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The dissemination of mobile antibiotic resistance genes (ARGs) via horizontal gene transfer is a significant threat to public health globally. The flow of ARGs into and between pathogens, however, remains poorly understood, limiting our ability to develop strategies for managing the antibiotic resistance crisis. Therefore, we aim to identify genetic and ecological factors that are fundamental for successful horizontal ARG transfer. We used a phylogenetic method to identify instances of horizontal ARG transfer in ~1 million bacterial genomes. This data was then integrated with >20,000 metagenomes representing animal, human, soil, water, and wastewater microbiomes to develop random forest models that can reliably predict horizontal ARG transfer between bacteria. Our results suggest that genetic incompatibility, measured as nucleotide composition dissimilarity, negatively influences the likelihood of transfer of ARGs between evolutionarily divergent bacteria. Conversely, environmental co-occurrence increases the likelihood, especially in humans and wastewater, in which several environment-specific dissemination patterns are observed. This study provides data-driven ways to predict the spread of ARGs and provides insights into the mechanisms governing this evolutionary process. The dynamics of antimicrobial resistance gene transfer remain unclear. Here, by integrating bacterial genome and metagenome data with machine learning the authors show that genetic incompatibility is a main limiting factor, while co-occurrence of bacteria in the human microbiome and wastewater contributes to gene transfer.

Background Bacterial communities in humans, animals, and the external environment maintain a large collection of antibiotic resistance genes (ARGs). However, few of these ARGs are well-characterized and thus established in existing resistance gene databases. In contrast, the remaining latent ARGs are typically unknown and overlooked in most sequencing-based studies. Our view of the resistome and its diversity is therefore incomplete, which hampers our ability to assess risk for promotion and spread of yet undiscovered resistance determinants. Results A reference database consisting of both established and latent ARGs (ARGs not present in current resistance gene repositories) was created. By analyzing more than 10,000 metagenomic samples, we showed that latent ARGs were more abundant and diverse than established ARGs in all studied environments, including the human- and animal-associated microbiomes. The pan-resistomes, i.e., all ARGs present in an environment, were heavily dominated by latent ARGs. In comparison, the core-resistome, i.e., ARGs that were commonly encountered, comprised both latent and established ARGs. We identified several latent ARGs shared between environments and/or present in human pathogens. Context analysis of these genes showed that they were located on mobile genetic elements, including conjugative elements. We, furthermore, identified that wastewater microbiomes had a surprisingly large pan- and core-resistome, which makes it a potentially high-risk environment for the mobilization and promotion of latent ARGs. Conclusions Our results show that latent ARGs are ubiquitously present in all environments and constitute a diverse reservoir from which new resistance determinants can be recruited to pathogens. Several latent ARGs already had high mobile potential and were present in human pathogens, suggesting that they may constitute emerging threats to human health. We conclude that the full resistome—including both latent and established ARGs—needs to be considered to properly assess the risks associated with antibiotic selection pressures. 5osJoaDX1hvXqrydXgmyjb Video Abstract

Antibiotic-resistant infections pose a growing threat to global health. This study reveals how genes conferring antibiotic resistance can move between bacteria that belong to different phyla lineages previously thought to be too evolutionarily distant for frequent gene exchange. By analyzing nearly 1 million resistance genes from over 400,000 bacterial genomes, the researchers uncovered hundreds of inter-phylum transfer events, exposing surprising patterns in how different classes of resistance genes spread. The findings highlight that conjugative systems are less common than expected in cross-phyla transfers and suggest that alternative mechanisms may play key roles. This new understanding of how resistance genes leap between vastly different bacterial groups can inform strategies to slow the emergence of drug-resistant infections, aiding in the development of more effective public health interventions.

