HIDDEN - Word Related Documents




#
Rank
Similarity
Title + Abs.
Year
PMID
012345
906700.9951PIPdb: a comprehensive plasmid sequence resource for tracking the horizontal transfer of pathogenic factors and antimicrobial resistance genes. Plasmids, as independent genetic elements, carrying resistance or virulence genes and transfer them among different pathogens, posing a significant threat to human health. Under the 'One Health' approach, it is crucial to control the spread of plasmids carrying such genes. To achieve this, a comprehensive characterization of plasmids in pathogens is essential. Here we present the Plasmids in Pathogens Database (PIPdb), a pioneering resource that includes 792 964 plasmid segment clusters (PSCs) derived from 1 009 571 assembled genomes across 450 pathogenic species from 110 genera. To our knowledge, PIPdb is the first database specifically dedicated to plasmids in pathogenic bacteria, offering detailed multi-dimensional metadata such as collection date, geographical origin, ecosystem, host taxonomy, and habitat. PIPdb also provides extensive functional annotations, including plasmid type, insertion sequences, integron, oriT, relaxase, T4CP, virulence factors genes, heavy metal resistance genes and antibiotic resistance genes. The database features a user-friendly interface that facilitates studies on plasmids across diverse host taxa, habitats, and ecosystems, with a focus on those carrying antimicrobial resistance genes (ARGs). We have integrated online tools for plasmid identification and annotation from assembled genomes. Additionally, PIPdb includes a risk-scoring system for identifying potentially high-risk plasmids. The PIPdb web interface is accessible at https://nmdc.cn/pipdb.202539460620
512610.9945Blanket antimicrobial resistance gene database with structural information, BOARDS, provides insights on historical landscape of resistance prevalence and effects of mutations in enzyme structure. Antimicrobial resistance (AMR) in pathogenic bacteria poses a significant threat to public health, yet there is still a need for development in the tools to deeply understand AMR genes based on genetic or structural information. In this study, we present an interactive web database named Blanket Overarching Antimicrobial-Resistance gene Database with Structural information (BOARDS, sbml.unist.ac.kr), a database that comprehensively includes 3,943 reported AMR gene information for 1,997 extended spectrum beta-lactamase (ESBL) and 1,946 other genes as well as a total of 27,395 predicted protein structures. These structures, which include both wild-type AMR genes and their mutants, were derived from 80,094 publicly available whole-genome sequences. In addition, we developed the rapid analysis and detection tool of antimicrobial-resistance (RADAR), a one-stop analysis pipeline to detect AMR genes across whole-genome sequencing (WGSs). By integrating BOARDS and RADAR, the AMR prevalence landscape for eight multi-drug resistant pathogens was reconstructed, leading to unexpected findings such as the pre-existence of the MCR genes before their official reports. Enzymatic structure prediction-based analysis revealed that the occurrence of mutations found in some ESBL genes was found to be closely related to the binding affinities with their antibiotic substrates. Overall, BOARDS can play a significant role in performing in-depth analysis on AMR.IMPORTANCEWhile the increasing antibiotic resistance (AMR) in pathogen has been a burden on public health, effective tools for deep understanding of AMR based on genetic or structural information remain limited. In this study, a blanket overarching antimicrobial-resistance gene database with structure information (BOARDS)-a web-based database that comprehensively collected AMR gene data with predictive protein structural information was constructed. Additionally, we report the development of a RADAR pipeline that can analyze whole-genome sequences as well. BOARDS, which includes sequence and structural information, has shown the historical landscape and prevalence of the AMR genes and can provide insight into single-nucleotide polymorphism effects on antibiotic degrading enzymes within protein structures.202438085058
326120.9945A global metagenomic map of urban microbiomes and antimicrobial resistance. We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.202134043940
