BiofilmMicrobiologyPeer-reviewed journal article
A model, mixed-species urinary catheter biofilm derived from spinal cord injury patients
Parisa NoorianiD, Kailey Hamann, M. Mozammel HoqueiD, Gustavo Espinoza-VergaraiD, Joyce ToiD, Dominic Leo, Priyadarshini Chari, Gerard Weber, Obaydullah Marial, Julie PryoriD, Iain G. DugginiD, Bonsan Bonne Lee, Scott A. Rice, Diane McDougaldiD
Abstract
Complex multispecies biofilms consistently colonise the interior of indwelling urinary catheters, causing persistent asymptomatic bacteriuria and frequent symptomatic episodes in long-term catheterized individuals. Simple single-species models often fail to capture the complexities of mixed-species interactions, leading to limited conclusions about microbial behaviour and treatment efficacy. Additionally, using lab-based organisms can obscure the genomic diversity found in real-world infections. The primary objective of this study was to establish a stable and reproducible in vitro biofilm model derived from the multi-species clinical flora associated with catheter-related infections, reflecting the dynamics of in vivo infections. Biofilm samples from clinical catheters of spinal cord injury (SCI) participants were used to establish polymicrobial macro-fluidic models within catheters. Metagenomic techniques using short-read Illumina and long-read Oxford Nanopore sequencing was used to assess the community composition, produce metagenome-assembled genomes (MAGs), analyse strain-level phylogeny diversity and single nucleotide polymorphisms (SNP) of isolates. Antibiotic resistance tests using our models highlighted the drastic differences between planktonic bacteria, single-species, and multispecies biofilms. In silico analysis of antibiotic resistance further revealed a high number of varied resistance genes present in these communities. The models developed and characterised in this study are expected to facilitate more effective strategies to prevent and treat catheter-associated infections.
- Citations
- 3
- FWCI
- 0.91
- Field percentile
- top 27%
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