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769801.0000Detecting horizontal gene transfer with metagenomics co-barcoding sequencing. Horizontal gene transfer (HGT) is the process through which genetic information is transferred between different genomes and that played a crucial role in bacterial evolution. HGT can enable bacteria to rapidly acquire antibiotic resistance and bacteria that have acquired resistance is spreading within the microbiome. Conventional methods of characterizing HGT patterns include short-read metagenomic sequencing (short-reads mNGS), long-read sequencing, and single-cell sequencing. These approaches present several limitations, such as short-read fragments, high amounts of input DNA, and sequencing costs, respectively. Here, we attempt to circumvent present limitations to detect HGT by developing a metagenomics co-barcode sequencing workflow (MECOS) and applying it to the human and mouse gut microbiomes. In addition to that, we have over 10-fold increased contig length compared to short-reads mNGS; we also obtained exceeding 30 million paired reads with co-barcode information. Applying the novel bioinformatic pipeline, we integrated this co-barcoding information and the context information from long reads, and observed over 50-fold HGT events after we corrected the potential wrong HGT events. Specifically, we detected approximately 3,000 HGT blocks in individual samples, encompassing ~6,000 genes and ~100 taxonomic groups, including loci conferring tetracycline resistance through ribosomal protection. MECOS provides a valuable tool for investigating HGT and advance our understanding on the evolution of natural microbial communities within hosts.IMPORTANCEIn this study, to better identify horizontal gene transfer (HGT) in individual samples, we introduce a new co-barcoding sequencing system called metagenomics co-barcoding sequencing (MECOS), which has three significant improvements: (i) long DNA fragment extraction, (ii) a special transposome insertion, (iii) hybridization of DNA to barcode beads, and (4) an integrated bioinformatic pipeline. Using our approach, we have over 10-fold increased contig length compared to short-reads mNGS, and observed over 50-fold HGT events after we corrected the potential wrong HGT events. Our results indicate the presence of approximately 3,000 HGT blocks, involving roughly 6,000 genes and 100 taxonomic groups in individual samples. Notably, these HGT events are predominantly enriched in genes that confer tetracycline resistance via ribosomal protection. MECOS is a useful tool for investigating HGT and the evolution of natural microbial communities within hosts, thereby advancing our understanding of microbial ecology and evolution.202438315121
378210.9997CRISPR spacers acquired from plasmids primarily target backbone genes, making them valuable for predicting potential hosts and host range. In recent years, there has been a surge in metagenomic studies focused on identifying plasmids in environmental samples. Although these studies have unearthed numerous novel plasmids, enriching our understanding of their environmental roles, a significant gap remains: the scarcity of information regarding the bacterial hosts of these newly discovered plasmids. Furthermore, even when plasmids are identified within bacterial isolates, the reported host is typically limited to the original isolate, with no insights into alternative hosts or the plasmid's potential host range. Given that plasmids depend on hosts for their existence, investigating plasmids without the knowledge of potential hosts offers only a partial perspective. This study introduces a method for identifying potential hosts and host ranges for plasmids through alignment with CRISPR spacers. To validate the method, we compared the PLSDB plasmids database with the CRISPR spacers database, yielding host predictions for 46% of the plasmids. When compared with reported hosts, our predictions achieved 84% concordance at the family level and 99% concordance at the phylum level. Moreover, the method frequently identified multiple potential hosts for a plasmid, thereby enabling predictions of alternative hosts and the host range. Notably, we found that CRISPR spacers predominantly target plasmid backbone genes while sparing functional genes, such as those linked to antibiotic resistance, aligning with our hypothesis that CRISPR spacers are acquired from plasmid-specific regions rather than insertion elements from diverse sources. Finally, we illustrate the network of connections among different bacterial taxa through plasmids, revealing potential pathways for horizontal gene transfer.IMPORTANCEPlasmids are notorious for their role in distributing antibiotic resistance genes, but they may also carry and distribute other environmentally important genes. Since plasmids are not free-living entities and rely on host bacteria for survival and propagation, predicting their hosts is essential. This study presents a method for predicting potential hosts for plasmids and offers insights into the potential paths for spreading functional genes between different bacteria. Understanding plasmid-host relationships is crucial for comprehending the ecological and clinical impact of plasmids and implications for various biological processes.202439508585
