SCAPP: an algorithm for improved plasmid assembly in metagenomes

Unveiling plasmid diversity and functionality in pristine groundwater

BACKGROUND

Plasmids are key in creating a dynamic reservoir of genetic diversity, yet their impact on Earth's continental subsurface-an important microbial reservoir-remains unresolved. We analyzed 32 metagenomic samples from six groundwater wells within a hillslope aquifer system to assess the genetic and functional diversity of plasmids and to evaluate the role of these plasmids in horizontal gene transfer (HGT).

RESULTS

Our results revealed 4,609 non-redundant mobile genetic elements (MGEs), with 14% (664) confidently classified as plasmids. These plasmids displayed well-specific populations, with fewer than 15% shared across wells. Plasmids were linked to diverse microbial phyla, including Pseudomonadota (42.17%), Nitrospirota (3.31%), Candidate Phyla Radiation (CPR) bacteria (2.56%), and Omnitrophota (2.11%). The presence of plasmids in the dominant CPR bacteria is significant, as this group remains underexplored in this context. Plasmid composition strongly correlated with well-specific microbial communities, suggesting local selection pressures. Functional analyses highlighted that conjugative plasmids carry genes crucial for metabolic processes, such as cobalamin biosynthesis and hydrocarbon degradation. Importantly, we found no evidence of high confidence emerging antibiotic resistance genes, contrasting with findings from sewage and polluted groundwater.

CONCLUSIONS

Overall, our study emphasizes the diversity, composition, and eco-evolutionary role of plasmids in the groundwater microbiome. The absence of known antibiotic resistance genes highlights the need to preserve groundwater in its pristine state to safeguard its unique genetic and functional landscape.

Deciphering antibiotic resistance genes and plasmids in pathogenic bacteria from 166 hospital effluents in Shanghai, China

Although previous studies using phenotypic or metagenomic approaches have revealed the patterns of antibiotic resistance genes (ARGs) in hospital effluents in local regions, limited information is available regarding the antibiotic resistome and plasmidome in human pathogenic bacteria in hospital effluents of megacity in China. To address this knowledge gap, we analyzed effluent samples from 166 hospitals across 13 geographical districts in Shanghai, China, using both cultivation-based approaches and metagenomics. A total of 357 strains were isolated from these samples, with the predominant species being Escherichia coli (n = 61), Aeromonas hydrophila (n = 57), Klebsiella pneumoniae (n = 48), and Aeromonas caviae (n = 42). Those identified indicator bacteria were classified into biosafety level 1 (BSL-1, 60 %) and BSL-2 (40 %). We identified 1237 ARG subtypes across 22 types, predominantly including beta-lactam, tetracycline, multidrug, polymyxin, and aminoglycoside resistance genes, using culture-enriched phenotypic metagenomics. Mobile genetic elements such as plasmids, transposons (tnpA), integrons (intI1), and insertion sequences (IS91) were abundant. We recovered 135 plasmids classified into mobilizable (n = 94) and non-mobilizable (n = 41) types. Additionally, 80 metagenome-assembled genomes (MAGs) were reconstructed from the hospital effluents for the assessment of ARG transmission risks, including genes for last-line antibiotics such as blaNDM, blaKPC, blaimiH, and mcr. This study is the first to comprehensively characterize and assess the risk of antimicrobial resistance levels and plasmidome in the hospital effluents of China's megacity, providing city-wide surveillance data and evidence to inform public health interventions.

Metagenomic analysis of pristine oil sheds new light on the global distribution of microbial genetic repertoire in hydrocarbon-associated ecosystems

Oil reservoirs are society's primary source of hydrocarbons. While microbial communities in industrially exploited oil reservoirs have been investigated in the past, pristine microbial communities in untapped oil reservoirs are little explored, as are distribution patterns of respective genetic signatures. Here, we show that a pristine oil sample contains a complex community consisting of bacteria and fungi for the degradation of hydrocarbons. We identified microorganisms and their pathways for the degradation of methane, n-alkanes, mono-aromatic, and polycyclic aromatic compounds in a metagenome retrieved from biodegraded petroleum encountered in a subsurface reservoir in the Barents Sea. Capitalizing on marker genes from metagenomes and public data mining, we compared the prokaryotes, putative viruses, and putative plasmids of the sampled site to those from 10 other hydrocarbon-associated sites, revealing a shared network of species and genetic elements across the globe. To test for the potential dispersal of the microbes and predicted elements via seawater, we compared our findings to the Tara Ocean dataset, resulting in a broad distribution of prokaryotic and viral signatures. Although frequently shared between hydrocarbon-associated sites, putative plasmids, however, showed little coverage in the Tara Oceans dataset, suggesting an undiscovered mode of transfer between hydrocarbon-affected ecosystems. Based on our analyses, genetic information is globally shared between oil reservoirs and hydrocarbon-associated sites, and we propose that currents and other physical occurrences within the ocean along with deep aquifers are major distributors of prokaryotes and viruses into these subsurface ecosystems.

Sequencing Strategy to Ensure Accurate Plasmid Assembly

Despite the wide use of plasmids in research and clinical production, the need to verify plasmid sequences is a bottleneck that is too often underestimated in the manufacturing process. Although sequencing platforms continue to improve, the method and assembly pipeline chosen still influence the final plasmid assembly sequence. Furthermore, few dedicated tools exist for plasmid assembly, especially for de novo assembly. Here, we evaluated short-read, long-read, and hybrid (both short and long reads) de novo assembly pipelines across three replicates of a 24-plasmid library. Consistent with previous characterizations of each sequencing technology, short-read assemblies had issues resolving GC-rich regions, and long-read assemblies commonly had small insertions and deletions, especially in repetitive regions. The hybrid approach facilitated the most accurate, consistent assembly generation and identified mutations relative to the reference sequence. Although Sanger sequencing can be used to verify specific regions, some GC-rich and repetitive regions were difficult to resolve using any method, suggesting that easily sequenced genetic parts should be prioritized in the design of new genetic constructs.

