RBP. Analysis of differentially destabilized transcripts identified a role for ProQ in the oxidative stress response. Our findings provide insights into posttranscriptional regulation by ProQ and CspC/E, and the importance of RBPs in regulating gene expression.

An expanded transcriptome atlas for Bacteroides thetaiotaomicron reveals a small RNA that modulates tetracycline sensitivity.

Plasticity in gene expression allows bacteria to adapt to diverse environments. This is particularly relevant in the dynamic niche of the human intestinal tract; however, transcriptional networks remain largely unknown for gut-resident bacteria. Here we apply differential RNA sequencing (RNA-seq) and conventional RNA-seq to the model gut bacterium Bacteroides thetaiotaomicron to map transcriptional units and profile their expression levels across 15 in vivo-relevant growth conditions. We infer stress- and carbon source-specific transcriptional regulons and expand the annotation of small RNAs (sRNAs). Integrating this expression atlas with published transposon mutant fitness data, we predict conditionally important sRNAs. These include MasB, which downregulates tetracycline tolerance. Using MS2 affinity purification and RNA-seq, we identify a putative MasB target and assess its role in the context of the MasB-associated phenotype. These data-publicly available through the Theta-Base web browser ( http://micromix.helmholtz-hiri.de/bacteroides/ )-constitute a valuable resource for the microbiome community.

Expanding the flexibility of base editing for high-throughput genetic screens in bacteria.

Genome-wide screens have become powerful tools for elucidating genotype-to-phenotype relationships in bacteria. Of the varying techniques to achieve knockout and knockdown, CRISPR base editors are emerging as promising options. However, the limited number of available, efficient target sites hampers their use for high-throughput screening. Here, we make multiple advances to enable flexible base editing as part of high-throughput genetic screening in bacteria. We first co-opt the Streptococcus canis Cas9 that exhibits more flexible protospacer-adjacent motif recognition than the traditional Streptococcus pyogenes Cas9. We then expand beyond introducing premature stop codons by mutating start codons. Next, we derive guide design rules by applying machine learning to an essentiality screen conducted in Escherichia coli. Finally, we rescue poorly edited sites by combining base editing with Cas9-induced cleavage of unedited cells, thereby enriching for intended edits. The efficiency of this dual system was validated through a conditional essentiality screen based on growth in minimal media. Overall, expanding the scope of genome-wide knockout screens with base editors could further facilitate the investigation of new gene functions and interactions in bacteria.

A comparative analysis of peptide-delivered antisense antibiotics using diverse nucleotide mimics.

Antisense oligomer (ASO)-based antibiotics that target mRNAs of essential bacterial genes have great potential for counteracting antimicrobial resistance and for precision microbiome editing. To date, the development of such antisense antibiotics has primarily focused on using phosphorodiamidate morpholino (PMO) and peptide nucleic acid (PNA) backbones, largely ignoring the growing number of chemical modalities that have spurred the success of ASO-based human therapy. Here, we directly compare the activities of seven chemically distinct 10mer ASOs, all designed to target the essential gene acpP upon delivery with a KFF-peptide carrier into Salmonella. Our systematic analysis of PNA, PMO, phosphorothioate (PTO)-modified DNA, 2'-methylated RNA (RNA-OMe), 2'-methoxyethylated RNA (RNA-MOE), 2'-fluorinated RNA (RNA-F), and 2'-4'-locked RNA (LNA) is based on a variety of in vitro and in vivo methods to evaluate ASO uptake, target pairing and inhibition of bacterial growth. Our data show that only PNA and PMO are efficiently delivered by the KFF peptide into Salmonella to inhibit bacterial growth. Nevertheless, the strong target binding affinity and in vitro translational repression activity of LNA and RNA-MOE make them promising modalities for antisense antibiotics that will require the identification of an effective carrier.

Improved prediction of bacterial CRISPRi guide efficiency from depletion screens through mixed-effect machine learning and data integration.

