Microbial Water Quality Assessment of Private Wells Using 16S rRNA Gene Amplicon Sequencing with a Nanopore Sequencer

Abstract

Private wells are used daily worldwide as convenient household water sources. In Japan, where water supply coverage is high, well water is occasionally used for non-potable purposes, such as irrigation and watering. Currently, the main microbiological test of well water is designed to detect Escherichia coli, which is an indicator of fecal contamination, using culture methods. Water use such as watering generates bioaerosols, which may cause airborne infection. However, many causative bacteria of aerosol-derived infections, such as Legionella spp., are difficult to detect using culture methods. Thus, more comprehensive modern assessment is desirable for securing the microbiological quality of well water. Here, the bacterial community structure of five private wells located in different environments was examined using the rapid and portable MinION sequencer, which enabled us to identify bacteria to the species level based on full-length 16S ribosomal RNA (rRNA) gene sequences. The results revealed the differences in the bacterial community structures of water samples from the five wells and detected Legionella pneumophila and Aeromonas hydrophila as new candidate microbial indicators. The comprehensive analysis method used in this study successfully detected bacteria causing opportunistic infections, which are difficult to detect by conventional methods. This approach is expected to be routinely applied in the future as a highly accurate method for assessing the microbiological quality of private well water.

INTRODUCTION

Private wells are used daily worldwide as convenient household water sources. In developing countries, they are considered important as a means of ensuring drinking water and, even in developed countries, well water is an important resource for drinking and other purposes in rural areas. The Environmental Protection Agency reported that more than 23 million households in the United States use private wells as their main source of household water, including for drinking.^1,2) In Japan, where water supply coverage is high (>98% of the population base), many private wells are unused. Some people use well water for non-potable purposes, such as irrigation (farming), watering gardens and trees, bathing, flushing toilets, and washing clothes. However, since the Great Hanshin-Awaji Earthquake in 1995, private wells have been recognized again as a potential source of household water in times of disaster.³⁾ A system to register privately owned wells with local governments has been promoted so that they can be used for household water supply at the community level in the event of a disaster.⁴⁾

In Japan, most private wells are shallow. Therefore, well water quality and quantity can easily be affected by the surrounding environment. The assessment of well water quality is left to the well owner, and regular testing is not mandated. The water quality assessment methods for well water are the same as those for tap water (which is treated by filtration and chlorination). The well-water assessment indicators include bacterial counts, absence of Escherichia coli, pH, chloride ions, nitrite-nitrogen, nitrate-nitrogen, total organic carbon (TOC), turbidity, color, odor, and taste. As science and technology have advanced, physicochemical testing of drinking water has developed; precise analysis of harmful chemical contaminants has been newly added to the array of tests, and simple methods using test strips are available for the traditional indicators pH, free chlorine, nitrite, nitric acid, arsenic, iron, lead, and others. However, the main microbiological test remains the measurement of the total bacterial count. A method for direct on-site monitoring of total and esterase-active bacterial cells number in freshwater environments using a portable microfluidic counting system has already been reported.⁵⁾ The culture method is designed to detect bacteria that cause waterborne diseases, especially E. coli, which is an indicator of fecal contamination. The microbiological quality of well water is important, and the current testing and monitoring methods may not be sufficient.

During water use (e.g., for agricultural watering), aerosols are generated. The definition of an aerosol is suspended nano/micro particles or droplets in the air, such as dusts, mists, or fumes.⁶⁾ Aerosols that include microorganisms are called “bioaerosols” and they may cause airborne infection. Legionella spp. are a typical cause of aerosol-derived infections. In Japan, the number of reported cases has been increasing since 2004.⁷⁾ The US Centers for Disease Control and Prevention (CDC) reported that the number of outbreaks of respiratory illness caused by Legionella has increased since 2000 (the crude national incidence rate increased 5.5-fold, from 0.42 per 100000 persons in 2000 to 2.29 per 100000 persons in 2017)^8–10) and that well water was one of the infection sources.^11–14) Mycobacterium avium complex (MAC), which causes pulmonary MAC disease is also known to be a cause of aerosol-derived infection.^15–18) These bacteria are ubiquitous in natural environments.¹⁹⁾ Many bacteria detected in the natural environment are slow-growing and difficult to detect by conventional culture methods. However, rapid advances in gene-targeted technology have made it possible to comprehensively analyze bacterial communities in the environment, including non-culturable bacteria.

