# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 9067 | 0 | 0.9857 | PIPdb: 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. | 2025 | 39460620 |
| 6508 | 1 | 0.9851 | Synergizing Ecotoxicology and Microbiome Data Is Key for Developing Global Indicators of Environmental Antimicrobial Resistance. The One Health concept recognises the interconnectedness of humans, plants, animals and the environment. Recent research strongly supports the idea that the environment serves as a significant reservoir for antimicrobial resistance (AMR). However, the complexity of natural environments makes efforts at AMR public health risk assessment difficult. We lack sufficient data on key ecological parameters that influence AMR, as well as the primary proxies necessary for evaluating risks to human health. Developing environmental AMR 'early warning systems' requires models with well-defined parameters. This is necessary to support the implementation of clear and targeted interventions. In this review, we provide a comprehensive overview of the current tools used globally for environmental AMR human health risk assessment and the underlying knowledge gaps. We highlight the urgent need for standardised, cost-effective risk assessment frameworks that are adaptable across different environments and regions to enhance comparability and reliability. These frameworks must also account for previously understudied AMR sources, such as horticulture, and emerging threats like climate change. In addition, integrating traditional ecotoxicology with modern 'omics' approaches will be essential for developing more comprehensive risk models and informing targeted AMR mitigation strategies. | 2024 | 39611949 |
| 5125 | 2 | 0.9850 | Do we still need Illumina sequencing data? Evaluating Oxford Nanopore Technologies R10.4.1 flow cells and the Rapid v14 library prep kit for Gram negative bacteria whole genome assemblies. The best whole genome assemblies are currently built from a combination of highly accurate short-read sequencing data and long-read sequencing data that can bridge repetitive and problematic regions. Oxford Nanopore Technologies (ONT) produce long-read sequencing platforms and they are continually improving their technology to obtain higher quality read data that is approaching the quality obtained from short-read platforms such as Illumina. As these innovations continue, we evaluated how much ONT read coverage produced by the Rapid Barcoding Kit v14 (SQK-RBK114) is necessary to generate high-quality hybrid and long-read-only genome assemblies for a panel of carbapenemase-producing Enterobacterales bacterial isolates. We found that 30× long-read coverage is sufficient if Illumina data are available, and that more (at least 100× long-read coverage is recommended for long-read-only assemblies. Illumina polishing is still improving single nucleotide variants (SNVs) and INDELs in long-read-only assemblies. We also examined if antimicrobial resistance genes could be accurately identified in long-read-only data, and found that Flye assemblies regardless of ONT coverage detected >96% of resistance genes at 100% identity and length. Overall, the Rapid Barcoding Kit v14 and long-read-only assemblies can be an optimal sequencing strategy (i.e., plasmid characterization and AMR detection) but finer-scale analyses (i.e., SNV) still benefit from short-read data. | 2024 | 38354391 |
| 9083 | 3 | 0.9848 | ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences. BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing. RESULTS: In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG. CONCLUSIONS: ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract. | 2024 | 38725076 |
| 6634 | 4 | 0.9847 | Making waves: The NORMAN antibiotic resistant bacteria and resistance genes database (NORMAN ARB&ARG)-An invitation for collaboration to tackle antibiotic resistance. With the global concerns on antibiotic resistance (AR) as a public health issue, it is pivotal to have data exchange platforms for studies on antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in the environment. For this purpose, the NORMAN Association is hosting the NORMAN ARB&ARG database, which was developed within the European project ANSWER. The present article provides an overview on the database functionalities, the extraction and the contribution of data to the database. In this study, AR data from three studies from China and Nepal were extracted and imported into the NORMAN ARB&ARG in addition to the existing AR data from 11 studies (mainly European studies) on the database. This feasibility study demonstrates how the scientific community can share their data on AR to generate an international evidence base to inform AR mitigation strategies. The open and FAIR data are of high potential relevance for regulatory applications, including the development of emission limit values / environmental quality standards in relation to AR. The growth in sharing of data and analytical methods will foster collaboration on risk management of AR worldwide, and facilitate the harmonization in the effort for identification and surveillance of critical hotspots of AR. The NORMAN ARB&ARG database is publicly available at: https://www.norman-network.com/nds/bacteria/. | 2024 | 38723350 |