Optical DNA mapping (ODM) is based on fluorescent labeling, stretching and imaging of single DNA molecules to obtain sequence-specific fluorescence profiles, DNA barcodes. These barcodes can be mapped to theoretical counterparts obtained from DNA reference sequences, which in turn allow for DNA identification in complex samples and for detecting structural changes in individual DNA molecules. There are several types of DNA labeling schemes for ODM and for each labeling type one or several types of match scoring methods are used. By combining the information from multiple labeling schemes one can potentially improve mapping confidence; however, combining match scores from different labeling assays has not been implemented yet. In this study, we introduce two theoretical methods for dealing with analysis of DNA molecules with multiple label types. In our first method, we convert the alignment scores, given as output from the different assays, into p-values using carefully crafted null models. We then combine the p-values for different label types using standard methods to obtain a combined match score and an associated combined p-value. In the second method, we use a block bootstrap approach to check for the uniqueness of a match to a database for all barcodes matching with a combined p-value below a predefined threshold. For obtaining experimental dual-labeled DNA barcodes, we introduce a novel assay where we cut plasmid DNA molecules from bacteria with restriction enzymes and the cut sites serve as sequence-specific markers, which together with barcodes obtained using the established competitive binding labeling method, form a dual-labeled barcode. All experimental data in this study originates from this assay, but we point out that our theoretical framework can be used to combine data from all kinds of available optical DNA mapping assays. We test our multiple labeling frameworks on barcodes from two different plasmids and synthetically generated barcodes (combined competitive-binding- and nick-labeling). It is demonstrated that by simultaneously using the information from all label types, we can substantially increase the significance when we match experimental barcodes to a database consisting of theoretical barcodes for all sequenced plasmids.

Improved diagnostic tools are vital for maintaining efficient treatment of antibiotic-resistant bacteria and for reducing antibiotic overconsumption. In our research, we introduce a new deep learning-based method capable of predicting untested antibiotic resistance phenotypes. The method uses transformers, a powerful artificial intelligence (AI) technique that efficiently leverages both antibiotic susceptibility tests (AST) and patient data simultaneously. The model produces predictions that can be used as time- and cost-efficient alternatives to results from cultivation-based diagnostic assays. Significantly, our study highlights the potential of AI technologies to address the increasing prevalence of antibiotic-resistant bacterial infections.

Antibiotic resistance is a growing threat to human health, caused in part by pathogens accumulating antibiotic resistance genes (ARGs) through horizontal gene transfer. New ARGs are typically not recognized until they have become widely disseminated, which limits our ability to reduce their spread. In this study, we use large-scale computational screening of bacterial genomes to identify previously undiscovered mobile ARGs in pathogens. From ~1 million genomes, we predict 1,071,815 genes encoding 34,053 unique aminoglycoside-modifying enzymes (AMEs). These cluster into 7,612 families (<70% amino acid identity) of which 88 are previously described. Fifty new AME families are associated with mobile genetic elements and pathogenic hosts. From these, 24 of 28 experimentally tested AMEs confer resistance to aminoglycoside(s) in Escherichia coli , with 17 providing resistance above clinical breakpoints. This study greatly expands the range of clinically relevant aminoglycoside resistance determinants and demonstrates that computational methods enable early discovery of potentially emerging ARGs. all Extensive bioinformatic screening predicts high numbers of aminoglycoside resistance genes from existing microbial whole genome sequences, thereby expanding the potential range of clinically relevant aminoglycoside resistance determinants.

Background Identification of pathogens is crucial to efficiently treat and prevent bacterial infections. However, existing diagnostic techniques are slow or have a too low resolution for well-informed clinical decisions. Methods In this study, we have developed an optical DNA mapping-based method for strain-level bacterial typing and simultaneous plasmid characterisation. For the typing, different taxonomical resolutions were examined and cultivated pure Escherichia coli and Klebsiella pneumoniae samples were used for parameter optimization. Finally, the method was applied to mixed bacterial samples and uncultured urine samples from patients with urinary tract infections. Results We demonstrate that optical DNA mapping of single DNA molecules can identify Escherichia coli and Klebsiella pneumoniae at the strain level directly from patient samples. At a taxonomic resolution corresponding to E. coli sequence type 131 and K. pneumoniae clonal complex 258 forming distinct groups, the average true positive prediction rates are 94% and 89%, respectively. The single-molecule aspect of the method enables us to identify multiple E. coli strains in polymicrobial samples. Furthermore, by targeting plasmid-borne antibiotic resistance genes with Cas9 restriction, we simultaneously identify the strain or subtype and characterize the corresponding plasmids. Conclusion The optical DNA mapping method is accurate and directly applicable to polymicrobial and clinical samples without cultivation. Hence, it has the potential to rapidly provide comprehensive diagnostics information, thereby optimizing early antibiotic treatment and opening up for future precision medicine management. Plain Language Summary For bacterial infections, it is important to rapidly and accurately identify and characterize the type of bacteria involved so that optimal antibiotic treatment can be given quickly to the patient. However, current diagnostic methods are sometimes slow and cannot be used for mixtures of bacteria. We have, therefore, developed a method to identify bacteria directly from patient samples. The method was tested on two common species of disease-causing bacteria – Escherichia coli and Klebsiella pneumoniae – and it could correctly identify the bacterial strain or subtype in both urine samples and mixtures. Hence, the method has the potential to provide fast diagnostic information for choosing the most suited antibiotic, thereby reducing the risk of death and suffering. Nyblom, Johnning et al. develop an optical DNA mapping approach for bacterial strain typing of patient samples. They demonstrate rapid identification of clinically relevant E. coli and K. pneumoniae strains, without the need for cultivation.

