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Detection of Legionella species, the influence of precipitation on the amount of Legionella DNA, and bacterial microbiome in aerosols from outdoor sites near asphalt roads in Toyama Prefecture, Japan

Abstract

Background

Legionellosis is caused by the inhalation of aerosolized water contaminated with Legionella bacteria. In this study, we investigated the prevalence of Legionella species in aerosols collected from outdoor sites near asphalt roads, bathrooms in public bath facilities, and other indoor sites, such as buildings and private homes, using amoebic co-culture, quantitative PCR, and 16S rRNA gene amplicon sequencing.

Results

Legionella species were not detected by amoebic co-culture. However, Legionella DNA was detected in 114/151 (75.5%) air samples collected near roads (geometric mean ± standard deviation: 1.80 ± 0.52 log10 copies/m3), which was comparable to the numbers collected from bathrooms [15/21 (71.4%), 1.82 ± 0.50] but higher than those collected from other indoor sites [11/30 (36.7%), 0.88 ± 0.56] (P < 0.05). The amount of Legionella DNA was correlated with the monthly total precipitation (r = 0.56, P < 0.01). It was also directly and inversely correlated with the daily total precipitation for seven days (r = 0.21, P = 0.01) and one day (r = − 0.29, P < 0.01) before the sampling day, respectively. 16S rRNA gene amplicon sequencing revealed that Legionella species were detected in 9/30 samples collected near roads (mean proportion of reads, 0.11%). At the species level, L. pneumophila was detected in 2/30 samples collected near roads (the proportion of reads, 0.09 and 0.11% of the total reads number in each positive sample). The three most abundant bacterial genera in the samples collected near roads were Sphingomonas, Streptococcus, and Methylobacterium (mean proportion of reads; 21.1%, 14.6%, and 1.6%, respectively). In addition, the bacterial diversity in outdoor environment was comparable to that in indoor environment which contains aerosol-generating features and higher than that in indoor environment without the features.

Conclusions

DNA from Legionella species was widely present in aerosols collected from outdoor sites near asphalt roads, especially during the rainy season. Our findings suggest that there may be a risk of exposure to Legionella species not only in bathrooms but also in the areas surrounding asphalt roads. Therefore, the possibility of contracting legionellosis in daily life should be considered.

Background

Legionella is the causative agent of Legionnaires’ disease, a severe form of legionellosis and a potentially fatal pneumonia [1]. At present, 62 Legionella species have been identified [2], approximately half of which have been demonstrated to be pathogenic to humans, and the majority is considered as potential human pathogens [3]. Legionella species are ubiquitous in natural environments, and they have also been found in artificial environments, such as cooling towers, baths, showers, and decorative fountains [4,5,6,7].

Legionellosis can be acquired through the inhalation of aerosolized water contaminated with Legionella [8]. Therefore, aquatic facilities are potential sources of sporadic cases and outbreaks of this disease. According to the National Epidemiological Surveillance of Infectious Diseases, public bath facilities are a major source of Legionella infections in Japan [9]. However, in many cases, the sources of infection remain unknown. The results of our previous study suggested that puddles on asphalt roads can serve as potential environmental reservoirs of L. pneumophila [10]. Several authors have suggested that Legionella present in puddles on roads could be spread by moving cars, which would result in the aerosolization of puddle water, especially on rainy days [11,12,13]. Recently, several studies have attempted to detect Legionella species in aerosols released from hot tap water in bathrooms, shower water, and compost [14,15,16]. These studies revealed that Legionella species are present in aerosols derived from these environments. The Coriolis µ, a portable cyclone-based air sampler for liquid medium, has been used to quantify Legionella in bioaerosols by quantitative PCR (qPCR) [14]. However, to date, the prevalence of Legionella species in aerosols from outdoor sites near asphalt roads has not been analyzed.

Typically, conventional plate culture has been used to detect Legionella in clinical and environmental samples. In some cases, amoebic co-culture has been used, because it can resuscitate viable but non-culturable (VBNC) Legionella cells [17] and has a higher sensitivity than plate culture method [18]. In addition, 16S rRNA gene amplicon sequencing has been widely used to detect bacterial pathogens in environmental samples, as metagenomic analysis using 16S rRNA genes is a powerful tool for analyzing microbial communities [19].

