A funkcionális metagenomika módszerének kiterjesztése kórokozó baktériumokra az antibiotikumrezisztencia vizsgálatának céljából

Számel Mónika
A funkcionális metagenomika módszerének kiterjesztése kórokozó baktériumokra az antibiotikumrezisztencia vizsgálatának céljából.
Doctoral thesis (PhD), University of Szeged.

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Abstract in foreign language

Antibiotic resistance has become one of the most pressing health problems in the past decades as newly developed antibiotics are less efficient to treat infections. Resistance in bacteria can emerge by mutations or by horizontal gene transfer. Since the examination of the latter process is ignored when a new antibiotic is tested for potential resistance in drug development pipelines, antibiotics lose their efficacy very fast. Functional metagenomics is a powerful tool to identify genes that are potential candidates to horizontal gene transfer. However, the technique relies on the usage of a single laboratory model strains, usually Escherichia coli. Our aim was to expand functional metagenomics to multiple, non-model bacteria. To achieve this goal, we developed a technique, called DEEPMINE, that utilizes hybrid transducing bacteriophage particles to transduce the libraries into different pathogens. By using these bacteriophages, we managed to transduce metagenomic libraries into pathogenic strains of Klebsiella pneumoniae, Salmonella enterica and Shigella sonnei. Next, we performed functional selection experiments in the presence of 13 antibiotics which revealed that multiple hosts identify more resistance genes than E. coli alone. We also found high variations in resistance levels when expressing the same resistance genes in the different hosts. Finally, our functional metagenomic screens revealed high number of mobile resistance genes against newly developed antibiotics.

Item Type: Thesis (Doctoral thesis (PhD))
Creators: Számel Mónika
Position, academic title, institution
MTMT author ID
Pál Csaba
tudományos tanácsadó, Biokémiai Intézet SZBK
Kintses Bálint
tudományos főmunkatárs, PhD, Biokémiai Intézet SZBK
Subjects: 01. Natural sciences > 01.06. Biological sciences
Divisions: Doctoral School of Biology
Discipline: Natural Sciences > Biology
Language: Hungarian
Date: 2023. June 01.
Item ID: 11630
MTMT identifier of the thesis: 34110668
doi: https://doi.org/10.14232/phd.11630
Date Deposited: 2023. Feb. 28. 15:49
Last Modified: 2023. Aug. 25. 11:55
Depository no.: B 7219
URI: https://doktori.bibl.u-szeged.hu/id/eprint/11630
Defence/Citable status: Defended.

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