| 6689 | 5 | 0.9847 | Wastewater-Based Epidemiology as a Complementary Tool for Antimicrobial Resistance Surveillance: Overcoming Barriers to Integration. This commentary highlights the potential of wastewater-based epidemiology (WBE) as a complementary tool for antimicrobial resistance (AMR) surveillance. WBE can support the early detection of resistance trends at the population level, including in underserved communities. However, several challenges remain, including technical variability, complexities in data interpretation, and regulatory gaps. An additional limitation is the uncertainty surrounding the origin of resistant bacteria and their genes in wastewater, which may derive not only from human sources but also from industrial, agricultural, or infrastructural contributors. Therefore, effective integration of WBE into public health systems will require standardized methods, sustained investment, and cross-sector collaboration. This could be achieved through joint monitoring initiatives that combine hospital wastewater data with agricultural and municipal surveillance to inform antibiotic stewardship policies. Overcoming these barriers could position WBE as an innovative tool for AMR monitoring, enhancing early warning systems and supporting more responsive, equitable, and preventive public health strategies. | 2025 | 40522150 |
| 6506 | 6 | 0.9845 | Mitigating antimicrobial resistance through effective hospital wastewater management in low- and middle-income countries. Hospital wastewater (HWW) is a significant environmental and public health threat, containing high levels of pollutants such as antibiotic-resistant bacteria (ARB), antibiotic-resistant genes (ARGs), antibiotics, disinfectants, and heavy metals. This threat is of particular concern in low- and middle-income countries (LMICs), where untreated effluents are often used for irrigating vegetables crops, leading to direct and indirect human exposure. Despite being a potential hotspot for the spread of antimicrobial resistance (AMR), existing HWW treatment systems in LMICs primarily target conventional pollutants and lack effective standards for monitoring the removal of ARB and ARGs. Consequently, untreated or inadequately treated HWW continues to disseminate ARB and ARGs, exacerbating the risk of AMR proliferation. Addressing this requires targeted interventions, including cost-effective treatment solutions, robust AMR monitoring protocols, and policy-driven strategies tailored to LMICs. This perspective calls for a paradigm shift in HWW management in LMIC, emphasizing the broader implementation of onsite treatment systems, which are currently rare. Key recommendations include developing affordable and contextually adaptable technologies for eliminating ARB and ARGs and enforcing local regulations for AMR monitoring and control in wastewater. Addressing these challenges is essential for protecting public health, preventing the environmental spread of resistance, and contributing to a global effort to preserve the efficacy of antibiotics. Recommendations include integrating scalable onsite technologies, leveraging local knowledge, and implementing comprehensive AMR-focused regulatory frameworks. | 2024 | 39944563 |
| 9075 | 7 | 0.9845 | CamPype: an open-source workflow for automated bacterial whole-genome sequencing analysis focused on Campylobacter. BACKGROUND: The rapid expansion of Whole-Genome Sequencing has revolutionized the fields of clinical and food microbiology. However, its implementation as a routine laboratory technique remains challenging due to the growth of data at a faster rate than can be effectively analyzed and critical gaps in bioinformatics knowledge. RESULTS: To address both issues, CamPype was developed as a new bioinformatics workflow for the genomics analysis of sequencing data of bacteria, especially Campylobacter, which is the main cause of gastroenteritis worldwide making a negative impact on the economy of the public health systems. CamPype allows fully customization of stages to run and tools to use, including read quality control filtering, read contamination, reads extension and assembly, bacterial typing, genome annotation, searching for antibiotic resistance genes, virulence genes and plasmids, pangenome construction and identification of nucleotide variants. All results are processed and resumed in an interactive HTML report for best data visualization and interpretation. CONCLUSIONS: The minimal user intervention of CamPype makes of this workflow an attractive resource for microbiology laboratories with no expertise in bioinformatics as a first line method for bacterial typing and epidemiological analyses, that would help to reduce the costs of disease outbreaks, or for comparative genomic analyses. CamPype is publicly available at https://github.com/JoseBarbero/CamPype . | 2023 | 37474912 |