769920.9997Effects of different assembly strategies on gene annotation in activated sludge. Activated sludge comprises diverse bacteria, fungi, and other microorganisms, featuring a rich repertoire of genes involved in antibiotic resistance, pollutant degradation, and elemental cycling. In this regard, hybrid assembly technology can revolutionize metagenomics by detecting greater gene diversity in environmental samples. Nonetheless, the optimal utilization and comparability of genomic information between hybrid assembly and short- or long-read technology remain unclear. To address this gap, we compared the performance of the hybrid assembly, short- and long-read technologies, abundance and diversity of annotated genes, and taxonomic diversity by analysing 46, 161, and 45 activated sludge metagenomic datasets, respectively. The results revealed that hybrid assembly technology exhibited the best performance, generating the most contiguous and longest contigs but with a lower proportion of high-quality metagenome-assembled genomes than short-read technology. Compared with short- or long-read technologies, hybrid assembly technology can detect a greater diversity of microbiota and antibiotic resistance genes, as well as a wider range of potential hosts. However, this approach may yield lower gene abundance and pathogen detection. Our study revealed the specific advantages and disadvantages of hybrid assembly and short- and long-read applications in wastewater treatment plants, and our approach could serve as a blueprint to be extended to terrestrial environments.202438734289
770030.9997Rapid identification of antibiotic resistance gene hosts by prescreening ARG-like reads. Effective risk assessment and control of environmental antibiotic resistance depend on comprehensive information about antibiotic resistance genes (ARGs) and their microbial hosts. Advances in sequencing technologies and bioinformatics have enabled the identification of ARG hosts using metagenome-assembled contigs and genomes. However, these approaches often suffer from information loss and require extensive computational resources. Here we introduce a bioinformatic strategy that identifies ARG hosts by prescreening ARG-like reads (ALRs) directly from total metagenomic datasets. This ALR-based method offers several advantages: (1) it enables the detection of low-abundance ARG hosts with higher accuracy in complex environments; (2) it establishes a direct relationship between the abundance of ARGs and their hosts; and (3) it reduces computation time by approximately 44-96% compared to strategies relying on assembled contigs and genomes. We applied our ALR-based strategy alongside two traditional methods to investigate a typical human-impacted environment. The results were consistent across all methods, revealing that ARGs are predominantly carried by Gammaproteobacteria and Bacilli, and their distribution patterns may indicate the impact of wastewater discharge on coastal resistome. Our strategy provides rapid and accurate identification of antibiotic-resistant bacteria, offering valuable insights for the high-throughput surveillance of environmental antibiotic resistance. This study further expands our knowledge of ARG-related risk management in future.202540059905
400740.9997Detecting horizontal gene transfer among microbiota: an innovative pipeline for identifying co-shared genes within the mobilome through advanced comparative analysis. Horizontal gene transfer (HGT) is a key driver in the evolution of bacterial genomes. The acquisition of genes mediated by HGT may enable bacteria to adapt to ever-changing environmental conditions. Long-term application of antibiotics in intensive agriculture is associated with the dissemination of antibiotic resistance genes among bacteria with the consequences causing public health concern. Commensal farm-animal-associated gut microbiota are considered the reservoir of the resistance genes. Therefore, in this study, we identified known and not-yet characterized mobilized genes originating from chicken and porcine fecal samples using our innovative pipeline followed by network analysis to provide appropriate visualization to support proper interpretation.202438099617
400850.9997Impacts of mobile genetic elements on antimicrobial resistance genes in gram-negative pathogens: Current insights and genomic approaches. Antimicrobial resistance threatens to take 10 million lives per year by 2050. It is a recognised global health crisis and understanding the historic and current spread of resistance determinants is important for informing surveillance and control measures. The 'inheritance' of resistance is difficult to track because horizontal transfer is common. Antimicrobial resistance genes (ARGs) spread rapidly between bacteria, plasmids and chromosomes due to different mobile genetic elements (MGEs). This movement can increase the range of species carrying an ARG, simplify acquisition of multi-resistance, or otherwise alter the selective advantage associated with carriage of the ARG. MGE activity is therefore a significant factor in understanding routes of ARG dissemination. Characterising the combinations of MGEs contributing to the movement of individual ARGs is crucial. Each MGE category has unique genetic characteristics, and distinct impacts on the location and expression of associated ARGs. Here, the ways in which MGEs can meaningfully associate with ARGs are discussed. Approaches for extracting information about MGE associations from bacterial genome sequences are also considered. Accurate and informative annotations of the genetic contexts of relevant ARGs provide crucial insight into the presence of MGEs and their locations relative to ARGs. Combining this genomic information with knowledge about relevant biological processes allows more accurate conclusions to be drawn about transmission and dissemination of ARGs.202641005125
965360.9997Evaluating the mobility potential of antibiotic resistance genes in environmental resistomes without metagenomics. Antibiotic resistance genes are ubiquitous in the environment. However, only a fraction of them are mobile and able to spread to pathogenic bacteria. Until now, studying the mobility of antibiotic resistance genes in environmental resistomes has been challenging due to inadequate sensitivity and difficulties in contig assembly of metagenome based methods. We developed a new cost and labor efficient method based on Inverse PCR and long read sequencing for studying mobility potential of environmental resistance genes. We applied Inverse PCR on sediment samples and identified 79 different MGE clusters associated with the studied resistance genes, including novel mobile genetic elements, co-selected resistance genes and a new putative antibiotic resistance gene. The results show that the method can be used in antibiotic resistance early warning systems. In comparison to metagenomics, Inverse PCR was markedly more sensitive and provided more data on resistance gene mobility and co-selected resistances.201627767072
965070.9997Plasmid-Encoded Traits Vary across Environments. Plasmids are key mobile genetic elements in bacterial evolution and ecology as they allow the rapid adaptation of bacteria under selective environmental changes. However, the genetic information associated with plasmids is usually considered separately from information about their environmental origin. To broadly understand what kinds of traits may become mobilized by plasmids in different environments, we analyzed the properties and accessory traits of 9,725 unique plasmid sequences from a publicly available database with known bacterial hosts and isolation sources. Although most plasmid research focuses on resistance traits, such genes made up <1% of the total genetic information carried by plasmids. Similar to traits encoded on the bacterial chromosome, plasmid accessory trait compositions (including general Clusters of Orthologous Genes [COG] functions, resistance genes, and carbon and nitrogen genes) varied across seven broadly defined environment types (human, animal, wastewater, plant, soil, marine, and freshwater). Despite their potential for horizontal gene transfer, plasmid traits strongly varied with their host's taxonomic assignment. However, the trait differences across environments of broad COG categories could not be entirely explained by plasmid host taxonomy, suggesting that environmental selection acts on the plasmid traits themselves. Finally, some plasmid traits and environments (e.g., resistance genes in human-related environments) were more often associated with mobilizable plasmids (those having at least one detected relaxase) than others. Overall, these findings underscore the high level of diversity of traits encoded by plasmids and provide a baseline to investigate the potential of plasmids to serve as reservoirs of adaptive traits for microbial communities. IMPORTANCE Plasmids are well known for their role in the transmission of antibiotic resistance-conferring genes. Beyond human and clinical settings, however, they disseminate many other types of genes, including those that contribute to microbially driven ecosystem processes. In this study, we identified the distribution of traits genetically encoded by plasmids isolated from seven broadly categorized environments. We find that plasmid trait content varied with both bacterial host taxonomy and environment and that, on average, half of the plasmids were potentially mobilizable. As anthropogenic activities impact ecosystems and the climate, investigating and identifying the mechanisms of how microbial communities can adapt will be imperative for predicting the impacts on ecosystem functioning.202336629415
964980.9997Bacteria of the order Burkholderiales are original environmental hosts of type II trimethoprim resistance genes (dfrB). It is consensus that clinically relevant antibiotic resistance genes have their origin in environmental bacteria, including the large pool of primarily benign species. Yet, for the vast majority of acquired antibiotic resistance genes, the original environmental host(s) has not been identified to date. Closing this knowledge gap could improve our understanding of how antimicrobial resistance proliferates in the bacterial domain and shed light on the crucial step of initial resistance gene mobilization in particular. Here, we combine information from publicly available long- and short-read environmental metagenomes as well as whole-genome sequences to identify the original environmental hosts of dfrB, a family of genes conferring resistance to trimethoprim. Although this gene family stands in the shadow of the more widespread, structurally different dfrA, it has recently gained attention through the discovery of several new members. Based on the genetic context of dfrB observed in long-read metagenomes, we predicted bacteria of the order Burkholderiales to function as original environmental hosts of the predominant gene variants in both soil and freshwater. The predictions were independently confirmed by whole-genome datasets and statistical correlations between dfrB abundance and taxonomic composition of environmental bacterial communities. Our study suggests that Burkholderiales in general and the family Comamonadaceae in particular represent environmental origins of dfrB genes, some of which now contribute to the acquired resistome of facultative pathogens. We propose that our workflow centered on long-read environmental metagenomes allows for the identification of the original hosts of further clinically relevant antibiotic resistance genes.202439658215
965490.9997Studying the Association between Antibiotic Resistance Genes and Insertion Sequences in Metagenomes: Challenges and Pitfalls. Antibiotic resistance is an issue in many areas of human activity. The mobilization of antibiotic resistance genes within the bacterial community makes it difficult to study and control the phenomenon. It is known that certain insertion sequences, which are mobile genetic elements, can participate in the mobilization of antibiotic resistance genes and in the expression of these genes. However, the magnitude of the contribution of insertion sequences to the mobility of antibiotic resistance genes remains understudied. In this study, the relationships between insertion sequences and antibiotic resistance genes present in the microbiome were investigated using two public datasets. The first made it possible to analyze the effects of different antibiotics in a controlled mouse model. The second dataset came from a study of the differences between conventional and organic-raised cattle. Although it was possible to find statistically significant correlations between the insertion sequences and antibiotic resistance genes in both datasets, several challenges remain to better understand the contribution of insertion sequences to the motility of antibiotic resistance genes. Obtaining more complete and less fragmented metagenomes with long-read sequencing technologies could make it possible to understand the mechanisms favoring horizontal transfers within the microbiome with greater precision.202336671375
7697100.9997Impact of sample multiplexing on detection of bacteria and antimicrobial resistance genes in pig microbiomes using long-read sequencing. The effects of sample multiplexing on the detection sensitivity of antimicrobial resistance genes (ARGs) and pathogenic bacteria in metagenomic sequencing remain underexplored in newer sequencing technologies such as Oxford Nanopore Technologies (ONT), despite its critical importance for surveillance applications. Here, we evaluate how different multiplexing levels (four and eight samples per flowcell) on two ONT platforms, GridION and PromethION, influence the detection of ARGs, bacterial taxa and pathogens. While overall resistome and bacterial community profiles remained comparable across multiplexing levels, ARG detection was more comprehensive in the four-plex setting with low-abundance genes. Similarly, pathogen detection was more sensitive in the four-plex, identifying a broader range of low abundant bacterial taxa compared to the eight-plex. However, triplicate sequencing of the same microbiomes revealed that these differences were primarily due to sequencing variability rather than multiplexing itself, as similar inconsistencies were observed across replicates. Given that eight-plex sequencing is more cost-effective while still capturing the overall resistome and bacterial community composition, it may be the preferred option for general surveillance. Lower multiplexing levels may be advantageous for applications requiring enhanced sensitivity, such as detailed pathogen research. These findings highlight the trade-off between multiplexing efficiency, sequencing depth, and cost in metagenomic studies.202540611965
7480110.9997Genetic compatibility and ecological connectivity drive the dissemination of antibiotic resistance genes. 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.202540090954