Integrative genomics would strengthen AMR understanding through ONE health approach

Emergence of drug-induced antimicrobial resistance (AMR) forms a crippling health and economic crisis worldwide, causing high mortality from otherwise treatable diseases and infections. Next Generation Sequencing (NGS) has significantly augmented detection of culture independent microbes, potential AMR in pathogens and elucidation of mechanisms underlying it. Here, we review recent findings of AMR evolution in pathogens aided by integrated genomic investigation strategies inclusive of bacteria, virus, fungi and AMR alleles. While AMR monitoring is dominated by data from hospital-related infections, we review genomic surveillance of both biotic and abiotic components involved in global AMR emergence and persistence. Identification of pathogen-intrinsic as well as environmental and/or host factors through robust genomics/bioinformatics, along with monitoring of type and frequency of antibiotic usage will greatly facilitate prediction of regional and global patterns of AMR evolution. Genomics-enabled AMR prediction and surveillance will be crucial - in shaping health and economic policies within the One Health framework to combat this global concern.

Sequencing Strategy to Ensure Accurate Plasmid Assembly

Despite the wide use of plasmids in research and clinical production, the need to verify plasmid sequences is a bottleneck that is too often underestimated in the manufacturing process. Although sequencing platforms continue to improve, the method and assembly pipeline chosen still influence the final plasmid assembly sequence. Furthermore, few dedicated tools exist for plasmid assembly, especially for de novo assembly. Here, we evaluated short-read, long-read, and hybrid (both short and long reads) de novo assembly pipelines across three replicates of a 24-plasmid library. Consistent with previous characterizations of each sequencing technology, short-read assemblies had issues resolving GC-rich regions, and long-read assemblies commonly had small insertions and deletions, especially in repetitive regions. The hybrid approach facilitated the most accurate, consistent assembly generation and identified mutations relative to the reference sequence. Although Sanger sequencing can be used to verify specific regions, some GC-rich and repetitive regions were difficult to resolve using any method, suggesting that easily sequenced genetic parts should be prioritized in the design of new genetic constructs.

PlasCAT: Plasmid Cloud Assembly Tool

SUMMARY

PlasCAT (Plasmid Cloud Assembly Tool) is an easy-to-use cloud-based bioinformatics tool that enables de novo plasmid sequence assembly from raw sequencing data. Nontechnical users can now assemble sequences from long reads and short reads without ever touching a line of code. PlasCAT uses high-performance computing servers to reduce run times on assemblies and deliver results faster.

AVAILABILITY AND IMPLEMENTATION

PlasCAT is freely available on the web at https://sequencing.genofab.com. The assembly pipeline source code and server code are available for download at https://bitbucket.org/genofabinc/workspace/projects/PLASCAT. Click the Cancel button to access the source code without authenticating. Web servers implemented in React.js and Python, with all major browsers supported.

Plasmids in the human gut reveal neutral dispersal and recombination that is overpowered by inflammatory diseases

Plasmids are pivotal in driving bacterial evolution through horizontal gene transfer. Here, we investigated 3467 human gut microbiome samples across continents and disease states, analyzing 11,086 plasmids. Our analyses reveal that plasmid dispersal is predominantly stochastic, indicating neutral processes as the primary driver of their wide distribution. We find that only 20-25% of plasmid DNA is being selected in various disease states, constraining its distribution across hosts. Selective pressures shape specific plasmid segments with distinct ecological functions, influenced by plasmid mobilization lifestyle, antibiotic usage, and inflammatory gut diseases. Notably, these elements are more commonly shared within groups of individuals with similar health conditions, such as Inflammatory Bowel Disease (IBD), regardless of geographic location across continents. These segments contain essential genes such as iron transport mechanisms- a distinctive gut signature of IBD that impacts the severity of inflammation. Our findings shed light on mechanisms driving plasmid dispersal and selection in the human gut, highlighting their role as carriers of vital gene pools impacting bacterial hosts and ecosystem dynamics.

A comparison of short-read, HiFi long-read, and hybrid strategies for genome-resolved metagenomics

Shotgun metagenomics enables the reconstruction of complex microbial communities at a high level of detail. Such an approach can be conducted using both short-read and long-read sequencing data, as well as a combination of both. To assess the pros and cons of these different approaches, we used 22 fecal DNA extracts collected weekly for 11 weeks from two respective lab mice to study seven performance metrics over four combinations of sequencing depth and technology: (i) 20 Gbp of Illumina short-read data, (ii) 40 Gbp of short-read data, (iii) 20 Gbp of PacBio HiFi long-read data, and (iv) 40 Gbp of hybrid (20 Gbp of short-read +20 Gbp of long-read) data. No strategy was best for all metrics; instead, each one excelled across different metrics. The long-read approach yielded the best assembly statistics, with the highest N50 and lowest number of contigs. The 40 Gbp short-read approach yielded the highest number of refined bins. Finally, the hybrid approach yielded the longest assemblies and the highest mapping rate to the bacterial genomes. Our results suggest that while long-read sequencing significantly improves the quality of reconstructed bacterial genomes, it is more expensive and requires deeper sequencing than short-read approaches to recover a comparable amount of reconstructed genomes. The most optimal strategy is study-specific and depends on how researchers assess the trade-off between the quantity and quality of recovered genomes.IMPORTANCEMice are an important model organism for understanding the gut microbiome. When studying these gut microbiomes using DNA techniques, researchers can choose from technologies that use short or long DNA reads. In this study, we perform an extensive benchmark between short- and long-read DNA sequencing for studying mice gut microbiomes. We find that no one approach was best for all metrics and provide information that can help guide researchers in planning their experiments.