669230.9944An omics-based framework for investigating the emerging antibiotic resistance gene: The case of estT. The escalating prevalence of antimicrobial resistance (AMR) constitutes a global public health crisis. This is exacerbated by the continuous emergence of new variants and the discovery of previously unrecognized antibiotic resistance genes (ARGs). While advanced AMR surveillance efforts include time-consuming epidemiological investigations and retrospective analyses, critical gaps often remain towards our understanding of the sources of newly identified ARGs. Here, we established a framework integrating omics-based epidemiological investigations, genomic feature analysis of ARGs-carrying bacteria and evolution analysis of novel ARGs. We took the novel resistance gene estT as an example and analyzed it following this framework. Our study revealed that the estT gene was widely prevalent, capable of cross-phyla transmission, and predominantly present in human- and animal-derived bacteria. We explored the genomic characteristics of estT-positive Escherichia coli, Bacillus spp., Mannheimia haemolytica, and Riemerella anatipestifer, uncovering their public health risks. Evolution analysis of estT homologs found historical connections between estTs and tet(X)s. This study provides a systematic strategy for the proactive surveillance of emerging ARGs, enabling omics-data-driven monitoring of ARG evolution and dissemination to mitigate the escalating crisis of AMR.202541160932
377640.9944FARME DB: a functional antibiotic resistance element database. Antibiotic resistance (AR) is a major global public health threat but few resources exist that catalog AR genes outside of a clinical context. Current AR sequence databases are assembled almost exclusively from genomic sequences derived from clinical bacterial isolates and thus do not include many microbial sequences derived from environmental samples that confer resistance in functional metagenomic studies. These environmental metagenomic sequences often show little or no similarity to AR sequences from clinical isolates using standard classification criteria. In addition, existing AR databases provide no information about flanking sequences containing regulatory or mobile genetic elements. To help address this issue, we created an annotated database of DNA and protein sequences derived exclusively from environmental metagenomic sequences showing AR in laboratory experiments. Our Functional Antibiotic Resistant Metagenomic Element (FARME) database is a compilation of publically available DNA sequences and predicted protein sequences conferring AR as well as regulatory elements, mobile genetic elements and predicted proteins flanking antibiotic resistant genes. FARME is the first database to focus on functional metagenomic AR gene elements and provides a resource to better understand AR in the 99% of bacteria which cannot be cultured and the relationship between environmental AR sequences and antibiotic resistant genes derived from cultured isolates.Database URL: http://staff.washington.edu/jwallace/farme.201728077567
326050.9943Profiles of phage in global hospital wastewater: Association with microbial hosts, antibiotic resistance genes, metal resistance genes, and mobile genetic elements. Hospital wastewater (HWW) is known to host taxonomically diverse microbial communities, yet limited information is available on the phages infecting these microorganisms. To fill this knowledge gap, we conducted an in-depth analysis using 377 publicly available HWW metagenomic datasets from 16 countries across 4 continents in the NCBI SRA database to elucidate phage-host dynamics and phage contributions to resistance gene transmission. We first assembled a metagenomic HWW phage catalog comprising 13,812 phage operational taxonomic units (pOTUs). The majority of these pOTUs belonged to the Caudoviricetes order, representing 75.29 % of this catalog. Based on the lifestyle of phages, we found that potentially virulent phages predominated in HWW. Specifically, 583 pOTUs have been predicted to have the capability to lyse 81 potentially pathogenic bacteria, suggesting the promising role of HWW phages as a viable alternative to antibiotics. Among all pOTUs, 1.56 % of pOTUs carry 108 subtypes of antibiotic resistance genes (ARGs), 0.96 % of pOTUs carry 76 subtypes of metal resistance genes (MRGs), and 0.96 % of pOTUs carry 22 subtypes of non-phage mobile genetic elements (MGEs). Predictions indicate that certain phages carrying ARGs, MRGs, and non-phage MGEs could infect bacteria hosts, even potential pathogens. This suggests that phages in HWW may contribute to the dissemination of resistance-associated genes in the environment. This meta-analysis provides the first global catalog of HWW phages, revealing their correlations with microbial hosts and pahge-associated ARGs, MRG, and non-phage MGEs. The insights gained from this research hold promise for advancing the applications of phages in medical and industrial contexts.202438513871
