Kleborate, a genotyping tool with primary utility for genomes belonging to the K. pneumoniae species complex, uses a curated version of the CARD v3.0.8 antimicrobial resistance (AMR) database to screen input genomes for AMR determinants. The spreadsheet contains details for changes made to the database, including exclusions, gene name changes, sequence replacements and additional inclusions of AMR entries from ARGannot.

PanRes database of antimicrobial resistance genes

Many different collections of antimicrobial resistance genes (ARGs) have been collected and used for various purposes. In order to develop a workflow for mass screening of public metagenomes, we recently gathered up and filtered in a number of these gene collections to produce PanRes.

For details, please see the methods section in the following publication:

"ARGfinder - a pipeline for large-scale analysis of antimicrobial resistance genes and their flanking regions in metagenomic datasets" (Unpublished, submitted)

Briefly, the PanRes gene collection is gathered from a combination of other resistance gene collections into one, so each unique sequence has an "pan_" identifier (PanRes_genes). A separate table (PanRes_data) provides an overview of all the genes, their origin database and which genes cluster together in high-identity clusters.

A number of previously published collections of ARGs were used in the creation of PanRes (See references):

ResFinder (downloaded 2023-01-20, (Bortolaia et al. 2020)),
ResFinderFG (version 2.0, (Gschwind et al. 2023))
CARD (version 3.2.5, (Alcock et al. 2023))
MegaRes (version 3.0.0, (Bonin et al. 2023))
AMRFinderPlus (version 3.11/2022-12-19.1, (Feldgarden et al. 2021))
ARGANNOT (V6_July2019, (Gupta et al. 2014))
The 'CsabaPal' collection (Provided by Csaba Pál and Zoltán Farkas in November 2022, Daruka et al. 2023))
BacMet (version 1.1, (Pal et al. 2014))

Important: Use of CARD data requires permission and licensing from McMaster University. Commercial or research redistribution is normally prohibited without written permission. License info: https://card.mcmaster.ca/about

About CARD: CARD provides curated information on antibiotic resistance genes, variants, mechanisms, and resistome prediction models. It includes expert-reviewed ARO ontology, reference sequences, and detection models for RGI.

Dataset Contents:

1. FASTA Sequences
2. Nucleotide & protein sequences

3. Model types: protein homolog, protein variant, protein overexpression, rRNA gene variant Headers include: prevalence_sequence_id, ARO name, ARO accession, detection model type.

4. CARD Short Names Standardized abbreviations for AMR genes to maintain programmatic compatibility.

5. Index Files a) index-for-model-sequences.txt.gz: Fields: sequence ID, model ID, ARO term, accession, model type, species, NCBI accession, assembly type, RGI confidence, % identity, bitscore, AMR family, mechanism, drug class.

b) card-genomes.txt.gz: Lists Perfect/Strict AMR hits per genome.

c) card_prevalence.txt.gz: Prevalence statistics across chromosomes, plasmids, WGS, genomic islands.

1. KMER Databases (for RGI) Includes 61-mer and 15-mer databases for pathogen-of-origin prediction.

Use Cases: - AMR detection - Genomic annotation - Resistome prediction - Machine learning - Comparative genomics - AMR surveillance research

Citation: Alcock et al. 2023. CARD 2023: expanded curation, support for machine learning, and resistome prediction. Nucleic Acids Research 51, D690–D699.