Background Broad-spectrum fluoroquinolone antibiotics are central in modern health care and are used to treat and prevent a wide range of bacterial infections. The recently discovered qnr genes provide a mechanism of resistance with the potential to rapidly spread between bacteria using horizontal gene transfer. As for many antibiotic resistance genes present in pathogens today, qnr genes are hypothesized to originate from environmental bacteria. The vast amount of data generated by shotgun metagenomics can therefore be used to explore the diversity of qnr genes in more detail. Results In this paper we describe a new method to identify qnr genes in nucleotide sequence data. We show, using cross-validation, that the method has a high statistical power of correctly classifying sequences from novel classes of qnr genes, even for fragments as short as 100 nucleotides. Based on sequences from public repositories, the method was able to identify all previously reported plasmid-mediated qnr genes. In addition, several fragments from novel putative qnr genes were identified in metagenomes. The method was also able to annotate 39 chromosomal variants of which 11 have previously not been reported in literature. Conclusions The method described in this paper significantly improves the sensitivity and specificity of identification and annotation of qnr genes in nucleotide sequence data. The predicted novel putative qnr genes in the metagenomic data support the hypothesis of a large and uncharacterized diversity within this family of resistance genes in environmental bacterial communities. An implementation of the method is freely available at http://bioinformatics.math.chalmers.se/qnr/ .

The draft genome sequence has been determined for an extended-spectrum-β-lactamase (ESBL)-producing (blaCTX-M-15) Escherichia coli strain (CCUG 62462), composed of 119 contigs and a total size of 5.27 Mb. This E. coli is serotype O25b and sequence type 131, a pandemic clonal group, causing worldwide antimicrobial-resistant infections.

In 2015, an outbreak caused by OXA-48-producing Enterobacteriaceae affected a neonatal intensive care unit at a Swedish University Hospital. The aim was to explore the transmission of OXA-48-producing strains between infants and the transfer of resistance plasmids between strains during the outbreak. Twenty-four outbreak isolates from ten suspected cases were whole-genome sequenced. A complete assembly was created for the index isolate ( Enterobacter cloacae ) and used as a mapping reference to detect its plasmids in the remaining isolates (17 Klebsiella pneumoniae , 4 Klebsiella aerogenes , and 2 Escherichia coli ). Strain typing was performed using core genome MLST and SNP analysis. As judged from sequencing and clinical epidemiological data, the outbreak involved nine cases (two developed sepsis) and four OXA-48-producing strains: E. cloacae ST1584 (index case), K. pneumoniae ST25 (eight cases), K. aerogenes ST93 (two cases), and E. coli ST453 (2 cases). Two plasmids from the index strain, p Ecl A2 and p Ecl A4, carrying bla OXA48 and bla CMY-4 , respectively, were traced to all K. pneumoniae ST25 isolates. Klebsiella aerogenes ST93 and E. coli ST453 harboured either only p Ecl A2, or both p Ecl A2 and p Ecl A4. One suspected case harbouring OXA-162-producing K. pneumoniae ST37 could be excluded from the outbreak. Once initiated by an E. cloacae strain, the outbreak was caused by the dissemination of a K. pneumoniae ST25 strain and involved inter-species horizontal transfer of two resistance plasmids, one of which carried bla OXA-48 . To our knowledge, this is the first description of an outbreak of OXA-48-producing Enterobacteriaceae in a neonatal setting in northern Europe.