511960.9943ROCker models for reliable detection and typing of short-read sequences carrying mcr, erm, mph, and lnu antibiotic resistance genes. Quantitative monitoring of emerging antimicrobial resistance genes (ARGs) using short-read sequences remains challenging due to the high frequency of amino acid functional domains and motifs shared with related but functionally distinct (non-target) proteins. To facilitate ARG monitoring efforts using unassembled short reads, we present novel ROCker models for mcr, mph, erm, and lnu ARG families, as well as models for variants of special public health concern within these families, including mcr-1, mphA, ermB, lnuF, lnuB, and lnuG genes. For this, we curated target gene sequence sets for model training and built these models using the recently updated ROCker V2 pipeline (Gerhardt et al., in review). To validate our models, we simulated reads from the whole genome of ARG-carrying isolates spanning a range of common read lengths and used them to challenge the filtering efficacy of ROCker versus common static filtering approaches, such as similarity searches using BLASTx with various e-value thresholds or hidden Markov models. ROCker models consistently showed F1 scores up to 10× higher (31% higher on average) and lower false-positive (by 30%, on average) and false-negative (by 16%, on average) rates based on 250 bp reads compared to alternative methods. The ROCker models and all related reference materials and data are freely available through http://enve-omics.ce.gatech.edu/rocker/models, further expanding the available model collection previously developed for other genes. Their application to short-read metagenomes, metatranscriptomes, and PCR amplicon data should facilitate more accurate classification and quantification of unassembled short-read sequences for these ARG families and specific genes.IMPORTANCEAntimicrobial resistance gene families encoding erm and mph genes confer resistance to the macrolide class of antimicrobials, which are used to treat a wide range of infections. Similarly, the mcr gene family confers resistance to polymyxin E (colistin), a drug of last resort for many serious drug-resistant bacterial infections, and the lnu gene family confers resistance to lincomycin, which is reserved for patients allergic to penicillin or where bacteria have developed resistance to other antimicrobials. Assessing the prevalence of these genes in clinical or environmental samples and monitoring their spread to new pathogens are thus important for quantifying the associated public health risk. However, detecting these and other resistance genes in short-read sequence data is technically challenging. Our ROCker bioinformatic pipeline achieves reliable detection and typing of broad-range target gene sequences in complex data sets, thus contributing toward solving an important problem in ongoing surveillance efforts of antimicrobial resistance.202541143534
455570.9943Characterization of known and novel clinically important antibiotic resistance genes and novel microbes from wastewater-impacted high Arctic fjord sediments. Arctic microbiota is enigmatic and highly underexplored. With the aim of understanding the resistome and microbiota of high-Arctic fjord sediments and the effect of wastewater discharge on sediment microbiota, we analyzed sediments from Advent fjord in Svalbard using metagenomics. We show the presence of 888 clinically relevant antibiotic resistance genes including genes coding resistance against last-resort antibiotics such as carbapenems, colistin, vancomycin, linezolid and tigecycline in the sediment microbiota. Using computational models, 478 novel β-lactamases belonging to 217 novel β-lactamase families were revealed in the sediment microbiota. Further, we identified hosts for 69 novel families and showed that these genes are widespread in the Arctic environment. We assembled 644 metagenome-assembled genomes (MAGs) from sediment metagenomes. Of these >97 % belonged to novel taxa with 89 bacterial MAGs representing seven putative novel phyla. These MAGs encoded important functions like nutrient cycling and methane metabolism etc. Our study demonstrated mixing of human associated bacteria and Arctic sediment microbiota. It provides the first comprehensive dataset of the distribution and diversity of novel microbes and β-lactamases in the wastewater-impacted high Arctic fjord sediments.202540424901
906880.9943TnCentral: a Prokaryotic Transposable Element Database and Web Portal for Transposon Analysis. We describe here the structure and organization of TnCentral (https://tncentral.proteininformationresource.org/ [or the mirror link at https://tncentral.ncc.unesp.br/]), a web resource for prokaryotic transposable elements (TE). TnCentral currently contains ∼400 carefully annotated TE, including transposons from the Tn3, Tn7, Tn402, and Tn554 families; compound transposons; integrons; and associated insertion sequences (IS). These TE carry passenger genes, including genes conferring resistance to over 25 classes of antibiotics and nine types of heavy metal, as well as genes responsible for pathogenesis in plants, toxin/antitoxin gene pairs, transcription factors, and genes involved in metabolism. Each TE has its own entry page, providing details about its transposition genes, passenger genes, and other sequence features required for transposition, as well as a graphical map of all features. TnCentral content can be browsed and queried through text- and sequence-based searches with a graphic output. We describe three use cases, which