Water quality parameters influence the abundance of pathogenic bacteria. The genera Aeromonas, Arcobacter, Klebsiella, and Mycobacterium are among the representative pathogenic bacteria identified in wastewater. However, information on the correlations between water quality and the abundance of these bacteria, as well as their reduction rate in existing wastewater treatment facilities (WTFs), is lacking. Hence, this study aimed to determine the abundance and reduction rates of these bacterial groups in WTFs. Sixty-eight samples (34 influent and 34 non-disinfected, treated, effluent samples) were collected from nine WTFs in Japan and Thailand. 16S rRNA gene amplicon sequencing analysis revealed the presence of Aeromonas, Arcobacter, and Mycobacterium in all influent wastewater and treated effluent samples. Quantitative real-time polymerase chain reaction (qPCR) was used to quantify the abundance of Aeromonas, Arcobacter, Klebsiella pneumoniae species complex (KpSC), and Mycobacterium. The geometric mean abundances of Aeromonas, Arcobacter, KpSC, and Mycobacterium in the influent wastewater were 1.2 × 104–2.4 × 105, 1.0 × 105–4.5 × 106, 3.6 × 102–4.3 × 104, and 6.9 × 103–5.5 × 104 cells mL−1, respectively, and their average log reduction values were 0.77–2.57, 1.00–3.06, 1.35–3.11, and −0.67–1.57, respectively. Spearman’s rank correlation coefficients indicated significant positive or negative correlations between the abundances of the potentially pathogenic bacterial groups and Escherichia coli as well as water quality parameters, namely, chemical/biochemical oxygen demand, total nitrogen, nitrate-nitrogen, nitrite-nitrogen, ammonium-nitrogen, suspended solids, volatile suspended solids, and oxidation-reduction potential. This study provides valuable information on the development and appropriate management of WTFs to produce safe, hygienic water.
Citation: Aoki M, Takemura Y, Kawakami S, Yoochatchaval W, Tran P. T, Tomioka N, et al. (2023) Quantitative detection and reduction of potentially pathogenic bacterial groups of Aeromonas, Arcobacter, Klebsiella pneumoniae species complex, and Mycobacterium in wastewater treatment facilities. PLoS ONE 18(9): e0291742. https://doi.org/10.1371/journal.pone.0291742
Editor: Zhi Zhou, Purdue University, UNITED STATES
Received: May 29, 2023; Accepted: September 5, 2023; Published: September 28, 2023
Copyright: © 2023 Aoki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The obtained raw 16S rRNA gene amplicon sequence data are available in the DNA Data Bank of Japan (DDBJ) Sequence Read Archive (DRA) (https://ddbj.nig.ac.jp) under accession numbers DRR457499–DRR457516. All other relevant data are within the paper and its Supporting information files.
Funding: This study was financially supported by Internal Competitive Research Funding (Type A) of the National Institute for Environmental Studies (to MA, YT, NT, YE, and KS) (https://www.nies.go.jp/). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Wastewater treatment facilities (WTFs) are vital components in reducing pathogens in untreated wastewater . The use of reclaimed water derived from treated wastewater for agricultural irrigation; environmental enhancement; groundwater recharge; portable reuse; and industrial, recreational, and urban use is gaining attention owing to rapid urbanization, especially in developing countries, and the increasing global water scarcity . Jones et al. estimated that 40.7 × 109 m3 year−1 of treated wastewater is intentionally reused for human purposes . A major concern regarding the discharge of wastewater into the environment and the use of treated wastewater is the inadequate removal of pathogenic microorganisms. Active pathogenic microorganisms in biologically treated wastewater can be effectively removed using ultraviolet light, ozonation, or chlorination disinfection . Although chlorination disinfection is the most common and cost-effective option, it produces mutagenic/carcinogenic disinfection by-products, such as trihalomethanes and haloacetic acids . As high pathogen reduction rates lead to low final disinfectant demand , the development and appropriate management of WTFs with high pathogen reduction rates are crucial to reduce not only disinfection costs but also the formation of disinfection by-products.
