Recipient Organization
UNIVERSITY OF VERMONT
(N/A)
BURLINGTON,VT 05405
Performing Department
(N/A)
Non Technical Summary
Antimicrobial resistance (AMR) is a critical global One Health problem impacting animal, human and environmental health. The World Health Organization declares antimicrobial resistance one of the top 10 global public health threats facing humanity (WHO, 2021). The US Department of Agriculture (USDA) has developed an Antimicrobial Resistance Action Plan and recognizes "controlling antibiotic resistance requires the adoption of a "One-Health" approach to disease surveillance that recognizes that resistance can arise in humans, animals, and the environment" (USDA). Emergence of resistant bacterial pathogens causing disease in animals, including dairy cattle results in decreased animal well-being and increased production costs. Antimicrobial resistance is an animal health issue, and increased disease surveillance is needed because we do not know the extent of the problem. A challenge is the costly and time-consuming nature of comprehensive antimicrobial resistance surveillance. This limits our ability to understand the extent of the problem and the impact of interventions. Pathogen surveillance has advanced in the genomic era. Recent advances in molecular biology techniques such as high-throughput or next generation nucleic acid sequencing have revolutionized strain typing, infectious disease epidemiological surveillance, and epidemiologic studies of pathogen dynamics within and between populations. These molecular methods have been applied to antimicrobial resistance surveillance, which differs from pathogen detection surveillance, as the goal is to identify antimicrobial resistance drivers to mitigate antimicrobial resistance development and spread. There are gaps in our knowledge and understanding of how AMR surveillance using high throughput metagenomic methods (i.e., characterizing the farm resistome) can inform farmer and veterinarian knowledge, opinion and actions related to antimicrobial resistance and antimicrobial use. This sabbatical project addresses those gaps with three objectives.During my sabbatical year, for the first objective, I will complete a field study on dairy farms where I will collect data on antimicrobial use practices for treatment of calf health issues and collect samples from calves and humans on the participating farms. I will focus on dairy calf health management because this is one of the most frequent reasons for antibiotic use on dairy farms and an age group where human contact with sick calves has previously been demonstrated to result in transmission of antibiotic resistant pathogens. Multiple samples will be collected over time to quantify antimicrobial resistant pathogen prevalence, possible changes in prevalence over time and possible spread of antimicrobial resistant pathogens between humans and calves on the farms. There are two general methods to identify antimicrobial resistance among potential bacterial pathogens from animals, humans, and the environment. The first is traditional culture-based methods, where the bacteria are isolated from the samples and then tested for susceptibility to a panel of antibiotics. This method is the standard for identification of antimicrobial resistant organisms, presumed to maximize diagnostic accuracy (sensitivity and specificity), yet culture-based susceptibility testing is labor intensive, costly and requires days to complete. The alternative is genetic sequence-based detection methods. Antimicrobial resistance is genetically encoded, and the presence of antimicrobial resistance markers can be identified from samples by identification of the gene sequences. This approach has the potential advantage for high-throughput, large-scale surveillance of animals, humans, and their environment. This approach also allows determination from both culturable and unculturable bacteria in samples. During the sabbatical, I will apply both approaches to the samples collected from animals, humans and the farm environment, with the goal of quantifying the diagnostic characteristics of gene sequence-based antimicrobial resistance detection methods. For the second objective, I will use the empirical data collected from the field study to develop a model of antimicrobial resistance spillover between humans and animals on dairy farms. The incorporation of metagenomics antimicrobial resistance data into models of pathogen spread between humans and animals on farms is a novel advance of epidemiological modeling and molecular epidemiology tools. Combining molecular epidemiology and contact or social network analysis into socio-molecular epidemiology has the potential to advance antibiotic resistance control practices. Under this objective, I will improve our understanding of how molecular genomics data can improve our understanding of the maintenance and spread of antimicrobial resistant pathogens on dairy farms. For the third objective, I will describe how monitoring antibiotic resistance on dairy farms may influence farmer attitudes and behaviors related to the problem of antibiotic resistance. Under this objective I will improve our understanding of how farmers and veterinarians might use genomic data that quantifies antimicrobial resistance on dairy farms. I will generate qualitative data that will help inform best practices for reporting antimicrobial resistance surveillance data to farmers and veterinarians and determine how these data may influence farmer and veterinarian attitude and behavior regarding antibiotic use. The emerging advanced molecular genetic approaches to antimicrobial resistance surveillance have the potential to increase our ability monitor antimicrobial resistance because the methods are faster and less labor intensive. This would allow for more frequent and more comprehensive surveillance. It is unclear if the availability of these tools would change farmer and veterinarian attitudes and behaviors regarding antimicrobial resistance and antibiotic use. This project will address that gap in knowledge, and will contrbute to our ability to mitigate antimicrobial resistance in agricultural production systems, with a focus on small- to medium-sized dairy farms.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Goals / Objectives
The goals of this sabbatical project are to advance my knowledge, skills, and experience in surveillance and quantification of antimicrobial resistant pathogens on dairy farms using next-generation high-throughput metagenomic sequencing methods. I am specifically interested in understanding methods to quantify the spread of antimicrobial resistant pathogens between cattle and humans on dairy farms, and to advance our understanding of how to combine metagenomic surveillance methods and contact network models in a "socio-molecular" epidemiological framework. I will also explore the application of qualitative research methods to understand dairy farmer and veterinarian attitudes and behaviors. The specific objectives of this one-year sabbatical project are to:1. Quantify the frequency of antimicrobial resistance spillover between humans and cattle on small to medium sized dairy farms using longitudinal metagenomic surveillance data.2. Implement a multi-host (humans and cattle) network epidemiological model of antimicrobial resistance spillover to predict the frequency of transfer of common gene sequence variants for antimicrobial resistance and taxonomic marker genes.3. Describe the degree of influence of antimicrobial resistance surveillance reporting on farmer and veterinarian attitudes and behaviors related to antimicrobial resistance and antibiotic use.