The main objective of this study was to determine whether Legionella species present in aerosols derived from outdoor sites near asphalt roads could be a source of Legionella infection. We investigated the prevalence of Legionella species in aerosols from outdoor sites near asphalt roads using amoebic co-culture and qPCR methods. Other types of sampling sites were used and compared; public bathrooms were used as high-risk areas for infection since Legionella can be frequently found here, and other indoor sites, such as buildings and private homes, were used as low-risk areas. In addition, to better understand the distribution of Legionella and the bacterial community in the air samples collected from outdoor and indoor environments, 16S rRNA gene amplicon sequencing was performed.

Results

Recovery of Legionella from spiked samples

In this study, the detection limit of the qPCR method was 10 Legionella colony-forming units (CFU) equivalents in 1 m3 of air, and the detection limits both of the amoebic co-culture and the plate culture methods were 104 CFU/m3, as determined using Legionella-spiked samples.

Prevalence of Legionella species in air samples

Amoebic co-culture

Of the 202 collected air samples, 150 (129 from roads and 21 from bathrooms) were tested by amoebic co-culture (Supplementary Table S1). Legionella species were not isolated by amoebic co-culture from any of the 150 air samples. Although 20 Legionella-suspected colonies grew on glycine-vancomycin-polymyxin B-cycloheximide (GVPC) agar plates, they also grew on blood agar plates, indicating that they do not belong to the genus Legionella. However, 10-fold more Legionella DNA was observed in 10 samples (10/150, 6.7%) using qPCR after amoebic co-culture than in identical samples analyzed without co-culture (Supplementary Table S1). PCR amplification of the macrophage infectivity potentiator (mip) gene was performed in these samples, and a PCR product was obtained from only one sample collected from a bathroom (Legionella-specific 16S rRNA genes, 1.45 log10 copies/m3) in which the amount of Legionella DNA was increased by 5.8 × 104-fold after amoebic co-culture (6.21 log10 copies/m3). Direct sequencing of the PCR product showed that the mip gene sequence in the sample had 92% identity with the mip gene of L. nautarum using BLAST.

qPCR

Legionella DNA was detected in 114/151 (75.5%) air samples collected near roads and at all 12 sampling sites (locations A‒L, Supplementary Table S1). Legionella DNA was also detected in 15/21 (71.4%) air samples collected from 14/17 bath facilities and 11/30 (36.7%) samples collected from 4/4 other indoor sites. The positivity rates for samples collected near roads and from bathrooms were significantly higher than those for samples collected from indoor sites other than bathrooms (P < 0.05; Fisher’s exact test followed by post hoc Holm test). The geometric means ± standard deviation (SD) (log10 copies/m3) of Legionella-specific 16S rRNA gene in the Legionella DNA-positive samples were 1.80 ± 0.52, 1.82 ± 0.50, and 0.88 ± 0.56 for roads (N = 114), bathrooms (N = 15), and other indoor sites (N = 11), respectively. The values for these three sampling source types were determined to be significantly different by one-way analysis of variance (one-way ANOVA) (P < 0.05). Moreover, the Tukey-Kramer method revealed that the amount of Legionella DNA was significantly different between the samples from roads and those collected in indoor sites other than bathrooms (P < 0.05) (Table 1).

Table 1 Prevalence of Legionella species in air samples

Geographic characterization of air samples collected from roads

At the 12 sampling sites near asphalt roads (locations A‒L, N = 151), the detection rates of Legionella DNA ranged from 60.0 to 93.3% (Supplementary Table S2). The geometric means ± SD (log10 copies/m3) of Legionella-specific 16S rRNA genes in the Legionella DNA-positive samples ranged from 1.54 ± 0.66 to 2.03 ± 0.42. The differences in the detection rates according to sampling site and the amount of Legionella-specific 16S rRNA genes according to sampling site were not significant by Fisher’s exact test followed by post hoc Holm test and one-way ANOVA, respectively (P ≥ 0.05).

