SAFPred: 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 bacteria. 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 SAFPred, a novel synteny-aware gene function prediction tool based on protein embeddings from state-of-the-art protein language models. SAFpred also leverages the unique operon structure of bacteria through conserved synteny. SAFPred 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 SAFPred 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. AVAILABILITY AND IMPLEMENTATION: https://github.com/AbeelLab/safpred.

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

Low-abundance members of microbial communities are difficult to study in their native habitats. This includes Escherichia coli, a minor, but common inhabitant of the gastrointestinal tract and opportunistic pathogen, including of the urinary tract, where it is the primary pathogen. While multi-omic analyses have detailed critical interactions between uropathogenic Escherichia coli (UPEC) and the bladder that mediate UTI outcome, comparatively little is known about UPEC in its pre-infection reservoir, partly due to its low abundance there (<1% relative abundance). To accurately and sensitively explore the genomes and transcriptomes of diverse E. coli in gastrointestinal communities, we developed E. coli PanSelect which uses a set of probes designed to specifically recognize and capture E. coli's broad pangenome from sequencing libraries. We demonstrated the ability of E. coli PanSelect to enrich, by orders of magnitude, sequencing data from diverse E. coli using a mock community and a set of human stool samples collected as part of a cohort study investigating drivers of recurrent urinary tract infections (rUTI). Comparisons of genomes and transcriptomes between E. coli residing in the gastrointestinal tracts of women with and without a history of rUTI suggest that rUTI gut E. coli are responding to increased levels of oxygen and nitrate, suggestive of mucosal inflammation, which may have implications for recurrent disease. E. coli PanSelect is well suited for investigations of native in vivo biology of E. coli in other environments where it is at low relative abundance, and the framework described here has broad applicability to other highly diverse, low abundance organisms.

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.

Clinical and Genomic Characterization of a Cohort of Patients With Klebsiella pneumoniae Bloodstream Infection.

BACKGROUND: The clinical and microbial factors associated with Klebsiella pneumoniae bloodstream infections (BSIs) are not well characterized. Prior studies have focused on highly resistant or hypervirulent isolates, limiting our understanding of K. pneumoniae strains that commonly cause BSI. We performed a record review and whole-genome sequencing to investigate the clinical characteristics, bacterial diversity, determinants of antimicrobial resistance, and risk factors for in-hospital death in a cohort of patients with K. pneumoniae BSI. METHODS: We identified 562 patients at Massachusetts General Hospital with K. pneumoniae BSIs between 2016 and 2022. We collected data on comorbid conditions, infection source, clinical outcomes, and antibiotic resistance and performed whole-genome sequencing on 108 sequential BSI isolates from 2021 to 2022. RESULTS: Intra-abdominal infection was the most common source of infection accounting for 34% of all BSIs. A respiratory tract source accounted for 6% of BSIs but was associated with a higher in-hospital mortality rate (adjusted odds ratio, 5.4 [95% confidence interval, 2.2-12.8]; P < .001 for comparison with other sources). Resistance to the first antibiotic prescribed was also associated with a higher risk of death (adjusted odds ratio, 5.2 [95% confidence interval, 2.2-12.4]; P < .001). BSI isolates were genetically diverse, and no clusters of epidemiologically and genetically linked cases were observed. Virulence factors associated with invasiveness were observed at a low prevalence, although an unexpected association between O-antigen type and the source of infection was found. CONCLUSIONS: These observations demonstrate the versatility of K. pneumoniae as an opportunistic pathogen and highlight the need for new approaches for surveillance and the rapid identification of patients with invasive antimicrobial-resistant K. pneumoniae infection.

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.

Gut microbiome perturbation, antibiotic resistance, and Escherichia coli strain dynamics associated with international travel: a metagenomic analysis.

