Antibiotic Resistance Characterization in Human Fecal and Environmental Resistomes using Metagenomics and Machine Learning

Antibiotic resistance is a global threat that can severely imperil public health. To curb the spread of antibiotic resistance, it is imperative that efforts commensurate with a “One Health” approach are undertaken. Given that interconnectivities among ecosystems can serve as conduits for the proliferation and dissemination of antibiotic resistance, it is increasingly being recognized that a robust global environmental surveillance framework is required to promote One Health. The ideal aim would be to develop approaches that inform global distribution of antibiotic resistance, help prioritize monitoring targets, present robust data analysis frameworks to profile resistance, and ultimately help build strategies to curb the dissemination of antibiotic resistance. The work described in this dissertation was aimed at evaluating and developing different data analysis paradigms and their applications in investigating and characterizing antibiotic resistance across different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The results in Chapter 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes.

Antibiotic resistance is a global threat that can severely imperil public health. To curb the spread of antibiotic resistance, it is imperative that efforts commensurate with a "One Health" approach are undertaken. Given that interconnectivities among ecosystems can serve as conduits for the proliferation and dissemination of antibiotic resistance, it is increasingly being recognized that a robust global environmental surveillance framework is required to promote One Health. The ideal aim would be to develop approaches that inform global distribution of antibiotic resistance, help prioritize monitoring targets, present robust data analysis frameworks to profile resistance, and ultimately help build strategies to curb the dissemination of antibiotic resistance. The work described in this dissertation was aimed at evaluating and developing different data analysis paradigms and their applications in investigating and characterizing antibiotic resistance across different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The results in Chapter 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes.