Rapid metagenomics analysis of EMS vehicles for monitoring pathogen load using nanopore DNA sequencing

Abstract

Pathogen monitoring, detection and removal are essential to public health and outbreak management. Systems are in place for monitoring the microbial load of hospitals and public health facilities with strategies to mitigate pathogen spread. However, no such strategies are in place for ambulances, which are tasked with transporting at-risk individuals in immunocompromised states. As standard culturing techniques require a laboratory setting, and are time consuming and labour intensive, our approach was designed to be portable, inexpensive and easy to use based on the MinION third-generation sequencing platform from Oxford Nanopore Technologies. We developed a transferable sampling-to-analysis pipeline to characterize the microbial community in emergency medical service vehicles. Our approach identified over sixty-eight organisms in ambulances to the genera level, with a proportion of these being connected with health-care associated infections, such as Clostridium spp. and Staphylococcus spp. We also monitored the microbiome of different locations across three ambulances over time, and examined the dynamic community of microorganisms found in emergency medical service vehicles. Observed differences identified hot spots, which may require heightened monitoring and extensive cleaning. Through metagenomics analysis it is also possible to identify how microorganisms spread between patients and colonize an ambulance over time. The sequencing results aid in the development of practices to mitigate disease spread, while also providing a useful tool for outbreak prediction through ongoing analysis of the ambulance microbiome to identify new and emerging pathogens. Overall, this pipeline allows for the tracking and monitoring of pathogenic microorganisms of epidemiological interest, including those related to health-care associated infections.

Fig 1. Nanopore sequencing workflow to rapidly identify bacteria in ambulances.

(A) Samples are acquired from three locations in three vehicles over a three-week period, followed by DNA isolation, 16S rRNA gene amplification and sample preparation. The prepared library is then sequenced using the MinION and analyzed to identify bacterial species present. Taxonomic identification is possible within 24 hours from initial sample acquisition. (B) Bioinformatics analysis pipeline for generating taxonomic data.

Fig 2. Diversity of bacterial genera detected in ambulances.

(A) Phylogenetic analysis of ambulance microbiota with confidence to the genus level. Colored segments correspond to unique phyla of bacteria. Organisms are unique to the EMS vehicles and were not identified in the control samples. Pathogenic genera are indicated by a red asterisk. (B) Relative abundance of bacterial genera with pathogenic species, represented as the average normalized sequencing reads detected over a three-week period from all ambulances for each sampling location and (C) from all sampling locations for each ambulance.

Fig 3. A comparison of fold-change of taxa found in ambulances relative to first sampling event.

Heatmap illustrating the log₂-fold change of organisms sequenced using ONT MinION relative to the first week of sampling. Yellow squares correspond to an increase in a given taxa at a corresponding week relative to Week 1, while blue squares indicate a decrease in abundance of a taxa relative to Week 1. Asterisks indicate a significant change relative to Week 1 (p<0.05). All changes from week 2 are significant relative to week 1 as indicated by the red asterisk.