DOI: 10.5061/dryad.69p8cz9cx
title: Metadata and antimicrobial resistance gene count data from dusts collected on Canadian vehicle filters

description: The role of bioaerosols in the dispersal of antimicrobial resistance genes
 (ARGs) and resistant microorganisms is poorly understood. In addition,
 bioaerosols are powerful composite samples representative of the
 surrounding environment and can be used as sentinels of many local
 habitats. Evidence suggests that using environmental DNA from dust
 collected on vehicle cabin air filters can define regional resistance
 profiles. Here, this method was used to investigate differences in
 resistance gene profiles, their underlying bacterial communities, and
 their links to anthropogenic and environmental variables across Canada. In
 total, 477 car filter samples were collected, with every province and
 territory being represented. DNA was extracted from filter dust.
 High‐throughput qPCR was used to detect and quantify a panel of 36 ARGs
 and 3 mobile genetic elements. Bacterial biomass was assessed using
 standard qPCR methods of the 16S rRNA gene, which was also used to assess
 bacterial biodiversity via metabarcoding. Results indicated that qepA
 dominates antimicrobial resistance profiles across Canada. However, after
 they were removed from the dataset, regional profiles were evident based
 on gene type and richness. Factors positively linked to total numbers of
 ARGs included human and livestock populations; whereas mean annual
 precipitation was negatively linked to resistance gene quantities.
 Measures of α‐diversity were generally greater in the western regions of
 Canada than in the east and the north. Community composition analyses
 showed similarities between the prairies and territories, which were
 separated from other regions. Finally, network analyses revealed a
 relatively stable group of core ARGs across regions, which were largely
 correlated with low‐abundance genera. Such findings suggest that rare taxa
 are key links in the diffusion of antimicrobial resistance in
 environmental contexts. Furthermore, this study highlights the potential
 application of vehicle air filters in building long‐term monitoring
 capacity of outdoor bioaerosols.
description: In total, 477 vehicle cabin air filters were collected from 51
 locations across Canada, through a network of participating mechanics,
 individuals, and municipal governments. Two additional filters were
 collected as a methodological control. We asked that filters be collected
 during routine vehicle maintenance and placed in sterile resealable
 plastic bags for transport to the <em>Institut universitaire de
 cardiologie et de pneumologie de Québec – Université Laval </em>in
 Quebec City, QC, Canada for processing. We also asked participants to
 include the odometer reading (km) since last change and the forward
 sorting area of their postal code for environmental and population
 metadata collection. This data is not presented to preserve anonymity.
 Filters were categorised into the 6 geographical regions of Canada as
 defined by Statistics Canada: British Columbia, Prairies, Ontario, Quebec,
 Atlantic, Territories. Filters were collected between summer 2020 and
 winter 2021. During this time, travel restrictions within some Canadian
 regions were in place and long-distance travel was discouraged, which
 helped control for effects of interregional travel. For
 each filter, metadata on the sampling location was collected from publicly
 available sources. Latitude, longitude (not presented to preserve
 anonymity) and elevation were recorded for each filter. Distance to the
 nearest major water body (i.e., ocean or Great Lake), was estimated using
 direct measurements from Google maps. Population and population density
 came from 2021 census data (Statistics Canada 2022b). Both mean annual
 temperature (MAT) and mean annual precipitation (MAP) were taken from the
 most recent records published by Environment and Climate Change Canada
 (2024). Estimates of local livestock populations for each location were
 taken from the 2021 census of agriculture (Statistics Canada 2022a) at the
 level of agricultural subdivision. Data on local hospital capacity based
 on number of beds in 2020-2021 was taken from <em>Ministère de la
 Santé et des Services sociaux</em> for Québec (2022) and from the
 Canadian Institute for Health Information (2022) for all other
 jurisdictions. These can be found in Table S2.