Meteorological characterizations of air samples collected from roads

We assessed the correlation between the climatic conditions (air temperature, relative humidity, total precipitation, and wind speed) and the amount of Legionella-specific 16S rRNA genes (log10 copies/m3) in the Legionella DNA-positive samples (Table 2). The amount of Legionella DNA was correlated with the monthly total precipitation (N = 17, r = 0.56, P < 0.01). It was also directly and inversely correlated with the daily total precipitation for seven days (N = 114, r = 0.21, P = 0.01) and one day (N = 114, r = − 0.29, P < 0.01) before the sampling day, respectively. The scatter plots of total precipitation and the amount of Legionella DNA are shown in Fig. 1. The detection rate and the geometric mean of Legionella DNA seven days before the sampling day at a daily total precipitation of > 10 mm (100%, 16/16 samples; 2.07 ± 0.32 log10 copies/m3, N = 16) were also higher than those at a daily total precipitation of ≤ 10 mm (72.6%, 98/135 samples; 1.75 ± 0.53 log10 copies/m3, N = 98) (P < 0.05; Fisher’s exact test and Student’s t-test, respectively). However, the geometric mean of Legionella DNA one day before the sampling day at a daily total precipitation of ≤ 10 mm (1.83 ± 0.49 log10 copies/m3, N = 99) was higher than that at a daily total precipitation of > 10 mm (1.55 ± 0.64 log10 copies/m3, N = 15; P < 0.05, Student’s t-test); the detection rates of Legionella DNA one day before the sampling day at daily total precipitation of ≤ 10 mm (75.0%, 99/132 samples) and > 10 mm (78.9%, 15/19 samples) were not significantly different (P ≥ 0.05, Fisher’s exact test).

Table 2 Correlation between the climatic conditions and the amount of Legionella DNAa
Fig. 1
figure1

Scatter plots of total precipitation and the amount of Legionella DNA. Total precipitation (mm) at each sampling time point and the amount of Legionella-specific 16 S rRNA gene (log10 copies/m3) in the Legionella DNA-positive air samples collected near roads in Toyama Prefecture, Japan. (a) Daily precipitation one day before sampling (lag day 1). (b) Daily precipitation seven days before sampling (lag day 7). (c) Monthly precipitation

16S rRNA gene amplicon sequencing

Sequencing analysis

16S rRNA gene amplicon sequencing was performed on randomly selected samples collected from roads (N = 30) and all samples collected from bathrooms (N = 21) and other indoor sites (N = 30). The median number of reads after quality filtering, denoising, merging, and removing chimeric sequences was 62,246 (range, 9,852‒246,625) from roads, 112,471 (range, 16,534‒341,587) from bathrooms, and 111,835 (range, 16,009‒250,862) from other indoor sites. A total of 8,174,054 reads (100,914 reads per sample) were assigned to 18,426 amplicon sequence variants (ASVs).

Legionella‑assigned reads

Reads from Legionella species were detected in 15/81 samples (19%) in total: 9/30 samples (30%) collected near roads (mean proportion of reads, 0.11%), 5/21 samples (24%) collected in bathrooms (mean, 0.04%), and 1/30 samples (3%) collected from indoor sites other than bathrooms (mean, 0.03%). The rate of positivity for samples collected near roads was significantly higher than that for samples collected from indoor sites other than bathrooms (P < 0.05; Fisher’s exact test followed by post hoc Holm test).

Among the 15 samples in which Legionella reads were detected, Legionella species was identified in 6 samples (Table 3). At the species level, L. pneumophila was identified in 2/30 samples collected near roads (sample no. 22 and 86; the proportion of reads, 0.09 and 0.11% of the total reads number in each positive sample) and in 1/21 samples collected in bathrooms (sample no. 98; 0.15%). L. birminghamensis was identified in 1/30 samples collected from roads (sample no. 107, 0.81%). L. geestiana was identified in 1/30 samples collected from roads (sample no. 35, 0.02%). L. nautarum was identified in 1/30 samples collected from indoor sites other than bathrooms (sample no. 159, 0.79%). The reads in the remaining 9 Legionella-positive samples could not be identified at the species level.

Table 3 Analysis of air samples containing Legionella-assigned reads at the species level

Characteristics of the bacterial communities

At the genus level, 485, 421, and 368 bacterial genera were detected in samples collected from roads, bathrooms, and other indoor sites, respectively (Supplementary Figure S1). The top three most abundant bacterial genera were Sphingomonas (21.1%), Streptococcus (14.6%), and Methylobacterium (1.6%) in the samples collected near roads; Sphingomonas (17.6%), Pseudomonas (5.4%), and Methylococcus (4.3%) in the samples collected in bathrooms, and Sphingomonas (19.2%), Achromobacter (5.0%), and Arthrobacter (3.8%) in the samples collected from indoor sites other than bathrooms.