A portable nanopore sequencer MinION (Oxford Nanopore Technologies) enables rapid sequencing of the full-length 16S ribosomal RNA (rRNA) gene sequence,^20–22) thereby providing comprehensive bacterial community data at the species level. The MinION system has already been successfully applied to monitoring of pathogenic or drug-resistant bacteria,²³⁾ as well as whole bacterial community structure.^24–26) Bacterial species information is necessary to understand the character of each bacterium, particularly its effects on humans. Since the water quality of shallow wells are highly susceptible to the surrounding environment, their associated bacterial assemblages would also change. Given such conditions, a rapid and comprehensive microbial quality assessment would be desirable to ensure the microbiological quality of well water. Here we aimed to understand the bacterial community structure in water from private wells located in different environments in Japan, using full-length 16S rRNA gene amplicon sequencing on the MinION sequencer. The results of this study provide fundamental data for potential new microbiological quality assessment methods, and new bacterial targets for assessing the quality of well water.

MATERIALS AND METHODS

Well Water Sampling

Well water samples were collected from four private wells in Osaka (samples N1, N2, W1, and W2) and a well in Wakayama prefecture (sample M) in western Japan (Fig. 1) between January and December 2021. All wells used in this study were shallow (depth ≤10 m). The sampling locations and the depths of the wells are shown in Table 1 and the sampling dates are shown in Table 2. The selected wells are located in different environments. Wells N1 and N2, and W1 and W2, are respectively located close to each other (within 20–50 m) but are used at different frequencies. During this study, all the water in wells W2 and M, which have not been used for several decades, was drained with a sump pump, and these wells were refilled with water naturally seeping from the ground.

Fig. 1. Sampling Locations

Table 1. Locations and Characteristics of Wells

Well name	Latitude	Longitude	Depth*(m)	Major purpose of use	Location characteristics
N1	34°25′04″N	135°27′31″E	2.9	Bath, laundry, watering the garden	Rural and agricultural area (on the grounds of a private home)
N2	34°25′04″N	135°27′30″E	2.7	Abandonment	Rural and agricultural area (adjacent to a field)
W1	34°27′09″N	135°33′42″E	4.8	Watering the garden	Residential area (on the grounds of a private home)
W2	34°27′07″N	135°33′44″E	4.8	Abandonment	Residential area (onsite shared parking lot)
M	34°16′10″N	135°17′24″E	4.0	Abandonment	Rural and agricultural area

* Depth from ground to bottom of well.