 <em>DNA Extraction</em> A section
 was cut out of each filter, including the two control filters, and weighed
 for DNA extraction. This section of material was placed in one half of a
 strainer bag and homogenised for 1 min in a 100 ml saline solution of 0.05
 tween20 with a paddle mixer (Fisher Scientific). This was done to dislodge
 fine dust from the filter. Next, a 50 ml aliquot of this solution was
 collected from the opposite side of the filter bag and differentially
 centrifuged for 3 min at 250 rpm to remove large particles and plant
 matter. Then 3 ml aliquots of supernatant were spun at 10,000 rpm to form
 pellets for DNA extraction. All DNA extractions were made with DNEasy
 PowerLyzer PowerSoil Kit (Qiagen, Toronto, ON, CA) following
 manufacturer’s instructions. <em>qPCR
 Analyses</em> Total bacterial biomass per g of
 filter material was calculated by qPCR of the 16S rRNA gene following Bach
 et al (2002). Primer sequences are presented in Table S3. These analyses
 were conducted on a Bio-Rad CFX-384 Touch<sup>TM</sup>
 Real-Time PCR Detection System (Bio-Rad, Montreal, QC, CA) with the
 following thermoprotocol: 95 °C for 3 min then 40 cycles of 95 °C for 20 s
 and 62 °C for 1 min. Data were used when efficiency curves were between
 90–110%. As part of quantification, DNA extraction blanks were included
 with all 16S rRNA gene qPCR analyses. If reactive, the starting quantity
 (Sq) values of corresponding blanks were subtracted from those true
 samples. An upper limit of 36 cycles was used. There were no reactions in
 no template qPCR negative controls. Total 16S rRNA gene
 copy numbers per gram of filter were estimated using the
 formula: 16S rRNA copy number per g filter =
 ((<em>Sq</em>*50)*(50/3.33))/<em>W</em>
 In which, <em>Sq</em> is the starting quantity
 estimate from qPCR reactions using 2 ml of DNA taken from a 100 ml
 reaction. This extraction was performed on a 3.33 ml aliquot of the 50 ml
 extraction solution. <em>W</em> is the weight of the filter in
 mg. Copy numbers were log<sub>10</sub>-transformed for
 statistical analyses. Next, the ARG profile of each
 filter was assessed with SmartChip HT-qPCR (TakaraBio USA, San Jose, CA,
 USA) using the same aliquot. In this case, extracts were screen against a
 predefined panel of 36 ARGs and 3 MGEs as outlined in George et al (2022).
 Table S3 outlines the primer sequences and target genes of the 3
 aminoglycoside, 10 beta-lactam, 1 colistin, 4 macrolide, 3 MGEs, 2
 quinolone, 2 sulfonamide, 10 tetracycline, and 4 vancomycin genes used.
 The mode of action of these genes was taken from the Comprehensive
 Antibiotic Resistance Database (https://card.mcmaster.ca). All genes used
 SYBR Green dye fluorescence except for <em>blaCTX-M-1 </em>and
 <em>mcr1</em>, which used FAM probes (Table S3). The SmartChip
 thermoprotocol was: 95 °C for 3 min; then 45 cycles of 95 °C, 10 s; 60 °C,
 30 s; 55 °C, 31 s; followed by a melting curve of 55 °C for 5 s + 0.5
 °C/cycle. On each chip a no template control and a positive control
 containing a mix of all targets at concentrations of
 ~10<sup>5</sup> copies ml <sup>-1</sup> was
 included. To eliminate false positives, melt curves were verified against
 those of the positive control. Relative copy numbers of target genes were
 calculated with the comparative CT method (Schmittgen and Livak
 2008): 𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑔𝑒𝑛𝑒 𝑐𝑜𝑝𝑦 𝑛𝑢𝑚𝑏𝑒𝑟 = 2<sup>
 ―(</sup><sup>𝐶𝑡</sup><sup>(</sup><sup>𝐴𝑅𝐺</sup><sup>) ― </sup><sup>𝐶𝑡</sup><sup>(16</sup><sup>𝑆</sup><sup>))</sup> Relative abundance of ARGs/MGEs was normalised by multiplying the copy number by 16S rRNA gene copy number. REFERENCES Bach, H.‐J., J. Tomanova, M. Schloter, and J. C. Munch. 2002. “Enumeration of Total Bacteria and Bacteria With Genes for Proteolytic Activity in Pure Cultures and in Environmental Samples by Quantitative PCR Mediated Amplification.” Journal of Microbiological Methods 49: 235–245 Canadian Institute for Health Information. 2022. Trends in Hospital Spending, 2009–2010 to 2020–2021—Data Tables—Series D: Beds Staffed and in Operation by Functional Centre. Ottawa: Canadian Institute for Health Information. George, P.B.L., F. Rossi, M. W. St‐Germain, et al. 2022. “Antimicrobial Resistance in the Environment: Towards Elucidating the Roles of Bioaerosols in Transmission and Detection of Antibacterial Resistance Genes.” Antibiotics 11: 974. Ministère de la Santé et des Services sociaux. 2022. Rapports statistiques annuels—Lits dressés par établissement et par installation. Quebec: Gouvernement du Québec.   Schmittgen, T. D., and K. J. Livak. 2008. “Analyzing Real‐Time PCR Data by the Comparative CT Method.” Nature Protocols 3: 1101–1108.   Statistics Canada. 2022a. Census of Agriculture <em>2021</em>. Ottawa: Government of Canada.   Statistics Canada. 2022b. Census of Population 2021. Ottawa: Government of Canada. 