The alpha diversity index based on Faith’s phylogenetic diversity, a qualitative measure of bacterial community richness that incorporates phylogenetic relationships between features, in air samples from roads were much higher value than those from other indoor sites [Q < 0.05; Kruskal-Wallis test with Benjamini-Hochberg false discovery rate (FDR) correction] (Supplementary Figure S2). Beta diversity analysis showed that a portion of the air samples collected from roads and indoor sites other than bathrooms were separate from samples collected from bathrooms (Supplementary Figure S3). Linear discriminant analysis (LDA) effect size (LEfSe) analysis to determine the significantly different taxa among the three sampling sources revealed 15 genera with LDA scores of at least 3.0 (Fig. 2; P < 0.05, pairwise Wilcoxon test). Specifically, we found that the following four genera were enriched in the samples (LDA score > 4.0): Pseudomonas, Vibrio, and Staphylococcus in samples from bathrooms (shown in red), and Achromobacter in samples from other indoor sites (shown in green).

Fig. 2
figure2

LEfSe analysis. LEfSe identified the most differentially abundant genera among the road (blue bar), bathroom (red bar), and other indoor sites (green bar). A total of 15 bacterial genera were considered significant. The threshold for the logarithmic LDA score is 3.0, with a P value less than 0.05 (pairwise Wilcoxon test)

Discussion

We demonstrated that DNA from Legionella species was widely present in aerosols derived from outdoor sites near asphalt roads, especially during the rainy season, regardless of sampling site. Legionella DNA was detected in more than 70% of the air samples collected near roads. The positivity rate for samples collected near roads was almost the same as that for samples collected in the bathrooms of public bath facilities, which are a major source of legionellosis in Japan and was significantly higher than that for samples collected from other indoor sites (P < 0.05). Similarly, Montagna et al. reported that Legionella DNA was detected in 72.7% (8/11) of air samples collected from bathrooms in healthcare facilities using a Coriolis µ air sampler by qPCR [14]. Low-level detection of Legionella DNA in air samples collected from other indoor sites (buildings and private homes) may be because airborne bacteria from the outdoor environment enter buildings and houses via ventilation components such as air-conditioning systems [20]. Although the sampling height in this study was not at 150–180 cm above ground level, which is the average height of an adult, owing to the stability of the air sampler, our results showed the existence of aerosols containing Legionella species near asphalt roads. Further investigation of the relationship between sampling height and the prevalence of Legionella species in aerosols is needed to reveal the risk of inhalation of aerosols containing Legionella species.

No Legionella species were isolated from any of the collected air samples (roads, bathrooms, and other indoor sites) by amoebic co-culture. The discrepant results for the amoebic co-culture and qPCR methods may be due to the detection limit of each method. Although our control experiment showed that the detection limit of the amoebic co-culture method was comparable to those of the plate culture method, Conza et al. reported that the detection limit of the amoebic co-culture method was 3 logs high sensitive than those of the plate culture method and that viable Legionella cells were isolated by amoebic co-culture from aerosols collected at composting facilities [15, 18]. Edagawa et al. reported that a combination of amoebic co-culture and qPCR might be useful to detect viable Legionella cells [21]. In this study, 10-fold more Legionella DNA was observed in 10 samples after amoebic co-culture, indicating that amoebic co-culture promoted the growth of Legionella cells such as VBNC state in the environment. The recovery of Legionella from air sample may be due to the state of Legionella cells. In addition, it is also possible Legionella species may not have been isolated because of the stress encountered during aerosolization and the air sampling process, which may have led to a loss of culturability [22]. In fact, Montagna et al. reported that viable Legionella cells in air samples collected using a Coriolis µ air sampler could not be isolated by culture [14]. Improvements in the amoebic co-culture method, such as the strains of amoeba used for co-culture and the incubation periods with the amoeba, may also allow the isolation of Legionella from aerosols. Nonetheless, new sampling and/or culture methods need to be established to allow the isolation of Legionella species from aerosols.

