Chari, P. | Search bioRxiv

bioRxiv2024

A model, mixed-species urinary catheter biofilm derived from spinal cord injury patients

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.

microbiology↗

bioRxiv2024

Prediction of symptomatic and asymptomatic bacteriuria in spinal cord injury patients using machine learning

BackgroundIndividuals with spinal cord injuries (SCI) frequently rely on urinary catheters to drain urine from the bladder, making them susceptible to asymptomatic and symptomatic catheter-associated bacteriuria and urinary tract infections (UTI). Proper identification of these conditions lacks precision, leading to inappropriate antibiotic use which promotes selection for drug-resistant bacteria. Since infection often leads to dysbiosis in the microbiome and correlates with health status, this study aimed to develop a machine learning-based diagnostic framework to predict potential UTI by monitoring urine and/or catheter microbiome data, thereby minimising unnecessary antibiotic use and improving patient health. ResultsMicrobial communities in 609 samples (309 catheter and 300 urine) with asymptomatic and symptomatic bacteriuria status were analysed using 16S rRNA gene sequencing from 27 participants over 18 months. Microbial community compositions were significantly different between asymptomatic and symptomatic bacteriuria, suggesting microbial community signatures have potential application as a diagnostic tool. A significant decrease in local (alpha) diversity was noted in symptomatic bacteriuria compared to the asymptomatic bacteriuria (P < 0.01). Beta diversity measured in weighted unifrac also showed a significant difference (P < 0.05) between groups. Supervised machine learning models trained on amplicon sequence variant (ASVs) counts and bacterial taxonomic abundances (Taxa) to classify symptomatic and asymptomatic bacteriuria with a 10-fold cross-validation approach. Combining urine and catheter microbiome data improved the model performance during cross-validation, yielding a mean area under the receiver operating characteristic curve (AUROC) of 0.91-0.98 (Interquartile range, IQR 0.93-0.96) and 0.78-0.91 (IQR 0.86-0.88) for ASVs and taxonomic features, respectively. ASVs and taxa features achieve a mean AUROC of 0.85-1 (IQR 0.93-0.98) and 0.69-0.99 (IQR 0.78-0.88) in the independent held-out test set, respectively, signifying their potential in differentiating symptomatic and asymptomatic bacteriuria states. ConclusionsOur findings demonstrate that signatures within catheter and urine microbiota could serve as tools to monitor the health status of SCI patients. Establishing an early warning system based on these microbial signatures could equip physicians with alternative management strategies, potentially reducing UTI episodes and associated hospital costs, thus significantly improving patient quality of life while mitigating the impact of drug-resistant UTI.

microbiology↗