illustrate how the search interface, results tables, and entry pages can be used to explore and compare TE. TnCentral also includes downloadable software to facilitate user-driven identification, with manual annotation, of certain types of TE in genomic sequences. Through the TnCentral homepage, users can also access TnPedia, which provides comprehensive reviews of the major TE families, including an extensive general section and specialized sections with descriptions of insertion sequence and transposon families. TnCentral and TnPedia are intuitive resources that can be used by clinicians and scientists to assess TE diversity in clinical, veterinary, and environmental samples. IMPORTANCE The ability of bacteria to undergo rapid evolution and adapt to changing environmental circumstances drives the public health crisis of multiple antibiotic resistance, as well as outbreaks of disease in economically important agricultural crops and animal husbandry. Prokaryotic transposable elements (TE) play a critical role in this. Many carry "passenger genes" (not required for the transposition process) conferring resistance to antibiotics or heavy metals or causing disease in plants and animals. Passenger genes are spread by normal TE transposition activities and by insertion into plasmids, which then spread via conjugation within and across bacterial populations. Thus, an understanding of TE composition and transposition mechanisms is key to developing strategies to combat bacterial pathogenesis. Toward this end, we have developed TnCentral, a bioinformatics resource dedicated to describing and exploring the structural and functional features of prokaryotic TE whose use is intuitive and accessible to users with or without bioinformatics expertise.202134517763
377790.9943A Bioinformatic Analysis of Integrative Mobile Genetic Elements Highlights Their Role in Bacterial Adaptation. Mobile genetic elements (MGEs) contribute to bacterial adaptation and evolution; however, high-throughput, unbiased MGE detection remains challenging. We describe MGEfinder, a bioinformatic toolbox that identifies integrative MGEs and their insertion sites by using short-read sequencing data. MGEfinder identifies the genomic site of each MGE insertion and infers the identity of the inserted sequence. We apply MGEfinder to 12,374 sequenced isolates of 9 prevalent bacterial pathogens, including Mycobacterium tuberculosis, Staphylococcus aureus, and Escherichia coli, and identify thousands of MGEs, including candidate insertion sequences, conjugative transposons, and prophage elements. The MGE repertoire and insertion rates vary across species, and integration sites often cluster near genes related to antibiotic resistance, virulence, and pathogenicity. MGE insertions likely contribute to antibiotic resistance in laboratory experiments and clinical isolates. Additionally, we identified thousands of mobility genes, a subset of which have unknown function opening avenues for exploration. Future application of MGEfinder to commensal bacteria will further illuminate bacterial adaptation and evolution.202031862382
7674100.9943Insights into gut microbiomes in stem cell transplantation by comprehensive shotgun long-read sequencing. The gut microbiome is a diverse ecosystem, dominated by bacteria; however, fungi, phages/viruses, archaea, and protozoa are also important members of the gut microbiota. Exploration of taxonomic compositions beyond bacteria as well as an understanding of the interaction between the bacteriome with the other members is limited using 16S rDNA sequencing. Here, we developed a pipeline enabling the simultaneous interrogation of the gut microbiome (bacteriome, mycobiome, archaeome, eukaryome, DNA virome) and of antibiotic resistance genes based on optimized long-read shotgun metagenomics protocols and custom bioinformatics. Using our pipeline we investigated the longitudinal composition of the gut microbiome in an exploratory clinical study in patients undergoing allogeneic hematopoietic stem cell transplantation (alloHSCT; n = 31). Pre-transplantation microbiomes exhibited a 3-cluster structure, characterized by Bacteroides spp. /Phocaeicola spp., mixed composition and Enterococcus abundances. We revealed substantial inter-individual and temporal variabilities of microbial domain compositions, human DNA, and antibiotic resistance genes during the course of alloHSCT. Interestingly, viruses and fungi accounted for substantial proportions of microbiome content in individual samples. In the course of HSCT, bacterial strains were stable or newly acquired. Our results demonstrate the disruptive potential of alloHSCTon the gut microbiome and pave the way for future comprehensive microbiome studies based on long-read metagenomics.202438374282
