Distinct Escherichia coli transcriptional profiles in the guts of recurrent UTI sufferers revealed by pan-genome hybrid selection.

Low-abundance members of microbial communities are difficult to study in their native habitat. This includes Escherichia coli, a minor, but common inhabitant of the gastrointestinal tract and opportunistic pathogen, including of the urinary tract, where it causes most infections. While our understanding of the interactions between uropathogenic Escherichia coli (UPEC) and the bladder is increasing, comparatively little is known about UPEC in its pre-infection reservoir, partly due to its low abundance there (<1% relative abundance). In order to specifically and sensitively explore the genomes and transcriptomes of diverse E. coli from gastrointestinal communities, we developed E. coli PanSelect, a set of probes designed to enrich E. coli's broad pangenome. First we demonstrated the ability of PanSelect to enrich diverse strains in an unbiased way using a mock community of known composition. Then we enriched E. coli DNA and RNA from human stool microbiomes by 158 and 30-fold, respectively. We also used E. coli PanSelect to explore the gene content and transcriptome of E. coli within the gut microbiomes of women with history of recurrent urinary tract infection (rUTI), finding differential regulation of pathways that suggests that the rUTI gut environment promotes respiratory vs fermentative metabolism. E. coli PanSelect technology holds promise for investigations of native in vivo biology of diverse E. coli in the gut and other environments, where it is a minor component of the microbial community, using unbiased, culture-free shotgun sequencing. This method could also be generally applied to other highly diverse, low abundance bacteria.

Global diversity of enterococci and description of 18 previously unknown species.

Enterococci are gut microbes of most land animals. Likely appearing first in the guts of arthropods as they moved onto land, they diversified over hundreds of millions of years adapting to evolving hosts and host diets. Over 60 enterococcal species are now known. Two species, Enterococcus faecalis and Enterococcus faecium, are common constituents of the human microbiome. They are also now leading causes of multidrug-resistant hospital-associated infection. The basis for host association of enterococcal species is unknown. To begin identifying traits that drive host association, we collected 886 enterococcal strains from widely diverse hosts, ecologies, and geographies. This identified 18 previously undescribed species expanding genus diversity by >25%. These species harbor diverse genes including toxins and systems for detoxification and resource acquisition. Enterococcus faecalis and E. faecium were isolated from diverse hosts highlighting their generalist properties. Most other species showed a more restricted distribution indicative of specialized host association. The expanded species diversity permitted the Enterococcus genus phylogeny to be viewed with unprecedented resolution, allowing features to be identified that distinguish its four deeply rooted clades, and the entry of genes associated with range expansion such as B-vitamin biosynthesis and flagellar motility to be mapped to the phylogeny. This work provides an unprecedentedly broad and deep view of the genus Enterococcus, including insights into its evolution, potential new threats to human health, and where substantial additional enterococcal diversity is likely to be found.

Persistent Salmonella infections in humans are associated with mutations in the BarA/SirA regulatory pathway.

Several bacterial pathogens, including Salmonella enterica, can cause persistent infections in humans by mechanisms that are poorly understood. By comparing genomes of isolates longitudinally collected from 256 prolonged salmonellosis patients, we identified repeated mutations in global regulators, including the barA/sirA two-component regulatory system, across multiple patients and Salmonella serovars. Comparative RNA-seq analysis revealed that distinct mutations in barA/sirA led to diminished expression of Salmonella pathogenicity islands 1 and 4 genes, which are required for Salmonella invasion and enteritis. Moreover, barA/sirA mutants were attenuated in an acute salmonellosis mouse model and induced weaker transcription of host immune responses. In contrast, in a persistent infection mouse model, these mutants exhibited long-term colonization and prolonged shedding. Taken together, these findings suggest that selection of mutations in global virulence regulators facilitates persistent Salmonella infection in humans, by attenuating Salmonella virulence and inducing a weaker host inflammatory response.

Discovery of a structural class of antibiotics with explainable deep learning.