Licensing Notice: Use or reproduction requires written permission from McMaster University. Please follow CARD licensing policies: https://card.mcmaster.ca/about

Antimicrobial resistance (AMR) remains a critical challenge in public health and research. AmrProfiler is a comprehensive tool with three specialized modules: identifying acquired AMR genes, resistance-associated point mutations, and rRNA gene mutations across nearly 18,000 bacterial species. By integrating and refining data from established databases, it provides a robust framework for AMR analysis. AmrProfiler is the first to systematically report mutations in rRNA genes, offering in-depth analysis of rRNA copy numbers and mutations—key for identifying potential rRNA-associated resistance mechanisms. Its curated database includes 7,600 unique AMR gene entries, 245 resistance-related genes, and over 4,300 mutations, alongside a detailed catalog of rRNA gene copy numbers and mutations. Supporting genome assemblies in multiple formats, AmrProfiler allows users to customize detection thresholds for AMR genes, mutations, and rRNA analysis. Validation with Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, and Staphylococcus epidermidis demonstrated that AmrProfiler accurately identified all AMR genes and mutations reported by other tools while also detecting additional resistance markers and mutations not previously recognized. By bridging AMR genotypes and phenotypes, AmrProfiler provides actionable insights that advance both research and clinical applications in antimicrobial resistance. AmrProfiler is freely available as an open-access web server without login at https://dianalab.e-ce.uth.gr/amrprofiler .

The uploaded files are :

1) amrprofiler-main.zip which is the repo from githup page (https://github.com/dianalabgr/amrprofiler)

2) three folders in zip format (databases.zip, db.zip, refseq.zip) to be unzipped inside the folder amrprofiler-main. These folders are needed for AmrProfiler to run locally. The file Readme.txt on the GitHub page explains how to run AmrProfiler locally.

This dataset contains the curated databases used by AMRProfiler and the source code. AmrProfiler integrates non-redundant AMR gene sequences and mutation records derived from multiple publicly available sources, including CARD, Reference Gene Catalog, and ResFinder.

Contents of this dataset:

✅ Source Code of AmrProfiler
✅ Curated AMRProfiler databases with non-redundant AMR gene entries.
✅ Processed metadata and annotations from various AMR databases.

Important Licensing Information:

📌 Included third-party data sources and their licenses:

• Reference Gene Catalog (NCBI) – Public domain, freely available for unrestricted use.
• ResFinder & PointFinder – Apache License 2.0, allowing free use, modification, and distribution.
• AMRProfiler Curation & Metadata – MIT License, allowing free use, modification, and redistribution under its terms.

By using this dataset, users acknowledge that they must comply with the respective licenses of each third-party database.

⚠ This dataset does NOT include raw CARD data. CARD is available for non-commercial academic research only and must be downloaded separately from its official source (https://card.mcmaster.ca/download). Users are responsible for complying with its terms of use.

The full licence of AmrProfiler can be found here (https://github.com/dianalabgr/amrprofiler/blob/main/Licence)

Pre-processed AMR data on S. aureus isolates from PATRIC database. The majority class size was decreased to reach the class ratio of 1:1 when the susceptible/resistant or resistant/susceptible class ratio exceeded 3.5.

Additional file 1: Pseudomonas aeruginosa ARDaP-compatible antimicrobial resistance variant database.

This study comprehensively investigates antimicrobial resistance (AMR) in Hong Kong across clinical, food, animal, and environmental reservoirs using a One Health approach. The data files were used to determine the current situation of the resistome profile in Gram-negative bacteria isolated from food and clinical isolates in Hong Kong. Genome analyses were conducted together with genome data downloaded from the NCBI database. The two Excel files were processed data, providing summaries of the genome analysis by sample types, and the zip file contained the raw data used in the analysis. Downloaded sequences were not included in the zip file.

The data set includes relative abundances of antimicrobial resistance genes in urban karst groundwater systems in western Kentucky. This dataset is not publicly accessible because: This data was generated and is being stored with the first author, a non-EPA researcher. It can be accessed through the following means: This data can be accessed by emailing the first author, Rachel Kaiser at rakaiser42@tntech.edu. Format: Excel spreadsheets with columns for samples, sites, collection date and relative abundances. This dataset is associated with the following publication: Rachel , K., J. Polk, T. Datta, S. Keely, N. Brinkman, R. Parekh, and G. Agga. Occurrence and Prevalence of Antimicrobial Resistance in Urban Karst Groundwater Systems Based on Targeted Resistome Analysis. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 874: 162571, (2023).

