Comparison of de-novo assembly tools for plasmid metagenome analysis. - Related Documents




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908001.0000Comparison of de-novo assembly tools for plasmid metagenome analysis. BACKGROUND: With the advent of next-generation sequencing techniques, culture-independent metagenome approaches have now made it possible to predict possible presence of genes in the environmental bacteria most of which may be non-cultivable. Short reads obtained from the deep sequencing can be assembled into long contigs some of which include plasmids. Plasmids are the circular double stranded DNA in bacteria and known as one of the major carriers of antibiotic resistance genes. OBJECTIVE: Metagenomic analyses, especially focused on plasmids, could help us predict dissemination mechanisms of antibiotic resistance genes in the environment. However, with the availability of a myriad of metagenomic assemblers, the selection of the most appropriate metagenome assembler for the plasmid metagenome study might be challenging. Therefore, in this study, we compared five open source assemblers to suggest most effective way of plasmid metagenome analysis. METHODS: IDBA-UD, MEGAHIT, SPAdes, SOAPdenovo2, and Velvet are compared for conducting plasmid metagenome analyses using two water samples. RESULTS: Our results clearly showed that abundance and types of antibiotic resistance genes on plasmids varied depending on the selection of assembly tools. IDBA-UD and MEGAHIT demonstrated the overall best assembly statistics with high N50 values with higher portion of longer contigs. CONCLUSION: These two assemblers also detected more diverse plasmids. Among the two, MEGAHIT showed more memory efficient assembly, therefore we suggest that the use of MEGAHIT for plasmid metagenome analysis may offer more diverse plasmids with less computer resource required. Here, we also summarized a fundamental plasmid metagenome work flow, especially for antibiotic resistance gene investigation.201931187446
466510.9996A 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
387420.9996Culture-enriched human gut microbiomes reveal core and accessory resistance genes. BACKGROUND: Low-abundance microorganisms of the gut microbiome are often referred to as a reservoir for antibiotic resistance genes. Unfortunately, these less-abundant bacteria can be overlooked by deep shotgun sequencing. In addition, it is a challenge to associate the presence of resistance genes with their risk of acquisition by pathogens. In this study, we used liquid culture enrichment of stools to assemble the genome of lower-abundance bacteria from fecal samples. We then investigated the gene content recovered from these culture-enriched and culture-independent metagenomes in relation with their taxonomic origin, specifically antibiotic resistance genes. We finally used a pangenome approach to associate resistance genes with the core or accessory genome of Enterobacteriaceae and inferred their propensity to horizontal gene transfer. RESULTS: Using culture-enrichment approaches with stools allowed assembly of 187 bacterial species with an assembly size greater than 1 million nucleotides. Of these, 67 were found only in culture-enriched conditions, and 22 only in culture-independent microbiomes. These assembled metagenomes allowed the evaluation of the gene content of specific subcommunities of the gut microbiome. We observed that differentially distributed metabolic enzymes were associated with specific culture conditions and, for the most part, with specific taxa. Gene content differences between microbiomes, for example, antibiotic resistance, were for the most part not associated with metabolic enzymes, but with other functions. We used a pangenome approach to determine if the resistance genes found in Enterobacteriaceae, specifically E. cloacae or E. coli, were part of the core genome or of the accessory genome of this species. In our healthy volunteer cohort, we found that E. cloacae contigs harbored resistance genes that were part of the core genome of the species, while E. coli had a large accessory resistome proximal to mobile elements. CONCLUSION: Liquid culture of stools contributed to an improved functional and comparative genomics study of less-abundant gut bacteria, specifically those associated with antibiotic resistance. Defining whether a gene is part of the core genome of a species helped in interpreting the genomes recovered from culture-independent or culture-enriched microbiomes.201930953542
965330.9996Evaluating 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
