DEVELOPING COMPUTATIONAL METHODS FOR SURVEILLANCE OF ANTIMICROBIAL RESISTANT AGENTS

PROJECT ABSTRACT Antimicrobial resistance is a critical public health issue. Infections with drug resistant pathogens are estimatedto cause an additional eight million hospitalization days annually over the hospitalizations that would be seen forinfections with susceptible agents. The use of antibiotics (in both clinical and agricultural settings) is being viewedas precursor for these infections and thus, is a major public health concern?particularly as outbreaks becomemore frequent and severe. However, scienti?c evidence describing the hazards associated with antibiotic useis lacking due to inability to quantify the risk of these practices. One promising avenue to elucidate this risk isto use shotgun metagenomics to identify the AMR genes in samples taken through systematic spatiotemporalsurveillance. The goal of this proposed work is to develop algorithms that will provide such a means foranalysis. The algorithms need to be scalable to very large datasets and thus, will require the developmentand use succinct data structures. In order to achieve this goal, the investigative team will develop the theoretical foundations and applied meth-ods needed to study AMR through the use of shotgun metagenomics. A major focus of the proposed work isdeveloping algorithms that can handle very large datasets. To achieve this scalability, we will create novel meansto create, compress, reconstruct and update very large de Bruijn graphs that metagenomics data in a mannerneeded to study AMR. In addition, we will pioneer the study of AMR through long read data by proposing newalgorithmic problems and solutions that use data. For example, identifying the location of speci?c genes in ametagenomics sample using long read data has not been proposed or studied. Thus, the algorithmic ideas andtechniques developed in this project will not only advance the study of AMR, but contribute to the growing domainof 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 antimicro-bial use in agriculture; (2) the effectiveness of interventions used to reduce resistant bacteria, and lastly, (3) thefactors that allow resistant bacteria to grow, thrive and evolve.A?1