Novel computational approaches to predict and reconstruct bacterial plasmids: Focus on the nosocomial pathogen ​Enterococcus faecium

Sergio Arredondo Alonso

Research output: ThesisDoctoral thesis 1 (Research UU / Graduation UU)


Plasmids are extrachromosomal elements that can disseminate between strains or even
between different bacterial species. Plasmids can also acquire novel genetic traits by acquisition of transposons or cointegration with other plasmids thereby providing host bacterial strains novel adaptive traits. These inherent characteristics make plasmids optimal vectors for disseminating antimicrobial resistance (AMR). Plasmid-mediated AMR
dissemination often follows a Russian-Doll model in which nested genomic elements
intervene in resistance propagation by vertical and horizontal transmission. However,
current epidemiological studies on AMR dissemination often solely focus on clonal
outbreaks. Traditional typing methods such as MLST, but also current whole genome
sequence (WGS)-based epidemiological studies fail to resolve the dynamics of plasmids
and thus challenge the monitoring of plasmid-mediated AMR. A main reason for this
focus on clonal dissemination is the challenge of reconstructing plasmid sequences from
short-read WGS as explained in chapter 2. The scope of this thesis was to develop a new
set of tools in order to overcome this limitation and accurately detect and trace plasmid
sequences, from short-read WGS data, in the nosocomial pathogen Enterococcus faecium.This was especially relevant to investigate the dissemination of antimicrobial resistance (AMR), in particular vancomycin resistance, as well as other disseminated genes.
Original languageEnglish
Awarding Institution
  • University Medical Center (UMC) Utrecht
  • Willems, Rob, Primary supervisor
  • Schürch, Anita, Co-supervisor
Award date21 Sept 2020
Publication statusPublished - 21 Sept 2020


  • plasmid
  • prediction
  • network
  • whole-genome sequencing data
  • short-read sequencing data
  • Enterococcus faecium
  • nosocomial pathogen
  • network analysis


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