CRISPR interference (CRISPRi) is the leading technique to silence gene expression in bacteria; however, design rules remain poorly defined. We develop a best-in-class prediction algorithm for guide silencing efficiency by systematically investigating factors influencing guide depletion in genome-wide essentiality screens, with the surprising discovery that gene-specific features substantially impact prediction. We develop a mixed-effect random forest regression model that provides better estimates of guide efficiency. We further apply methods from explainable AI to extract interpretable design rules from the model. This study provides a blueprint for predictive models for CRISPR technologies where only indirect measurements of guide activity are available.

Ribosome profiling reveals the fine-tuned response of Escherichia coli to mild and severe acid stress.

IMPORTANCE: Bacteria react very differently to survive in acidic environments, such as the human gastrointestinal tract. Escherichia coli is one of the extremely acid-resistant bacteria and has a variety of acid-defense mechanisms. Here, we provide the first genome-wide overview of the adaptations of E. coli K-12 to mild and severe acid stress at both the transcriptional and translational levels. Using ribosome profiling and RNA sequencing, we uncover novel adaptations to different degrees of acidity, including previously hidden stress-induced small proteins and novel key transcription factors for acid defense, and report mRNAs with pH-dependent differential translation efficiency. In addition, we distinguish between acid-specific adaptations and general stress response mechanisms using denoising autoencoders. This workflow represents a powerful approach that takes advantage of next-generation sequencing techniques and machine learning to systematically analyze bacterial stress responses.

Vector-borne Trypanosoma brucei parasites develop in artificial human skin and persist as skin tissue forms.

Transmission of Trypanosoma brucei by tsetse flies involves the deposition of the cell cycle-arrested metacyclic life cycle stage into mammalian skin at the site of the fly's bite. We introduce an advanced human skin equivalent and use tsetse flies to naturally infect the skin with trypanosomes. We detail the chronological order of the parasites' development in the skin by single-cell RNA sequencing and find a rapid activation of metacyclic trypanosomes and differentiation to proliferative parasites. Here we show that after the establishment of a proliferative population, the parasites enter a reversible quiescent state characterized by slow replication and a strongly reduced metabolism. We term these quiescent trypanosomes skin tissue forms, a parasite population that may play an important role in maintaining the infection over long time periods and in asymptomatic infected individuals.

DksA is a conserved master regulator of stress response in Acinetobacter baumannii.

Coordination of bacterial stress response mechanisms is critical for long-term survival in harsh environments for successful host infection. The general and specific stress responses of well-studied Gram-negative pathogens like Escherichia coli are controlled by alternative sigma factors, archetypically RpoS. The deadly hospital pathogen Acinetobacter baumannii is notoriously resistant to environmental stresses, yet it lacks RpoS, and the molecular mechanisms driving this incredible stress tolerance remain poorly defined. Here, using functional genomics, we identified the transcriptional regulator DksA as a master regulator for broad stress protection and virulence in A. baumannii. Transcriptomics, phenomics and in vivo animal studies revealed that DksA controls ribosomal protein expression, metabolism, mutation rates, desiccation, antibiotic resistance, and host colonization in a niche-specific manner. Phylogenetically, DksA was highly conserved and well-distributed across Gammaproteobacteria, with 96.6% containing DksA, spanning 88 families. This study lays the groundwork for understanding DksA as a major regulator of general stress response and virulence in this important pathogen.

Grad-seq analysis of Enterococcus faecalis and Enterococcus faecium provides a global view of RNA and protein complexes in these two opportunistic pathogens.

Enterococcus faecalis and Enterococcus faecium are major nosocomial pathogens. Despite their relevance to public health and their role in the development of bacterial antibiotic resistance, relatively little is known about gene regulation in these species. RNA-protein complexes serve crucial functions in all cellular processes associated with gene expression, including post-transcriptional control mediated by small regulatory RNAs (sRNAs). Here, we present a new resource for the study of enterococcal RNA biology, employing the Grad-seq technique to comprehensively predict complexes formed by RNA and proteins in E. faecalis V583 and E. faecium AUS0004. Analysis of the generated global RNA and protein sedimentation profiles led to the identification of RNA-protein complexes and putative novel sRNAs. Validating our data sets, we observe well-established cellular RNA-protein complexes such as the 6S RNA-RNA polymerase complex, suggesting that 6S RNA-mediated global control of transcription is conserved in enterococci. Focusing on the largely uncharacterized RNA-binding protein KhpB, we use the RIP-seq technique to predict that KhpB interacts with sRNAs, tRNAs, and untranslated regions of mRNAs, and might be involved in the processing of specific tRNAs. Collectively, these datasets provide departure points for in-depth studies of the cellular interactome of enterococci that should facilitate functional discovery in these and related Gram-positive species. Our data are available to the community through a user-friendly Grad-seq browser that allows interactive searches of the sedimentation profiles (https://resources.helmholtz-hiri.de/gradseqef/).

