Improving our Understanding of the Ecology of Antimicrobial Resistance in Food Production using Bayesian Model and Machine Learning Approach

Antimicrobial resistance (AMR) is a critical public health issue. Infections with resistant pathogens are estimated to cause an additional 8 million hospitalization days, and methicillin-resistant Staphylococcus aureus (MRSA) infections alone caused 9,650 deaths in the US in 2011. Meat production systems are thought to contribute to the problem by harboring a reservoir of AMR that interfaces with humans either through persistence in the food chain or dissemination of wastes into the environment. Antimicrobial use in food producing animals is often cited as a driver of AMR in food production, but this blanket statement fails to recognize the extremely varied contexts in which such use occurs.Unfortunately, scientific research has been largely unable to provide consistent guidance as to which specific use practices require modification in order to protect public health. Our group has been researching antimicrobial use in food animals for over a decade. We have typically determined resistance status based on either the failure of indicator organisms to grow in antimicrobial-impregnated cultures, or on the presence of PCR-amplified fragments in sample DNA. Using these methods, we have conducted many large, well-designed prospective cohort studies of antimicrobial use and resistance in commercial settings. Despite the meticulous thought that went into these studies, we continue to grapple with anomalous and often contradictory results. One study involving feedlot cattle found weak positive associations between tetracycline exposures and resistance, while another showed increasing prevalence of tetracycline resistance in the absence of any tetracycline use. These are but a few examples from a large body of literature that can best be described as consistently inconsistent. Within this context, it is unsurprising that policy makers and producers alike are hard-pressed to formulate evidence-based policies for AMR mitigation.Given our experience in this area, we came to believe that culture- and PCR-based methods were inadequate for identifying and understanding use-resistance patterns that occur within a complex ecosystem. For example, cattle feces contain thousands of bacterial taxa. In this context, studies that generalize AMR findings gleaned from one or two indicator bacteria commit ecological fallacy. PCR-based methods may circumvent this problem, but typically focus on one or two antimicrobial drug classes, while many cross-class resistance determinants travel frequently and readily between distantly related bacteria. Given these shortcomings, we have adopted a shotgun metagenomics approach as a potential means of providing more consistent and actionable answers to the question of AMR in food production. Through two studies, we have identified over 350 unique AMR genes in cattle production systems, a degree of diversity that is impossible to identify using culture- and PCR-based approaches. We have found that different biomes - but not different cattle production systems - harbor distinct AMR profiles. Preliminary evidence also suggests that currently utilized harvest interventions not only reduce pathogen load, but may also serve to mitigate transmittance of AMR into retail beef. Further, our data show a decrease in AMR diversity throughout cattle production, perhaps indicating selective pressure on bacterial populations.These findings are immensely intriguing, yet barely scratch the surface of what we could find using more advanced statistical and computational methods. Unfortunately, these methods have only just begun to find their way into microbiome research, and have not been applied to metagenomics projects in agriculture. Specifically, there is an urgent need to apply advanced statistical and computational methods towards finding complex associations between metadata factors (such as management practices and antimicrobial use) and patterns of AMR in metagenomic sequence data - as well as an urgent need for people who can appropriately use these methods. The goal of this project is to understand drivers of AMR by uncovering complex patterns of interactions between multiple ecosystems operating at different levels - from the genes, to the bacteria, to the host, to the environment, and to the management practices utilized in food production. We propose to adapt existing Bayesian modeling and machine learning methods to achieve this goal. At this point, we have assembled all of the necessary ingredients to achieve this - metagenomic and related datasets, a validated bioninformatics pipeline, sufficient computational capacity, existing statistical methods, and individuals with expertise in how to apply them. Now begins the hard work of identifying the most appropriate and useful statistical methods for our data, and modifying them to the specific nature of metagenomic data and the specific question of AMR in food production. Therefore, the specific objectives are: Objective #1 - identify a set of tools that can be used to execute hierarchical Bayesian modeling and machine learning methods on metagenomic datasets.Objective #2 - test, optimize and validate the tools identified in Objective #1 using our own metagenomic datasets.Objective #3 - apply optimized tools to new and existing metagenomic data to uncover novel associations between genes, microbes, host, environment and management practices that influence AMR dynamics in food production systems.