Table 2. Physicochemical Characteristics of Well Water

Well name	Sample ID	Sampling date	Temperature (°C)		Turbidity (NTU)a)	Color (TCU)b)	pH	TOCc) (mg/L)	Remarks
			Water	Atmosphere
	N1 0401	1 Apr 2021	13.3	15.8	0.1	0.5	6.89	0.4	Sunny
N1	N1 0922 d)	22 Sep 2021	23.0	27.8	1.4	9.4	9.06	2.1	Sunny after a brief downpour
	N1 1015	15 Oct 2021	21.5	23.0	0.5	4.5	9.26	2.0	Sunny
	N1 1221	21 Dec 2021	16.5	13.5	0.3	4.0	10.9	1.9	Sunny
	N2 0401	1 Apr 2021	13.0	16.2	0.5	2.8	6.92	0.5	Sunny
N2	N2 0922 d)	22 Sep 2021	23.0	27.5	2.3	6.7	7.56	1.5	Sunny after a brief downpour
	N2 1015	15 Oct 2021	21.0	22.8	0.3	1.3	7.22	0.8	Sunny
	N2 122	21 Dec 2021	14.2	13.0	0.4	2.7	6.98	1.1	Sunny
	W1 0401	1 Apr 2021	13.3	19.6	1.1	1.8	6.88	0.5	Sunny
W1	W1 0922	22 Sep 2021	21.5	30.0	4.2	10.5	7.43	0.8	Cloudy
	W1 1015	15 Oct 2021	20.5	26.8	0.7	3.9	7.76	0.6	Sunny
	W1 1221	21 Dec 2021	14.5	12.5	0.3	1.1	7.01	0.8	Sunny
	W2 0119	19 Jan 2021	12.7	4.2	2.3	39.9	6.02	17.4	Sunny
	W2 0209-1e)	9 Feb 2021	14.0	10.0	1.6	8.5	7.04	1.6	Sunny
	W2 0209-2f)	9 Feb 2021	13.5	7.8	40.6	18.5	7.07	0.9	Sunny
W2	W2 0401	1 Apr 2021	14.0	20.6	0.9	7.3	6.85	0.7	Sunny
	W2 0922	22 Sep 2021	22.7	29.5	0.5	4.6	7.11	0.7	Cloudy
	W2 1015	15 Oct 2021	21.5	25.8	0.3	1.8	7.19	0.6	Sunny
	W2 1221	21 Dec 2021	16.0	12.0	0.6	4.4	7.18	0.8	Sunny
	M 0504	4 May 2021	15.8	26.0	1.2	14.6	7.08	2.7	Sunny
	M 0523-1e)	23 May 2021	18.8	22.0	3.9	21.3	7.03	4.1	Sunny
M	M 0523-2f)	23 May 2021	15.8	26.0	5.3	10.5	6.87	2.6	Sunny
	M 0829	29 Aug 2021	26.2	33.0	1.6	12.5	6.70	2.6	Sunny
	M 1206	6 Dec 2021	13.8	9.5	0.5	3.6	6.80	2.6	Sunny

a) Nephelometric unit. b) True color unit. c) Total organic carbon. d) Concrete sewer pipes were laid by sewer works approximately two weeks ago. e) Before drainage. f) After drainage/water refill.

For sampling, from each well, 1.5 L of water was collected into glass bottles sterilized at 180 °C for 2 h using a sterile tin bucket. The bottles were rinsed thoroughly with water from the same source before sample collection. From each sample, 1 L of the water was used for nucleic acid analysis, and 0.5 L for chemical analyses and bacterial enumeration. Water samples were transported to the laboratory in a cool-box with ice packs and stored in a refrigerator until analysis. Sample preparation for bacterial counting was performed within 24 h of sampling.

Culture Dependent Methods for Enumeration of Bacteria in Well Water Samples

Within 24 h of sampling, 1 mL of each well water sample was diluted in 15 mL of standard methods agar (SMA; 5 mg/mL peptone, 2.5 mg/mL yeast extract, 1 mg/mL glucose, and 1.5% agar; Nissui Pharmaceutical Co., Ltd., Tokyo, Japan) and incubated at 36 ± 1 °C for 24 h, or in Difco R2A agar (R2A; Becton Dickinson and Company, NJ, U.S.A.) and incubated at 20 ± 1 °C for 7 d, before counting.

Direct Counting Methods for Enumeration of Bacteria in Well Water Samples

Direct bacterial cell counts were performed within 24 h of sampling. Bacterial cells in a sample were trapped on a sterile polycarbonate filter (pore size 0.22 µm; Advantec, Tokyo, Japan) and fluorescently stained with SYBR Green I (TaKaRa, Shiga, Japan), which detects total bacterial cells, or 6-carboxyfluorescein diacetate (6-CFDA; Dojindo, Kumamoto, Japan), which detects esterase active bacterial cells. In >20 microscopic fields, fluorescently stained cells were counted under an epifluorescence microscope at a magnification of ×1000 and the number of cells per milliliter of well water was calculated.

Physicochemical Analyses

Water temperature and atmospheric temperature were measured on site. Water turbidity, color, and pH were determined within 24 h of sampling in the laboratory. Turbidity and color were measured using a water analyzer (WA 6000, Nippon Denshoku Industries Co., Ltd., Tokyo, Japan). pH was measured using a digital pH meter (F-73, Horiba Co., Kyoto, Japan). TOC was measured using a TOC analyzer (TOC-V CSH, Shimadzu Corp., Kyoto, Japan) within 72 h of sampling.