Improved Bacterial Single-Cell RNA-Seq through Automated MATQ-Seq and Cas9-Based Removal of rRNA Reads.

Bulk RNA sequencing technologies have provided invaluable insights into host and bacterial gene expression and associated regulatory networks. Nevertheless, the majority of these approaches report average expression across cell populations, hiding the true underlying expression patterns that are often heterogeneous in nature. Due to technical advances, single-cell transcriptomics in bacteria has recently become reality, allowing exploration of these heterogeneous populations, which are often the result of environmental changes and stressors. In this work, we have improved our previously published bacterial single-cell RNA sequencing (scRNA-seq) protocol that is based on multiple annealing and deoxycytidine (dC) tailing-based quantitative scRNA-seq (MATQ-seq), achieving a higher throughput through the integration of automation. We also selected a more efficient reverse transcriptase, which led to reduced cell loss and higher workflow robustness. Moreover, we successfully implemented a Cas9-based rRNA depletion protocol into the MATQ-seq workflow. Applying our improved protocol on a large set of single Salmonella cells sampled over different growth conditions revealed improved gene coverage and a higher gene detection limit compared to our original protocol and allowed us to detect the expression of small regulatory RNAs, such as GcvB or CsrB at a single-cell level. In addition, we confirmed previously described phenotypic heterogeneity in Salmonella in regard to expression of pathogenicity-associated genes. Overall, the low percentage of cell loss and high gene detection limit makes the improved MATQ-seq protocol particularly well suited for studies with limited input material, such as analysis of small bacterial populations in host niches or intracellular bacteria. IMPORTANCE Gene expression heterogeneity among isogenic bacteria is linked to clinically relevant scenarios, like biofilm formation and antibiotic tolerance. The recent development of bacterial single-cell RNA sequencing (scRNA-seq) enables the study of cell-to-cell variability in bacterial populations and the mechanisms underlying these phenomena. Here, we report a scRNA-seq workflow based on MATQ-seq with increased robustness, reduced cell loss, and improved transcript capture rate and gene coverage. Use of a more efficient reverse transcriptase and the integration of an rRNA depletion step, which can be adapted to other bacterial single-cell workflows, was instrumental for these improvements. Applying the protocol to the foodborne pathogen Salmonella, we confirmed transcriptional heterogeneity across and within different growth phases and demonstrated that our workflow captures small regulatory RNAs at a single-cell level. Due to low cell loss and high transcript capture rates, this protocol is uniquely suited for experimental settings in which the starting material is limited, such as infected tissues.

An integrated transcriptomics-functional genomics approach reveals a small RNA that modulates Bacteroides thetaiotaomicron sensitivity to tetracyclines.

Gene expression plasticity allows bacteria to adapt to diverse environments, tie their metabolism to available nutrients, and cope with stress. This is particularly relevant in a niche as dynamic and hostile as the human intestinal tract, yet transcriptional networks remain largely unknown in gut Bacteroides spp. Here, we map transcriptional units and profile their expression levels in Bacteroides thetaiotaomicron over a suite of 15 defined experimental conditions that are relevant in vivo , such as variation of temperature, pH, and oxygen tension, exposure to antibiotic stress, and growth on simple carbohydrates or on host mucin-derived glycans. Thereby, we infer stress- and carbon source-specific transcriptional regulons, including conditional expression of capsular polysaccharides and polysaccharide utilization loci, and expand the annotation of small regulatory RNAs (sRNAs) in this organism. Integrating this comprehensive expression atlas with transposon mutant fitness data, we identify conditionally important sRNAs. One example is MasB, whose inactivation led to increased bacterial tolerance of tetracyclines. Using MS2 affinity purification coupled with RNA sequencing, we predict targets of this sRNA and discuss their potential role in the context of the MasB-associated phenotype. Together, this transcriptomic compendium in combination with functional sRNA genomics-publicly available through a new iteration of the 'Theta-Base' web browser (www.helmholtz-hiri.de/en/datasets/bacteroides-v2)-constitutes a valuable resource for the microbiome and sRNA research communities alike.

