Antibiotic resistance is critical to global health. This thesis examines the relationship between antibiotic resistance and genomic variations in E. coli from wastewater. E. coli is of interest as it causes urinary tract and other infections. Wastewater is a good source because it is a melting pot for E. coli from diverse origins.
The research delves into two key aspects: including or excluding antibiotic resistance data and the level of granularity in representing genomic variations. The former is important because there is more genomic data than antibiotic resistance data. Consequently, relying solely on genomic data, this thesis studies positive selection in E. coli to identify mutations and genes favored by evolution. This study demonstrates the preferential selection of known antibiotic resistance genes and mutations, particularly mutations located on functionally important locations of outer membrane porins, and may hence have a direct effect on structure and function.
Encouraged by these results, the study was expanded to include antibiotic resistance data and to examine genomic variations at three resolution levels: single mutations, unitigs (genome words) that may contain multiple mutations, and whole coding genome using machine learning classifier models that capture dependencies among multiple mutations and other genomic variations. Representation of single mutations detects well-known resistance mutations as well as potentially novel mechanisms related to biofilm formation and translation. By exploring larger genomic units such as genome words, the analysis confirms the findings from single mutations and additionally uncovers joint mutations in both known and novel genes. Finally, machine learning models, including AI language models, were trained to predict antibiotic resistance based on the whole coding genome. This achieved an accuracy of over 90% in predicting antibiotic resistance when sufficient data were available.
Overall, this thesis unveils new antibiotic resistance mechanisms, conducts one of the largest studies of positive selection in E. coli, and stands out as one of the pioneering studies that utilizes AI language models for antibiotic resistance prediction.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:87637 |
| Date | 24 October 2023 |
| Creators | Malekian Boroujeni, Negin |
| Contributors | Schroeder, Michael, Ploy, Marie-Cécile, Berendonk, Thomas U., Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 10.3390/ijms22116063, 10.1038/s41598-022-11432-0 |
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