510940.9996PlasmidHostFinder: Prediction of Plasmid Hosts Using Random Forest. Plasmids play a major role facilitating the spread of antimicrobial resistance between bacteria. Understanding the host range and dissemination trajectories of plasmids is critical for surveillance and prevention of antimicrobial resistance. Identification of plasmid host ranges could be improved using automated pattern detection methods compared to homology-based methods due to the diversity and genetic plasticity of plasmids. In this study, we developed a method for predicting the host range of plasmids using machine learning-specifically, random forests. We trained the models with 8,519 plasmids from 359 different bacterial species per taxonomic level; the models achieved Matthews correlation coefficients of 0.662 and 0.867 at the species and order levels, respectively. Our results suggest that despite the diverse nature and genetic plasticity of plasmids, our random forest model can accurately distinguish between plasmid hosts. This tool is available online through the Center for Genomic Epidemiology (https://cge.cbs.dtu.dk/services/PlasmidHostFinder/). IMPORTANCE Antimicrobial resistance is a global health threat to humans and animals, causing high mortality and morbidity while effectively ending decades of success in fighting against bacterial infections. Plasmids confer extra genetic capabilities to the host organisms through accessory genes that can encode antimicrobial resistance and virulence. In addition to lateral inheritance, plasmids can be transferred horizontally between bacterial taxa. Therefore, detection of the host range of plasmids is crucial for understanding and predicting the dissemination trajectories of extrachromosomal genes and bacterial evolution as well as taking effective countermeasures against antimicrobial resistance.202235382558
510150.9996Identification 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
511560.9996Search Engine for Antimicrobial Resistance: A Cloud Compatible Pipeline and Web Interface for Rapidly Detecting Antimicrobial Resistance Genes Directly from Sequence Data. BACKGROUND: Antimicrobial resistance remains a growing and significant concern in human and veterinary medicine. Current laboratory methods for the detection and surveillance of antimicrobial resistant bacteria are limited in their effectiveness and scope. With the rapidly developing field of whole genome sequencing beginning to be utilised in clinical practice, the ability to interrogate sequencing data quickly and easily for the presence of antimicrobial resistance genes will become increasingly important and useful for informing clinical decisions. Additionally, use of such tools will provide insight into the dynamics of antimicrobial resistance genes in metagenomic samples such as those used in environmental monitoring. RESULTS: Here we present the Search Engine for Antimicrobial Resistance (SEAR), a pipeline and web interface for detection of horizontally acquired antimicrobial resistance genes in raw sequencing data. The pipeline provides gene information, abundance estimation and the reconstructed sequence of antimicrobial resistance genes; it also provides web links to additional information on each gene. The pipeline utilises clustering and read mapping to annotate full-length genes relative to a user-defined database. It also uses local alignment of annotated genes to a range of online databases to provide additional information. We demonstrate SEAR's application in the detection and abundance estimation of antimicrobial resistance genes in two novel environmental metagenomes, 32 human faecal microbiome datasets and 126 clinical isolates of Shigella sonnei. CONCLUSIONS: We have developed a pipeline that contributes to the improved capacity for antimicrobial resistance detection afforded by next generation sequencing technologies, allowing for rapid detection of antimicrobial resistance genes directly from sequencing data. SEAR uses raw sequencing data via an intuitive interface so can be run rapidly without requiring advanced bioinformatic skills or resources. Finally, we show that SEAR is effective in detecting antimicrobial resistance genes in metagenomic and isolate sequencing data from both environmental metagenomes and sequencing data from clinical isolates.201526197475
965670.9996Use of sequence barcodes for tracking horizontal gene transfer of antimicrobial resistance genes in a microbial community. One of the most important knowledge gaps in the antimicrobial resistance crisis is the lack of understanding regarding how genes spread from their environmental origins to bacteria pathogenic to humans. In this study our aim was to create a system that allows the conduction of experiments in laboratory settings that mimic the complexity of natural communities with multiple resistance genes and mobile genetic elements circulating at the same time. Here we report a new sequence-based barcode system that allows simultaneous tracking of the spread of antimicrobial resistance genes from multiple genetic origins. We tested this concept with an experiment in which we added an antimicrobial resistance gene to different genetic environments in alive and dead donors and let the gene spread naturally in an artificial microbial community under different environmental conditions to provide examples of factors that can be investigated. We used emulsion, paired-isolation, and concatenation polymerase chain reaction to detect the new gene carriers and metagenomic analysis to see changes in the genetic environment. We observed the genes moving and were able to recognise the barcode from the gene sequences, thus validating the idea of barcode use. We also saw that temperature and gene origin had effects on the number of new host species. Our results confirmed that our system worked and can be further developed for more complicated experiments.202540800620
769880.9996Detecting 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
429690.9996Twenty-first century molecular methods for analyzing antimicrobial resistance in surface waters to support One Health assessments. Antimicrobial resistance (AMR) in the environment is a growing global health concern, especially the dissemination of AMR into surface waters due to human and agricultural inputs. Within recent years, research has focused on trying to understand the impact of AMR in surface waters on human, agricultural and ecological health (One Health). While surface water quality assessments and surveillance of AMR have historically utilized culture-based methods, culturing bacteria has limitations due to difficulty in isolating environmental bacteria and the need for a priori information about the bacteria for selective isolation. The use of molecular techniques to analyze AMR at the genetic level has helped to overcome the difficulties with culture-based techniques since they do not require advance knowledge of the bacterial population and can analyze uncultivable environmental bacteria. The aim of this review is to provide an overview of common contemporary molecular methods available for analyzing AMR in surface waters, which include high throughput real-time polymerase chain reaction (HT-qPCR), metagenomics, and whole genome sequencing. This review will also feature how these methods may provide information on human and animal health risks. HT-qPCR works at the nanoliter scale, requires only a small amount of DNA, and can analyze numerous gene targets simultaneously, but may lack in analytical sensitivity and the ability to optimize individual assays compared to conventional qPCR. Metagenomics offers more detailed genomic information and taxonomic resolution than PCR by sequencing all the microbial genomes within a sample. Its open format allows for the discovery of new antibiotic resistance genes; however, the quantity of DNA necessary for this technique can be a limiting factor for surface water samples that typically have low numbers of bacteria per sample volume. Whole genome sequencing provides the complete genomic profile of a single environmental isolate and can identify all genetic elements that may confer AMR. However, a main disadvantage of this technique is that it only provides information about one bacterial isolate and is challenging to utilize for community analysis. While these contemporary techniques can quickly provide a vast array of information about AMR in surface waters, one technique does not fully characterize AMR nor its potential risks to human, animal, or ecological health. Rather, a combination of techniques (including both molecular- and culture-based) are necessary to fully understand AMR in surface waters from a One Health perspective.202133774111
9654100.9996Studying 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
6595110.9996Methodological aspects of investigating the resistome in pig farm environments. A typical One Health issue, antimicrobial resistance (AMR) development and its spread among people, animals, and the environment attracts significant research attention. The animal sector is one of the major contributors to the development and dissemination of AMR and accounts for more than 50 % of global antibiotics usage. The use of antibiotics exerts a selective pressure for resistant bacteria in the exposed microbiome, but many questions about the epidemiology of AMR in farm environments remain unanswered. This is connected to several methodological challenges and limitations, such as inconsistent sampling methods, complexity of farm environment samples and the lack of standardized protocols for sample collection, processing and bioinformatical analysis. In this project, we combined metagenomics and bioinformatics to optimise the methodology for reproducible research on the resistome in complex samples from the indoor farm environment. The work included optimizing sample collection, transportation, and storage, as well as DNA extraction, sequencing, and bioinformatic analysis, such as metagenome assembly and antibiotic resistance gene (ARG) detection. Our studies suggest that the current most optimal and cost-effective pipeline for ARG search should be based on Illumina sequencing of sock sample material at high depth (at least 25 M 250 bp PE for AMR gene families and 43 M for gene variants). We present a computational analysis utilizing MEGAHIT assembly to balance the identification of bacteria carrying ARGs with the potential loss of diversity and abundance of resistance genes. Our findings indicate that searching against multiple ARG databases is essential for detecting the highest diversity of ARGs.202539954816