Recent advances in high-throughput DNA sequencing approaches (e.g., 16S ribosomal RNA [rRNA] gene amplicon sequencing and metagenomic sequencing) have enabled researchers to reveal the phylogenetic diversity, relative abundance, and putative pathogenic traits of potentially pathogenic bacteria in wastewater environments [7–10]. Representative and frequently observed potentially pathogenic bacteria in wastewater, identified by conventional cultivation and high-throughput DNA sequencing approaches, include those from the genera Aeromonas, Arcobacter, Klebsiella, and Mycobacterium [7–11]. Galagoda et al. recently investigated the dynamics of potentially pathogenic bacteria in a Japanese municipal WTF using quantitative 16S rRNA gene sequencing and a novel pathogen database . While 69 potentially pathogenic bacterial genera were detected in the influent wastewater, only 13 potentially pathogenic bacterial genera, including abundant levels of Aeromonas, Arcobacter, Klebsiella, and Mycobacterium, were quantitatively detected in the chlorine-disinfected, treated effluent. These data indicate the potential importance of better understanding the abundance and persistence of these potentially pathogenic bacteria in WTFs to reduce health risks associated with treated wastewater. However, to the best of our knowledge, the group-level abundance and persistence of Aeromonas, Arcobacter, Klebsiella, and Mycobacterium in the existing WTFs in East Asian regions remain largely unknown owing to limited investigation [10, 12].
The genus Aeromonas belongs to the family Aeromonadaceae within the phylum Pseudomonadota (formerly Proteobacteria) and inhabits various aquatic environments . Aeromonas species are important human pathogens capable of causing various diseases, including gastroenteritis as well as blood-borne, skin, soft tissue, intra-abdominal, respiratory tract, urogenital tract, and eye infections [13, 14]. The most common human pathogenic Aeromonas species are Ae. hydrophila, Ae. caviae, Ae. veronii, and Ae. dhakensis [15, 16]. Biological wastewater treatment can reduce the abundance of Aeromonas. For instance, a 1.14 and 2.57 log reduction of Aeromonas was reported in a Moroccan basin wastewater treatment system  and a Brazilian sanitary sewage stabilization pond , respectively, after biological treatment.
Members of the genus Arcobacter are aerotolerant Campylobacter-like bacteria with a psychrotrophic nature and currently belong to the family Arcobacteraceae within the phylum Campylobacterota (formerly Epsilonproteobacteria). They are zoonotic food and waterborne pathogens that cause gastroenteritis and bacteremia in humans . The majority of Arcobacter infections in humans and animals are caused by Ar. butzleri, Ar. cryaerophilus, Ar. skirrowii, and Ar. thereius . Virulence genes responsible for adhesion, invasion, and cytotoxicity have been reported not only in the Arcobacter species mentioned above but also in other taxonomically diverse Arcobacter members [20, 21]. Although the predominant mode of transmission is contaminated food, contaminated water is also a potential source of Arcobacter infection . In the existing WTFs in Beijing, Denmark, and Southern Arizona, Arcobacter reductions ranging from 1 to 3 logs were confirmed through molecular biological analyses [9, 12, 22].
The genus Klebsiella belongs to the family Enterobacteriaceae within the phylum Pseudomonadota. Environmental Klebsiella isolates have pathogenic potential [23, 24], and natural aquatic environments are potential reservoirs for the growth and spread of Klebsiella species . The K. pneumoniae species complex (KpSC) generally refers to closely related Klebsiella species that share a 95–96% average nucleotide identity with K. pneumoniae, a causative agent of pneumonia, urinary tract infections, and wound infections . KpSC includes seven phylogroups: K. pneumoniae (Kp 1), K. quasipneumoniae subsp. quasipneumoniae (Kp 2), K. variicola subsp. variicola (Kp 3), K. quasipneumoniae subsp. similipneumoniae (Kp 4), K. variicola subsp. tropica (Kp 5), K. quasivariicola (Kp 6), and K. africana (Kp 7). All KpSC bacteria are potentially pathogenic to humans and animals, and some strains associated with this group are hypervirulent and/or antibiotic-resistant strains . In a municipal WTF, a 4.23–4.33 log reduction of K. pneumoniae was confirmed through a K. pneumoniae-specific quantitative real-time polymerase chain reaction (qPCR) assay . However, to the best of our knowledge, the group-level abundance and persistence of KpSC in the existing WTFs have not been reported yet owing to the general lack of their quantification before the development of a highly specific qPCR assay, as reported by Barbier et al. .