Veterinary-Human AMR Data Exchange Market Outlook

According to our latest research, the global Veterinary-Human AMR Data Exchange market size reached USD 1.23 billion in 2024, with a robust compound annual growth rate (CAGR) of 12.8% projected through 2033. By the end of the forecast period, the market is expected to achieve a value of USD 3.66 billion. The primary growth factor for this market is the escalating threat of antimicrobial resistance (AMR), which has necessitated seamless data integration between veterinary and human health sectors to enhance surveillance, policy-making, and clinical interventions.

The growth of the Veterinary-Human AMR Data Exchange market is primarily driven by the increasing global prevalence of antimicrobial resistance, which poses a significant threat to both human and animal health. This has led governments, international health bodies, and private sector stakeholders to invest heavily in integrated data exchange platforms. These platforms enable real-time sharing and analysis of AMR data, helping to identify emerging resistance patterns, facilitate timely interventions, and support the development of targeted antimicrobial stewardship programs. Furthermore, the growing adoption of digital health technologies in both veterinary and human healthcare settings is accelerating the deployment of advanced data exchange solutions, making data-driven decision-making more accessible and effective.

Another crucial growth driver is the rising awareness and regulatory mandates surrounding One Health initiatives. The One Health approach emphasizes the interconnectedness of human, animal, and environmental health, thereby underscoring the need for integrated AMR surveillance systems. Regulatory agencies, such as the World Health Organization (WHO) and the World Organisation for Animal Health (OIE), are advocating for the harmonization of AMR data collection and exchange protocols. This has prompted the development of interoperable software, hardware, and service solutions that can seamlessly bridge the gap between veterinary and human health data repositories. The implementation of these systems is further supported by funding from international organizations and public-private partnerships, which are fostering innovation and market expansion.

Technological advancements in big data analytics, artificial intelligence, and cloud computing are also propelling the Veterinary-Human AMR Data Exchange market forward. These technologies enable the aggregation, normalization, and analysis of vast and diverse AMR datasets from multiple sources, including surveillance networks, diagnostic laboratories, and prescription databases. The integration of genomic data into AMR surveillance has further enhanced the precision and timeliness of resistance detection, allowing for more effective outbreak containment and resource allocation. As a result, stakeholders across the healthcare continuum are increasingly adopting sophisticated data exchange platforms to support research, clinical decision-making, and policy formulation.

From a regional perspective, North America currently dominates the Veterinary-Human AMR Data Exchange market, owing to its advanced healthcare infrastructure, strong regulatory framework, and substantial investments in digital health technologies. Europe follows closely, driven by robust public health initiatives and cross-border data sharing agreements. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rising awareness of AMR, expanding veterinary and human healthcare sectors, and increasing government support for One Health initiatives. Latin America and the Middle East & Africa are also showing steady progress, albeit at a slower pace, due to improving healthcare infrastructure and growing international collaborations.

Component Analysis

The Component segment of the Veterinary-Human AMR Data Exchange market is categorized into soft

Increasing antimicrobial resistance (AMR) poses a substantial threat to global health and economies, which has led many countries and regions to develop AMR National Action Plans (NAPs). However, inadequate logistical capacity, funding, and essential information can hinder NAP policymaking, especially in low-to-middle-income countries (LMICs). Therefore, major gaps exist between aspirations and actions, such as fully operationalized environmental AMR surveillance programs in NAPs. To help bridge knowledge gaps, we compiled a multilingual database that contains policy guidance from 146 countries composed of NAPs, internal reports, and other guidance documents on AMR mitigations, including environmental considerations. Leveraging this database, we developed an AMR-Policy GPT, a large language model with advanced retrieval-augmented generation capabilities. This prototype model can search and summarize evidence from plans, metadata, and technical knowledge and provide traceable references from global document databases. It was then manually validated to show its proficiency in accurately managing diverse inquiries while minimizing misinformation. Overall, the AMR-Policy GPT offers a prototype that, with the deepening of its database and further road testing, has the potential to support inclusive, evidence-informed AMR policy guidance to support governments, research, and public agencies. A conversational version of our prototype is available at www.liuhuibot.com/amrpolicy.