DNA Extraction

Within 8 h of sampling, bacterial cells present in the samples were trapped onto Supor polyethersulfone membrane filters (pore size 0.2 µm) using MicroFunnel filter funnels (Pall, NY, U.S.A.) and stored at −80 °C. Bacterial DNA was extracted using a DNeasy PowerWater Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions, and prepared in 100 µL of TE buffer. The DNA was quantified using a Qubit 4 Fluorometer and a dsDNA HS Assay Kit (Thermo Fisher Scientific, MA, U.S.A.) and stored at −20 °C.

PCR Amplification, 16S rRNA Gene Sequencing, and Base Calling

The DNA extract was used to build a 16S rRNA gene sequencing library for nanopore sequencing using a 16S Barcoding kit (Oxford Nanopore Technologies (ONT), Oxford, U.K.). The library construction was performed following the manufacturer’s instructions. DNA amplification was carried out using 10 ng of DNA template and 25 µL of LongAmp Taq 2 × Master Mix (New England Biolabs, Ipswich, MA, U.S.A.) with the following thermal cycling conditions: initial denaturation for 1 min at 95 °C; denaturation for 20 s at 95 °C, annealing for 30 s at 55 °C, and extension for 2 min at 65 °C (25 cycles); and final extension for 5 min at 65 °C. The PCR products (total 50–100 ng) were used to build the 16S rRNA gene library and loaded onto a flow cell (R9.4.1, ONT) following the manufacturer’s protocol. The sequencing run was performed for 8–72 h using ONT’s MinKNOW software (version 21.06.13). Concurrently, base calls were made using Guppy software (version 4.3.2; ONT). Homology analysis was performed with the EPI2ME FASTA pipeline. The taxonomic classification of base-called reads, along with their frequency, was analyzed using the FASTQ 16S workflow (version 2021.09.09) with default values in the quality filtering, except for quality score ≥8 and minimum length filter 1200 bases. Finally, the numbers of obtained reads per sample (classified at the phylum level) were as follows: N1, 15902–477300 reads (median 226913); N2, 15553–362854 reads (median 197214); W1, 25829–300568 reads (median 160922); W2, 12775–264700 reads (median 38831); and M, 80065–371126 reads (median 122620).

Bacterial Community Structure Analysis

In this study, the relative abundances (%) at the species, genus, and phylum levels were calculated using the total reads classified at the phylum level as 100%. In the analysis of bacterial community structures at the phylum or genus level, a minimum abundance cutoff of 0.1% was applied for each taxonomic group in each sample. The minimum abundance cutoff was set at 0.05% for the species level classification.

Non-metric Multidimensional Scaling (NMDS) Analysis

To visualize differences in bacterial community structures, NMDS ordination was performed based on Bray–Curtis dissimilarities calculated from relative community composition at the genus level (minimum cutoff 0.1%) using the metaMDS function (distance = “bray,” k = 2, trymax = 100) in the vegan package in R version 4.1.0 (2021-05-18; http://www.r-project.org/). The NMDS results were plotted using the ggplot2 package version 3.3.6 (Wickham et al.).

RESULTS AND DISCUSSION

Physicochemical Characteristics of Well Water and Bacterial Abundance

Table 2 shows the results of physicochemical analyses of the 24 water samples collected from the five wells. Wells W2 and M had been left for a long time before the first sample was taken. In well W2, rotting pieces of wood were floating in the water. The initial water samples from wells W2 (W2 0119) and M (M 0504) had high turbidity and color. Draining and refilling these wells improved the water color and turbidity, particularly the color in well W2. Thereafter, both turbidity and color decreased to ¹/₂ to ¹/₁₀th of the values for samples W2 0119 and M 0504. The values were comparable to those for wells N1 and N2.