5114120.9996Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing. Whole genome sequencing (WGS) is a key tool in identifying and characterising disease-associated bacteria across clinical, agricultural, and environmental contexts. One increasingly common use of genomic and metagenomic sequencing is in identifying the type and range of antimicrobial resistance (AMR) genes present in bacterial isolates in order to make predictions regarding their AMR phenotype. However, there are a large number of alternative bioinformatics software and pipelines available, which can lead to dissimilar results. It is, therefore, vital that researchers carefully evaluate their genomic and metagenomic AMR analysis methods using a common dataset. To this end, as part of the Microbial Bioinformatics Hackathon and Workshop 2021, a 'gold standard' reference genomic and simulated metagenomic dataset was generated containing raw sequence reads mapped against their corresponding reference genome from a range of 174 potentially pathogenic bacteria. These datasets and their accompanying metadata are freely available for use in benchmarking studies of bacteria and their antimicrobial resistance genes and will help improve tool development for the identification of AMR genes in complex samples.202235705638
9650130.9996Plasmid-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
4666140.9996Large Circular Plasmids from Groundwater Plasmidomes Span Multiple Incompatibility Groups and Are Enriched in Multimetal Resistance Genes. Naturally occurring plasmids constitute a major category of mobile genetic elements responsible for harboring and transferring genes important in survival and fitness. A targeted evaluation of plasmidomes can reveal unique adaptations required by microbial communities. We developed a model system to optimize plasmid DNA isolation procedures targeted to groundwater samples which are typically characterized by low cell density (and likely variations in the plasmid size and copy numbers). The optimized method resulted in successful identification of several hundred circular plasmids, including some large plasmids (11 plasmids more than 50 kb in size, with the largest being 1.7 Mb in size). Several interesting observations were made from the analysis of plasmid DNA isolated in this study. The plasmid pool (plasmidome) was more conserved than the corresponding microbiome distribution (16S rRNA based). The circular plasmids were diverse as represented by the presence of seven plasmid incompatibility groups. The genes carried on these groundwater plasmids were highly enriched in metal resistance. Results from this study confirmed that traits such as metal, antibiotic, and phage resistance along with toxin-antitoxin systems are encoded on abundant circular plasmids, all of which could confer novel and advantageous traits to their hosts. This study confirms the ecological role of the plasmidome in maintaining the latent capacity of a microbiome, enabling rapid adaptation to environmental stresses.IMPORTANCE Plasmidomes have been typically studied in environments abundant in bacteria, and this is the first study to explore plasmids from an environment characterized by low cell density. We specifically target groundwater, a significant source of water for human/agriculture use. We used samples from a well-studied site and identified hundreds of circular plasmids, including one of the largest sizes reported in plasmidome studies. The striking similarity of the plasmid-borne ORFs in terms of taxonomical and functional classifications across several samples suggests a conserved plasmid pool, in contrast to the observed variability in the 16S rRNA-based microbiome distribution. Additionally, the stress response to environmental factors has stronger conservation via plasmid-borne genes as marked by abundance of metal resistance genes. Last, identification of novel and diverse plasmids enriches the existing plasmid database(s) and serves as a paradigm to increase the repertoire of biological parts that are available for modifying novel environmental strains.201930808697
4623150.9995Capturing the Resistome: a Targeted Capture Method To Reveal Antibiotic Resistance Determinants in Metagenomes. Identification of the nucleotide sequences encoding antibiotic resistance elements and determination of their association with antibiotic resistance are critical to improve surveillance and monitor trends in antibiotic resistance. Current methods to study antibiotic resistance in various environments rely on extensive deep sequencing or laborious culturing of fastidious organisms, both of which are heavily time-consuming operations. An accurate and sensitive method to identify both rare and common resistance elements in complex metagenomic samples is needed. Referencing the sequences in the Comprehensive Antibiotic Resistance Database, we designed a set of 37,826 probes to specifically target over 2,000 nucleotide sequences associated with antibiotic resistance in clinically relevant bacteria. Testing of this probe set on DNA libraries generated from multidrug-resistant bacteria to selectively capture resistance genes reproducibly produced higher numbers of reads on target at a greater length of coverage than shotgun sequencing. We also identified additional resistance gene sequences from human gut microbiome samples that sequencing alone was not able to detect. Our method to capture the resistome enables a sensitive means of gene detection in diverse environments where genes encoding antibiotic resistance represent less than 0.1% of the metagenome.201931611361