| 5115 | 8 | 0.9845 | Search Engine for Antimicrobial Resistance: A Cloud Compatible Pipeline and Web Interface for Rapidly Detecting Antimicrobial Resistance Genes Directly from Sequence Data. BACKGROUND: Antimicrobial resistance remains a growing and significant concern in human and veterinary medicine. Current laboratory methods for the detection and surveillance of antimicrobial resistant bacteria are limited in their effectiveness and scope. With the rapidly developing field of whole genome sequencing beginning to be utilised in clinical practice, the ability to interrogate sequencing data quickly and easily for the presence of antimicrobial resistance genes will become increasingly important and useful for informing clinical decisions. Additionally, use of such tools will provide insight into the dynamics of antimicrobial resistance genes in metagenomic samples such as those used in environmental monitoring. RESULTS: Here we present the Search Engine for Antimicrobial Resistance (SEAR), a pipeline and web interface for detection of horizontally acquired antimicrobial resistance genes in raw sequencing data. The pipeline provides gene information, abundance estimation and the reconstructed sequence of antimicrobial resistance genes; it also provides web links to additional information on each gene. The pipeline utilises clustering and read mapping to annotate full-length genes relative to a user-defined database. It also uses local alignment of annotated genes to a range of online databases to provide additional information. We demonstrate SEAR's application in the detection and abundance estimation of antimicrobial resistance genes in two novel environmental metagenomes, 32 human faecal microbiome datasets and 126 clinical isolates of Shigella sonnei. CONCLUSIONS: We have developed a pipeline that contributes to the improved capacity for antimicrobial resistance detection afforded by next generation sequencing technologies, allowing for rapid detection of antimicrobial resistance genes directly from sequencing data. SEAR uses raw sequencing data via an intuitive interface so can be run rapidly without requiring advanced bioinformatic skills or resources. Finally, we show that SEAR is effective in detecting antimicrobial resistance genes in metagenomic and isolate sequencing data from both environmental metagenomes and sequencing data from clinical isolates. | 2015 | 26197475 |
| 6696 | 9 | 0.9845 | The Role of Gulls as Reservoirs of Antibiotic Resistance in Aquatic Environments: A Scoping Review. The role of wildlife with long-range dispersal such as gulls in the global dissemination of antimicrobial resistance (AMR) across natural and anthropogenic aquatic environments remains poorly understood. Antibiotic-resistant bacteria have been detected in resident and migratory gulls worldwide for more than a decade, suggesting gulls as either sentinels of AMR pollution from anthropogenic sources or independent reservoirs that could maintain and disperse AMR across aquatic environments. However, confirming either of these roles remains challenging and incomplete. In this review, we present current knowledge on the geographic regions where AMR has been detected in gulls, the molecular characterization of resistance genes, and the evidence supporting the capacity of gulls to disperse AMR across regions or countries. We identify several limitations of current research to assess the role of gulls in the spread of AMR including most studies not identifying the source of AMR, few studies comparing bacteria isolated in gulls with other wild or domestic species, and almost no study performing longitudinal sampling over a large period of time to assess the maintenance and dispersion of AMR by gulls within and across regions. We suggest future research required to confirm the role of gulls in the global dispersion of AMR including the standardization of sampling protocols, longitudinal sampling using advanced satellite tracking, and whole-genome sequencing typing. Finally, we discuss the public health implications of the spread of AMR by gulls and potential solutions to limit its spread in aquatic environments. | 2021 | 34367104 |