It seems plausible that precipitation is positively associated with the occurrence of legionellosis [23]. Our study also showed a positive correlation between the monthly total precipitation and the amount of Legionella DNA present in aerosols collected near roads. According to previous studies, the number of legionellosis cases in Japan peak in July, which is in the second half of the rainy season [24, 25]. Thus, there is a risk of contracting legionellosis near asphalt roads, especially during the rainy season. The amount of Legionella DNA was also directly and inversely correlated with the daily total precipitation of seven days and one day before sampling, respectively. Our results suggest that Legionella may have multiplied within amoeba present in the environment after precipitation at the seven-day-before sampling time point, whereas the dry condition during the day before the sampling time point may have favored the release of aerosols of small particle sizes from the ground into the atmosphere because of splashing or the effect of wind. Alternatively, the survival rate of Legionella in aerosols may be different due to climatic conditions.

We assessed the distribution of Legionella and the bacterial community in air samples using 16S rRNA gene amplicon sequencing. Reads from Legionella species were detected in 30% of the air samples collected near roads, which was similar to the proportion of reads detected in bathrooms and was significantly higher than those detected in other indoor sites. L. pneumophila, a major causative agent of Legionnaires’ disease [26], was also detected in aerosols collected near roads and in bathrooms. In outdoor environment, other non-L. pneumophila species, such as L. longbeachae, derived from soil were also important for legionellosis [27], indicating the importance of the detection of non-L. pneumophila and species identification. Our results suggest that legionellosis may be acquired by inhaling aerosols from areas near asphalt roads, as well as in bathrooms. The detection rate of Legionella reads by 16S rRNA gene amplicon sequencing was lower than that by qPCR (75.5%). This may be due to the PCR amplification bias of the 16S rRNA gene. Conversely, the detection rate and geometric mean of Legionella DNA by qPCR for samples in which Legionella reads were detected using 16S rRNA gene amplicon sequencing (93.3%, 14/15 samples; 2.04 ± 0.56, log10 copies/m3) were significantly higher than those in the samples in which Legionella reads were not detected by 16S rRNA gene amplicon sequencing (57.6%, 38/66 samples; 1.49 ± 0.56 log10 copies/m3) (P < 0.05; Fisher’s exact test and Student’s t-test, respectively). Thus, the qPCR results for Legionella DNA were correlated with the 16S rRNA gene amplicon sequencing results.

Our study revealed that the bacterial diversity in outdoor environment was comparable to that in indoor environment which contains aerosol-generating features and higher than that in indoor environment without the features. In addition, the bacterial community composition differed in air samples from outdoor and indoor environments. Then, we analyzed what genera account for the diversity and significance by LEfSe analysis. Sphingomonas was the most frequently detected genus in the air samples. This genus has been found in various environments, such as soil, water, clinical specimens, air, and other locations [28,29,30], indicating the opportunity for these bacteria to be released into the air. Pseudomonas and Achromobacter were significantly enriched in air samples from bathrooms and other indoor sites, respectively. These genera have frequently been detected in bathwater and moist indoor environments [31, 32]. Thus, our results agreed with those reported in other studies. These findings provide a foundation for understanding the health effects of aerosols in these environments.

In this study, there are several limitations. First, it has not been investigated whether DNA from Legionella species detected in air samples is derived from VBNC or dead cells. Although various stress factors may induce Legionella cells to enter a VBNC state, these cells can still directly infect human macrophages and amoebae, indicating that VBNC Legionella cells can cause disease in humans [33, 34]. Several studies have shown that VBNC Legionella in water samples regain culturability in amoebic co-culture [17, 35]. Although Legionella is sensitive to environmental stresses such as desiccation and UV irradiation, amoeba cysts, which are resistant amoebal forms that can survive under stress conditions [36], may play a role in the survival and transmission of airborne Legionella. Further investigations using techniques such as ethidium monoazide with qPCR, which can selectively quantify viable Legionella cells because ethidium monoazide prevents the amplification of DNA from membrane-damaged dead cells [37], are needed to obtain a detailed understanding of the state of Legionella in aerosols. Second, regarding the outdoor sites near asphalt roads, information on the sampling sites such as traffic volume at the sampling time and the precise number of people walking on foot on the neighboring was missing. A risk of contracting legionellosis depends not only on the concentration of Legionella in aerosols, but also on the exposure to Legionella cells. However, the exposure assessment was not investigated in this study. Furthermore, we could investigated only 12 sampling sites. Thus, it remains unclear how the road conditions influenced aerosolization of water. The relevance on the road conditions and Legionella prevalence in aerosols seems an interesting topic for future research.