Uncovering microbiomes of the rice phyllosphere using long-read metagenomic sequencing

The plant microbiome is crucial for plant growth, yet many important questions remain, such as the identification of specific bacterial species in plants, their genetic content, and location of these genes on chromosomes or plasmids. To gain insights into the genetic makeup of the rice-phyllosphere, we perform a metagenomic analysis using long-read sequences. Here, 1.8 Gb reads are assembled into 26,067 contigs including 142 circular sequences. Within these contigs, 669 complete 16S rRNA genes are clustered into 166 bacterial species, 121 of which show low identity (<97%) to defined sequences, suggesting novel species. The circular contigs contain novel chromosomes and a megaplasmid, and most of the smaller circular contigs are defined as novel plasmids or bacteriophages. One circular contig represents the complete chromosome of a difficult-to-culture bacterium Candidatus Saccharibacteria. Our findings demonstrate the efficacy of long-read-based metagenomics for profiling microbial communities and discovering novel sequences in plant-microbiome studies.

Diverse plasmid systems and their ecology across human gut metagenomes revealed by PlasX and MobMess

Plasmids alter microbial evolution and lifestyles by mobilizing genes that often confer fitness in changing environments across clades. Yet our ecological and evolutionary understanding of naturally occurring plasmids is far from complete. Here we developed a machine-learning model, PlasX, which identified 68,350 non-redundant plasmids across human gut metagenomes and organized them into 1,169 evolutionarily cohesive 'plasmid systems' using our sequence containment-aware network-partitioning algorithm, MobMess. Individual plasmids were often country specific, yet most plasmid systems spanned across geographically distinct human populations. Cargo genes in plasmid systems included well-known determinants of fitness, such as antibiotic resistance, but also many others including enzymes involved in the biosynthesis of essential nutrients and modification of transfer RNAs, revealing a wide repertoire of likely fitness determinants in complex environments. Our study introduces computational tools to recognize and organize plasmids, and uncovers the ecological and evolutionary patterns of diverse plasmids in naturally occurring habitats through plasmid systems.

A cryptic plasmid is among the most numerous genetic elements in the human gut

Plasmids are extrachromosomal genetic elements that often encode fitness-enhancing features. However, many bacteria carry "cryptic" plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes and is 14 times as numerous as crAssphage, currently established as the most abundant extrachromosomal genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from one's mother. pBI143 can transfer between Bacteroidales, and although it does not appear to impact bacterial host fitness in vivo, it can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an alternative approach to track human colonic inflammatory states.

Genomic surveillance for antimicrobial resistance - a One Health perspective

Antimicrobial resistance (AMR) - the ability of microorganisms to adapt and survive under diverse chemical selection pressures - is influenced by complex interactions between humans, companion and food-producing animals, wildlife, insects and the environment. To understand and manage the threat posed to health (human, animal, plant and environmental) and security (food and water security and biosecurity), a multifaceted 'One Health' approach to AMR surveillance is required. Genomic technologies have enabled monitoring of the mobilization, persistence and abundance of AMR genes and mutations within and between microbial populations. Their adoption has also allowed source-tracing of AMR pathogens and modelling of AMR evolution and transmission. Here, we highlight recent advances in genomic AMR surveillance and the relative strengths of different technologies for AMR surveillance and research. We showcase recent insights derived from One Health genomic surveillance and consider the challenges to broader adoption both in developed and in lower- and middle-income countries.

Insights into the circular: The cryptic plasmidome and its derived antibiotic resistome in the urban water systems

Plasmids have been a concern in the dissemination and evolution of antibiotic resistance in the environment. In this study, we investigated the total pool of plasmids (plasmidome) and its derived antibiotic resistance genes (ARGs) in different compartments of urban water systems (UWSs) in three European countries representing different antibiotic usage regimes. We applied a direct plasmidome approach using wet-lab methods to enrich circular DNA in the samples, followed by shotgun sequencing and in silico contig circularisation. We identified 9538 novel sequences in a total of 10,942 recovered circular plasmids. Of these, 66 were identified as conjugative, 1896 mobilisable and 8970 non-mobilisable plasmids. The UWSs' plasmidome was dominated by small plasmids (≤10 Kbp) representing a broad diversity of mobility (MOB) types and incompatibility (Inc) groups. A shared collection of plasmids from different countries was detected in all treatment compartments, and plasmids could be source-tracked in the UWSs. More than half of the ARGs-encoding plasmids carried mobility genes for mobilisation/conjugation. The richness and abundance of ARGs-encoding plasmids generally decreased with the flow, while we observed that non-mobilisable ARGs-harbouring plasmids maintained their abundance in the Spanish wastewater treatment plant. Overall, our work unravels that the UWS plasmidome is dominated by cryptic (i.e., non-mobilisable, non-typeable and previously unknown) plasmids. Considering that some of these plasmids carried ARGs, were prevalent across three countries and could persist throughout the UWSs compartments, these results should alarm and call for attention.