9079110.9943Review, Evaluation, and Directions for Gene-Targeted Assembly for Ecological Analyses of Metagenomes. Shotgun metagenomics has greatly advanced our understanding of microbial communities over the last decade. Metagenomic analyses often include assembly and genome binning, computationally daunting tasks especially for big data from complex environments such as soil and sediments. In many studies, however, only a subset of genes and pathways involved in specific functions are of interest; thus, it is not necessary to attempt global assembly. In addition, methods that target genes can be computationally more efficient and produce more accurate assembly by leveraging rich databases, especially for those genes that are of broad interest such as those involved in biogeochemical cycles, biodegradation, and antibiotic resistance or used as phylogenetic markers. Here, we review six gene-targeted assemblers with unique algorithms for extracting and/or assembling targeted genes: Xander, MegaGTA, SAT-Assembler, HMM-GRASPx, GenSeed-HMM, and MEGAN. We tested these tools using two datasets with known genomes, a synthetic community of artificial reads derived from the genomes of 17 bacteria, shotgun sequence data from a mock community with 48 bacteria and 16 archaea genomes, and a large soil shotgun metagenomic dataset. We compared assemblies of a universal single copy gene (rplB) and two N cycle genes (nifH and nirK). We measured their computational efficiency, sensitivity, specificity, and chimera rate and found Xander and MegaGTA, which both use a probabilistic graph structure to model the genes, have the best overall performance with all three datasets, although MEGAN, a reference matching assembler, had better sensitivity with synthetic and mock community members chosen from its reference collection. Also, Xander and MegaGTA are the only tools that include post-assembly scripts tuned for common molecular ecology and diversity analyses. Additionally, we provide a mathematical model for estimating the probability of assembling targeted genes in a metagenome for estimating required sequencing depth.201931749830
7700120.9942Rapid identification of antibiotic resistance gene hosts by prescreening ARG-like reads. Effective risk assessment and control of environmental antibiotic resistance depend on comprehensive information about antibiotic resistance genes (ARGs) and their microbial hosts. Advances in sequencing technologies and bioinformatics have enabled the identification of ARG hosts using metagenome-assembled contigs and genomes. However, these approaches often suffer from information loss and require extensive computational resources. Here we introduce a bioinformatic strategy that identifies ARG hosts by prescreening ARG-like reads (ALRs) directly from total metagenomic datasets. This ALR-based method offers several advantages: (1) it enables the detection of low-abundance ARG hosts with higher accuracy in complex environments; (2) it establishes a direct relationship between the abundance of ARGs and their hosts; and (3) it reduces computation time by approximately 44-96% compared to strategies relying on assembled contigs and genomes. We applied our ALR-based strategy alongside two traditional methods to investigate a typical human-impacted environment. The results were consistent across all methods, revealing that ARGs are predominantly carried by Gammaproteobacteria and Bacilli, and their distribution patterns may indicate the impact of wastewater discharge on coastal resistome. Our strategy provides rapid and accurate identification of antibiotic-resistant bacteria, offering valuable insights for the high-throughput surveillance of environmental antibiotic resistance. This study further expands our knowledge of ARG-related risk management in future.202540059905
6798130.9942Diet-driven diversity of antibiotic resistance genes in wild bats: implications for public health. Wild bats may serve as reservoirs for antibiotic resistance genes (ARGs) and antibiotic-resistant bacteria, potentially contributing to antibiotic resistance and pathogen transmission. However, current assessments of bats' antibiotic resistance potential are limited to culture-dependent bacterial snapshots. In this study, we present metagenomic evidence supporting a strong association between diet, gut microbiota, and the resistome, highlighting bats as significant vectors for ARG propagation. We characterized gut microbiota, ARGs, and mobile genetic elements (MGEs) in bats with five distinct diets: frugivory, insectivory, piscivory, carnivory, and sanguivory. Our analysis revealed high levels of ARGs in bat guts, with limited potential for horizontal transfer, encompassing 1106 ARGs conferring resistance to 26 antibiotics. Multidrug-resistant and polymyxin-resistant genes were particularly prevalent among identified ARG types. The abundance and diversity of ARGs/MGEs varied significantly among bats with different dietary habits, possibly due to diet-related differences in microbial composition. Additionally, genetic linkage between high-risk ARGs and multiple MGEs was observed on the genomes of various zoonotic pathogens, indicating a potential threat to human health from wild bats. Overall, our study provides a comprehensive analysis of the resistome in wild bats and underscores the role of dietary habits in wildlife-associated public health risks.202539892320