Conclusions

Here, we demonstrated that DNA from Legionella species was widely present in aerosols collected from outdoor sites near asphalt roads, especially during the rainy season, regardless of the sampling site. Our findings suggest that there may be a risk of exposure to Legionella species not only in bathrooms but also in the areas surrounding asphalt roads. Therefore, the possibility of contracting legionellosis in daily life should be considered. Further studies are needed to evaluate the risk of legionellosis and develop prevention strategies.

Methods

Air samples

We investigated outdoor sites near asphalt roads, indoor sites in bathrooms next to bathtubs in public bath facilities, and other indoor sites in Toyama Prefecture, Japan (Supplementary Table S1). A total of 202 air samples were collected, including 151 samples from 12 outdoor sites (locations A‒L) near asphalt roads, on various days in sunny, cloudy, and rainy weather for almost 1 year from June 2016 to April 2017. The numbers of air samples collected near roads at the 12 sampling sites were as follows: A, N = 15; B, N = 12; C, N = 12; D, N = 11; E, N = 10; F, N = 13; G, N = 11; H, N = 11; I, N = 17; J, N = 13; K, N = 11; and L, N = 15. Traffic volume (vehicles/24 h) was shown in Supplementary Table S2 [38]. Although it was not the data at the time of air sampling, we used them as the latest published data (2015) for reference. These sampling sites were located near the external city roads, and there were several people walking on foot on the neighboring roads during the sampling period.

Twenty-one samples were collected from bathrooms next to bathtubs in 17 public bath facilities from October 2016 to February 2017 (16 were collected while operating aerosol-generating devices in a bathtub, such as jet circulation with or without air induction bubbles). The remaining 30 samples were collected in rooms and corridors from two buildings and two private houses from March 2017 to February 2018 (indoor sites other than bathrooms). At the sites, devices that generate aerosols of water, such as showers and taps, were not operated during the sampling period.

Sample collection

For each sampling, a portable cyclone-based air sampler (Coriolis µ; Bertin Technologies, France) was placed approximately 70 cm from the ground. The air samples were collected in 15 mL of a solution containing 0.005% Tween 80 at a flow rate of 300 L/min for 10 min. Depending on the climatic conditions, the remaining volume after sampling was 6‒14 mL, and each sample solution was vortexed for 1 min.

Control experiment

We assessed the detection limits of the culture, amoebic co-culture, and qPCR methods from the air samples as described previously with a slight modification [18]. In this study, air samples were collected in the field with a Coriolis µ under the conditions described above. For our experiments, we used 15 mL of sterilized 0.005% Tween 80 solution spiked with known amounts of Legionella. Five 14.7 mL aliquots of sterilized 0.005% Tween 80 were spiked with 0.3 mL of serial dilutions of L. pneumophila (ATCC33152) to obtain suspensions containing 3–30,000 CFU in 15 mL of 0.005% Tween 80. Then, a collector cone was filled with the spiked solution and connected to the Coriolis µ. After operating the Coriolis µ at a flow rate of 300 L/min for 10 min in a pre-cleaned biological safety cabinet, the spiked solution was used for culture, amoebic co-culture, and qPCR. Fifteen milliliters of sterile 0.005% Tween 80 solution was used as a negative control. All measurements were performed in duplicate.

qPCR

For qPCR, DNA was extracted from a 2 mL aliquot of sample solution. The suspension was centrifuged at 20,000 × g at room temperature for 5 min and then resuspended in 100 µL of 5% (w/v) Chelex-100 solution ([39]; Bio-Rad Laboratories, CA, USA). The suspension was boiled for 10 min and then centrifuged at 20,000 × g for 5 min at room temperature. The supernatant was used as the DNA template, and qPCR was carried out using the CycleavePCR Legionella (16S rRNA) Detection Kit (Takara Bio, Shiga, Japan) and a Thermal Cycler Dice Real Time System II (Takara Bio). According to the manufacturer’s instructions, this qPCR assay was positive for 74 Legionella strains (48 Legionella species) and negative for 14 non-Legionella strains (11 non-Legionella species: Shigella sonnei, Escherichia coli, Vibrio parahaemolyticus, Campylobacter jejuni, Salmonella enterica, Clostridium botulinum, Clostridium perfringens, Staphylococcus aureus, Yersinia enterocolitica, Listeria monocytogenes, and Bacillus cereus), and 1 CFU of L. pneumophila corresponded to 23 copies of 16S rRNA genes, which was calculated from DNA standard curve.