Mixed waste contamination selects for a mobile genetic element population enriched in multiple heavy metal resistance genes

Mobile genetic elements (MGEs) like plasmids, viruses, and transposable elements can provide fitness benefits to their hosts for survival in the presence of environmental stressors. Heavy metal resistance genes (HMRGs) are frequently observed on MGEs, suggesting that MGEs may be an important driver of adaptive evolution in environments contaminated with heavy metals. Here, we report the meta-mobilome of the heavy metal-contaminated regions of the Oak Ridge Reservation subsurface. This meta-mobilome was compared with one derived from samples collected from unimpacted regions of the Oak Ridge Reservation subsurface. We assembled 1615 unique circularized DNA elements that we propose to be MGEs. The circular elements from the highly contaminated subsurface were enriched in HMRG clusters relative to those from the nearby unimpacted regions. Additionally, we found that these HMRGs were associated with Gamma and Betaproteobacteria hosts in the contaminated subsurface and potentially facilitate the persistence and dominance of these taxa in this region. Finally, the HMRGs were associated with conjugative elements, suggesting their potential for future lateral transfer. We demonstrate how our understanding of MGE ecology, evolution, and function can be enhanced through the genomic context provided by completed MGE assemblies.

Plasmid-encoded toxin defence mediates mutualistic microbial interactions

Gut environments harbour dense microbial ecosystems in which plasmids are widely distributed. Plasmids facilitate the exchange of genetic material among microorganisms while enabling the transfer of a diverse array of accessory functions. However, their precise impact on microbial community composition and function remains largely unexplored. Here we identify a prevalent bacterial toxin and a plasmid-encoded resistance mechanism that mediates the interaction between Lactobacilli and Enterococci. This plasmid is widespread across ecosystems, including the rumen and human gut microbiota. Biochemical characterization of the plasmid revealed a defence mechanism against reuterin, a toxin produced by various gut microbes, such as Limosilactobacillus reuteri. Using a targeted metabolomic approach, we find reuterin to be prevalent across rumen ecosystems with impacts on microbial community structure. Enterococcus strains carrying the protective plasmid were isolated and their interactions with L. reuteri, the toxin producer, were studied in vitro. Interestingly, we found that by conferring resistance against reuterin, the plasmid mediates metabolic exchange between the defending and the attacking microbial species, resulting in a beneficial relationship or mutualism. Hence, we reveal here an ecological role for a plasmid-coded defence system in mediating a beneficial interaction.

Synthetic Nuclease-Producing Microbiome Achieves Efficient Removal of Extracellular Antibiotic Resistance Genes from Wastewater Effluent

Antibiotic resistance gene (ARG) transmission poses significant threats to human health. The effluent of wastewater treatment plants is demonstrated as a hotspot source of ARGs released into the environment. In this study, a synthetic microbiome containing nuclease-producing Deinococcus radiodurans was constructed to remove extracellular ARGs. Results of quantitative polymerase chain reaction (qPCR) showed significant reduction in plasmid RP4-associated ARGs (by more than 3 orders of magnitude) and reduction of indigenous ARG sul1 and mobile genetic element (MGE) intl1 (by more than 1 order of magnitude) in the synthetic microbiome compared to the control without D. radiodurans. Metagenomic analysis revealed a decrease in ARG and MGE diversity in extracellular DNA (eDNA) of the treated group. Notably, whereas eight antibiotic-resistant plasmids with mobility risk were detected in the control, only one was detected in the synthetic microbiome. The abundance of the nuclease encoding gene exeM, quantified by qPCR, indicated its enrichment in the synthetic microbiome, which ensures stable eDNA degradation even when D. radiodurans decreased. Moreover, intracellular ARGs and MGEs and pathogenic ARG hosts in the river receiving treated effluent were lower than those in the river receiving untreated effluent. Overall, this study presents a new approach for removing extracellular ARGs and further reducing the risk of ARG transmission in receiving rivers.

plASgraph2: using graph neural networks to detect plasmid contigs from an assembly graph

Identification of plasmids from sequencing data is an important and challenging problem related to antimicrobial resistance spread and other One-Health issues. We provide a new architecture for identifying plasmid contigs in fragmented genome assemblies built from short-read data. We employ graph neural networks (GNNs) and the assembly graph to propagate the information from nearby nodes, which leads to more accurate classification, especially for short contigs that are difficult to classify based on sequence features or database searches alone. We trained plASgraph2 on a data set of samples from the ESKAPEE group of pathogens. plASgraph2 either outperforms or performs on par with a wide range of state-of-the-art methods on testing sets of independent ESKAPEE samples and samples from related pathogens. On one hand, our study provides a new accurate and easy to use tool for contig classification in bacterial isolates; on the other hand, it serves as a proof-of-concept for the use of GNNs in genomics. Our software is available at https://github.com/cchauve/plasgraph2 and the training and testing data sets are available at https://github.com/fmfi-compbio/plasgraph2-datasets.

What are the missing pieces needed to stop antibiotic resistance?

As recognized by several international agencies, antibiotic resistance is nowadays one of the most relevant problems for human health. While this problem was alleviated with the introduction of new antibiotics into the market in the golden age of antimicrobial discovery, nowadays few antibiotics are in the pipeline. Under these circumstances, a deep understanding on the mechanisms of emergence, evolution and transmission of antibiotic resistance, as well as on the consequences for the bacterial physiology of acquiring resistance is needed to implement novel strategies, beyond the development of new antibiotics or the restriction in the use of current ones, to more efficiently treat infections. There are still several aspects in the field of antibiotic resistance that are not fully understood. In the current article, we make a non-exhaustive critical review of some of them that we consider of special relevance, in the aim of presenting a snapshot of the studies that still need to be done to tackle antibiotic resistance.