4550140.9942Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming. Anthropogenic environments such as those created by intensive farming of livestock, have been proposed to provide ideal selection pressure for the emergence of antimicrobial-resistant Escherichia coli bacteria and antimicrobial resistance genes (ARGs) and spread to humans. Here, we performed a longitudinal study in a large-scale commercial poultry farm in China, collecting E. coli isolates from both farm and slaughterhouse; targeting animals, carcasses, workers and their households and environment. By using whole-genome phylogenetic analysis and network analysis based on single nucleotide polymorphisms (SNPs), we found highly interrelated non-pathogenic and pathogenic E. coli strains with phylogenetic intermixing, and a high prevalence of shared multidrug resistance profiles amongst livestock, human and environment. Through an original data processing pipeline which combines omics, machine learning, gene sharing network and mobile genetic elements analysis, we investigated the resistance to 26 different antimicrobials and identified 361 genes associated to antimicrobial resistance (AMR) phenotypes; 58 of these were known AMR-associated genes and 35 were associated to multidrug resistance. We uncovered an extensive network of genes, correlated to AMR phenotypes, shared among livestock, humans, farm and slaughterhouse environments. We also found several human, livestock and environmental isolates sharing closely related mobile genetic elements carrying ARGs across host species and environments. In a scenario where no consensus exists on how antibiotic use in the livestock may affect antibiotic resistance in the human population, our findings provide novel insights into the broader epidemiology of antimicrobial resistance in livestock farming. Moreover, our original data analysis method has the potential to uncover AMR transmission pathways when applied to the study of other pathogens active in other anthropogenic environments characterised by complex interconnections between host species.202235333870
5127150.9941ResFinderFG v2.0: a database of antibiotic resistance genes obtained by functional metagenomics. Metagenomics can be used to monitor the spread of antibiotic resistance genes (ARGs). ARGs found in databases such as ResFinder and CARD primarily originate from culturable and pathogenic bacteria, while ARGs from non-culturable and non-pathogenic bacteria remain understudied. Functional metagenomics is based on phenotypic gene selection and can identify ARGs from non-culturable bacteria with a potentially low identity shared with known ARGs. In 2016, the ResFinderFG v1.0 database was created to collect ARGs from functional metagenomics studies. Here, we present the second version of the database, ResFinderFG v2.0, which is available on the Center of Genomic Epidemiology web server (https://cge.food.dtu.dk/services/ResFinderFG/). It comprises 3913 ARGs identified by functional metagenomics from 50 carefully curated datasets. We assessed its potential to detect ARGs in comparison to other popular databases in gut, soil and water (marine + freshwater) Global Microbial Gene Catalogues (https://gmgc.embl.de). ResFinderFG v2.0 allowed for the detection of ARGs that were not detected using other databases. These included ARGs conferring resistance to beta-lactams, cycline, phenicol, glycopeptide/cycloserine and trimethoprim/sulfonamide. Thus, ResFinderFG v2.0 can be used to identify ARGs differing from those found in conventional databases and therefore improve the description of resistomes.202337207327
7698160.9941Detecting 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
7722170.9941Genome-resolving metagenomics reveals wild western capercaillies (Tetrao urogallus) as avian hosts for antibiotic-resistance bacteria and their interactions with the gut-virome community. The gut microbiome is a critical component of avian health, influencing nutrient uptake and immune functions. While the gut microbiomes of agriculturally important birds have been studied, the microbiomes of wild birds still need to be explored. Filling this knowledge gap could have implications for the microbial rewilding of captive birds and managing avian hosts for antibiotic-resistant bacteria (ARB). Using genome-resolved metagenomics, we recovered 112 metagenome-assembled genomes (MAGs) from the faeces of wild and captive western capercaillies (Tetrao urogallus) (n = 8). Comparisons of bacterial diversity between the wild and captive capercaillies suggest that the reduced diversity in the captive individual could be due to differences in diet. This was further substantiated through the analyses of 517,657 clusters of orthologous groups (COGs), which revealed that gene functions related to amino