In well N1, the three samples taken from September onward had pH 9–11. This could be because sewer construction works were taking place in the vicinity of wells N1 and N2.²⁷⁾ The water quality of shallow wells is directly influenced by the nearby groundwater environment. In these sewer works, approximately 1 m of soil was excavated upstream of the wells under an asphalt paved road, gravel was placed on the bottom, and a concrete sewer pipe was laid. The September samples from wells N1 and N2 were taken approximately 2 weeks after this construction work and had higher turbidity, color, and TOC than the April samples; turbidity and color values then decreased over time.

Bacterial counts determined by plate counting and total direct counting using an epifluorescence microscope are shown in Fig. 2. The bacterial counts for well N1, which is used on a daily basis, ranged from <3 × 10¹ to 5.8 × 10² colony-forming units (CFU)/mL on SMA medium, and from 1.0 × 10² to 5.7 × 10³ CFU/mL on R2A medium. These values were less than half those in the samples from well N2, which is unused. Although the data are limited, sampling from wells W2 and M showed that after draining and refilling the water, the numbers of CFU/mL determined using SMA and R2A decreased over time to the same levels as those in wells N2 and W1, regardless of changes in atmospheric and water temperature. The number of CFU/mL determined using R2A decreased by more than one order of magnitude from the initial values in both wells W2 and M.

Fig. 2. Bacterial Abundance in Well Water

Vertical arrows indicate the timing of sewer works. Solid arrows indicate samples taken before drainage, and dotted arrows indicate those taken after drainage/water refill. Open circles indicate the total number of bacterial cells counted by SYBR Green I staining. Closed circles indicate the number of esterase-active bacterial cells counted by 6-carboxyfluorescein diacetate (6-CFDA) staining. Open squares indicate the number of colony-forming units (CFU) on R2A medium incubated at 20 °C for 7 d. Closed squares indicate the number of CFU on standard agar medium incubated at 36 °C for 24 h. Open crosses indicate less than 3.0 × 10¹.

Bacterial Community Composition at the Phylum Level

In this study, we performed comprehensive analyses by amplicon sequencing targeting full-length bacterial 16S rRNA genes. Relative abundances of each taxonomic group were classified at the phylum level. In the phylum Proteobacteria, the classification was expanded to the class level (Fig. 3). Proteobacteria, which are frequently detected in water and other environmental samples, accounted for >65% of all samples. By class, Alphaproteobacteria and Betaproteobacteria were predominant. At the class level, Alphaproteobacteria, Betaproteobacteria and Gammaproteobacteria, which have been reported to be detected in the water environment,^13,28,29) were predominant. Bacteroidetes and Firmicutes were detected, which are the bacteria that make up the majority of human and animal gut microbiota.^30,31) Acidobacteria, Nitrospirae, and Verrucomicrobia were also detected in all the wells. After the sewerage works (see above), the pH became alkaline in well N1 (samples N1 0922, N1 1015, and N1 1221). The microbiota was then characterized by Deinococcus–Thermus, which are detected in the natural environment and in hot springs in extreme conditions. Planctomycetes, which are oligotrophic and slow growing, were detected in well W1.

Fig. 3. Bacterial Community Structure in Well Water at the Phylum Level, Based on 16S rRNA Gene Amplicon Sequencing Using MinION

In the phylum Proteobacteria, the classification was expanded to the class level. Vertical blue arrows indicate the timing of sewer works. Vertical red arrows indicate the timing of drainage and refill of well water. Solid arrows indicate samples taken before drainage, and dotted arrows indicate those taken after drainage/water refill.

Bacterial Community Composition at the Genus Level

Heat maps were drawn using bacterial relative abundance (cutoff 0.1%) to investigate the changes in bacterial community composition at the genus level (Fig. 4). Bacterial genera listed as harmful to humans in the WHO water quality guidelines³²⁾ are presented in three categories: respiratory tract infection-causing bacteria, gastrointestinal tract infection-causing bacteria, and nonpathogenic bacteria (Fig. 4A). All the five most frequent bacterial genera in each well water sample, other than those listed as harmful to humans in the WHO water quality guidelines, are listed in Fig. 4B.