Global transmission of broad-host-range plasmids derived from the human gut microbiome

Broad-host-range (BHR) plasmids in human gut bacteria are of considerable interest for their ability to mediate horizontal gene transfer (HGT) across large phylogenetic distance. However, the human gut plasmids, especially the BHR plasmids, remain largely unknown. Here, we identified the plasmids in the draft genomes of gut bacterial isolates from Chinese and American donors, resulting in 5372 plasmid-like clusters (PLCs), of which, 820 PLCs (comPLCs) were estimated with > 60% completeness genomes and only 155 (18.9%) were classified to known replicon types (n = 37). We observed that 175 comPLCs had a broad host range across distinct bacterial genera, of which, 71 were detected in at least two human populations of Chinese, American, Spanish, and Danish, and 13 were highly prevalent (>10%) in at least one human population. Haplotype analyses of two widespread PLCs demonstrated their spreading and evolutionary trajectory, suggesting frequent and recent exchanges of the BHR plasmids in environments. In conclusion, we obtained a large collection of plasmid sequences in human gut bacteria and demonstrated that a subset of the BHR plasmids can be transmitted globally, thus facilitating extensive HGT (e.g. antibiotic resistance genes) events. This study highlights the potential implications of the plasmids for global human health.

Characterization of the Environmental Plasmidome of the Red Sea

Plasmids contribute to microbial diversity and adaptation, providing microorganisms with the ability to thrive in a wide range of conditions in extreme environments. However, while the number of marine microbiome studies is constantly increasing, very little is known about marine plasmids, and they are very poorly represented in public databases. To extend the repertoire of environmental marine plasmids, we established a pipeline for the de novo assembly of plasmids in the marine environment by analyzing available microbiome metagenomic sequencing data. By applying the pipeline to data from the Red Sea, we identified 362 plasmid candidates. We showed that the distribution of plasmids corresponds to environmental conditions, particularly, depth, temperature, and physical location. At least 7 of the 362 candidates are most probably real plasmids, based on a functional analysis of their open reading frames (ORFs). Only one of the seven has been described previously. Three plasmids were identified in other public marine metagenomic data from different locations all over the world; these plasmids contained different cassettes of functional genes at each location. Analysis of antibiotic and metal resistance genes revealed that the same positions that were enriched with genes encoding resistance to antibiotics were also enriched with resistance to metals, suggesting that plasmids contribute site-dependent phenotypic modules to their ecological niches. Finally, half of the ORFs (50.8%) could not be assigned to a function, emphasizing the untapped potential of the unique marine plasmids to provide proteins with multiple novel functions. IMPORTANCE Marine plasmids are understudied and hence underrepresented in databases. Plasmid functional annotation and characterization is complicated but, if successful, may provide a pool of novel genes and unknown functions. Newly discovered plasmids and their functional repertoire are potentially valuable tools for predicting the dissemination of antimicrobial resistance, providing vectors for molecular cloning and an understanding of plasmid-bacterial interactions in various environments.

Plasmer: an Accurate and Sensitive Bacterial Plasmid Prediction Tool Based on Machine Learning of Shared k-mers and Genomic Features

Identification of plasmids in bacterial genomes is critical for many factors, including horizontal gene transfer, antibiotic resistance genes, host-microbe interactions, cloning vectors, and industrial production. There are several in silico methods to predict plasmid sequences in assembled genomes. However, existing methods have evident shortcomings, such as unbalance in sensitivity and specificity, dependency on species-specific models, and performance reduction in sequences shorter than 10 kb, which has limited their scope of applicability. In this work, we proposed Plasmer, a novel plasmid predictor based on machine-learning of shared k-mers and genomic features. Unlike existing k-mer or genomic-feature based methods, Plasmer employs the random forest algorithm to make predictions using the percent of shared k-mers with plasmid and chromosome databases combined with other genomic features, including alignment E value and replicon distribution scores (RDS). Plasmer can predict on multiple species and has achieved an average the area under the curve (AUC) of 0.996 with accuracy of 98.4%. Compared to existing methods, tests of both sliding sequences and simulated and de novo assemblies have consistently shown that Plasmer has outperforming accuracy and stable performance across long and short contigs above 500 bp, demonstrating its applicability for fragmented assemblies. Plasmer also has excellent and balanced performance on both sensitivity and specificity (both >0.95 above 500 bp) with the highest F1-score, which has eliminated the bias on sensitivity or specificity that was common in existing methods. Plasmer also provides taxonomy classification to help identify the origin of plasmids. IMPORTANCE In this study, we proposed a novel plasmid prediction tool named Plasmer. Technically, unlike existing k-mer or genomic features-based methods, Plasmer is the first tool to combine the advantages of the percent of shared k-mers and the alignment score of genomic features. This has given Plasmer (i) evident improvement in performance compared to other methods, with the best F1-score and accuracy on sliding sequences, simulated contigs, and de novo assemblies; (ii) applicability for contigs above 500 bp with highest accuracy, enabling plasmid prediction in fragmented short-read assemblies; (iii) excellent and balanced performance between sensitivity and specificity (both >0.95 above 500 bp) with the highest F1-score, which eliminated the bias on sensitivity or specificity that commonly existed in other methods; and (iv) no dependency of species-specific training models. We believe that Plasmer provides a more reliable alternative for plasmid prediction in bacterial genome assemblies.

