AN INTEGRATIVE EXPERIMENTAL AND COMPUTATIONAL FRAMEWORK FOR THE GENOME-SCALE FLUX ANALYSIS OF ANTIBIOTIC RESISTANCE

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dc.contributor.advisorDwyer, Daniel Jen_US
dc.contributor.authorMack, Seanen_US
dc.contributor.departmentChemical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2020-07-14T05:31:37Z
dc.date.available2020-07-14T05:31:37Z
dc.date.issued2020en_US
dc.description.abstractThe prevalence of antibiotic-resistant bacterial pathogens demands the development of novel therapeutic approaches. An attractive target is metabolism, where the impact of antibiotics and resistance remains poorly understood. Numerous omics-driven studies have identified a metabolic response due to antibiotic stress and suggested that metabolism adjusts to accommodate the genetic burden of resistance. Arising from these data is the hypothesis that context-specific modification of metabolism is a key component of antibiotic resistance and stress. Further exploration of the relationship between metabolism, antibiotic stress, and resistance is clearly needed. To elucidate metabolic signatures of antibiotic resistance, we analyzed the metabolic behaviors of wild-type and resistant strains of Escherichia coli through a combined transcriptomic and fluxomic analysis. Specifically, we compared wild-type E. coli to isogenic strains expressing integrated copies of tetRA and dhfr resistance genes, respectively under normal and antibiotic stress conditions. From comprehensive genome-scale (GS) flux predictions, we observed a resistance-associated metabolic phenotype as well as mechanism- and target-specific metabolic shifts. Furthermore, we identified a distinct metabolic response to antibiotic stress in both resistant strains. To improve our computational framework, we developed NetRed, NetRed-MFA, and NetFlow, each designed to reduce complexity of GS flux analysis. Through lossless reduction of genome-scale models (GSMs), NetRed generated a comprehensive minimal model for aerobic and anaerobic growth in E. coli and rapidly elucidated the mechanism driving artemisinin production in yeast. NetRed-MFA extended the original algorithm by incorporating full carbon mapping to generate reduced models for 13C metabolic flux analysis. NetFlow leveraged GS carbon mapping to isolate the major carbon flows through a core network and GSM; from the GSM subnetwork, we identified a mechanistic relationship between a triple-knockout and increased lycopene production in E. coli. Our resistance work represents the first application of quantitative flux analysis to study the metabolism of resistant bacteria and should provide significant insight into the role of metabolic adaptation in antibiotic resistance. The developed tools each dramatically improve the interpretation of GS flux predictions and the mechanistic understanding of metabolic perturbations. Taken together, this dissertation describes a comprehensive framework for the prediction, comparison, and interpretation of altered metabolic states.en_US
dc.identifierhttps://doi.org/10.13016/zdy9-ootr
dc.identifier.urihttp://hdl.handle.net/1903/26281
dc.language.isoenen_US
dc.subject.pqcontrolledBioengineeringen_US
dc.subject.pqcontrolledBioinformaticsen_US
dc.subject.pqcontrolledMolecular biologyen_US
dc.subject.pquncontrolledAntibiotic Resistanceen_US
dc.subject.pquncontrolledComputational Biologyen_US
dc.subject.pquncontrolledFlux Analysisen_US
dc.subject.pquncontrolledMetabolic Modelingen_US
dc.subject.pquncontrolledMetabolismen_US
dc.subject.pquncontrolledNetwork Reductionen_US
dc.titleAN INTEGRATIVE EXPERIMENTAL AND COMPUTATIONAL FRAMEWORK FOR THE GENOME-SCALE FLUX ANALYSIS OF ANTIBIOTIC RESISTANCEen_US
dc.typeDissertationen_US

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