{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/379399"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/379399","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Measuring the abundance, diversity and overlap of Antimicrobial Resistance genes associated with humans, livestock and the environment in low- and middle-income country settings","abstract":"Antimicrobial resistance (AMR) is one of the greatest threats to infectious disease management and is also challenging UN Sustainable Development Goals (UN-SDGs) in achieving health, food security and environmental well-being. Conventional AMR assessment techniques involve phenotypic characterisation or detection of a limited number of antimicrobial resistance genes (ARGs) using PCR-based techniques. With the inaccessibility of sequencing-based platforms in resource-limited settings, microfluidics-based high-throughput qPCR (HT-qPCR) holds the potential to study AMR with increased granularity. In this thesis, using Fluidigm HT-qPCR, I measured the abundance and diversity of AMR at a One Health, human-animal-environment interface. I additionally employed the same platform to evaluate the impact of intervention in reducing AMR in the poultry production system and to examine differential gene expression of clinical Klebsiella pneumoniae isolates under the exposure of three antimicrobials. The One Health AMR study in Fiji investigated 239 individual resistance markers and revealed that ARGs conferring resistance to multiple classes of antimicrobials (MDR) were predominant across the sample sources. The diversity analysis showed an overlapping resistome between human stool samples and water samples collected from open spots of the settlements. This study was followed by measuring the impact of heavy farm-level antimicrobial usage (AMU) on AMR, where chicken flocks receiving antimicrobials showed a higher ARG abundance compared to the control flock. A univariable linear regression analysis found that the age of the birds and the number of classes of antimicrobials administered explained the variation in AMR significantly. Temporal dynamics depicted that ARGs of different antimicrobial classes follow distinct patterns of persistence. As an intervention, provision to veterinary advice resulted in an overall 74.2% reduction in AMU and 10.7% in ARG abundance. Additionally, a study of colistin-resistance mediated LPS modification demonstrated that K. pneumoniae with the ability to alter LPS expression triggered more inflammatory cytokines (IL-1β, IL-6, CXCL-8, IL-10 and TNF-α) compared to naive one and it was dependent on LPS configuration (L-Ara4N-LPS vs pEtN-LPS). Finally, the genetic determinants of AMR and other stress regulators of K. pneumoniae exhibited dose-dependent gene expression in response to meropenem, ciprofloxacin and tigecycline exposure. A good concordance was observed between Fluidigm HT-qPCR and RNA-seq in measuring differential gene expression upon exposure to antimicrobials. My work substantiates the application of microfluidics in the study of AMR and advocates its use for more extensive One Health studies in this area.","abstract_html":"Antimicrobial resistance (AMR) is one of the greatest threats to infectious disease management and is also challenging UN Sustainable Development Goals (UN-SDGs) in achieving health, food security and environmental well-being. Conventional AMR assessment techniques involve phenotypic characterisation or detection of a limited number of antimicrobial resistance genes (ARGs) using PCR-based techniques. With the inaccessibility of sequencing-based platforms in resource-limited settings, microfluidics-based high-throughput qPCR (HT-qPCR) holds the potential to study AMR with increased granularity. In this thesis, using Fluidigm HT-qPCR, I measured the abundance and diversity of AMR at a One Health, human-animal-environment interface. I additionally employed the same platform to evaluate the impact of intervention in reducing AMR in the poultry production system and to examine differential gene expression of clinical Klebsiella pneumoniae isolates under the exposure of three antimicrobials. The One Health AMR study in Fiji investigated 239 individual resistance markers and revealed that ARGs conferring resistance to multiple classes of antimicrobials (MDR) were predominant across the sample sources. The diversity analysis showed an overlapping resistome between human stool samples and water samples collected from open spots of the settlements. This study was followed by measuring the impact of heavy farm-level antimicrobial usage (AMU) on AMR, where chicken flocks receiving antimicrobials showed a higher ARG abundance compared to the control flock. A univariable linear regression analysis found that the age of the birds and the number of classes of antimicrobials administered explained the variation in AMR significantly. Temporal dynamics depicted that ARGs of different antimicrobial classes follow distinct patterns of persistence. As an intervention, provision to veterinary advice resulted in an overall 74.2% reduction in AMU and 10.7% in ARG abundance. Additionally, a study of colistin-resistance mediated LPS modification demonstrated that K. pneumoniae with the ability to alter LPS expression triggered more inflammatory cytokines (IL-1β, IL-6, CXCL-8, IL-10 and TNF-α) compared to naive one and it