Spatial Mapping of Mobile Genetic Elements and their Cognate Hosts in Complex Microbiomes

The frequent exchange of mobile genetic elements (MGEs) between bacteria accelerates the spread of functional traits, including antimicrobial resistance, within the human microbiome. Yet, progress in understanding these intricate processes has been hindered by the lack of tools to map the spatial spread of MGEs in complex microbial communities, and to associate MGEs to their bacterial hosts. To overcome this challenge, we present an imaging approach that pairs single molecule DNA Fluorescence In Situ Hybridization (FISH) with multiplexed ribosomal RNA FISH, thereby enabling the simultaneous visualization of both MGEs and host bacterial taxa. We used this methodology to spatially map bacteriophage and antimicrobial resistance (AMR) plasmids in human oral biofilms, and we studied the heterogeneity in their spatial distributions and demonstrated the ability to identify their host taxa. Our data revealed distinct clusters of both AMR plasmids and prophage, coinciding with densely packed regions of host bacteria in the biofilm. These results suggest the existence of specialized niches that maintain MGEs within the community, possibly acting as local hotspots for horizontal gene transfer. The methods introduced here can help advance the study of MGE ecology and address pressing questions regarding antimicrobial resistance and phage therapy.

Metagenomic analysis reveals gut plasmids as diagnosis markers for colorectal cancer

Background

Colorectal cancer (CRC) is linked to distinct gut microbiome patterns. The efficacy of gut bacteria as diagnostic biomarkers for CRC has been confirmed. Despite the potential to influence microbiome physiology and evolution, the set of plasmids in the gut microbiome remains understudied.

Methods

We investigated the essential features of gut plasmid using metagenomic data of 1,242 samples from eight distinct geographic cohorts. We identified 198 plasmid-related sequences that differed in abundance between CRC patients and controls and screened 21 markers for the CRC diagnosis model. We utilize these plasmid markers combined with bacteria to construct a random forest classifier model to diagnose CRC.

Results

The plasmid markers were able to distinguish between the CRC patients and controls [mean area under the receiver operating characteristic curve (AUC = 0.70)] and maintained accuracy in two independent cohorts. In comparison to the bacteria-only model, the performance of the composite panel created by combining plasmid and bacteria features was significantly improved in all training cohorts (mean AUC_composite = 0.804 and mean AUC_bacteria = 0.787) and maintained high accuracy in all independent cohorts (mean AUC_composite = 0.839 and mean AUC_bacteria = 0.821). In comparison to controls, we found that the bacteria-plasmid correlation strength was weaker in CRC patients. Additionally, the KEGG orthology (KO) genes in plasmids that are independent of bacteria or plasmids significantly correlated with CRC.

Conclusion

We identified plasmid features associated with CRC and showed how plasmid and bacterial markers could be combined to further enhance CRC diagnosis accuracy.

A highly conserved and globally prevalent cryptic plasmid is among the most numerous mobile genetic elements in the human gut

Plasmids are extrachromosomal genetic elements that often encode fitness enhancing features. However, many bacteria carry 'cryptic' plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes, and is 14 times as numerous as crAssphage, currently established as the most abundant genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from one's mother. pBI143 can transfer between Bacteroidales and although it does not appear to impact bacterial host fitness in vivo, can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an inexpensive alternative for detecting human colonic inflammatory states.

Plasmids of the urinary microbiota

Studies of the last decade have identified a phylogenetically diverse community of bacteria within the urinary tract of individuals with and without urinary symptoms. Mobile genetic elements (MGEs), including plasmids and phages, within this niche have only recently begun to be explored. These MGEs can expand metabolic capacity and increase virulence, as well as confer antibiotic resistance. As such, they have the potential to contribute to urinary symptoms. While plasmids for some of the bacterial taxa found within the urinary microbiota (urobiome) have been well characterized, many urinary species are under-studied with few genomes sequenced to date. Using a two-pronged bioinformatic approach, we have conducted a comprehensive investigation of the plasmid content of urinary isolates representative of 102 species. The bioinformatic tools plasmidSPAdes and Recycler were used in tandem to identify plasmid sequences from raw short-read sequence data followed by manual curation. In total, we identified 603 high-confidence plasmid sequences in 20 different genera of the urobiome. In total, 70 % of these high-confidence plasmids exhibit sequence similarity to plasmid sequences from the gut. This observation is primarily driven by plasmids from E. coli , which is found in both anatomical niches. To confirm our bioinformatic predictions, long-read sequencing was performed for 23 of the E. coli isolates in addition to two E. coli strains that were sequenced as part of a prior study. Overall, 66.95 % of these predictions were confirmed highlighting the strengths and weaknesses of current bioinformatic tools. Future studies of the urobiome, especially concerning under-studied species in the urobiome, should employ long-read sequencing to expand the catalogue of plasmids for this niche.

Improved microbial genomes and gene catalog of the chicken gut from metagenomic sequencing of high-fidelity long reads

BACKGROUND

Due to the importance of chicken production and the remarkable influence of the gut microbiota on host health and growth, tens of thousands of metagenome-assembled genomes (MAGs) have been constructed for the chicken gut microbiome. However, due to the limitations of short-read sequencing and assembly technologies, most of these MAGs are far from complete, are of lower quality, and include contaminant reads.

