Developing Computational Methods for Surveillance of Antimicrobial Resistant Agents

Principal Investigator: Christina Boucher

Sponsor: National Institutes of Health

Start Date: November 26, 2018

End Date: October 31, 2023

Amount: $2,139,795


Antimicrobial resistance is a critical public health issue. Infections with drug resistant pathogens are estimated to cause an additional eight million hospitalization days annually over the hospitalizations that would be seen for infections with susceptible agents. The use of antibiotics (in both clinical and agricultural settings) is being viewed as precursor for these infections and thus, is a major public health concern—particularly as outbreaks become more frequent and severe. However, scientific evidence describing the hazards associated with antibiotic use is lacking due to inability to quantify the risk of these practices. One promising avenue to elucidate this risk is to use shotgun metagenomics to identify the AMR genes in samples taken through systematic spatiotemporal surveillance. The goal of this proposed work is to develop algorithms that will provide such a means for analysis. The algorithms need to be scalable to very large datasets and thus, will require the development and use succinct data structures. In order to achieve this goal, the investigative team will develop the theoretical foundations and applied methods needed to study AMR through the use of shotgun metagenomics. A major focus of the proposed work is developing algorithms that can handle very large datasets. To achieve this scalability, we will create novel means to create, compress, reconstruct and update very large de Bruijn graphs that metagenomics data in a manner needed to study AMR. In addition, we will pioneer the study of AMR through long read data by proposing new algorithmic problems and solutions that use data. For example, identifying the location of specific genes in a metagenomics sample using long read data has not been proposed or studied. Thus, the algorithmic ideas and techniques developed in this project will not only advance the study of AMR, but contribute to the growing domain of big data analysis and pan-genomics. Lastly, we plan to apply our methods to samples collected from both agricultural and clinical settings in Florida. Analysis of preliminary and new data will allow us to conclude about (1) the public risk associated with antimicrobial use in agriculture; (2) the effectiveness of interventions used to reduce resistant bacteria, and lastly, (3) the factors that allow resistant bacteria to grow, thrive and evolve.

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