5101120.9996Identification of Key Features Pivotal to the Characteristics and Functions of Gut Bacteria Taxa through Machine Learning Methods. BACKGROUND: Gut bacteria critically influence digestion, facilitate the breakdown of complex food substances, aid in essential nutrient synthesis, and contribute to immune system balance. However, current knowledge regarding intestinal bacteria remains insufficient. OBJECTIVE: This study aims to discover essential differences for different intestinal bacteria. METHODS: This study was conducted by investigating a total of 1478 gut bacterial samples comprising 235 Actinobacteria, 447 Bacteroidetes, and 796 Firmicutes, by utilizing sophisticated machine learning algorithms. By building on the dataset provided by Chen et al., we engaged sophisticated machine learning techniques to further investigate and analyze the gut bacterial samples. Each sample in the dataset was described by 993 unique features associated with gut bacteria, including 342 features annotated by the Antibiotic Resistance Genes Database, Comprehensive Antibiotic Research Database, Kyoto Encyclopedia of Genes and Genomes, and Virulence Factors of Pathogenic Bacteria. We employed incremental feature selection methods within a computational framework to identify the optimal features for classification. RESULTS: Eleven feature ranking algorithms selected several key features as pivotal to the characteristics and functions of gut bacteria. These features appear to facilitate the identification of specific gut bacterial species. Additionally, we established quantitative rules for identifying Actinobacteria, Bacteroidetes, and Firmicutes. CONCLUSION: This research underscores the significant potential of machine learning in studying gut microbes and enhances our understanding of the multifaceted roles of gut bacteria.202540671232
3774130.9996Forecasting the dissemination of antibiotic resistance genes across bacterial genomes. Antibiotic resistance spreads among bacteria through horizontal transfer of antibiotic resistance genes (ARGs). Here, we set out to determine predictive features of ARG transfer among bacterial clades. We use a statistical framework to identify putative horizontally transferred ARGs and the groups of bacteria that disseminate them. We identify 152 gene exchange networks containing 22,963 bacterial genomes. Analysis of ARG-surrounding sequences identify genes encoding putative mobilisation elements such as transposases and integrases that may be involved in gene transfer between genomes. Certain ARGs appear to be frequently mobilised by different mobile genetic elements. We characterise the phylogenetic reach of these mobilisation elements to predict the potential future dissemination of known ARGs. Using a separate database with 472,798 genomes from Streptococcaceae, Staphylococcaceae and Enterobacteriaceae, we confirm 34 of 94 predicted mobilisations. We explore transfer barriers beyond mobilisation and show experimentally that physiological constraints of the host can explain why specific genes are largely confined to Gram-negative bacteria although their mobile elements support dissemination to Gram-positive bacteria. Our approach may potentially enable better risk assessment of future resistance gene dissemination.202133893312
3783140.9996Ecology drives a global network of gene exchange connecting the human microbiome. Horizontal gene transfer (HGT), the acquisition of genetic material from non-parental lineages, is known to be important in bacterial evolution. In particular, HGT provides rapid access to genetic innovations, allowing traits such as virulence, antibiotic resistance and xenobiotic metabolism to spread through the human microbiome. Recent anecdotal studies providing snapshots of active gene flow on the human body have highlighted the need to determine the frequency of such recent transfers and the forces that govern these events. Here we report the discovery and characterization of a vast, human-associated network of gene exchange, large enough to directly compare the principal forces shaping HGT. We show that this network of 10,770 unique, recently transferred (more than 99% nucleotide identity) genes found in 2,235 full bacterial genomes, is shaped principally by ecology rather than geography or phylogeny, with most gene exchange occurring between isolates from ecologically similar, but geographically separated, environments. For example, we observe 25-fold more HGT between human-associated bacteria than among ecologically diverse non-human isolates (P = 3.0 × 10(-270)). We show that within the human microbiome this ecological architecture continues across multiple spatial scales, functional classes and ecological niches with transfer further enriched among bacteria that inhabit the same body site, have the same oxygen tolerance or have the same ability to cause disease. This structure offers a window into the molecular traits that define ecological niches, insight that we use to uncover sources of antibiotic resistance and identify genes associated with the pathology of meningitis and other diseases.201122037308
3882150.9996Clusters of Antibiotic Resistance Genes Enriched Together Stay Together in Swine Agriculture. Antibiotic resistance is a worldwide health risk, but the influence of animal agriculture on the genetic context and enrichment of individual antibiotic resistance alleles remains unclear. Using quantitative PCR followed by amplicon sequencing, we quantified and sequenced 44 genes related to antibiotic resistance, mobile genetic elements, and bacterial phylogeny in microbiomes from U.S. laboratory swine and from swine farms from three Chinese regions. We identified highly abundant resistance clusters: groups of resistance and mobile genetic element alleles that cooccur. For example, the abundance of genes conferring resistance to six classes of antibiotics together with class 1 integrase