The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis1-9. Deep learning approaches have aided in exploring chemical spaces1,10-15; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.

Utility of Mycobacterium tuberculosis genome sequencing snapshots to assess transmission dynamics over time.

We explored the utility of brief Mycobacterium tuberculosis whole-genome sequencing (WGS) "snapshots" at a sentinel site within Lima, Peru for evaluating local transmission dynamics over time. Within a 17 km2 area, 15/70 (21%) isolates with WGS collected during 2011-2012 and 22/81 (27%) collected during 2020-2021 were clustered (p = 0.414), and additional isolates clustered with those from outside the area. Isolates from the later period were disproportionately related to large historic clusters in Lima from the earlier period. WGS snapshots at a sentinel site may not be useful for monitoring transmission, but monitoring the persistence of large transmission clusters might be.

Genomic epidemiology and antibiotic susceptibility profiling of uropathogenic Escherichia coli among children in the United States.

Escherichia coli is the most common cause of urinary tract infections (UTIs) in children, and yet the underlying mechanisms of virulence and antibiotic resistance and the overall population structure of the species is poorly understood within this age group. To investigate whether uropathogenic E. coli (UPEC) from children who developed pyelonephritis carried specific genetic markers, we generated whole-genome sequence data for 96 isolates from children with UTIs. This included 57 isolates from children with either radiologically confirmed pyelonephritis or cystitis and 27 isolates belonging to the well-known multidrug-resistant sequence type ST131, selected to investigate their population structure and antibiotic resistance characteristics. We observed a UPEC population structure that is similar to those reported in adults. In comparison with prior investigations, we found that the full pap operon was more common among UPEC from pediatric cases of pyelonephritis. Further, in contrast with recent reports that the P-fimbriae adhesin-encoding papGII allele is substantially more prevalent in invasive UPEC from adults, we found papGII was common to both invasive and non-invasive UPEC from children. Among the set of ST131 isolates from children with UTIs, we found antibiotic resistance was correlated with known genetic markers of resistance, as in adults. Unexpectedly, we observed that fimH30, an allele of the fimbrial gene fimH often used as a proxy to type ST131 isolates into the most drug-resistant subclade C, was carried by some of the subclade A and subclade B isolates, suggesting that the fimH30 allele could confer a selective advantage for UPEC. IMPORTANCE Urinary tract infections (UTIs), which are most often caused by Escherichia coli, are not well studied in children. Here, we examine genetic characteristics that differentiate UTI-causing bacteria in children that either remain localized to the bladder or are involved in more serious kidney infections. We also examine patterns of antibiotic resistance among strains from children that are part of E. coli sequence type 131, a group of bacteria that commonly cause UTIs and are known to have high levels of drug resistance. This work provides new insight into the virulence and antibiotic resistance characteristics of the bacteria that cause UTIs in children.

The carbapenem inoculum effect provides insight into the molecular mechanisms underlying carbapenem resistance in Enterobacterales.

Carbapenem-resistant Enterobacterales (CRE) are important pathogens that can develop resistance via multiple molecular mechanisms, including hydrolysis or reduced antibiotic influx. Identifying these mechanisms can improve pathogen surveillance, infection control, and patient care. We investigated how resistance mechanisms influence the carbapenem inoculum effect (IE), a phenomenon where inoculum size affects antimicrobial susceptibility testing (AST). We demonstrated that seven different carbapenemases impart a meropenem IE in Escherichia coli. Across 110 clinical CRE isolates, the carbapenem IE strictly depended on resistance mechanism: all carbapenemase-producing CRE (CP-CRE) exhibited a strong IE, whereas porin-deficient CRE displayed none. Concerningly, 50% and 24% of CP-CRE isolates changed susceptibility classification to meropenem and ertapenem, respectively, across the allowable inoculum range in clinical guidelines. The meropenem IE, and the ratio of ertapenem to meropenem minimal inhibitory concentration (MIC) at standard inoculum, reliably identified CP-CRE. Understanding how resistance mechanisms affect AST could improve diagnosis and guide therapies for CRE infections.