DRIAMS Database of ResIstance against Antimicrobials with MALDI-TOF Mass Spectrometry

Large Swiss dataset of **300k samples of 804 unique pathogens - bacteria & fungi **- with their sensitivity / resistance to 52 different antimicrobials.

For each site, the data consists of MALDI-TOF mass spectra in the form of .txt files and a meta-data file. (i) The meta-data, incl. species and antimicrobial resistance corresponding to each spectra, is part of the id folder (ii) The remaining folders store the MALDI-TOF mass spectra in various stages of preprocessing: raw all spectra as extracted from the MALDI-TOF MS instrument, preprocessed all spectra after the application of an established preprocessing pipeline and binned_6000 all spectra after the application of an established preprocessing pipeline and binning along the mass-to-charge-ratio axis with a bin size of 3Da, resulting in 6000 feature bins.

For details on the dataset extraction, quality control, preprocessing and properties, please refer to the Methods section in the preprint corresponding to the publication https://doi.org/10.1101/2020.07.30.228411.

I've dl the original from https://doi.org/10.5061/dryad.bzkh1899q It takes 3-4 hours to dl a 153Gb zip. Open the zip and tar files well over 300-400 Gb. Removed the raw and pre-processed files.

What's left are the features of 804 unique bacteria & fungi. Each pathogen has 6k bins of mass spec, usually used for pathogen identification in clinical labs. The labels are the actual sensitivity / resistance to 52 antimicrobials for each pathogen.

Article related to this dataset

Please NOTE: "...many proteins causing resistance are beyond the effective mass range of MALDI-TOF mass spectra. For example, the penicillin-binding protein in S. aureus has a mass of approximately 76,400 Da35 ,beta-lactamases in E. coli and K. pneumoniae weigh approximately 30,000 Da36–39, and the E. coli outer membrane porin OmpC weighs approx. 40,300 Da4..."

The database contains the evidence presented by the Data Visualization tool (available on EFSA website) accompanying the publication of the 2015 European Union Summary Report on antimicrobial resistance (AMR). Data correspond to occurrence of resistance in Salmonella from animals and humans, occurrence of resistance in E.Coli in animals and prevalence of ESBL-producing E.coli in animals and meat, in EU Member States.

Neisseria gonorrhoeae antimicrobial resistance (AMR) surveillance is essential for tracking the emergence and spread of AMR strains in local, national and international populations. This is crucial for developing or refining treatment guidelines. N. gonorrhoeae multiantigen sequence typing (NG-MAST) is beneficial for describing the molecular epidemiology of gonococci at national and international levels. Elucidation of AMR determinants to β-lactam drugs, is a means of monitoring the development of resistance. In Ghana, little is known about the current gonococcal AMR prevalence and no characterization of gonococcal isolates has been previously performed. In this study, gonococcal isolates (n = 44) collected from five health facilities in Ghana from 2012 to 2015, were examined using AMR testing, NG-MAST and sequencing of penA. High rates of resistance were identified to tetracycline (100%), benzylpenicillin (90.9%), and ciprofloxacin (81.8%). One isolate had a high cefixime MIC (0.75 μg/ml). Twenty-eight NG-MAST sequence types (STs) were identified, seventeen of which were novel. The isolate with the high cefixime MIC contained a mosaic penA-34 allele and belonged to NG-MAST ST1407, an internationally spreading multidrug-resistant clone that has accounted for most cefixime resistance in many countries. In conclusion, AMR testing, NG-MAST, and sequencing of the AMR determinant penA, revealed high rates of resistance to tetracycline, benzylpenicillin, and ciprofloxacin; as well as a highly diverse population of N. gonorrhoeae in Ghana. It is imperative to continue with enhanced AMR surveillance and to understand the molecular epidemiology of gonococcal strains circulating in Ghana and other African countries.