9657160.9995Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome. Antibiotic resistance in pathogens is extensively studied, and yet little is known about how antibiotic resistance genes of typical gut bacteria influence microbiome dynamics. Here, we leveraged genomes from metagenomes to investigate how genes of the premature infant gut resistome correspond to the ability of bacteria to survive under certain environmental and clinical conditions. We found that formula feeding impacts the resistome. Random forest models corroborated by statistical tests revealed that the gut resistome of formula-fed infants is enriched in class D beta-lactamase genes. Interestingly, Clostridium difficile strains harboring this gene are at higher abundance in formula-fed infants than C. difficile strains lacking this gene. Organisms with genes for major facilitator superfamily drug efflux pumps have higher replication rates under all conditions, even in the absence of antibiotic therapy. Using a machine learning approach, we identified genes that are predictive of an organism's direction of change in relative abundance after administration of vancomycin and cephalosporin antibiotics. The most accurate results were obtained by reducing annotated genomic data to five principal components classified by boosted decision trees. Among the genes involved in predicting whether an organism increased in relative abundance after treatment are those that encode subclass B2 beta-lactamases and transcriptional regulators of vancomycin resistance. This demonstrates that machine learning applied to genome-resolved metagenomics data can identify key genes for survival after antibiotics treatment and predict how organisms in the gut microbiome will respond to antibiotic administration. IMPORTANCE The process of reconstructing genomes from environmental sequence data (genome-resolved metagenomics) allows unique insight into microbial systems. We apply this technique to investigate how the antibiotic resistance genes of bacteria affect their ability to flourish in the gut under various conditions. Our analysis reveals that strain-level selection in formula-fed infants drives enrichment of beta-lactamase genes in the gut resistome. Using genomes from metagenomes, we built a machine learning model to predict how organisms in the gut microbial community respond to perturbation by antibiotics. This may eventually have clinical applications.201829359195
6597170.9995Exploiting 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
9918180.9995Linking plasmid-based beta-lactamases to their bacterial hosts using single-cell fusion PCR. The horizonal transfer of plasmid-encoded genes allows bacteria to adapt to constantly shifting environmental pressures, bestowing functional advantages to their bacterial hosts such as antibiotic resistance, metal resistance, virulence factors, and polysaccharide utilization. However, common molecular methods such as short- and long-read sequencing of microbiomes cannot associate extrachromosomal plasmids with the genome of the host bacterium. Alternative methods to link plasmids to host bacteria are either laborious, expensive, or prone to contamination. Here we present the One-step Isolation and Lysis PCR (OIL-PCR) method, which molecularly links plasmid-encoded genes with the bacterial 16S rRNA gene via fusion PCR performed within an emulsion. After validating this method, we apply it to identify the bacterial hosts of three clinically relevant beta-lactamases within the gut microbiomes of neutropenic patients, as they are particularly vulnerable multidrug-resistant infections. We successfully detect the known association of a multi-drug resistant plasmid with Klebsiella pneumoniae, as well as the novel associations of two low-abundance genera, Romboutsia and Agathobacter. Further investigation with OIL-PCR confirmed that our detection of Romboutsia is due to its physical association with Klebsiella as opposed to directly harboring the beta-lactamase genes. Here we put forth a robust, accessible, and high-throughput platform for sensitively surveying the bacterial hosts of mobile genes, as well as detecting physical bacterial associations such as those occurring within biofilms and complex microbial communities.202134282723
5112190.9995Genome-Based Prediction of Bacterial Antibiotic Resistance. Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences.201930381421