| 6576 | 10 | 0.9845 | Wastewater-based AMR surveillance associated with tourism on a Caribbean island (Guadeloupe). OBJECTIVES: Antimicrobial resistance (AMR) is a major public health concern worldwide. International travel is a risk factor for acquiring antibiotic-resistant bacteria (ARB) and antibiotic-resistance genes (ARGs). Therefore, understanding the transmission of ARB and ARGs is instrumental in tackling AMR. This longitudinal study aimed to assess the benefit of wastewater monitoring in Guadeloupe to evaluate the role of tourism in the spread of AMR. METHODS: A wastewater-based surveillance (WBS) study was conducted to monitor AMR in Guadeloupe in 2022 during dry and wet seasons. We characterized the resistome, microbiome and exposome of water samples collected in wastewater treatment facilities of two cities with different levels of tourism activities, in the content of aircraft toilets, and the pumping station receiving effluents from hotels. RESULTS: The results show that the WBS approach facilitates the differentiation of various untreated effluents concerning exposome, microbiome, and resistome, offering insights into AMR dissemination. Additionally, the findings reveal that microbiome and exposome are comparable across sites and seasons, while resistome characterisation at specific locations may be pertinent for health surveillance. The microbiome of aircraft was predominantly composed of anaerobic bacteria from human intestinal microbiota, whereas the other locations exhibited a blend of human and environmental bacteria. Notably, individuals arriving by air have not introduced clinically significant resistance genes. Exposome compounds have been shown to influence the resistome's variance. CONCLUSIONS: Clear differences were seen between the aircraft and the local sampling sites, indicating that the contribution of tourism to the observed resistance in Guadeloupe is not significant. | 2025 | 40154781 |
| 6686 | 11 | 0.9844 | The Impact of Wastewater on Antimicrobial Resistance: A Scoping Review of Transmission Pathways and Contributing Factors. BACKGROUND/OBJECTIVES: Antimicrobial resistance (AMR) is a global issue driven by the overuse of antibiotics in healthcare, agriculture, and veterinary settings. Wastewater and treatment plants (WWTPs) act as reservoirs for antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs). The One Health approach emphasizes the interconnectedness of human, animal, and environmental health in addressing AMR. This scoping review analyzes wastewater's role in the AMR spread, identifies influencing factors, and highlights research gaps to guide interventions. METHODS: This scoping review followed the PRISMA-ScR guidelines. A comprehensive literature search was conducted across the PubMed and Web of Science databases for articles published up to June 2024, supplemented by manual reference checks. The review focused on wastewater as a source of AMR, including hospital effluents, industrial and urban sewage, and agricultural runoff. Screening and selection were independently performed by two reviewers, with conflicts resolved by a third. RESULTS: Of 3367 studies identified, 70 met the inclusion criteria. The findings indicated that antibiotic residues, heavy metals, and microbial interactions in wastewater are key drivers of AMR development. Although WWTPs aim to reduce contaminants, they often create conditions conducive to horizontal gene transfer, amplifying resistance. Promising interventions, such as advanced treatment methods and regulatory measures, exist but require further research and implementation. CONCLUSIONS: Wastewater plays a pivotal role in AMR dissemination. Targeted interventions in wastewater management are essential to mitigate AMR risks. Future studies should prioritize understanding AMR dynamics in wastewater ecosystems and evaluating scalable mitigation strategies to support global health efforts. | 2025 | 40001375 |
| 6635 | 12 | 0.9843 | Antimicrobial resistance dashboard application for mapping environmental occurrence and resistant pathogens. An antibiotic resistance (AR) Dashboard application is being developed regarding the occurrence of antibiotic resistance genes (ARG) and bacteria (ARB) in environmental and clinical settings. The application gathers and geospatially maps AR studies, reported occurrence and antibiograms, which can be downloaded for offline analysis. With the integration of multiple data sets, the database can be used on a regional or global scale to identify hot spots for ARGs and ARB; track and link spread and transmission, quantify environmental or human factors influencing presence and persistence of ARG harboring organisms; differentiate natural ARGs from those distributed via human or animal activity; cluster and compare ARGs connections in different environments and hosts; and identify genes that can be used as proxies to routinely monitor anthropogenic pollution. To initially populate and develop the AR Dashboard, a qPCR ARG array was tested with 30 surface waters, primary influent from three waste water treatment facilities, ten clinical isolates from a regional hospital and data from previously published studies including river, park soil and swine farm samples. Interested users are invited to download a beta version (available on iOS or Android), submit AR information using the application, and provide feedback on current and prospective functionalities. | 2016 | 26850162 |