AMR Interoperability Platform Market Outlook

According to our latest research, the global AMR Interoperability Platform market size reached USD 1.34 billion in 2024, reflecting robust adoption across healthcare and laboratory environments. The market is projected to expand at a CAGR of 13.2% from 2025 to 2033, with the total value forecasted to reach USD 4.19 billion by 2033. This growth is fueled by the urgent need for seamless data integration and exchange to combat antimicrobial resistance (AMR) globally, as healthcare systems increasingly prioritize interoperability for effective surveillance and response.

The primary growth driver for the AMR Interoperability Platform market is the intensifying global threat posed by antimicrobial resistance, which has prompted governments and health organizations to invest in advanced digital infrastructure. Interoperability platforms are crucial for enabling the real-time sharing and analysis of AMR data across disparate systems, including hospitals, laboratories, and public health agencies. The increasing frequency of AMR outbreaks and the need for coordinated responses have made the integration of electronic health records (EHR), laboratory information management systems (LIMS), and surveillance databases a critical priority. The market benefits from heightened regulatory mandates and funding initiatives aimed at digitizing healthcare, particularly in developed economies.

Another significant factor propelling market growth is the rapid technological advancement in healthcare IT solutions. The evolution of cloud computing, artificial intelligence, and big data analytics has revolutionized how AMR data is collected, stored, and interpreted. Modern AMR interoperability platforms leverage these technologies to facilitate seamless data exchange, automate reporting, and provide actionable insights for clinicians and policymakers. The integration of advanced cybersecurity protocols ensures data integrity and patient privacy, further increasing user confidence and adoption rates. Moreover, the growing trend of remote diagnostics and telemedicine has amplified the demand for interoperable platforms that can support decentralized healthcare delivery.

The expansion of collaborative networks among healthcare providers, research institutes, and government agencies also significantly boosts the AMR Interoperability Platform market. Cross-sector partnerships are increasingly common, as stakeholders recognize the need for a unified approach to AMR surveillance and management. These collaborations drive the standardization of data formats and protocols, making it easier to aggregate and analyze information on a global scale. The proliferation of public-private partnerships and international health initiatives, such as the Global Antimicrobial Resistance Surveillance System (GLASS), underscores the strategic importance of interoperable platforms in achieving global health security objectives.

From a regional perspective, North America continues to dominate the AMR Interoperability Platform market, accounting for the largest share in 2024, followed by Europe and the Asia Pacific. The United States, in particular, benefits from a mature healthcare IT ecosystem, extensive funding for AMR research, and stringent regulatory frameworks that promote interoperability. Europe is witnessing accelerated growth due to the European Union's coordinated AMR action plans and investments in digital health infrastructure. Meanwhile, the Asia Pacific region is emerging as a high-growth market, driven by increasing healthcare digitization in countries like China and India and rising awareness about AMR. Latin America and the Middle East & Africa are gradually adopting interoperability platforms, with international aid and governmental initiatives playing a pivotal role in market penetration.