acids and carbohydrate metabolisms were more abundant in wild capercaillies. Metagenomics mining of resistome identified 751 antibiotic resistance genes (ARGs), of which 40.7 % were specific to wild capercaillies suggesting that capercaillies could be potential reservoirs for hosting ARG-associated bacteria. Additionally, the core resistome shared between wild and captive capercaillies indicates that birds can acquire these ARG-associated bacteria naturally from the environment (43.1 % of ARGs). The association of 26 MAGs with 120 ARGs and 378 virus operational taxonomic units (vOTUs) also suggests a possible interplay between these elements, where putative phages could have roles in modulating the gut microbiota of avian hosts. These findings can have important implications for conservation and human health, such as avian gut microbiota rewilding, identifying the emerging threats or opportunities due to phage-microbe interactions, and monitoring the potential spread of ARG-associated bacteria from wild avian populations.202337018898
3268180.9941Resistomic features and novel genetic element identified in hospital wastewater with short- and long-read metagenomics. The global spread of antimicrobial resistance (AMR) poses a serious threat to public health, with hospital wastewater treatment plants (WWTPs) recognized as a key hotspot for resistant pathogens and antibiotic resistance genes (ARGs). This study employed advanced hybrid sequencing platforms to provide a comprehensive resistomic analysis of a Qingdao WWTP in China, revealing previously uncovered AMR transmission risks. We identified 175 ARG subtypes conferring resistance to 38 antimicrobials, including the last-resort antibiotics, highlighting the extensive and concerning resistance reservoir within this environment. Multidrug resistance genes predominated, followed by ARGs targeting aminoglycoside, β-lactam, tetracycline, glycopeptide, and macrolide classes, reflecting clinically relevant resistance patterns. Co-occurrence analysis revealed ARGs were strongly associated with mobile genetic elements, especially for ARGs targeting sulfonamide, glycopeptide, macrolide, tetracycline, aminoglycoside, and β-lactam classes, providing concrete evidence of their high dissemination potential. A striking 85 % of 131 metagenome-assembled genomes (MAGs) carried ARGs, demonstrating prevalent resistance in the wastewater microbiome. Furthermore, the identification of several rarely studied genomic islands (GIs), including those conferring resistance to antibiotics and heavy metals, and notably, the novel variant GIAS409 carrying transposases and heavy metal resistance operons, reveals a significant and previously neglected mechanism for co-selection and dissemination. This study significantly advances our understanding of AMR dynamics in hospital WWTPs, demonstrating that current treatment approaches (42 % ARG removal) have limited efficacy and that WWTP may serve as potential hotspots for multidrug resistance development. Collectively, these findings emphasize the urgent need for improved wastewater management to safeguard public health.202540915207
3116190.9941Prediction of Antibiotic Resistance Genes in Cyanobacterial Strains by Whole Genome Sequencing. Cyanobacteria are ubiquitous in freshwater environments, but their role in aquatic resistome remains unclear. In this work, we performed whole genome sequencing on 43 cyanobacterial strains isolated from Portuguese fresh/wastewaters. From 43 available non-axenic unicyanoabacterial cultures (containing only one cyanobacterial strain and their co-occurring bacteria), it was possible to recover 41 cyanobacterial genomes from the genomic assemblies using a genome binning software, 26 of which were classified as high-quality based on completeness, contamination, N50 and contig number thresholds. By using the comprehensive antibiotic resistance database (CARD) on the assembled samples, we detected four antibiotic resistance gene (ARG) variants, conferring resistance in pathogenic bacteria to tetracyclines, fluoroquinolones (adeF-type) and macrolides (ermF-type, mefC-type and mphG-type). Among these, adeF-type was the most prevalent gene, found across 11 cyanobacterial genomes from the Nostocales order. Planktothrix presented the highest variety of close ARG matches, with hits for the macrolide resistance genes ermF-type, mefC-type and mphG-type. An analysis of the genomic assemblies also revealed an additional 12 ARGs in bacteria from the phyla Firmicutes, Proteobacteria and Bacteroidetes, present in the cyanobacterial cultures, foreseeing the horizontal gene transfer of ARGs with cyanobacteria. Additionally, more than 200 partial ARGs were detected on each recovered cyanobacterial genome, allowing for future studies of antibiotic resistance genotype/phenotype in cyanobacteria. These findings highlight the importance of further efforts to understand the role of cyanobacteria on the aquatic resistome from a One Health perspective.202540572139