The genus Mycobacterium belongs to the family Mycobacteriaceae within the phylum Actinomycetota (formerly Actinobacteria). Mycobacterium species are generally classified into two distinct groups: the Mycobacterium tuberculosis complex (MTC) and nontuberculous mycobacteria (NTM) . In humans, MTC members cause pulmonary/extrapulmonary tuberculosis, whereas NTM members cause major clinical diseases, such as chronic pulmonary disease, disseminated disease in severely immunocompromised patients, skin and soft tissue infections, and superficial lymphadenitis, particularly cervical lymphadenitis in children . NTM are known as “environmental mycobacteria” owing to their ubiquitous distribution in the environment [29, 30]. The rate of NTM infections is increasing, with the possibility of turning serious in the most vulnerable individuals . Radomski et al. developed a specific qPCR assay for the genus Mycobacterium . This qPCR assay was later applied to reveal the behavior of Mycobacterium in a WTF located in Paris, which used a combined process of physical-chemical decantation followed by biofiltration . The reported data showed that most of the Mycobacterium population in the WTF influent was removed by a physical–chemical decantation process (2.4 log reduction) and then completely removed through subsequent biofiltration.
Indicators for fecal contamination, such as Escherichia coli and total fecal coliforms, have been used as water quality criteria for the use of reclaimed or recycled water and to adhere to environmental and effluent standards [34–37]. Many previous studies have used these indicators to assess the pathogen reduction performance of WTFs . However, poor positive correlations between the presence/abundance of fecal indicators and pathogenic bacteria have been reported in several water environments [17, 38–41]. In addition, water quality parameters can influence the abundance of pathogenic bacteria in water environments [17, 33, 40]. For instance, Boussaid et al. revealed a significant negative correlation between Aeromonas abundance and chemical oxygen demand (COD) in the treated effluent of a Moroccan basin wastewater treatment system . Furthermore, Mycobacterium abundance in a WTF in Paris was related to the COD, biochemical oxygen demand (BOD), orthophosphates, and suspended solids (SS) concentrations . Information on such correlations could be useful to develop and appropriately manage WTFs with high pathogen reduction rates; however, such correlation data in wastewater and related environments are limited. Therefore, this study aimed to determine the abundance and reduction rates of the above-mentioned four potentially pathogenic bacterial groups in the influent wastewater and treated effluent of existing WTFs in Japan and Thailand. In addition, we aimed to reveal the correlation among the abundances of E. coli, the potentially pathogenic bacterial groups, and various water quality parameters.
Materials and methods
Collection of wastewater samples
Wastewater sampling from six decentralized WTFs (A, B, C, D, E, and F) in Tokushima, Japan, was conducted between November 2021 and August 2022. Wastewater sampling from three centralized domestic WTFs (G, H, and I) in Bangkok, Thailand, was conducted between November 2020 and December 2021. The nine investigated WTFs are summarized in Table 1. WTFs A and B were small-scale on-site domestic WTFs, known as the Johkasou system , that treated domestic wastewater from individual, private houses (design wastewater flow: 1.0 and 1.4 m3 day−1, respectively). WTF C was a rural community WTF that used a combined system of an anaerobic filter bed and contact aeration (design wastewater flow: 164.7 m3 day−1). WTFs D, E, and F were small-scale WTFs, known as community plants, that treated domestic wastewater from different housing complexes (design wastewater flow: 266, 400, and 850 m3 day−1, respectively). WTF D used a contact aeration system, whereas WTFs E and F used an extended aeration system. WTFs G, H, and I were centralized domestic WTFs that used contact stabilization activated sludge, cyclic activated sludge, and activated sludge with nutrient removal systems, respectively . The treatment capacities of WTFs G, H, and I were 30,000, 200,000, and 350,000 m3 day−1, respectively. Tertiary treatment based on chemical precipitation was not applied in any of the analyzed WTFs. A total of 68 grab samples were collected, consisting of 34 influent wastewater and 34 treated effluent samples. The WTFs located in Tokushima, Japan, considered for this study routinely used chlorination to disinfect treated wastewater before discharging to the environment; however, we collected the treated effluent samples before the disinfection process. The treated effluents from WTFs G, H, and I in Bangkok, Thailand, were not subjected to any disinfection process . Wastewater sampling was permitted by the owners of the Johkasou systems; the Environment Division, Naka-cho, Tokushima Prefecture; the Environmental Preservation Division, Tokushima City, Tokushima Prefecture; and the Drainage and Sewage Department, Bangkok Metropolitan Administration, Bangkok.