Global diversity of enterococci and description of 18 novel species.

Enterococci are commensal gut microbes of most land animals. They diversified over hundreds of millions of years adapting to evolving hosts and host diets. Of over 60 known enterococcal species, Enterococcus faecalis and E. faecium uniquely emerged in the antibiotic era among leading causes of multidrug resistant hospital-associated infection. The basis for the association of particular enterococcal species with a host is largely unknown. To begin deciphering enterococcal species traits that drive host association, and to assess the pool of Enterococcus-adapted genes from which known facile gene exchangers such as E. faecalis and E. faecium may draw, we collected 886 enterococcal strains from nearly 1,000 specimens representing widely diverse hosts, ecologies and geographies. This provided data on the global occurrence and host associations of known species, identifying 18 new species in the process expanding genus diversity by >25%. The novel species harbor diverse genes associated with toxins, detoxification, and resource acquisition. E. faecalis and E. faecium were isolated from a wide diversity of hosts highlighting their generalist properties, whereas most other species exhibited more restricted distributions indicative of specialized host associations. The expanded species diversity permitted the Enterococcus genus phylogeny to be viewed with unprecedented resolution, allowing features to be identified that distinguish its four deeply rooted clades as well as genes associated with range expansion, such as B-vitamin biosynthesis and flagellar motility. Collectively, this work provides an unprecedentedly broad and deep view of the genus Enterococcus, potential threats to human health, and new insights into its evolution.

zol & fai: large-scale targeted detection and evolutionary investigation of gene clusters.

Many universally and conditionally important genes are genomically aggregated within clusters. Here, we introduce fai and zol, which together enable large-scale comparative analysis of different types of gene clusters and mobile-genetic elements (MGEs), such as biosynthetic gene clusters (BGCs) or viruses. Fundamentally, they overcome a current bottleneck to reliably perform comprehensive orthology inference at large scale across broad taxonomic contexts and thousands of genomes. First, fai allows the identification of orthologous or homologous instances of a query gene cluster of interest amongst a database of target genomes. Subsequently, zol enables reliable, context-specific inference of protein-encoding ortholog groups for individual genes across gene cluster instances. In addition, zol performs functional annotation and computes a variety of statistics for each inferred ortholog group. These programs are showcased through application to: (i) longitudinal tracking of a virus in metagenomes, (ii) discovering novel population-genetic insights of two common BGCs in a fungal species, and (iii) uncovering large-scale evolutionary trends of a virulence-associated gene cluster across thousands of genomes from a diverse bacterial genus.

SAP: Synteny-aware gene function prediction for bacteria using protein embeddings.

Motivation: Today, we know the function of only a small fraction of the protein sequences predicted from genomic data. This problem is even more salient for bacteria, which represent some of the most phylogenetically and metabolically diverse taxa on Earth. This low rate of bacterial gene annotation is compounded by the fact that most function prediction algorithms have focused on eukaryotes, and conventional annotation approaches rely on the presence of similar sequences in existing databases. However, often there are no such sequences for novel bacterial proteins. Thus, we need improved gene function prediction methods tailored for prokaryotes. Recently, transformer-based language models - adopted from the natural language processing field - have been used to obtain new representations of proteins, to replace amino acid sequences. These representations, referred to as protein embeddings, have shown promise for improving annotation of eukaryotes, but there have been only limited applications on bacterial genomes. Results: To predict gene functions in bacteria, we developed SAP, a novel synteny-aware gene function prediction tool based on protein embeddings from state-of-the-art protein language models. SAP also leverages the unique operon structure of bacteria through conserved synteny. SAP outperformed both conventional sequence-based annotation methods and state-of-the-art methods on multiple bacterial species, including for distant homolog detection, where the sequence similarity to the proteins in the training set was as low as 40%. Using SAP to identify gene functions across diverse enterococci, of which some species are major clinical threats, we identified 11 previously unrecognized putative novel toxins, with potential significance to human and animal health.