description: # Metadata and antimicrobial resistance gene count data from dusts
 collected on Canadian vehicle filters
 [https://doi.org/10.5061/dryad.69p8cz9cx](https://doi.org/10.5061/dryad.69p8cz9cx) ## Description of the data and file structure ### Files and variables #### File: TableS2.xlsx **Description:** Environmental metadata for each filter collected in this study. Please note that identifying data like city, postal code, latitude, and longitude are not presented so as to preserve participant anonymity. ##### Variables * Filter: The ID number of each individual filter collected. * Province: The Canadian province or territory in which each filter was collected using official two-letter abbreviations (BC = British Columbia, AB = Alberta, SK = Saskatchewan, MB = Manitoba, ON = Ontario, QC = Québec, NB = New Brunswick, NS = Nova Scotia, PE = Prince Edward Island, NL = Newfoundland and Labrador, YK = Yukon, NT = Northwest Territotries, NU = Nunavut). * KM_driven: The odometer reading since last change of cabin filter. * Filter_section_weight_g: The weight of each section of filter used for analyses in g. * Pop_Centre_Class: The population size classification of each population centre from which filter was collected based on Statistics Canada's definitions (Large &gt; 100,000, medium = &gt; 30,000, &lt; 99,999 , small &lt; 30,000, &gt; 1,000) with all places with &lt; 1,000 population classified as rural. * Ecozone: The ecozones from which each filter was collected according to the Ecological Framework for Canada. * Geographical_region: The region each filter was collected from following Statistics Canada's definitions (British Columbia, Prairies, Ontario, Québec, Atlantic, Territories). * Population_2021: Population values of the location from which each filter was collected in the 2021 Census of Canada (Statistics Canada). * Population_density_km2: Population density per km2 values of the location from which each filter was collected in the 2021 Census of Canada (Statistics Canada). * Hospital_beds: Number of hospital beds present in each location from which filters were collected in 2020-2021 see data descritption for sources. * Cattle_Number: Number of cattle in the agricultural subdivision from whihc each filter was collected from Statistics Canada's 2021 Census of Agriculture. * Pig_Number: Number of pig in the agricultural subdivision from whihc each filter was collected from Statistics Canada's 2021 Census of Agriculture. * Sheep_Number: Number of sheep in the agricultural subdivision from whihc each filter was collected from Statistics Canada's 2021 Census of Agriculture. * Poultry_Number: Number of poultry in the agricultural subdivision from whihc each filter was collected from Statistics Canada's 2021 Census of Agriculture. * Other_Number: Number of other in the agricultural subdivision from whihc each filter was collected from Statistics Canada's 2021 Census of Agriculture. * MAP_mm: Mean annual precipitation in mm for each location from which a filter was collected based on Environment and Climate Change Canada Climate Normals. * MAT_C: Mean annual temperature in Celcius for each location from which a filter was collected based on Environment and Climate Change Canada Climate Normals. * Elevation_m: Elevation in m of each location from which a filter was collected. * Distance_coast_km: Distance from each location from which as filter was collected to the nearest coastline (ocean or Great Lakes) as-the-crow-flies as calculated from Google Maps. * Please note: In all cases where data was unavailable, NA has been used. Also, BLANK is used on categorical variables to denote new filters used as method controls (i.e., blanks) in this analysis. #### File: TableS5.csv **Description:** Underlying data for Fig 1F and G as well as Fig S2. ##### Variables * Filter: The ID number of each individual filter collected. * Geographical_region: The region each filter was collected from following Statistics Canada's definitions (British Columbia, Prairies, Ontario, Québec, Atlantic, Territories). * Gene: The gene detected (see Table S3 for the complete list). * Copy number: Approximate copy number as calculated by the formulae presented in the data description. * Type: The antimmicrobial family targeted by the gene. * Action: The mode of action as defined by the CARD database. ## Access information Other publicly accessible locations of the data: * None Data was derived from the following sources: * Please see methods