RESULTS

We generated 332 Gb of high-fidelity (HiFi) long reads from the 5 chicken intestinal compartments and assembled 461 and 337 microbial genomes, of which 53% and 55% are circular, at the species and strain levels, respectively. For the assembled microbial genomes, approximately 95% were regarded as complete according to the "RNA complete" criteria, which requires at least 1 full-length ribosomal RNA (rRNA) operon encoding all 3 types of rRNA (16S, 23S, and 5S) and at least 18 copies of full-length transfer RNA genes. In comparison with the short-read-derived chicken MAGs, 384 (83% of 461) and 89 (26% of 337) strain-level and species-level genomes in this study are novel, with no matches to previously reported sequences. At the gene level, one-third of the 2.5 million genes in the HiFi-derived gene catalog are novel and cannot be matched to the short-read-derived gene catalog. Moreover, the HiFi-derived genomes have much higher continuity and completeness, as well as lower contamination; the HiFi-derived gene catalog has a much higher ratio of complete gene structures. The dominant phylum in our HiFi-assembled genomes was Firmicutes (82.5%), and the foregut was highly enriched in 5 genera: Ligilactobacillus, Limosilactobacillus, Lactobacillus, Weissella, and Enterococcus, all of which belong to the order Lactobacillales. Using GTDB-Tk, all 337 species-level genomes were successfully classified at the order level; however, 2, 35, and 189 genomes could not be classified into any known family, genus, and species, respectively. Among these incompletely classified genomes, 9 and 49 may belong to novel genera and species, respectively, because their 16S rRNA genes have identities lower than 95% and 97% to any known 16S rRNA genes.

CONCLUSIONS

HiFi sequencing not only produced metagenome assemblies and gene structures with markedly improved quality but also recovered a substantial portion of novel genomes and genes that were missed in previous short-read-based metagenome studies. The novel genomes and species obtained in this study will facilitate gut microbiome and host-microbiota interaction studies, thereby contributing to the sustainable development of poultry resources.

Globally Disseminated Multidrug Resistance Plasmids Revealed by Complete Assembly of Multidrug Resistant Escherichia coli and Klebsiella pneumoniae Genomes from Diarrheal Disease in Botswana

Antimicrobial resistance is a disseminated global health challenge because many of the genes that cause resistance can transfer horizontally between bacteria. Despite the central role of extrachromosomal DNA elements called plasmids in driving the spread of resistance, the detection and surveillance of plasmids remains a significant barrier in molecular epidemiology. We assessed two DNA sequencing platforms alone and in combination for laboratory diagnostics in Botswana by annotating antibiotic resistance genes and plasmids in extensively drug resistant bacteria from diarrhea in Botswana. Long-read Nanopore DNA sequencing and high accuracy basecalling effectively estimated the architecture and gene content of three plasmids in Escherichia coli HUM3355 and two plasmids in Klebsiella pneumoniae HUM7199. Polishing the assemblies with Illumina reads increased base calling precision with small improvements to gene prediction. All five plasmids encoded one or more antibiotic resistance genes, usually within gene islands containing multiple antibiotic and metal resistance genes, and four plasmids encoded genes associated with conjugative transfer. Two plasmids were almost identical to antibiotic resistance plasmids sequenced in Europe and North America from human infection and a pig farm. These One Health connections demonstrate how low-, middle-, and high-income countries collectively benefit from increased whole genome sequencing capacity for surveillance and tracking of infectious diseases and antibiotic resistance genes that can transfer between animal hosts and move across continents.

3CAC: improving the classification of phages and plasmids in metagenomic assemblies using assembly graphs

MOTIVATION

Bacteriophages and plasmids usually coexist with their host bacteria in microbial communities and play important roles in microbial evolution. Accurately identifying sequence contigs as phages, plasmids and bacterial chromosomes in mixed metagenomic assemblies is critical for further unraveling their functions. Many classification tools have been developed for identifying either phages or plasmids in metagenomic assemblies. However, only two classifiers, PPR-Meta and viralVerify, were proposed to simultaneously identify phages and plasmids in mixed metagenomic assemblies. Due to the very high fraction of chromosome contigs in the assemblies, both tools achieve high precision in the classification of chromosomes but perform poorly in classifying phages and plasmids. Short contigs in these assemblies are often wrongly classified or classified as uncertain.

RESULTS

Here we present 3CAC, a new three-class classifier that improves the precision of phage and plasmid classification. 3CAC starts with an initial three-class classification generated by existing classifiers and improves the classification of short contigs and contigs with low confidence classification by using proximity in the assembly graph. Evaluation on simulated metagenomes and on real human gut microbiome samples showed that 3CAC outperformed PPR-Meta and viralVerify in both precision and recall, and increased F1-score by 10-60 percentage points.

AVAILABILITY AND IMPLEMENTATION

The 3CAC software is available on https://github.com/Shamir-Lab/3CAC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Precise genotyping of circular mobile elements from metagenomic data uncovers human-associated plasmids with recent common ancestors

Mobile genetic elements with circular genomes play a key role in the evolution of microbial communities. Their circular genomes correspond to circular walks in metagenome graphs, and yet, assemblies derived from natural microbial communities produce graphs riddled with spurious cycles, complicating the accurate reconstruction of circular genomes. We present DomCycle, an algorithm that reconstructs likely circular genomes based on the identification of so-called 'dominant' graph cycles. In the implementation we leverage paired reads to bridge assembly gaps and scrutinize cycles through a nucleotide-level analysis, making the approach robust to misassembly artifacts. We validated the approach using simulated and real sequencing data. Application of DomCycle to 32 publicly available DNA shotgun sequence data sets from diverse natural environments led to the reconstruction of hundreds of circular mobile genomes. Clustering revealed 20 highly prevalent and cryptic plasmids that have clonal population structures with recent common ancestors. This method facilitates the study of microbial communities that evolve through horizontal gene transfer.