| 9076 | 13 | 0.9843 | ResiDB: An automated database manager for sequence data. The amount of publicly available DNA sequence data is drastically increasing, making it a tedious task to create sequence databases necessary for the design of diagnostic assays. The selection of appropriate sequences is especially challenging in genes affected by frequent point mutations such as antibiotic resistance genes. To overcome this issue, we have designed the webtool resiDB, a rapid and user-friendly sequence database manager for bacteria, fungi, viruses, protozoa, invertebrates, plants, archaea, environmental and whole genome shotgun sequence data. It automatically identifies and curates sequence clusters to create custom sequence databases based on user-defined input sequences. A collection of helpful visualization tools gives the user the opportunity to easily access, evaluate, edit, and download the newly created database. Consequently, researchers do no longer have to manually manage sequence data retrieval, deal with hardware limitations, and run multiple independent software tools, each having its own requirements, input and output formats. Our tool was developed within the H2020 project FAPIC aiming to develop a single diagnostic assay targeting all sepsis-relevant pathogens and antibiotic resistance mechanisms. ResiDB is freely accessible to all users through https://residb.ait.ac.at/. | 2021 | 33495705 |
| 2525 | 14 | 0.9843 | Review of antimicrobial resistance surveillance programmes in livestock and meat in EU with focus on humans. OBJECTIVES: In this review, we describe surveillance programmes reporting antimicrobial resistance (AMR) and resistance genes in bacterial isolates from livestock and meat and compare them with those relevant for human health. METHODS: Publications on AMR in European countries were assessed. PubMed was reviewed and AMR monitoring programmes were identified from reports retrieved by Internet searches and by contacting national authorities in EU/European Economic Area (EEA) member states. RESULTS: Three types of systems were identified: EU programmes, industry-funded supranational programmes and national surveillance systems. The mandatory EU-financed programme has led to some harmonization in national monitoring and provides relevant information on AMR and extended-spectrum β-lactamase/AmpC- and carbapenemase-producing bacteria. At the national level, AMR surveillance systems in livestock apply heterogeneous sampling, testing and reporting modalities, resulting in results that cannot be compared. Most reports are not publicly available or are written in a local language. The industry-funded monitoring systems undertaken by the Centre Européen d'Etudes pour la Santé Animale (CEESA) examines AMR in bacteria in food-producing animals. CONCLUSIONS: Characterization of AMR genes in livestock is applied heterogeneously among countries. Most antibiotics of human interest are included in animal surveillance, although results are difficult to compare as a result of lack of representativeness of animal samples. We suggest that EU/EEA countries provide better uniform AMR monitoring and reporting in livestock and link them better to surveillance systems in humans. Reducing the delay between data collection and publication is also important to allow prompt identification of new resistance patterns. | 2018 | 28970159 |
| 6720 | 15 | 0.9843 | Human, animal and environmental contributors to antibiotic resistance in low-resource settings: integrating behavioural, epidemiological and One Health approaches. Antibiotic resistance (ABR) is recognized as a One Health challenge because of the rapid emergence and dissemination of resistant bacteria and genes among humans, animals and the environment on a global scale. However, there is a paucity of research assessing ABR contemporaneously in humans, animals and the environment in low-resource settings. This critical review seeks to identify the extent of One Health research on ABR in low- and middle-income countries (LMICs). Existing research has highlighted hotspots for environmental contamination; food-animal production systems that are likely to harbour reservoirs or promote transmission of ABR as well as high and increasing human rates of colonization with ABR commensal bacteria such as Escherichia coli However, very few studies have integrated all three components of the One Health spectrum to understand the dynamics of transmission and the prevalence of community-acquired resistance in humans and animals. Microbiological, epidemiological and social science research is needed at community and population levels across the One Health spectrum in order to fill the large gaps in knowledge of ABR in low-resource settings. | 2018 | 29643217 |