and the abundance of IS6100-type transposons in three Chinese regions are directly correlated. These resistance cluster genes likely colocalize in microbial genomes in the farms. Resistance cluster alleles were dramatically enriched (up to 1 to 10% as abundant as 16S rRNA) and indicate that multidrug-resistant bacteria are likely the norm rather than an exception in these communities. This enrichment largely occurred independently of phylogenetic composition; thus, resistance clusters are likely present in many bacterial taxa. Furthermore, resistance clusters contain resistance genes that confer resistance to antibiotics independently of their particular use on the farms. Selection for these clusters is likely due to the use of only a subset of the broad range of chemicals to which the clusters confer resistance. The scale of animal agriculture and its wastes, the enrichment and horizontal gene transfer potential of the clusters, and the vicinity of large human populations suggest that managing this resistance reservoir is important for minimizing human risk. IMPORTANCE: Agricultural antibiotic use results in clusters of cooccurring resistance genes that together confer resistance to multiple antibiotics. The use of a single antibiotic could select for an entire suite of resistance genes if they are genetically linked. No links to bacterial membership were observed for these clusters of resistance genes. These findings urge deeper understanding of colocalization of resistance genes and mobile genetic elements in resistance islands and their distribution throughout antibiotic-exposed microbiomes. As governments seek to combat the rise in antibiotic resistance, a balance is sought between ensuring proper animal health and welfare and preserving medically important antibiotics for therapeutic use. Metagenomic and genomic monitoring will be critical to determine if resistance genes can be reduced in animal microbiomes, or if these gene clusters will continue to be coselected by antibiotics not deemed medically important for human health but used for growth promotion or by medically important antibiotics used therapeutically.201627073098
4665160.9996A comprehensive survey of integron-associated genes present in metagenomes. BACKGROUND: Integrons are genomic elements that mediate horizontal gene transfer by inserting and removing genetic material using site-specific recombination. Integrons are commonly found in bacterial genomes, where they maintain a large and diverse set of genes that plays an important role in adaptation and evolution. Previous studies have started to characterize the wide range of biological functions present in integrons. However, the efforts have so far mainly been limited to genomes from cultivable bacteria and amplicons generated by PCR, thus targeting only a small part of the total integron diversity. Metagenomic data, generated by direct sequencing of environmental and clinical samples, provides a more holistic and unbiased analysis of integron-associated genes. However, the fragmented nature of metagenomic data has previously made such analysis highly challenging. RESULTS: Here, we present a systematic survey of integron-associated genes in metagenomic data. The analysis was based on a newly developed computational method where integron-associated genes were identified by detecting their associated recombination sites. By processing contiguous sequences assembled from more than 10 terabases of metagenomic data, we were able to identify 13,397 unique integron-associated genes. Metagenomes from marine microbial communities had the highest occurrence of integron-associated genes with levels more than 100-fold higher than in the human microbiome. The identified genes had a large functional diversity spanning over several functional classes. Genes associated with defense mechanisms and mobility facilitators were most overrepresented and more than five times as common in integrons compared to other bacterial genes. As many as two thirds of the genes were found to encode proteins of unknown function. Less than 1% of the genes were associated with antibiotic resistance, of which several were novel, previously undescribed, resistance gene variants. CONCLUSIONS: Our results highlight the large functional diversity maintained by integrons present in unculturable bacteria and significantly expands the number of described integron-associated genes.202032689930
6597170.9996Exploiting a targeted resistome sequencing approach in assessing antimicrobial resistance in retail foods. BACKGROUND: With the escalating risk of antimicrobial resistance (AMR), there are limited analytical options available that can comprehensively assess the burden of AMR carried by clinical/environmental samples. Food can be a potential source of AMR bacteria for humans, but its significance in driving the clinical spread of AMR remains unclear, largely due to the lack of holistic-yet-sensitive tools for surveillance and evaluation. Metagenomics is a culture-independent approach well suited for uncovering genetic determinants of defined microbial traits, such as AMR, present within unknown bacterial communities. Despite its popularity, the conventional approach of non-selectively sequencing a sample's metagenome (namely, shotgun-metagenomics) has several technical drawbacks that lead to uncertainty about its effectiveness for AMR assessment; for instance, the low discovery rate of resistance-associated genes due to their naturally small genomic footprint within the vast metagenome. Here, we describe the development of a targeted resistome sequencing method and demonstrate its application in the characterization of the AMR gene profile of bacteria associated with several retail foods. RESULT: A targeted-metagenomic sequencing workflow using a customized bait-capture system targeting over 4,000 referenced AMR genes and 263 plasmid replicon sequences was validated against both mock and sample-derived bacterial community preparations. Compared to shotgun-metagenomics, the targeted method consistently provided for improved recovery of resistance gene targets with a much-improved target detection efficiency (> 300-fold). Targeted resistome analyses conducted on 36 retail-acquired food samples (fresh sprouts, n = 10; ground meat, n = 26) and their corresponding bacterial enrichment cultures (n = 36) reveals in-depth features regarding the identity and diversity of AMR genes, most of which were otherwise undetected by the whole-metagenome shotgun sequencing method. Furthermore, our findings suggest that foodborne Gammaproteobacteria could be the major reservoir of food-associated AMR genetic determinants, and that the resistome structure of the selected high-risk food commodities are, to a large extent, dictated by microbiome composition. CONCLUSIONS: For metagenomic sequencing-based surveillance of AMR, the target-capture method presented herein represents a more sensitive and efficient approach to evaluate the resistome profile of complex food or environmental samples. This study also further implicates retail foods as carriers of diverse resistance-conferring genes indicating a potential impact on the dissemination of AMR.202336991496
9554180.9996A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling. The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can identify ARGs relatively more accurately, the identifying process is time-consuming and specialized knowledge is required. With the rapid development of whole genome sequencing technology, researchers attempt to identify ARGs by computing sequence similarity from public databases. However, these computational methods might fail to detect ARGs due to the low sequence identity to known ARGs. Moreover, existing methods cannot effectively address the issue of multidrug resistance prediction for ARGs, which is a great challenge to clinical treatments. To address the challenges, we propose an end-to-end multi-label learning framework for predicting ARGs. More specifically, the task of ARGs prediction is modeled as a problem of multi-label learning, and a deep neural network-based end-to-end framework is proposed, in which a specific loss function is introduced to employ the advantage of multi-label learning for ARGs prediction. In addition, a dual-view modeling mechanism is employed to make full use of the semantic associations among two views of ARGs, i.e. sequence-based information and structure-based information. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs prediction.202235272349
7478190.9996Global analysis of the metaplasmidome: ecological drivers and spread of antibiotic resistance genes across ecosystems. BACKGROUND: Plasmids act as vehicles for the rapid spread of antibiotic resistance genes (ARGs). However, few studies of the resistome at the community level distinguish between ARGs carried by mobile genetic elements and those carried by chromosomes, and these studies have been limited to a few ecosystems. This is the first study to focus on ARGs carried by the metaplasmidome on a global scale. RESULTS: This study shows that only a small fraction of the plasmids reconstructed from 27 ecosystems representing 9 biomes are catalogued in public databases. The abundance of ARGs harboured by the metaplasmidome was significantly explained by bacterial richness. Few plasmids with or without ARGs were shared between ecosystems or biomes, suggesting that plasmid distribution on a global scale is mainly driven by ecology rather than geography. The network linking plasmids to their hosts shows that these mobile elements have thus been shared between bacteria across geographically distant environmental niches. However, certain plasmids carrying ARGs involved in human health were identified as being shared between multiple ecosystems and hosted by a wide variety of hosts. Some of these mobile elements, identified as keystone plasmids, were characterised by an enrichment in antibiotic resistance genes (ARGs) and CAS-CRISPR components which may explain their ecological success. The ARGs accounted for 9.2% of the recent horizontal transfers between bacteria and plasmids. CONCLUSIONS: By comprehensively analysing the plasmidome content of ecosystems, some key habitats have emerged as particularly important for monitoring the spread of ARGs in relation to human health. Of particular note is the potential for air to act as a vector for long-distance transport of ARGs and accessory genes across ecosystems and continents. Video Abstract.202540108678