Metagenomic characterization of ambulances across the USA

Abstract

Background: Microbial communities in our built environments have great influence on human health and disease. A variety of built environments have been characterized using a metagenomics-based approach, including some healthcare settings. However, there has been no study to date that has used this approach in pre-hospital settings, such as ambulances, an important first point-of-contact between patients and hospitals.

Results: We sequenced 398 samples from 137 ambulances across the USA using shotgun sequencing. We analyzed these data to explore the microbial ecology of ambulances including characterizing microbial community composition, nosocomial pathogens, patterns of diversity, presence of functional pathways and antimicrobial resistance, and potential spatial and environmental factors that may contribute to community composition. We found that the top 10 most abundant species are either common built environment microbes, microbes associated with the human microbiome (e.g., skin), or are species associated with nosocomial infections. We also found widespread evidence of antimicrobial resistance markers (hits ~ 90% samples). We identified six factors that may influence the microbial ecology of ambulances including ambulance surfaces, geographical-related factors (including region, longitude, and latitude), and weather-related factors (including temperature and precipitation).

Conclusions: While the vast majority of microbial species classified were beneficial, we also found widespread evidence of species associated with nosocomial infections and antimicrobial resistance markers. This study indicates that metagenomics may be useful to characterize the microbial ecology of pre-hospital ambulance settings and that more rigorous testing and cleaning of ambulances may be warranted.

Keywords: Ambulance; Antimicrobial resistance; Classification; Hospital-acquired infections; Metagenomics; Microbial ecology; Nosocomial pathogens; Pre-hospital setting; Taxonomy; Whole-genome sequencing.

Fig. 1

Sample collection and workflow. a Map of sample collection areas across the USA (cities not specified to protect privacy). Darker orange signifies a greater number of samples were collected as indicated in key. Sample collection was clustered in five regions labeled East, West, West Coast, Southwest/West Coast, and Southeast. b Workflow figure including laboratory and computational approaches used

Fig. 2

Top ranking features (species) during random forest classification training (128 trees) when the overlap dataset was used. Features were identified in terms of random forest importance scores, indicating their contribution to classification performance for a given class. The relative abundances (RPK) for each top ranking feature across all samples were binned (x-axis). The frequency of each feature across samples falling into these bins is shown (y-axis). Bars shaded red indicate the highest ranking feature for a given class. High ranking features with large frequencies at bin 0 suggest that those features are rare, but if present, highly influence the classifier to classify a sample in that feature’s corresponding class. a Surface. b Region

Fig. 3

HUMAnN2 functional analysis results. Breakdown of superclasses of pathways identified and their relative proportions across the entire dataset (a), number of hits for top pathways identified across the entire dataset (b), and number of hits for different taxa across the entire dataset (c). All results determined from the annotations posted on the MetaCyc database for each identified pathway

Fig. 4

Functional analysis including Human Microbiome Project annotated ambulance species for overlap results and AMR hits. a Proportions of species identified in ambulances associated with indicated human body parts. b Deviation of ambulance body part associations from HMP database indicates HMP proportions are not driving patterns observed in ambulances and that heart, skin, and blood associated species are overrepresented. c Skin associated species varied significantly across surfaces, shared letter(s) on the x-axis between surfaces indicates statistical equivalence. d Boxplot of AMR hits across cities with boxplots colored by region

Fig. 5

Potential factors driving variation in alpha diversity (calculated using MetaPhlAn2 results). a Region had a significant effect on alpha diversity (univariate ANOVA: p = 0.001; east removed due to small sample size). b Apha diversity increases with mean temperature (bivariate regression: p = 0.001; r = 0.161). c Alpha diversity decreases with latitude (bivariate regression: p = 0.0003; r = −0.179). Interesting because follows latitudinal diversity gradient (LDG)