Sep 16: Novel computational approaches to predict and reconstruct bacterial plasmids
Newly developed user-friendly bioinformatics tools to predict and reconstruct bacterial plasmid sequences provide new insights in dissemination patterns of antimicrobial resistance of a wide range of bacterial species. This was concluded by Sergio Arredondo-Alonso who received his PhD this week from Utrecht University.
Plasmids are extrachromosomal elements that can disseminate between bacterial strains or different species. Plasmids can also acquire novel genetic traits by acquisition of transposons or co-integration 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, including 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. 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 particular from the nosocomial pathogen Enterococcus faecium.
New tools
Current bioinformatics tools to predict and reconstruct bacterial plasmids from short-read sequencing data have significant limitations and challenges. A benchmark of available tools concluded that none of these was able to accurately reconstruct plasmid sequences in an automated fashion. Sergio Arredondo (Group Rob Willems, Department of Medical Microbiology, UMC Utrecht) and coworkers developed two novel user-friendly machine-learning tools that appeared to be able to confidently predict and reconstruct bacterial plasmids, outperforming currently available methods.
Clonal dissemination dominant
One of the tools was used to study dissemination of vancomycin resistance in a set of 309 vancomycin-resistant E. faecium (VRE) isolates from patients from 32 Dutch hospitals. He observed that clonal dissemination was the major driver of vancomycin resistance spread in Dutch hospitals followed by horizontal plasmid-mediated dissemination.
Arredondo-Alonso stresses that these newly-developed tools are not E. faecium specific, and thus can be applied to study the role of plasmids in bacterial adaptation and AMR dissemination in a wide variety of bacterial species, including important pathogens such as Escherichia coli or Klebsiella pneumonia.
PhD defense
Sergio Arredondo Alonso (1993, Barcelona) received his PhD on September 21, 2020 at Utrecht University. The title of his thesis was “Novel computational approaches to predict and reconstruct bacterial plasmids”. Supervisor was prof. dr. Rob Willems and co-supervisor was dr. Anita Schürch (both Department of Medical Microbiology, UMC Utrecht). Sergio will continue his career as a post-doctoral fellow at the University of Oslo.