Wastewater sample filtration and genomic DNA extraction
Filtration of wastewater samples and subsequent genomic DNA extraction were performed as described previously . Briefly, wastewater subsamples (40 mL each) were initially fixed by adding 10 mL of ethanol. Next, 2.5–10 mL of ethanol-fixed samples (net sample filtration volume: 2.0–8.0 mL) were filtered through an Omnipore polytetrafluoroethylene membrane filter (diameter, 25 mm; pore size, 0.2 μm; Merck KGaA, Darmstadt, Germany), and the filters were stored at −80 °C until DNA extraction. DNA extraction from the resulting filters was performed using an Extrap Soil DNA Kit Plus ver.2 (BioDynamics Laboratory Inc., Tokyo, Japan) according to the manufacturer’s instructions. As a slight modification, the final DNA elution volume in TE buffer (10 mM Tris-HCl and 1 mM EDTA [pH 8.0]) was set to 50 μL. The concentrations of the extracted DNA were determined using the Qubit dsDNA HS Assay Kit and the Qubit 4 Fluorometer (Thermo Fisher Scientific K.K., Tokyo, Japan).
PCR primer design for quantitative detection of Aeromonas
A new PCR primer set Aero581F/Aero848R, targeting the 16S rRNA gene of the genus Aeromonas, was designed using the probe design function of ARB v.6.0.6  with the All-Species Living Tree Project (LTP) database (release 132) . The in silico coverage and specificity of the primer set Aero581F/Aero848R and previously reported primer sets targeting the 16S rRNA gene of the genus Aeromonas were verified using TestPrime v.1.0  against the SILVA reference database (release 138.1) (sequence collection: non-redundant [RefNR] and LTP) [46, 48].
qPCR assays were performed using the oligonucleotide primer/probe sets listed in Table 2 [27, 32, 40]. The detailed procedures of qPCR assays, qPCR data processing, and the determination of the limit of detection (LoD) and limit of quantification (LoQ) of the qPCR assays are described in S1 Text. All standard DNA fragments used in the qPCR assays were synthesized by Eurofins Genomics K.K. (Tokyo, Japan), and the sequences are listed in S1 Table. The determined LoD values for Aeromonas, Arcobacter, KpSC, and Mycobacterium quantification were 0.31, 0.63, 3.1, and 3.1 cells mL-sample−1, respectively, whereas the determined LoQ values were 3.1, 6.3, 31, and 31 cells mL-sample−1, respectively. Abundances below the LoQ were assigned a value of LoQ/2 for the calculation of geometric mean abundances and reduction rates of the potentially pathogenic bacterial groups.
16S rRNA gene amplicon sequencing analysis
Amplicon sequencing was performed targeting the prokaryotic 16S rRNA gene hypervariable region V4. The analyzed influent wastewater and treated effluent samples from the six WTFs in Japan (i.e., A, B, C, D, E, and F) were collected in November 2021, whereas those from the three WTFs in Thailand (i.e., G, H, and I) were collected in October 2021. 16S rRNA gene amplicon libraries were prepared using a two-step tailed PCR method with the 515F/806R primer set , as described previously . Briefly, the first PCR targeting the 16S rRNA genes in the genomic DNA extracts obtained using the above-mentioned DNA extraction method was performed using TaKaRa Ex Taq HS (Takara Bio Inc., Shiga, Japan). Thermal cycling for the first PCR consisted of an initial denaturation at 94 °C for 2 min, followed by 30 amplification cycles (94 °C for 30 s, 50 °C for 30 s, and 72 °C for 30 s) and a final extension step at 72 °C for 5 min. The first PCR products were purified using the AMPure XP reagent (Beckman Coulter Inc., Brea, CA, USA), and the purified products were subsequently subjected to a second PCR using primers with unique index sequences . 16S rRNA gene amplicon sequencing was performed on a MiSeq system using a MiSeq Reagent Kit v3 (Illumina, Inc., San Diego, CA, USA) at the Bioengineering Lab. Co., Ltd., Kanagawa, Japan.