| 9082 | 16 | 0.9843 | GeneMates: an R package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure. BACKGROUND: Horizontal gene transfer contributes to bacterial evolution through mobilising genes across various taxonomical boundaries. It is frequently mediated by mobile genetic elements (MGEs), which may capture, maintain, and rearrange mobile genes and co-mobilise them between bacteria, causing horizontal gene co-transfer (HGcoT). This physical linkage between mobile genes poses a great threat to public health as it facilitates dissemination and co-selection of clinically important genes amongst bacteria. Although rapid accumulation of bacterial whole-genome sequencing data since the 2000s enables study of HGcoT at the population level, results based on genetic co-occurrence counts and simple association tests are usually confounded by bacterial population structure when sampled bacteria belong to the same species, leading to spurious conclusions. RESULTS: We have developed a network approach to explore WGS data for evidence of intraspecies HGcoT and have implemented it in R package GeneMates ( github.com/wanyuac/GeneMates ). The package takes as input an allelic presence-absence matrix of interested genes and a matrix of core-genome single-nucleotide polymorphisms, performs association tests with linear mixed models controlled for population structure, produces a network of significantly associated alleles, and identifies clusters within the network as plausible co-transferred alleles. GeneMates users may choose to score consistency of allelic physical distances measured in genome assemblies using a novel approach we have developed and overlay scores to the network for further evidence of HGcoT. Validation studies of GeneMates on known acquired antimicrobial resistance genes in Escherichia coli and Salmonella Typhimurium show advantages of our network approach over simple association analysis: (1) distinguishing between allelic co-occurrence driven by HGcoT and that driven by clonal reproduction, (2) evaluating effects of population structure on allelic co-occurrence, and (3) direct links between allele clusters in the network and MGEs when physical distances are incorporated. CONCLUSION: GeneMates offers an effective approach to detection of intraspecies HGcoT using WGS data. | 2020 | 32972363 |
| 3776 | 17 | 0.9842 | FARME 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. | 2017 | 28077567 |
| 6694 | 18 | 0.9842 | Interconnected microbiomes and resistomes in low-income human habitats. Antibiotic-resistant infections annually claim hundreds of thousands of lives worldwide. This problem is exacerbated by exchange of resistance genes between pathogens and benign microbes from diverse habitats. Mapping resistance gene dissemination between humans and their environment is a public health priority. Here we characterized the bacterial community structure and resistance exchange networks of hundreds of interconnected human faecal and environmental samples from two low-income Latin American communities. We found that resistomes across habitats are generally structured by bacterial phylogeny along ecological gradients, but identified key resistance genes that cross habitat boundaries and determined their association with mobile genetic elements. We also assessed the effectiveness of widely used excreta management strategies in reducing faecal bacteria and resistance genes in these settings representative of low- and middle-income countries. Our results lay the foundation for quantitative risk assessment and surveillance of resistance gene dissemination across interconnected habitats in settings representing over two-thirds of the world's population. | 2016 | 27172044 |
| 6713 | 19 | 0.9842 | Human Colonization with Antibiotic-Resistant Bacteria from Nonoccupational Exposure to Domesticated Animals in Low- and Middle-Income Countries: A Critical Review. Data on community-acquired antibiotic-resistant bacterial infections are particularly sparse in low- and middle-income countries (LMICs). Limited surveillance and oversight of antibiotic use in food-producing animals, inadequate access to safe drinking water, and insufficient sanitation and hygiene infrastructure in LMICs could exacerbate the risk of zoonotic antibiotic resistance transmission. This critical review compiles evidence of zoonotic exchange of antibiotic-resistant bacteria (ARB) or antibiotic resistance genes (ARGs) within households and backyard farms in LMICs, as well as assesses transmission mechanisms, risk factors, and environmental transmission pathways. Overall, substantial evidence exists for exchange of antibiotic resistance between domesticated animals and in-contact humans. Whole bacteria transmission and horizontal gene transfer between humans and animals were demonstrated within and between households and backyard farms. Further, we identified water, soil, and animal food products as environmental transmission pathways for exchange of ARB and ARGs between animals and humans, although directionality of transmission is poorly understood. Herein we propose study designs, methods, and topical considerations for priority incorporation into future One Health research to inform effective interventions and policies to disrupt zoonotic antibiotic resistance exchange in low-income communities. | 2022 | 35947446 |