Project Methods
To quantify the frequency of antimicrobial resistance spillover between humans and cattle on small to medium sized dairy farms I will extend our current sample collection approaches for surveillance of AMR. In this project I will enroll 10 commercial Vermont dairy farms in longitudinal study design. These farms will be a non-probability convenience sample of dairy farms recruited from farms that have successfully collaborated in past research projects. Each farm will be visited three times at approximately 3-week intervals. Samples collected at each farm visit will include bulk tank milk (i.e., milk filter) sample (n=1 per farm per visit), pooled calf fecal samples from at least 3 randomly selected calves from each of two age groups (2 - 4 weeks of age and 5 to 7 weeks of age, n=2 samples per farm per visit) human nasal swab and fecal samples (average 2 people per farm repeated at each farm visit) from humans that administer antimicrobials or work with sick animals that receive antimicrobials, (n=4 samples per farm per visit), and two water samples, one from the farm water source (e.g., drop house in milking parlor) and one from an outdoor environmental water source. Samples will be transported to the lab and DNA extracted using established methods using the ZymoBIOMICS™ DNA Microprep Kit (Zymo Research Corporation, Irvine, CA) with bead beating following manufacturer's instructions for each of the sample types. A series of no DNA negative sample collection and extraction controls will be processed for sequencing in parallel with sets of the farm samples. All samples will be stored until the end of the sampling period and randomized for extraction and sequencing to reduce batch effects. All DNA sequencing will be conducted at the University of Vermont Advanced Genome Technologies Core Facility. Extracted DNA will be submitted for quality and yield analysis using Qubit® and Nanodrop® systems, followed by quality control assessment using HS DNA Bioanalyzer®. Sequencing libraries will be constructed using Illumina DNASeq-Chipseq® paired-end library prep kits. After sequencing, raw data will be trimmed and demultiplexed into FASTQ files and then submitted to quality control using the FastQC software. Sequencing data will be cleaned and curated for downstream analyses and deposited in NCBI. For downstream analysis to characterize microbial taxa in the samples, FASTQ files will be submitted to a taxonomic sequence classifier (e.g., Kraken2 using Kraken2 database). For downstream analysis targeting antimicrobial resistance genes (i.e., the resistome), FASTQ files will be submitted to a customized bioinformatic pipeline AmrPlusPlus, using the MEGARes database for antimicrobial drugs or to the ResFinder 4.0 platform. Alternative approaches for analyzing and reporting the data will be explored during my sabbatical visit to the Research Group for Genomic Epidemiology, Danish Technical University.To develop and implement a network model I will use our observed metagenomic resistome data to inform a contact network for antibiotic resistance transmission between calves and their caretakers. I will develop a structured Susceptible-Infectious-Recovered (SIR) resistome network model informed by observed and reported contact patterns on the enrolled farms where the resistome data informs the status of each individual node in the network. I will explore the metagenomic data for the ability to incorporate strain diversity in the models based on AMR sequence variants so that we can identify potential AMR gene variation among hosts within the network.To describe the degree of influence of antimicrobial resistance surveillance reporting on farmer and veterinarian attitudes and behaviors related to antimicrobial resistance and antibiotic use, I will conduct interviews and focus groups following the approaches used by the population medicine researches at the University of Guelph. Briefly, building on information from a literature review, I will develop a semi-structured interview guide building on previously developed materials, with modifications to address the primary questions regarding use of antimicrobial resistance surveillance data. I will conduct three to five focus groups with a target study population of 30 dairy farmers from Vermont. I will coordinate with veterinarians to recruit farmers by personal or email invitation. I will also conduct one to two focus groups with 10 to 16 dairy practice veterinarians from Vermont. The focus groups will be audio recorded and transcribed. Thematic analysis will be used to identify, analyze, and report patterns within the data.For literature reviews, I will follow established guidelines for systematic reviews of observational studies, and structure my research questions based on the guidelines described by Munn et al. (2015), who provide an appraisal checklist for studies reporting prevalence data. I will follow the Strengthening the Reporting of Observational Studies in Epidemiology - Veterinary (STROBE-Vet) guidelines for reporting the results.During the sabbatical year I will make an effort to communicate my research findings with my collaborators in the laboratory settings, and with other scientists at the institutions where I will be a visiting scientist. I will also present our findings at scientific conferences. The measures of success of the project will be the submission of abstracts for presentations at scientific conferences, the submission of publications to the peer-review literature, and the completion of one grant proposal for extramural funding.