BACKGROUND: Culture-based studies have shown that acquisition of extended-spectrum ß-lactamase-producing Enterobacterales is common during international travel; however, little is known about the role of the gut microbiome before and during travel, nor about acquisition of other antimicrobial-resistant organisms. We aimed to identify (1) whether the gut microbiome provided colonisation resistance against antimicrobial-resistant organism acquisition, (2) the effect of travel and travel behaviours on the gut microbiome, and (3) the scale and global heterogeneity of antimicrobial-resistant organism acquisition. METHODS: In this metagenomic analysis, participants were recruited at three US travel clinics (Boston, MA; New York, NY; and Salt Lake City, UT) before international travel. Participants had to travel internationally between Dec 8, 2017, and April 30, 2019, and have DNA extractions for stool samples both before and after travel for inclusion. Participants were excluded if they had at least one low coverage sample (<1 million read pairs). Stool samples were collected at home before and after travel, sent to a clinical microbiology laboratory to be screened for three target antimicrobial-resistant organisms (extended-spectrum ß-lactamase-producing Enterobacterales, carbapenem-resistant Enterobacterales, and mcr-mediated colistin-resistant Enterobacterales), and underwent DNA extraction and shotgun metagenomic sequencing. We profiled metagenomes for taxonomic composition, antibiotic-resistant gene content, and characterised the Escherichia coli population at the strain level. We analysed pre-travel samples to identify the gut microbiome risk factors associated with acquisition of the three targeted antimicrobial resistant organisms. Pre-travel and post-travel samples were compared to identify microbiome and resistome perturbation and E coli strain acquisition associated with travel. FINDINGS: A total of 368 individuals travelled between the required dates, and 296 had DNA extractions available for both before and after travel. 29 travellers were excluded as they had at least one low coverage sample, leaving a final group of 267 participants. We observed a perturbation of the gut microbiota, characterised by a significant depletion of microbial diversity and enrichment of the Enterobacteriaceae family. Metagenomic strain tracking confirmed that 67% of travellers acquired new strains of E coli during travel that were phylogenetically distinct from their pre-travel strains. We observed widespread enrichment of antibiotic-resistant genes in the gut, with a median 15% (95% CI 10-20, p<1 × 10-10) increase in burden (reads per kilobase per million reads). This increase included antibiotic-resistant genes previously classified as threats to public health, which were 56% (95% CI 36-91, p=2 × 10-11) higher in abundance after travel than before. Fluoroquinolone antibiotic-resistant genes were aquired by 97 (54%) of 181 travellers with no detected pre-travel carriage. Although we found that visiting friends or relatives, travel to south Asia, and eating uncooked vegetables were risk factors for acquisition of the three targeted antimicrobial resistant organisms, we did not observe an association between the pre-travel microbiome structure and travel-related antimicrobial-resistant organism acquisition. INTERPRETATION: This work highlights a scale of E coli and antimicrobial-resistant organism acquisition by US travellers not apparent from previous culture-based studies, and suggests that strategies to control antimicrobial-resistant organisms addressing international traveller behaviour, rather than modulating the gut microbiome, could be worthwhile. FUNDING: US Centers for Disease Control and Prevention and National Institute of Allergy and Infectious Diseases.

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.

Evaluating the relationship between community water and sanitation access and the global burden of antibiotic resistance: an ecological study.

BACKGROUND: Antibiotic resistance is a leading cause of death, with the highest burden occurring in low-resource settings. There is little evidence on the potential for water, sanitation, and hygiene (WASH) access to reduce antibiotic resistance in humans. We aimed to determine the relationship between the burden of antibiotic resistance in humans and community access to drinking water and sanitation. METHODS: In this ecological study, we linked publicly available, geospatially tagged human faecal metagenomes (from the US National Center for Biotechnology Information Sequence Read Archive) with georeferenced household survey datasets that reported access to drinking water sources and sanitation facility types. We used generalised linear models with robust SEs to estimate the relationship between the abundance of antibiotic resistance genes (ARGs) in human faecal metagenomes and community-level coverage of improved drinking water and sanitation within a defined radii of faecal metagenome coordinates. FINDINGS: We identified 1589 metagenomes from 26 countries. The mean abundance of ARGs, in units of log10 ARG fragments per kilobase per million mapped reads classified as bacteria, was highest in Africa compared with Europe (p=0·014), North America (p=0·0032), and the Western Pacific (p=0·011), and second highest in South-East Asia compared with Europe (p=0·047) and North America (p=0·014). Increased access to improved water and sanitation was associated with lower ARG abundance (effect estimate -0·22, [95% CI -0·39 to -0·05]) and the association was stronger in urban (-0·32 [-0·63 to 0·00]) than in rural (-0·16 [-0·38 to 0·07]) areas. INTERPRETATION: Although additional studies to investigate causal effects are needed, increasing access to water and sanitation could be an effective strategy to curb the proliferation of antibiotic resistance in low-income and middle-income countries. FUNDING: Bill & Melinda Gates Foundation.