Fig. 4. Relative Abundances of Frequently Detected Bacterial Genera

A, bacteria listed in the WHO guidelines for drinking water quality; B, Non-etiologic agents and non-waterborne etiologic agents. Vertical blue arrows indicate the timing of sewer works. Solid arrows indicate samples taken before drainage, and dotted arrows indicate those taken after drainage/water refill. A minimum abundance cutoff of 0.1% for each taxonomic group in a sample was applied.

Among the bacteria listed in the WHO water quality guidelines (Fig. 4A), Legionella, a typical respiratory tract infection-causing organism, and Pseudomonas, a cause of opportunistic infections, were detected in all our wells. In wells W2 and M, they were detected before drainage, and after drainage and water refill. Aeromonas and Campylobacter are causative agents of gastrointestinal infections.³³⁾ Campylobacter were detected in sample W2 0922 (0.5%). Aeromonas were detected more frequently and were abundant in well N2. Spore-forming Bacillus and Clostridium were detected, but their relative abundance was low (0.1–0.5%). Escherichia, Salmonella, Shigella, and Vibrio spp., which are associated with fecal contamination and gastrointestinal infections, were not detected. Most of the wells sampled in this study were located in areas with septic tanks and sewerage systems, so the possibility of human fecal contamination from the surrounding environments was considered low. In addition, Legionella spp. are known to persist in biofilms, and the nonpathogenic, biofilm-producing bacteria Flavobacterium spp., Sphingomonas, and Caulobacter spp. were detected in the samples in which Legionella spp. were detected.³⁴⁾

Among the bacterial genera not mentioned in the WHO water quality guidelines (Fig. 4B), the following were detected in most of our well water samples, with the highest values exceeding 5%: Curvibacter and Reyranella, which are reported to survive in soil and aquatic environments; Polynucleobacter, which has been detected in mineral water; Undibacterium, Rhodoferax, and Geobacter, which are associated with metal metabolism; and Novosphingobium, Hydrogenophaga, Methylotenera, Nitrospira, Terrimonas, Hyphomicrobium, Limnohabitans, and Sphingomonas, which are associated with chemical metabolism. Malikia and Meiothermus were dominant in well N1, whereas Vogesella were abundant in well N2. Before drainage, the relative abundance of Flectobacillus in well W2 was 30%. Species level analysis revealed that members of this genus included F. rhizosphaerae and F. roseus. It was previously reported that F. rhizosphaerae and F. roseus had been isolated from soil and freshwater.^35,36) Sulfurimonas, which is often isolated from sulfidic habitats,³⁷⁾ was detected in well W2 (W2 0922) at approximately 10%. The characteristic bacterial genera in well W2 differed before drainage and after drainage/water refill.

Bacterial Species Analyses

Bacteria listed in the WHO water quality guidelines, were analyzed at the species level (Table 3). L. pneumophila was detected ubiquitously in the five wells; it was detected in both wells W2 and M before drainage and after drainage/water refill. A. hydrophila was detected in well N2 (N2 1015) at the highest value of 4.5%, while it was not detected in well N1. Wells N1 and N2 are 20–30 m apart; well N1 is routinely used for household water supply, whereas well N2 is adjacent to a field and surrounded by vegetation in all seasons. The field adjacent to N2 is fertilized with fertilizers made from vegetable or livestock-derived manure, which was assumed to be a contributing factor.

Table 3. Relative Abundance of Aeromans hydrophila and Legionella pneumophila Compared with the Total Reads