Isolation of Legionella species

Amoebic co-culture was performed as described previously [40]. Acanthamoeba species isolated from cooling tower water were incubated in proteose peptone-yeast extract-glucose-cysteine medium at 30°C for 5‒7 days. Cells were washed and resuspended in phosphate-buffered saline, and then 0.5 mL of the suspension (approximately 1.0 × 105 cells) was added to the remaining amount of the sampling solution (4‒12 mL) after qPCR sampling (2 mL). To prevent evaporation, the samples were incubated at 35°C in 50 mL screw-cap tubes. After 4 weeks, the sample was mixed with equal volumes of 0.2 mol/L KCl-HCl buffer (pH 2.2) for 15 min at room temperature, and 0.2 mL was spread on a GVPC agar plate (Nissui Pharmaceutical Co., Tokyo, Japan). The agar plates were incubated at 35 °C for 7 days in a humidified chamber. Candidate colonies that were smooth gray with characteristic outward structures of Legionella, i.e., cut-glass-like or mosaic-like in appearance were viewed under a stereomicroscope with oblique illumination [41] and were subcultured on a buffered charcoal-yeast extract (BCYE) agar plate with L-cysteine (bioMérieux, Lyon, France) and a blood agar plate (Eiken Chemical, Tokyo, Japan). Colonies growing only on the BCYE agar plate and not on the blood agar plate were presumed to belong to the genus Legionella.

Sequencing of the mip gene

For samples in which the amount of Legionella DNA increased after amoebic co-culture, the species of Legionella was determined. DNA was extracted from a 2 mL sample of the amoebic co-culture using Chelex-100 solution as described above. The mip gene was directly amplified and sequenced as previously described [42].

Next-generation sequencing for 16S rRNA gene amplicon

DNA samples extracted with Chelex-100 solution were used for next-generation sequencing. For air samples randomly selected from roads (N = 30) and collected from bathrooms (N = 21) and other indoor sites (N = 30), the bacterial 16 S V3–4 region was amplified using Tks Gflex DNA Polymerase (Takara Bio) with the primers 341F (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-3′) and 806R (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-3′). A 16S rRNA gene amplicon library was prepared according to the 16S Metagenomic Sequencing Library Preparation protocol for Illumina MiSeq (Illumina, San Diego, CA, USA) and then sequenced using an Illumina MiSeq instrument with the MiSeq Reagent Kit v3 (600 cycles).

Bioinformatic processing

Microbiome bioinformatics was performed using QIIME2 version 2019.7 [43]. Imported demultiplexed sequence data were denoised using DADA2 [44] (via q2-dada2). All ASVs were aligned with mafft [45] (via q2-alignment), which was used to construct a phylogeny with fasttree2 [46] (via q2-phylogeny). Taxonomy, from kingdom to species, was assigned to ASVs using the q2-feature-classifier [47] based on the classify-sklearn naïve Bayes taxonomy classifier against the GreenGenes 13_8 99 % OTU database [48]. However, the short fragments generated by 16S rRNA amplicon sequencing (< 500 bp in this study) limit their use for 16S rRNA gene-based bacterial identification at the species level due to the sequence similarity of the variable regions [49]. Alpha (Faith’s phylogenetic diversity [50]) and beta diversity metrics (weighted UniFrac [51]) were performed to assess the diversity of the microbial communities in the air samples. These metrics were estimated using q2-diversity after samples were rarefied (subsampled without replacement) to 9,852 sequences per sample. The coordinate points of beta diversity were shown in Supplementary Table S3. Different taxa with significant differences between sampling source types were assessed using the LEfSe program with the following parameters (alpha value for pairwise Wilcoxon test set at 0.05; the logarithmic LDA score threshold was set at 3.0) using the Galaxy Hutlab online platform [52] (https://huttenhower.sph.harvard.edu/galaxy/).