Comparison of long- and short-read metagenomic assembly for low-abundance species and resistance genes.

Recent technological and computational advances have made metagenomic assembly a viable approach to achieving high-resolution views of complex microbial communities. In previous benchmarking, short-read (SR) metagenomic assemblers had the highest accuracy, long-read (LR) assemblers generated the most contiguous sequences and hybrid (HY) assemblers balanced length and accuracy. However, no assessments have specifically compared the performance of these assemblers on low-abundance species, which include clinically relevant organisms in the gut. We generated semi-synthetic LR and SR datasets by spiking small and increasing amounts of Escherichia coli isolate reads into fecal metagenomes and, using different assemblers, examined E. coli contigs and the presence of antibiotic resistance genes (ARGs). For ARG assembly, although SR assemblers recovered more ARGs with high accuracy, even at low coverages, LR assemblies allowed for the placement of ARGs within longer, E. coli-specific contigs, thus pinpointing their taxonomic origin. HY assemblies identified resistance genes with high accuracy and had lower contiguity than LR assemblies. Each assembler type's strengths were maintained even when our isolate was spiked in with a competing strain, which fragmented and reduced the accuracy of all assemblies. For strain characterization and determining gene context, LR assembly is optimal, while for base-accurate gene identification, SR assemblers outperform other options. HY assembly offers contiguity and base accuracy, but requires generating data on multiple platforms, and may suffer high misassembly rates when strain diversity exists. Our results highlight the trade-offs associated with each approach for recovering low-abundance taxa, and that the optimal approach is goal-dependent.

Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery.

Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein-ligand interactions between 296 proteins spanning Escherichia coli's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning-based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true-positive rate to false-positive rate. This work indicates that advances in modeling protein-ligand interactions, particularly using machine learning-based approaches, are needed to better harness AlphaFold2 for drug discovery.

Longitudinal multi-omics analyses link gut microbiome dysbiosis with recurrent urinary tract infections in women.

Recurrent urinary tract infections (rUTIs) are a major health burden worldwide, with history of infection being a significant risk factor. While the gut is a known reservoir for uropathogenic bacteria, the role of the microbiota in rUTI remains unclear. We conducted a year-long study of women with (n = 15) and without (n = 16) history of rUTI, from whom we collected urine, blood and monthly faecal samples for metagenomic and transcriptomic interrogation. During the study 24 UTIs were reported, with additional samples collected during and after infection. The gut microbiome of individuals with a history of rUTI was significantly depleted in microbial richness and butyrate-producing bacteria compared with controls, reminiscent of other inflammatory conditions. However, Escherichia coli gut and bladder populations were comparable between cohorts in both relative abundance and phylogroup. Transcriptional analysis of peripheral blood mononuclear cells revealed expression profiles indicative of differential systemic immunity between cohorts. Altogether, these results suggest that rUTI susceptibility is in part mediated through the gut-bladder axis, comprising gut dysbiosis and differential immune response to bacterial bladder colonization, manifesting in symptoms.

Modulating the evolutionary trajectory of tolerance using antibiotics with different metabolic dependencies.

Antibiotic tolerance, or the ability of bacteria to survive antibiotic treatment in the absence of genetic resistance, has been linked to chronic and recurrent infections. Tolerant cells are often characterized by a low metabolic state, against which most clinically used antibiotics are ineffective. Here, we show that tolerance readily evolves against antibiotics that are strongly dependent on bacterial metabolism, but does not arise against antibiotics whose efficacy is only minimally affected by metabolic state. We identify a mechanism of tolerance evolution in E. coli involving deletion of the sodium-proton antiporter gene nhaA, which results in downregulated metabolism and upregulated stress responses. Additionally, we find that cycling of antibiotics with different metabolic dependencies interrupts evolution of tolerance in vitro, increasing the lifetime of treatment efficacy. Our work highlights the potential for limiting the occurrence and extent of tolerance by accounting for antibiotic dependencies on bacterial metabolism.