Design and off-target prediction for antisense oligomers targeting bacterial mRNAs with the MASON web server.

Antisense oligomers (ASOs), such as peptide nucleic acids (PNAs), designed to inhibit the translation of essential bacterial genes, have emerged as attractive sequence- and species-specific programmable RNA antibiotics. Yet, potential drawbacks include unwanted side effects caused by their binding to transcripts other than the intended target. To facilitate the design of PNAs with minimal off-target effects, we developed MASON (make antisense oligomers now), a web server for the design of PNAs that target bacterial mRNAs. MASON generates PNA sequences complementary to the translational start site of a bacterial gene of interest and reports critical sequence attributes and potential off-target sites. We based MASON's off-target predictions on experiments in which we treated Salmonella enterica serovar Typhimurium with a series of 10-mer PNAs derived from a PNA targeting the essential gene acpP but carrying two serial mismatches. Growth inhibition and RNA-sequencing (RNA-seq) data revealed that PNAs with terminal mismatches are still able to target acpP, suggesting wider off-target effects than anticipated. Comparison of these results to an RNA-seq data set from uropathogenic Escherichia coli (UPEC) treated with eleven different PNAs confirmed that our findings are not unique to Salmonella We believe that MASON's off-target assessment will improve the design of specific PNAs and other ASOs.

INRI-seq enables global cell-free analysis of translation initiation and off-target effects of antisense inhibitors.

Ribosome profiling (Ribo-seq) is a powerful method for the transcriptome-wide assessment of protein synthesis rates and the study of translational control mechanisms. Yet, Ribo-seq also has limitations. These include difficulties with the analysis of translation-modulating molecules such as antibiotics, which are often toxic or challenging to deliver into living cells. Here, we have developed in vitro Ribo-seq (INRI-seq), a cell-free method to analyze the translational landscape of a fully customizable synthetic transcriptome. Using Escherichia coli as an example, we show how INRI-seq can be used to analyze the translation initiation sites of a transcriptome of interest. We also study the global impact of direct translation inhibition by antisense peptide nucleic acid (PNA) to analyze PNA off-target effects. Overall, INRI-seq presents a scalable, sensitive method to study translation initiation in a transcriptome-wide manner without the potentially confounding effects of extracting ribosomes from living cells.

Comprehensive analysis of PNA-based antisense antibiotics targeting various essential genes in uropathogenic Escherichia coli.

Antisense peptide nucleic acids (PNAs) that target mRNAs of essential bacterial genes exhibit specific bactericidal effects in several microbial species, but our mechanistic understanding of PNA activity and their target gene spectrum is limited. Here, we present a systematic analysis of PNAs targeting 11 essential genes with varying expression levels in uropathogenic Escherichia coli (UPEC). We demonstrate that UPEC is susceptible to killing by peptide-conjugated PNAs, especially when targeting the widely-used essential gene acpP. Our evaluation yields three additional promising target mRNAs for effective growth inhibition, i.e.dnaB, ftsZ and rpsH. The analysis also shows that transcript abundance does not predict target vulnerability and that PNA-mediated growth inhibition is not universally associated with target mRNA depletion. Global transcriptomic analyses further reveal PNA sequence-dependent but also -independent responses, including the induction of envelope stress response pathways. Importantly, we show that 9mer PNAs are generally as effective in inhibiting bacterial growth as their 10mer counterparts. Overall, our systematic comparison of a range of PNAs targeting mRNAs of different essential genes in UPEC suggests important features for PNA design, reveals a general bacterial response to PNA conjugates and establishes the feasibility of using PNA antibacterials to combat UPEC.