was dependent on LPS configuration (L-Ara4N-LPS vs pEtN-LPS). Finally, the genetic determinants of AMR and other stress regulators of K. pneumoniae exhibited dose-dependent gene expression in response to meropenem, ciprofloxacin and tigecycline exposure. A good concordance was observed between Fluidigm HT-qPCR and RNA-seq in measuring differential gene expression upon exposure to antimicrobials. My work substantiates the application of microfluidics in the study of AMR and advocates its use for more extensive One Health studies in this area.","abstract_has_math":false,"creators":["Dutta, Avijit"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Baker, Stephen","Dougan, Gordon"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-08-21","date_published":"2024-08-21","updated_at":"2026-07-22T22:24:18Z","subjects":["Antimicrobial resistance","Colistin resistance","Differential expression","HT-qPCR","Immunity","One Health"],"languages":["eng"],"rights":[],"rights_urls":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/20a286c2-2214-42e3-9021-0e9215bbd842/download","http://purl.org/NET/rdflicense/allrightsreserved"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.115504","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Baker, Stephen","Dougan, Gordon"]},{"key":"dc:creator","label":"Author","values":["Dutta, Avijit"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2024-08-21"]},{"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/1810/379399"]},{"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":["Antimicrobial resistance","Colistin resistance","Differential expression","HT-qPCR","Immunity","One Health"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/20a286c2-2214-42e3-9021-0e9215bbd842/download","http://purl.org/NET/rdflicense/allrightsreserved"]},{"key":"dc:rights.embargodate","label":"Dc Rights Embargodate","values":["2026-01-29"]},{"key":"dc:rights.embargotype","label":"Dc Rights Embargotype","values":["embargo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.115504"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/23978ee3-36f1-4c63-a25b-36e60fe50f1d/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Antimicrobial resistance (AMR) is one of the greatest threats to infectious disease management and is also challenging UN Sustainable Development Goals (UN-SDGs) in achieving health, food security and environmental well-being. 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The diversity analysis showed an overlapping resistome between human stool samples and water samples collected from open spots of the settlements. This study was followed by measuring the impact of heavy farm-level antimicrobial usage (AMU) on AMR, where chicken flocks receiving antimicrobials showed a higher ARG abundance compared to the control flock. A univariable linear regression analysis found that the age of the birds and the number of classes of antimicrobials administered explained the variation in AMR significantly. Temporal dynamics depicted that ARGs of different antimicrobial classes follow distinct patterns of persistence. As an intervention, provision to veterinary advice resulted in an overall 74.2% reduction in AMU and 10.7% in ARG abundance. Additionally, a study of colistin-resistance mediated LPS modification demonstrated that K. pneumoniae with the ability to alter LPS expression triggered more inflammatory cytokines (IL-1β, IL-6, CXCL-8, IL-10 and TNF-α) compared to naive one and it was dependent on LPS configuration (L-Ara4N-LPS vs pEtN-LPS). Finally, the genetic determinants of AMR and other stress regulators of K. pneumoniae exhibited dose-dependent gene expression in response to meropenem, ciprofloxacin and tigecycline exposure. A good concordance was observed between Fluidigm HT-qPCR and RNA-seq in measuring differential gene expression upon exposure to antimicrobials. 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The diversity analysis showed an overlapping resistome between human stool samples and water samples collected from open spots of the settlements. This study was followed by measuring the impact of heavy farm-level antimicrobial usage (AMU) on AMR, where chicken flocks receiving antimicrobials showed a higher ARG abundance compared to the control flock. A univariable linear regression analysis found that the age of the birds and the number of classes of antimicrobials administered explained the variation in AMR significantly. Temporal dynamics depicted that ARGs of different antimicrobial classes follow distinct patterns of persistence. As an intervention, provision to veterinary advice resulted in an overall 74.2% reduction in AMU and 10.7% in ARG abundance. Additionally, a study of colistin-resistance mediated LPS modification demonstrated that K. pneumoniae with the ability to alter LPS expression triggered more inflammatory cytokines (IL-1β, IL-6, CXCL-8, IL-10 and TNF-α) compared to naive one and it was dependent on LPS configuration (L-Ara4N-LPS vs pEtN-LPS). Finally, the genetic determinants of AMR and other stress regulators of K. pneumoniae exhibited dose-dependent gene expression in response to meropenem, ciprofloxacin and tigecycline exposure. A good concordance was observed between Fluidigm HT-qPCR and RNA-seq in measuring differential gene expression upon exposure to antimicrobials. 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