Well name	Sample ID	Sampling date	Relative abundance (%)
			A. hydrophila		L. pneumophila
	N1 0401	1 Apr 2021	—e)	(—)	0.14	(83.0)
N1	N1 0922b)	22 Sep 2021	—	(—)	0.17	(84.7)
	N1 1015	15 Oct 2021	—	(—)	0.23	(87.3)
	N1 1221	21 Dec 2021	—	(—)	0.09	(86.6)
	N2 0401	1 Apr 2021	0.05	(86.3)	0.44	(82.3)
N2	N2 0922b)	22 Sep 2021	1.06	(89.1)	0.05	(83.9)
	N2 1015	15 Oct 2021	4.45	(93.2)	0.22	(88.6)
	N2 1221	21 Dec 2021	0.27	(92.7)	0.21	(86.8)
	W1 0401	1 Apr 2021	—	(—)	0.05	(85.2)
W1	W1 0922	22 Sep 2021	0.83	(89.0)	0.13	(83.5)
	W1 1015	15 Oct 2021	0.51	(93.2)	0.11	(86.4)
	W1 1221	21 Dec 2021	0.07	(93.2)	0.22	(87.3)
	W2 0119	19 Jan 2021	—	(—)	—	(—)
	W2 0209-1c)	9 Feb 2021	—	(—)	0.09	(85.5)
	W2 0209-2d)	9 Feb 2021	—	(—)	0.09	(86.0)
W2	W2 0401	1 Apr 2021	—	(—)	0.51	(83.4)
	W2 0922	22 Sep 2021	—	(—)	0.19	(83.3)
	W2 1015	15 Oct 2021	2.00	(93.1)	0.13	(86.6)
	W2 1221	21 Dec 2021	0.26	(93.2)	0.20	(86.6)
	M 0504	4 May 2021	—	(—)	0.18	(85.2)
	M 0523-1c)	23 May 2021	0.05	(89.5)	0.64	(85.6)
M	M 0523-2d)	23 May 2021	—	(—)	0.09	(84.7)
	M 0829	29 Aug 2021	0.15	(89.3)	0.06	(83.1)
	M 1206	6 Dec 2021	—	(—)	1.12	(86.6)

Numbers in brackets indicate average accuracy^a) (%). a) Average accuracy indicates the average percentage match with the 16S rRNA gene database used in EPI2ME software (ONT). b) Concrete sewer pipes were laid by sewer works approximately two weeks ago. c) Before drainage. d) After drainage/water refill. e) —: < 0.05% (Relative abundance).

L. pneumophila and A. hydrophila have been detected in routinely used well water.^13,14) In the CDC’s Legionella spp. report, well water and drinking water were reported to be sources of Legionella spp.^11,12) In Japan, the main use of well water has changed from drinking to irrigating agriculture and watering gardens and trees. Therefore, it is necessary to consider well water bacteria that may cause respiratory tract infections via bioaerosols. Hence, we consider it necessary to monitor L. pneumophila, other bacteria of the genus Legionella, Pseudomonas, and bacteria, including MAC (which were not detected in this study), as well water indicator organisms in the future. Although there have been no reports of outbreaks of A. hydrophila in Japan in recent years, it is still challenge as a causative agent of diarrheal infections in other parts of the world.³⁸⁾ Well water may be used as drinking water in times of disaster. Therefore, it is necessary to use gastrointestinal infection-causing bacteria as indicator bacteria for the safety and quality of well water.

NMDS of the Bacteria Classified at the Genus Level

Data on bacterial genera from our samples were used to analyze the similarities of bacterial community structures between samples. The distance between points indicates the degree of difference in relative bacterial composition (Fig. 5). Points clustered closely have similar bacterial community structures. The arrows indicate bacterial genera that characterize the clustering.

Fig. 5. Nonparametric Multidimensional Scaling Analysis of Bacterial Communities at the Genus Level in Well Water Samples N1, N2, W1, and W2 (A), and Samples M and W2 (B)

The arrows indicate bacterial genera that characterize the clustering.

Comparisons were made between 16 samples from wells N1, N2, W1, and W2 (Fig. 5A). For well N1, three samples (N1 0922, N1 1015, and N1 1221) were taken after sewer construction works (see above), and these samples clustered together. For well N2, samples N2 0401, N2 1015, and N2 1221 clustered together, but sample N2 0922, which was taken immediately after the sewer construction works, did not. For well W1, the samples other than W1 0401 clustered closely. Three samples from well W2 (W2 0401, W2 1015, and W2 1221), all sampled after drainage/water refill, clustered together.