Inter-species geographic signatures for tracing horizontal gene transfer and long-term persistence of carbapenem resistance.

BACKGROUND: Carbapenem-resistant Enterobacterales (CRE) are an urgent global health threat. Inferring the dynamics of local CRE dissemination is currently limited by our inability to confidently trace the spread of resistance determinants to unrelated bacterial hosts. Whole-genome sequence comparison is useful for identifying CRE clonal transmission and outbreaks, but high-frequency horizontal gene transfer (HGT) of carbapenem resistance genes and subsequent genome rearrangement complicate tracing the local persistence and mobilization of these genes across organisms. METHODS: To overcome this limitation, we developed a new approach to identify recent HGT of large, near-identical plasmid segments across species boundaries, which also allowed us to overcome technical challenges with genome assembly. We applied this to complete and near-complete genome assemblies to examine the local spread of CRE in a systematic, prospective collection of all CRE, as well as time- and species-matched carbapenem-susceptible Enterobacterales, isolated from patients from four US hospitals over nearly 5 years. RESULTS: Our CRE collection comprised a diverse range of species, lineages, and carbapenem resistance mechanisms, many of which were encoded on a variety of promiscuous plasmid types. We found and quantified rearrangement, persistence, and repeated transfer of plasmid segments, including those harboring carbapenemases, between organisms over multiple years. Some plasmid segments were found to be strongly associated with specific locales, thus representing geographic signatures that make it possible to trace recent and localized HGT events. Functional analysis of these signatures revealed genes commonly found in plasmids of nosocomial pathogens, such as functions required for plasmid retention and spread, as well survival against a variety of antibiotic and antiseptics common to the hospital environment. CONCLUSIONS: Collectively, the framework we developed provides a clearer, high-resolution picture of the epidemiology of antibiotic resistance importation, spread, and persistence in patients and healthcare networks.

StrainGE: a toolkit to track and characterize low-abundance strains in complex microbial communities.

Human-associated microbial communities comprise not only complex mixtures of bacterial species, but also mixtures of conspecific strains, the implications of which are mostly unknown since strain level dynamics are underexplored due to the difficulties of studying them. We introduce the Strain Genome Explorer (StrainGE) toolkit, which deconvolves strain mixtures and characterizes component strains at the nucleotide level from short-read metagenomic sequencing with higher sensitivity and resolution than other tools. StrainGE is able to identify strains at 0.1x coverage and detect variants for multiple conspecific strains within a sample from coverages as low as 0.5x.

Global phylogenomic analyses of Mycobacterium abscessus provide context for non cystic fibrosis infections and the evolution of antibiotic resistance.

Mycobacterium abscessus (MAB) is an emerging pathogen that leads to chronic lung infections. To date, the global population structure of non-cystic fibrosis (CF) MAB and evolutionary patterns of drug resistance emergence have not been investigated. Here we construct a global dataset of 1,279 MAB whole genomes from CF or non-CF patients. We utilize whole genome analysis to assess relatedness, phylogeography, and drug resistance evolution. MAB isolates from CF and non-CF hosts are interspersed throughout the phylogeny, such that the majority of dominant circulating clones include isolates from both populations, indicating that global spread of MAB clones is not sequestered to CF contexts. We identify a large clade of M. abscessus harboring the erm(41) T28C mutation, predicted to confer macrolide susceptibility in this otherwise macrolide-resistant species. Identification of multiple evolutionary events within this clade, consistent with regain of wild type, intrinsic macrolide resistance, underscores the critical importance of macrolides in MAB.

Genetic determinants facilitating the evolution of resistance to carbapenem antibiotics.