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.

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.

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.

The Role of Mobile Genetic Elements in Virulence Factor Carriage from Symptomatic and Asymptomatic Cases of Escherichia coli Bacteriuria.

Uropathogenic Escherichia coli (UPEC) is extremely diverse genotypically and phenotypically. Individual strains can variably carry diverse virulence factors, making it challenging to define a molecular signature for this pathotype. For many bacterial pathogens, mobile genetic elements (MGEs) constitute a major mechanism of virulence factor acquisition. For urinary E. coli, the total distribution of MGEs and their role in the acquisition of virulence factors is not well defined, including in the context of symptomatic infection versus asymptomatic bacteriuria (ASB). In this work, we characterized 151 isolates of E. coli, derived from patients with either urinary tract infection (UTI) or ASB. For both sets of E. coli, we catalogued the presence of plasmids, prophage, and transposons. We analyzed MGE sequences for the presence of virulence factors and antimicrobial resistance genes. These MGEs were associated with only ~4% of total virulence associated genes, while plasmids contributed to ~15% of antimicrobial resistance genes under consideration. Our analyses suggests that, across strains of E. coli, MGEs are not a prominent driver of urinary tract pathogenesis and symptomatic infection. IMPORTANCE Escherichia coli is the most common etiological agent of urinary tract infections (UTIs), with UTI-associated strains designated "uropathogenic" E. coli or UPEC. Across urinary strains of E. coli, the global landscape of MGEs and its relationship to virulence factor carriage and clinical symptomatology require greater clarity. Here, we demonstrate that many of the putative virulence factors of UPEC are not associated with acquisition due to MGEs. The current work enhances our understanding of the strain-to-strain variability and pathogenic potential of urine-associated E. coli and points toward more subtle genomic differences distinguishing ASB from UTI isolates.

Intrinsic Resistance to Colistin in the Genus Hafnia.

A bacterial species is considered to be intrinsically resistant to an antimicrobial when nearly all of the wild-type isolates (i.e., those without acquired resistance) exhibit minimum inhibitory concentration (MIC) values that are sufficiently high such that susceptibility testing is unnecessary, and that the antimicrobial should not be considered for therapy. Accordingly, knowledge of intrinsic resistance influences both the selection of treatment regimens and the approach to susceptibility testing in the clinical laboratory, where unexpected results also facilitate the recognition of microbial identification or susceptibility testing errors. Previously, limited data have suggested that Hafnia spp. may be intrinsically resistant to colistin. We evaluated the in vitro activity of colistin against 119 Hafniaceae that were isolated from human samples: 75 (63%) from routine clinical cultures and 44 (37%) from stool samples of travelers undergoing screening for antimicrobial resistant organisms. Broth microdilution colistin MICs were ≥4 µg/mL for 117 of 119 (98%) isolates. Whole-genome sequencing of 96 of the isolates demonstrated that the colistin-resistant phenotype was not lineage-specific. 2 of the 96 (2%) isolates harbored mobile colistin resistance genes. Compared to whole-genome sequencing, VITEK MS matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and VITEK 2 GN ID failed to consistently distinguish between Hafnia alvei, Hafnia paralvei, and Obesumbacterium proteus. In conclusion, using a reference antimicrobial susceptibility testing method and a genetically diverse collection of isolates, we found Hafnia spp. to be intrinsically resistant to colistin. The recognition of this phenotype will help inform rational approaches by which to perform antimicrobial susceptibility testing and therapy for patients with infections that are caused by Hafnia spp.

Development of a prediction model for the acquisition of extended spectrum beta-lactam-resistant organisms in U.S. international travellers.