The plot distributions for wells N1 and N2, and wells W1 and W2, which are respectively located in close proximity, were also observed. Loose clustering was observed for the eight samples from wells W1 and W2, indicating that the constituent bacteria of these two wells were similar. The characteristic bacteria were Novosphingobium and Terrimonas. Wells N1 and N2 are close together, but the cluster of well N1 samples (N1 0922, N1 1015, and N1 1221) taken after the sewer construction works separated from the cluster of well N2 samples (N2 0401, N2 1015, and N2 1221), and the bacterial genera characterizing each cluster were different. The bacteria of the former cluster were characterized by Rickettsia, Zoogloea, Hydrogeophaga, Meiothermus, Gemmatimonas, Malikia, Phenylobacterium, and Nitrosomonas (each classified within the top 10 bacteria in each sample). After the sewer construction, the pH of the well water became alkaline (9–11), which is thought to have allowed certain bacterial taxa to selectively inhabit the well water. These bacteria cannot be detected by culture methods. Comprehensive analysis by amplicon sequencing targeting the 16S rRNA gene allowed timely observation of changes in bacterial community structure due to changes in water quality.

The water that was initially present in unused wells W2 and M was drained with a sump pump, and they then refilled with water naturally seeping from the ground. We waited until water partially refilled the wells and then sampled: water in the well 5.5 h after the start of refilling (to depth 0.6 m) for well W2, and 3 h after the start of refilling (to depth 1 m) for well M, was taken as samples W2 0209-2 and M 0523-2, respectively. The similarity of the 12 samples taken in total from wells W2 and M was analyzed by NMDS (Fig. 5B). In both wells W2 and M, there was distance between the plots for the samples taken before drainage (W2 0209-1, M 0523-1) and after water refill (W2 0209-2, M 0523-2). This may be due to the temporary effects of detached material from the wall of the well, bottom sediment, or fallen wood chips during the drainage and water refill. In well W2, characteristic bacterial genera were found to be different in three samples taken before drainage and immediately after drainage/water refill (W2 0119, W2 0209-1, and W2 0209-2), and in four samples taken later after drainage/water refill (W2 0401, W2 0922, W2 1015, and W2 1221). We suggest that there were changes in the bacterial community structure before drainage and after drainage/water refill. The samples taken before drainage and immediately after drainage/water refill (W2 0119, W2 0209-1, and W2 0209-2) were characterized by Methylotenera and Hydrogeophaga, which are involved in the metabolism of chemicals, as well as Novosphingobium. Wells W1 and W2 are located near a main road, and it is possible that their proximity to metalworking and other factories may have influenced their location.

A New Approach for the Quality Assessment of Well Water

This study showed that the bacterial community structure can be affected by water quality changes in shallow wells. Thus, regular bacterial monitoring would be necessary to secure the bacteriological quality of well water. Combination of the conventional culture-dependent method and gene-targeted methods is useful for complete assessment of bacteriological water quality. Recent studies have utilized modern molecular technologies to assess the microbial quality of drinking water. For example, Brumfield et al. and Han et al. reported detailed bacterial community structure in bottled mineral water, tap water and the source water using shotgun sequencing or high-throughput sequencing.^28,29) Analysis of specific region of 16S rRNA genes, such as V4, using massively parallel sequencers, such as Illumina's MiSeq, allows identification at the genus level. Shotgun sequencing analysis provides detailed taxonomic data from kingdom to bacterial species. On the other hand, the comprehensive analysis method targeting full-length 16S rRNA genes used in this study enables rapid and portable species-level analysis of bacteria, including those causing opportunistic infections, which are difficult to detect by conventional methods. It was suggested that the results obtained from the aforementioned analysis methods and the analysis methods targeting full-length 16S rRNA gene used in this study are comparable in analysis resolution. This approach is expected to be routinely applied as a highly accurate method of assessing the microbiological quality of water in the future.

Acknowledgments

We acknowledge Mr. Sakae Wada and Mr. Yoshiharu Nagatani for providing well water samples. We also thank Dr. Mako Kawai at Himeji Dokkyo University for technical assistance to this study.

Conflict of Interest

The authors declare no conflict of interest.

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