Component Analysis

The component segment of the AMR Interoperability Platform market comprises software, hardware, and services, each playing a unique role in facilitating seamless data integration and exchange. Software solutions form the backbone of interoperability platforms, enabling the aggregation, standardization, and analysis of AMR data from diverse sources. These solutions are designed to interface with various electronic health record systems, laboratory management software, and surveillance databases, ensuring that critical data can be accessed and interpreted in real time. The demand for robu

House flies (Musca domestica L.) are a global pest ubiquitous in urban and agricultural settings. Their dependence on microbe-rich substrates for development, as well as ability to acquire and transmit pathogenic and antimicrobial resistant (AMR) bacteria, make house flies a risk to human and animal health. Large livestock operations, like confined cattle, are environments which are conducive to both house flies and developing AMR due to large accumulations of animal feed and waste. However, little is known about what factors influence bacterial abundances and AMR prevalence carried by house flies in confined cattle operations. Adult house flies (n=6/fly sex/location) were collected on alternating weeks mid-August through early October of 2019 from a dairy and beef feedlot cattle operation in each of three Kansas counties (Riley, Marion, and Washington). We enumerated colony forming units (CFUs) of culturable aerobic bacteria and suspected coliforms (SC) from house fly homogenates on nonselective (tryptic soy agar, TSA) and selective (violet-red bile agar, VRBA) media to investigate factors, such as fly sex, farm type, location, and climate, which may be associated with bacterial abundances carried by house flies. Further, we screened unique morphotypes of SC isolates for tetracycline (Tet) resistance, then tested for additional resistance to florfenicol (Flo), enrofloxacin (Enr) ceftiofur (Cef), and ampicillin (Amp) to identify multi-drug resistant (MDR) isolates. AMR isolates were identified via 16S rRNA Sanger sequencing or, in select cases, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS).Resources in this dataset:Resource File: Raw_fly_CFU_counts.xlsxResource description: Raw CFU counts for culturable aerobic bacteria (TSA) and suspected coliforms (VRBA) cultured from replica plating of ten-fold serially diluted house fly homogenates.Resource File: Raw_daily_avg_climate_Jul-Oct 2019.xlsxResource description: Raw climate data downloaded from Kansas Mesonet weather stations for sampling period.Resource File: Metadata_bacterial_isolates.xlsxResource description: Spreadsheet gives information linking individual isolate (Isolate ID #) data resources with which fly they originated from and other collection information (fly sex, farm type, collection date, county).Resource File: Raw_isolates_disk_susceptibility.xlsxResource description: Measured inhibition zones (in millimeters) of individual isolates which underwent disk susceptibility testing against 5 antibiotics (Tet, Flo, Enr, Cef, Amp).Resource file: Raw_isolates_MALDI-TOF_outputs.xlsxResource description: Spreadsheet of best and second-best matches of Bruker MALDI Biotyper Identification Results for individual isolates (Sample ID). Each isolate was measured in duplicate.All trimmed Sanger sequence reads of AMR isolates are publicly available at GenBank (PQ636534 - PQ636762).The code repository for 16S sequence analysis for this project can be found here:https://github.com/vlpickens04/Sanger_Phred_Code

The distribution of antimicrobial resistance (AMR) genes for the EU and European Free Trade Association (EFTA) countries data was obtained from the global Vibrio parahaemolyticus genomes based on a collection of nearly 10,000 genomes. Some of the strains are from the collection of prof. Jaime Martinez-Urtaza (Department of Genetics and Microbiology, Universitat Autònoma de Barcelona) or are part of ongoing studies to expand the genome collection; other genomes were retrieved from the European Nucleotide Archive (ENA at https://www.ebi.ac.uk/ena/browser/home) and the National Center for Biotechnology Information (NCBI) [GenBank at https://www.ncbi.nlm.nih.gov/genbank/; RefSeq at https://www.ncbi.nlm.nih.gov/refseq/; SRA at https://www.ncbi.nlm.nih.gov/sra]. For detection of AMR genes, a resistance genes detection pipeline based on one of the standard databases (CARD database at https://card.mcmaster.ca/) was used. The phylogenetic tree was prepared and includes the reference genome from Japan "Osaka" as reference. The RIMD 2210633 strain has been added as the global reference strain which has been historically used for all the phylogenetic analysis of V. parahaemolyticus. The metadata includes the source of the strain, i.e., country, origin (clinical, environmental or unclear), date of isolation, and subtype. The antibiotic-resistant genes are shown as present, absent or not applicable. To build the ARGs European V. parahaemolyticus tree, the Parsnp tool, a fast core-genome multi-aligner and SNP detector, from the Harvest suite was used (Treangen et al., 2014). Parsnp calculates the MUMi distances between the reference genome (RIMD_2210633) and each one of the 152 genomes used in this study. The resulting Newick formatted core genome SNP tree was then uploaded onto the webtool I-Tol (Letunic and Bork, 2021), midpoint rooted and the metadata of the samples was incorporated.