In this era of rising antibiotic resistance, in contrast to our increasing understanding of mechanisms that cause resistance, our understanding of mechanisms that influence the propensity to evolve resistance remains limited. Here, we identified genetic factors that facilitate the evolution of resistance to carbapenems, the antibiotic of 'last resort', in Klebsiella pneumoniae, the major carbapenem-resistant species. In clinical isolates, we found that high-level transposon insertional mutagenesis plays an important role in contributing to high-level resistance frequencies in several major and emerging carbapenem-resistant lineages. A broader spectrum of resistance-conferring mutations for select carbapenems such as ertapenem also enables higher resistance frequencies and, importantly, creates stepping-stones to achieve high-level resistance to all carbapenems. These mutational mechanisms can contribute to the evolution of resistance, in conjunction with the loss of systems that restrict horizontal resistance gene uptake, such as the CRISPR-Cas system. Given the need for greater antibiotic stewardship, these findings argue that in addition to considering the current efficacy of an antibiotic for a clinical isolate in antibiotic selection, considerations of future efficacy are also important. The genetic background of a clinical isolate and the exact antibiotic identity can and should also be considered as they are determinants of a strain's propensity to become resistant. Together, these findings thus provide a molecular framework for understanding acquisition of carbapenem resistance in K. pneumoniae with important implications for diagnosing and treating this important class of pathogens.

Clinically relevant mutations in core metabolic genes confer antibiotic resistance.

Although metabolism plays an active role in antibiotic lethality, antibiotic resistance is generally associated with drug target modification, enzymatic inactivation, and/or transport rather than metabolic processes. Evolution experiments of Escherichia coli rely on growth-dependent selection, which may provide a limited view of the antibiotic resistance landscape. We sequenced and analyzed E. coli adapted to representative antibiotics at increasingly heightened metabolic states. This revealed various underappreciated noncanonical genes, such as those related to central carbon and energy metabolism, which are implicated in antibiotic resistance. These metabolic alterations lead to lower basal respiration, which prevents antibiotic-mediated induction of tricarboxylic acid cycle activity, thus avoiding metabolic toxicity and minimizing drug lethality. Several of the identified metabolism-specific mutations are overrepresented in the genomes of >3500 clinical E. coli pathogens, indicating clinical relevance.

Limited effects of long-term daily cranberry consumption on the gut microbiome in a placebo-controlled study of women with recurrent urinary tract infections.

BACKGROUND: Urinary tract infections (UTIs) affect 15 million women each year in the United States, with > 20% experiencing frequent recurrent UTIs. A recent placebo-controlled clinical trial found a 39% reduction in UTI symptoms among recurrent UTI sufferers who consumed a daily cranberry beverage for 24 weeks. Using metagenomic sequencing of stool from a subset of these trial participants, we assessed the impact of cranberry consumption on the gut microbiota, a reservoir for UTI-causing pathogens such as Escherichia coli, which causes > 80% of UTIs. RESULTS: The overall taxonomic composition, community diversity, carriage of functional pathways and gene families, and relative abundances of the vast majority of observed bacterial taxa, including E. coli, were not changed significantly by cranberry consumption. However, one unnamed Flavonifractor species (OTU41), which represented ≤1% of the overall metagenome, was significantly less abundant in cranberry consumers compared to placebo at trial completion. Given Flavonifractor's association with negative human health effects, we sought to determine OTU41 characteristic genes that may explain its differential abundance and/or relationship to key host functions. Using comparative genomic and metagenomic techniques, we identified genes in OTU41 related to transport and metabolism of various compounds, including tryptophan and cobalamin, which have been shown to play roles in host-microbe interactions. CONCLUSION: While our results indicated that cranberry juice consumption had little impact on global measures of the microbiome, we found one unnamed Flavonifractor species differed significantly between study arms. This suggests further studies are needed to assess the role of cranberry consumption and Flavonifractor in health and wellbeing in the context of recurrent UTI. TRIAL REGISTRATION: Clinical trial registration number: ClinicalTrials.gov NCT01776021 .