{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/393684"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/393684","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Studying plasmid relatedness and evolution through structural variation","abstract":"Bacterial cells contain the chromosome (or chromosomes), plus several small DNA molecules which occur at a broad copy number range (from none to potentially hundreds), and replicate independently, called plasmids. They are genetically extremely diverse, and in many cases are able to spread \"horizontally\" between unrelated cells. The ability to spread, while carrying genes coding for locally adaptive functions (e.g. antibiotic resistance, inter-bacterial warfare, virulence), explains the critical role plasmids play in bacterial evolution. Dense sequencing in laboratories and hospitals clearly show that structural changes (gene gain/loss, inversions) occur at comparable or higher rates than SNPs. This presents two challenges for analysis of plasmids. First, dramatic structural changes make it hard to define evolving units for further study (species equivalents). Second, the sophisticated maximum likelihood and Bayesian approaches used in phylogenetics are not available, as we do not have good evolutionary models for plasmid-style genome evolution. In this thesis, I solve the first problem by using rearrangement distances between genomes to identify plausible species-equivalents. Plasmids are studied at a coarse level, as a sequence of signed integers (representing genes or aligned blocks), and the distance between two plasmids is the minimum number of rearrangement events between them. I introduce a software workflow pling, which builds a network of relatedness between plasmids on the basis of sequence similarity and rearrangement distances, and uses this to cluster plasmids into their evolving units. I apply pling to nosocomial plasmids to demonstrate the utility of its clusters for tracking plasmid spread across a bacterial phylogeny. To tackle the second problem, I study plasmids from NORM, a national epidemiological collection of E. coli from Norway covering 16 years. After identifying evolving units of plasmids, I map their phylogenetic distribution, locating clades where they are evolving vertically. This then allows me to perform ancestral reconstruction of structural changes. Together, this provides a framework for understanding how established plasmids change evolutionarily, and estimate the rates of structural and mutational change.","abstract_html":"Bacterial cells contain the chromosome (or chromosomes), plus several small DNA molecules which occur at a broad copy number range (from none to potentially hundreds), and replicate independently, called plasmids. They are genetically extremely diverse, and in many cases are able to spread "horizontally" between unrelated cells. The ability to spread, while carrying genes coding for locally adaptive functions (e.g. antibiotic resistance, inter-bacterial warfare, virulence), explains the critical role plasmids play in bacterial evolution. Dense sequencing in laboratories and hospitals clearly show that structural changes (gene gain/loss, inversions) occur at comparable or higher rates than SNPs. This presents two challenges for analysis of plasmids. First, dramatic structural changes make it hard to define evolving units for further study (species equivalents). Second, the sophisticated maximum likelihood and Bayesian approaches used in phylogenetics are not available, as we do not have good evolutionary models for plasmid-style genome evolution. In this thesis, I solve the first problem by using rearrangement distances between genomes to identify plausible species-equivalents. Plasmids are studied at a coarse level, as a sequence of signed integers (representing genes or aligned blocks), and the distance between two plasmids is the minimum number of rearrangement events between them. I introduce a software workflow pling, which builds a network of relatedness between plasmids on the basis of sequence similarity and rearrangement distances, and uses this to cluster plasmids into their evolving units. I apply pling to nosocomial plasmids to demonstrate the utility of its clusters for tracking plasmid spread across a bacterial phylogeny. To tackle the second problem, I study plasmids from NORM, a national epidemiological collection of E. coli from Norway covering 16 years. After identifying evolving units of plasmids, I map their phylogenetic distribution, locating clades where they are evolving vertically. This then allows me to perform ancestral reconstruction of structural changes. Together, this provides a framework for understanding how established plasmids change evolutionarily, and estimate the rates of structural and mutational change.","abstract_has_math":false,"creators":["Frolova, Daria"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Iqbal, Zamin"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-08-27","date_published":"2025-08-27","updated_at":"2026-07-22T22:24:27Z","subjects":["plasmids","genomics","bioinformatics"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/b519fcfa-2ace-4029-97df-6599b66b5b8c/download","https://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.123913","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Iqbal, Zamin"]},{"key":"dc:creator","label":"Author","values":["Frolova, Daria"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2025-08-27"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/393684"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["plasmids","genomics","bioinformatics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/b519fcfa-2ace-4029-97df-6599b66b5b8c/download","https://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.123913"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/fc2dbad8-0a47-4945-a20d-058d7c6edba4/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Bacterial cells contain the chromosome (or chromosomes), plus several small DNA molecules which occur at a broad copy number range (from none to potentially hundreds), and replicate independently, called plasmids. 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Plasmids are studied at a coarse level, as a sequence of signed integers (representing genes or aligned blocks), and the distance between two plasmids is the minimum number of rearrangement events between them. I introduce a software workflow pling, which builds a network of relatedness between plasmids on the basis of sequence similarity and rearrangement distances, and uses this to cluster plasmids into their evolving units. I apply pling to nosocomial plasmids to demonstrate the utility of its clusters for tracking plasmid spread across a bacterial phylogeny. To tackle the second problem, I study plasmids from NORM, a national epidemiological collection of E. coli from Norway covering 16 years. After identifying evolving units of plasmids, I map their phylogenetic distribution, locating clades where they are evolving vertically. This then allows me to perform ancestral reconstruction of structural changes. 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The ability to spread, while carrying genes coding for locally adaptive functions (e.g. antibiotic resistance, inter-bacterial warfare, virulence), explains the critical role plasmids play in bacterial evolution. Dense sequencing in laboratories and hospitals clearly show that structural changes (gene gain/loss, inversions) occur at comparable or higher rates than SNPs. This presents two challenges for analysis of plasmids. First, dramatic structural changes make it hard to define evolving units for further study (species equivalents). Second, the sophisticated maximum likelihood and Bayesian approaches used in phylogenetics are not available, as we do not have good evolutionary models for plasmid-style genome evolution. In this thesis, I solve the first problem by using rearrangement distances between genomes to identify plausible species-equivalents. 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