Statistical analysis

To investigate the relationships between the rate of Legionella DNA detection and sampling source type and between the detection rate and sampling site (locations A‒L), Fisher’s exact test followed by post hoc Holm test were performed. Fisher’s exact test was also used to compare the rates of Legionella DNA detection in samples according to total precipitation (high and low). We also investigated the relationships between the amount of Legionella DNA and sampling source type and between the amount of Legionella DNA and sampling site (locations A‒L) using one-way ANOVA followed by Tukey–Kramer multiple comparisons. Student’s t-test was performed to compare the amount of Legionella DNA between samples with high and low total precipitation. These tests were performed using the R statistical software package (version 3.0.0). Pearson’s correlation coefficient (r) between climatic conditions (air temperature, relative humidity, total precipitation, and wind speed) and the amount of Legionella DNA was calculated using Excel (Microsoft, Redmond, WA, USA). An absolute Pearson’s r value ≥ 0.20 was considered indicative of a correlation, and a P value less than 0.05 was considered significant. The Kruskal-Wallis test with Benjamini-Hochberg FDR correction (Q value [53]) was performed to compare alpha diversity indices using QIIME2. The Q value cutoff was set to 0.05.

Meteorological data collection

During the study period (2016‒2018), meteorological data were obtained from two main weather stations in Fushiki and Toyama, Toyama Prefecture, Japan [54]. The meteorological data used in this study were monthly or daily values of air temperature (mean, °C), relative humidity (mean, %), total precipitation (mm), and wind speed (mean, m/s). To consider the period for Legionella growth in the environment, the amount of Legionella DNA (log10 copies/m3) and the daily meteorological values for lag day- 0 to 7 were compared. To analyze seasonal trends, the amount of Legionella DNA (log10 copies/m3) and monthly values were compared (Supplementary Table S4).

Availability of data and materials

The datasets supporting the conclusions of this article are deposited in the DNA Data Bank of Japan (http://www.ddbj.nig.ac.jp/) under accession numbers LC472487 (http://getentry.ddbj.nig.ac.jp/getentry/na/LC472487/?format=flatfile&filetype=html&trace=true&show_suppressed=false&limit=10) for the mip gene and DRA008310 (https://ddbj.nig.ac.jp/DRASearch/submission?acc=DRA008310) and DRA009422 (https://ddbj.nig.ac.jp/DRASearch/submission?acc=DRA009422) for the 16 S rRNA gene amplicons. The data deposited is publicly available.

Abbreviations

qPCR:

Quantitative PCR

GVPC:

Glycine-vancomycin-polymyxin B-cycloheximide

CFU:

Colony-forming units

SD:

Standard deviation

ANOVA:

Analysis of variance

mip:

Macrophage infectivity potentiator

VBNC:

Viable but non-culturable

ASV:

Amplicon sequence variants

LDA:

Linear discriminant analysis

LEfSe:

Linear discriminant analysis effect size

BCYE:

Buffered charcoal-yeast extract

FDR:

False discovery rate

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Acknowledgements

We thank Editage for the English language review. A portion of the results of this study was presented as an oral communication at the 6th meeting of the ESGLI Study Group for Legionella Infections Conference, Athens, Greece, September 10–12, 2019.

Funding

This work was supported by Health and Labor Sciences research grants (grant numbers H28-Shinko-Ippan-006 and 19LA1006 to J.A.-M) and JSPS KAKENHI (grant number 20K18922). The funding bodies had no role in the design of the study and collection, analysis, interpretation of data and in writing the manuscript.

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JK carried out the experiments, performed the bioinformatics and statistical analysis, and was a major contributor in writing the manuscript. MW, KK, TK, and KU participated in the analysis. FK, JA-M, and JI participated in writing the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jun-ichi Kanatani.

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Kanatani, Ji., Watahiki, M., Kimata, K. et al. Detection of Legionella species, the influence of precipitation on the amount of Legionella DNA, and bacterial microbiome in aerosols from outdoor sites near asphalt roads in Toyama Prefecture, Japan. BMC Microbiol 21, 215 (2021). https://doi.org/10.1186/s12866-021-02275-2

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Keywords

  • Legionella species
  • Legionellosis
  • Aerosols
  • Asphalt roads
  • Microbiome