BACKGROUND: Extended spectrum beta-lactamase producing Enterobacterales (ESBL-PE) present a risk to public health by limiting the efficacy of multiple classes of beta-lactam antibiotics against infection. International travellers may acquire these organisms and identifying individuals at high risk of acquisition could help inform clinical treatment or prevention strategies. METHODS: We used data collected from a cohort of 528 international travellers enrolled in a multicentre US-based study to derive a clinical prediction rule (CPR) to identify travellers who developed ESBL-PE colonization, defined as those with new ESBL positivity in stool upon return to the United States. To select candidate features, we used data collected from pre-travel and post-travel questionnaires, alongside destination-specific data from external sources. We utilized LASSO regression for feature selection, followed by random forest or logistic regression modelling, to derive a CPR for ESBL acquisition. RESULTS: A CPR using machine learning and logistic regression on 10 features has an internally cross-validated area under the receiver operating characteristic curve (cvAUC) of 0.70 (95% confidence interval 0.69-0.71). We also demonstrate that a four-feature model performs similarly to the 10-feature model, with a cvAUC of 0.68 (95% confidence interval 0.67-0.69). This model uses traveller's diarrhoea, and antibiotics as treatment, destination country waste management rankings and destination regional probabilities as predictors. CONCLUSIONS: We demonstrate that by integrating traveller characteristics with destination-specific data, we could derive a CPR to identify those at highest risk of acquiring ESBL-PE during international travel.

Strain Tracking with Uncertainty Quantification.

The ability to detect and quantify microbiota over time has a plethora of clinical, basic science, and public health applications. One of the primary means of tracking microbiota is through sequencing technologies. When the microorganism of interest is well characterized or known a priori, targeted sequencing is often used. In many applications, however, untargeted bulk (shotgun) sequencing is more appropriate; for instance, the tracking of infection transmission events and nucleotide variants across multiple genomic loci, or studying the role of multiple genes in a particular phenotype. Given these applications, and the observation that pathogens (e.g. Clostridioides difficile, Escherichia coli, Salmonella enterica) and other taxa of interest can reside at low relative abundance in the gastrointestinal tract, there is a critical need for algorithms that accurately track low-abundance taxa with strain level resolution. Here we present a sequence quality- and time-aware model, ChronoStrain, that introduces uncertainty quantification to gauge low-abundance species and significantly outperforms the current state-of-the-art on both real and synthetic data. ChronoStrain leverages sequences' quality scores and the samples' temporal information to produce a probability distribution over abundance trajectories for each strain tracked in the model. We demonstrate Chronostrain's improved performance in capturing post-antibiotic E. coli strain blooms among women with recurrent urinary tract infections (UTIs) from the UTI Microbiome (UMB) Project. Other strain tracking models on the same data either show inconsistent temporal colonization or can only track consistently using very coarse groupings. In contrast, our probabilistic outputs can reveal the relationship between low-confidence strains present in the sample that cannot be reliably assigned a single reference label (either due to poor coverage or novelty) while simultaneously calling high-confidence strains that can be unambiguously assigned a label. We also include and analyze newly sequenced cultured samples from the UMB Project.

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.

Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies.

Whether the human fetus and the prenatal intrauterine environment (amniotic fluid and placenta) are stably colonized by microbial communities in a healthy pregnancy remains a subject of debate. Here we evaluate recent studies that characterized microbial populations in human fetuses from the perspectives of reproductive biology, microbial ecology, bioinformatics, immunology, clinical microbiology and gnotobiology, and assess possible mechanisms by which the fetus might interact with microorganisms. Our analysis indicates that the detected microbial signals are likely the result of contamination during the clinical procedures to obtain fetal samples or during DNA extraction and DNA sequencing. Furthermore, the existence of live and replicating microbial populations in healthy fetal tissues is not compatible with fundamental concepts of immunology, clinical microbiology and the derivation of germ-free mammals. These conclusions are important to our understanding of human immune development and illustrate common pitfalls in the microbial analyses of many other low-biomass environments. The pursuit of a fetal microbiome serves as a cautionary example of the challenges of sequence-based microbiome studies when biomass is low or absent, and emphasizes the need for a trans-disciplinary approach that goes beyond contamination controls by also incorporating biological, ecological and mechanistic concepts.