The accession IDs for the genomes included in the metadata are accessible in the following databases according to the first characters:
* GCA: GenBank (https://www.ncbi.nlm.nih.gov/genbank/)
* GCF: RefSeq (https://www.ncbi.nlm.nih.gov/refseq/)
* ERR: ENA (https://www.ebi.ac.uk/ena/browser/home)
* SRR: SRA (https://www.ncbi.nlm.nih.gov/sra)

References

Letunic I and Bork P, 2021. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res, 49:W293-w296. doi: 10.1093/nar/gkab301

Treangen TJ, Ondov BD, Koren S and Phillippy AM, 2014. The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes. Genome Biol, 15:524. doi: 10.1186/s13059-014-0524-x

South Korea LI: ME: AMR: DB data was reported at 95.800 % in Sep 2025. This records a decrease from the previous number of 96.100 % for Aug 2025. South Korea LI: ME: AMR: DB data is updated monthly, averaging 96.400 % from Jan 2023 (Median) to Sep 2025, with 33 observations. The data reached an all-time high of 96.800 % in Apr 2023 and a record low of 94.300 % in Feb 2025. South Korea LI: ME: AMR: DB data remains active status in CEIC and is reported by Korea Life Insurance Association. The data is categorized under Global Database’s South Korea – Table KR.RG: Life Insurance: IFRS 17: Management Efficiency.

Background Livestock systems have been proposed as a reservoir for antimicrobial-resistant (AMR) bacteria and AMR genetic determinants that may infect or colonise humans, yet quantitative evidence regarding their epidemiological role remains lacking. Here we used a combination of genomics, epidemiology and ecology to investigate patterns of AMR gene carriage in Escherichia coli, regarded as a sentinel organism. Methods We conducted a structured epidemiological survey of 99 households across Nairobi, Kenya, and whole genome sequenced E. coli isolates from 311 human, 606 livestock, and 399 wildlife faecal samples. We used statistical models to investigate the prevalence of AMR carriage and characterise AMR gene diversity and structure of AMR genes in different host populations across the city. We also investigated house-hold level risk factors for exchange of AMR genes between sympatric humans and livestock. Findings We detected 56 unique acquired genes along with 13 point mutations present in variable proportions in human and animal isolates, known to confer resistance to nine antibiotic classes. We find that AMR gene community composition is not associated with host species, but AMR genes were frequently co-located, potentially enabling the acquisition and dispersal of multi-drug resistance in a single step. We find that whilst keeping livestock had no influence on human AMR gene carriage, the potential for AMR transmission across human-livestock interfaces is greatest when manure is poorly disposed of and in larger households. Conclusions Findings of widespread carriage of AMR bacteria in human and animal populations, including in long-distance wildlife species, in community settings, highlight the value of evidence-based surveillance to address antimicrobial resistance on a global scale. Our genomic analysis provided in-depth understanding of AMR determinants at the interfaces of One-Health sectors that will inform AMR prevention and control.

A multi-fasta file of unique alleles of ARGs in the AMRFinder database

The global antibiotic-resistant infection treatment market is booming, projected to reach $45 billion by 2033. Learn about market drivers, trends, restraints, key players (Pfizer, Johnson & Johnson, Merck), and regional analysis in this comprehensive market overview. Explore the challenges and opportunities in combating drug-resistant bacteria and the development of new therapies.