Generating lineage-resolved, complete metagenome-assembled genomes from complex microbial communities

Microbial communities might include distinct lineages of closely related organisms that complicate metagenomic assembly and prevent the generation of complete metagenome-assembled genomes (MAGs). Here we show that deep sequencing using long (HiFi) reads combined with Hi-C binning can address this challenge even for complex microbial communities. Using existing methods, we sequenced the sheep fecal metagenome and identified 428 MAGs with more than 90% completeness, including 44 MAGs in single circular contigs. To resolve closely related strains (lineages), we developed MAGPhase, which separates lineages of related organisms by discriminating variant haplotypes across hundreds of kilobases of genomic sequence. MAGPhase identified 220 lineage-resolved MAGs in our dataset. The ability to resolve closely related microbes in complex microbial communities improves the identification of biosynthetic gene clusters and the precision of assigning mobile genetic elements to host genomes. We identified 1,400 complete and 350 partial biosynthetic gene clusters, most of which are novel, as well as 424 (298) potential host-viral (host-plasmid) associations using Hi-C data.

OUP accepted manuscript

Despite recent methodology and reference database improvements for taxonomic profiling tools, metagenomic assembly and genomic binning remain important pillars of metagenomic analysis workflows. In case reference information is lacking, genomic binning is considered to be a state-of-the-art method in mixed culture metagenomic data analysis. In this light, our previously published tool BusyBee Web implements a composition-based binning method efficient enough to function as a rapid online utility. Handling assembled contigs and long nanopore generated reads alike, the webserver provides a wide range of supplementary annotations and visualizations. Half a decade after the initial publication, we revisited existing functionality, added comprehensive visualizations, and increased the number of data analysis customization options for further experimentation. The webserver now allows for visualization-supported differential analysis of samples, which is computationally expensive and typically only performed in coverage-based binning methods. Further, users may now optionally check their uploaded samples for plasmid sequences using PLSDB as a reference database. Lastly, a new application programming interface with a supporting python package was implemented, to allow power users fully automated access to the resource and integration into existing workflows. The webserver is freely available under: https://www.ccb.uni-saarland.de/busybee.

PLSDB: advancing a comprehensive database of bacterial plasmids

Plasmids are known to contain genes encoding for virulence factors and antibiotic resistance mechanisms. Their relevance in metagenomic data processing is steadily growing. However, with the increasing popularity and scale of metagenomics experiments, the number of reported plasmids is rapidly growing as well, amassing a considerable number of false positives due to undetected misassembles. Here, our previously published database PLSDB provides a reliable resource for researchers to quickly compare their sequences against selected and annotated previous findings. Within two years, the size of this resource has more than doubled from the initial 13,789 to now 34,513 entries over the course of eight regular data updates. For this update, we aggregated community feedback for major changes to the database featuring new analysis functionality as well as performance, quality, and accessibility improvements. New filtering steps, annotations, and preprocessing of existing records improve the quality of the provided data. Additionally, new features implemented in the web-server ease user interaction and allow for a deeper understanding of custom uploaded sequences, by visualizing similarity information. Lastly, an application programming interface was implemented along with a python library, to allow remote database queries in automated workflows. The latest release of PLSDB is freely accessible under https://www.ccb.uni-saarland.de/plsdb.

Recovering Escherichia coli Plasmids in the Absence of Long-Read Sequencing Data

The incidence of infections caused by multidrug-resistant E. coli strains has risen in the past years. Antibiotic resistance in E. coli is often mediated by acquisition and maintenance of plasmids. The study of E. coli plasmid epidemiology and genomics often requires long-read sequencing information, but recently a number of tools that allow plasmid prediction from short-read data have been developed. Here, we reviewed 25 available plasmid prediction tools and categorized them into binary plasmid/chromosome classification tools and plasmid reconstruction tools. We benchmarked six tools (MOB-suite, plasmidSPAdes, gplas, FishingForPlasmids, HyAsP and SCAPP) that aim to reliably reconstruct distinct plasmids, with a special focus on plasmids carrying antibiotic resistance genes (ARGs) such as extended-spectrum beta-lactamase genes. We found that two thirds (n = 425, 66.3%) of all plasmids were correctly reconstructed by at least one of the six tools, with a range of 92 (14.58%) to 317 (50.23%) correctly predicted plasmids. However, the majority of plasmids that carried antibiotic resistance genes (n = 85, 57.8%) could not be completely recovered as distinct plasmids by any of the tools. MOB-suite was the only tool that was able to correctly reconstruct the majority of plasmids (n = 317, 50.23%), and performed best at reconstructing large plasmids (n = 166, 46.37%) and ARG-plasmids (n = 41, 27.9%), but predictions frequently contained chromosome contamination (40%). In contrast, plasmidSPAdes reconstructed the highest fraction of plasmids smaller than 18 kbp (n = 168, 61.54%). Large ARG-plasmids, however, were frequently merged with sequences derived from distinct replicons. Available bioinformatic tools can provide valuable insight into E. coli plasmids, but also have important limitations. This work will serve as a guideline for selecting the most appropriate plasmid reconstruction tool for studies focusing on E. coli plasmids in the absence of long-read sequencing data.