The resulting sequence data were initially subjected to primer trimming using Cutadapt v.4.1 with default parameters . Sequence processing was then performed with mothur v.1.48.0  using the SILVA reference database (release 138.1) , following MiSeq standard operating procedures . The assembly and quality filtering of the paired-end amplicon reads were performed using the “make.contigs” command with the following options: maxambig = 0, maxlength = 275, maxhomop = 8, and trimoverlap = t. For operational taxonomic unit (OTU)-based analysis, the obtained sequences were clustered into OTUs at 97% sequence similarity using the OptiClust clustering algorithm . The closest cultured relatives of dominant Aeromonas, Arcobacter, Pseudarcobacter, and Mycobacterium OTUs were determined using the EzBioCloud 16S-based ID service with the 16S database v.2021.07.07  and the representative sequences of the dominant OTUs. The habitat preferences of the dominant OTUs were analyzed using the ProkAtlas Online Toolkit with default parameters .
Water quality analysis
Water quality analysis of the influent wastewater and treated effluent samples collected from the six WTFs located in Tokushima, Japan (i.e., A, B, C, D, E, and F) was performed as described below. Water temperature, pH, and dissolved oxygen (DO) concentrations were measured using MM-42DP portable water quality meters connected to a pH probe MM4-PH or DO probe MM4-DDO (DKK-TOA CORPORATION, Tokyo, Japan). The oxidation-reduction potential (ORP) values were measured using an MM-41DP portable water quality meter connected to an ORP probe MM4-ORP (DKK-TOA CORPORATION). COD determined using the potassium dichromate method (CODCr), BOD, total nitrogen (TN), ammonium-nitrogen (NH4+-N), nitrite-nitrogen (NO2−-N), nitrate-nitrogen (NO3−-N), SS, and volatile suspended solids (VSS) concentrations were measured according to the Japanese Industrial Standards (JIS) K 0102 methods . The water quality parameters of the influent wastewater and treated effluent samples collected from the three WTFs in Bangkok, Thailand (i.e., G, H, and I) were determined as described previously [44, 58].
Measurement of colony-forming units (CFUs) of E. coli
The CFUs of E. coli of all analyzed samples were determined through the Compact Dry “Nissui” E. coli/Coliform Count (EC) method (Nissui Pharmaceutical Co., Ltd., Tokyo, Japan) . In brief, we observed the formation of blue or bluish-purple colored E. coli colonies in selective medium containing two chromogenic enzyme substrates: 5-bromo-4-chloro-3-indoxyl-β-D-glucuronic acid cyclohexylammonium salt (X-Gluc) and 5-bromo-6-chloro-3-indoxyl-β-D-galactopyranoside (Magenta-Gal).
Calculation and statistical analysis
The removal rates (%) of CODCr, BOD, TN, SS, and VSS were calculated using Eq (1): (1) where Cinf and Ceff represent the influent wastewater and treated effluent concentrations, respectively.
The log reduction values (LRVs) of E. coli and the potentially pathogenic bacterial groups were calculated using Eq (2): (2) where Ninf and Neff are the abundances of E. coli (CFU mL−1) or a potentially pathogenic bacterial group (cells mL−1) in the influent wastewater and treated effluent, respectively.
Statistical differences in the average LRVs of the different WTFs were assessed using Student’s t-test with the Benjamini–Hochberg (BH) correction. Spearman’s rank correlation coefficient (ρ) calculation with the BH correction was performed to reveal the possible correlations among the abundances of E. coli, potentially pathogenic bacterial groups, and water quality parameters. The obtained quantitative data of E. coli CFU counts and potentially pathogenic bacterial abundances were log-transformed before the statistical analyses. Pooled data of all influent wastewater and treated effluent samples of WTFs were used to calculate the Spearman’s ρ values. Spearman’s ρ values were calculated using the corr.test function of the R package psych v.2.2.9 , and the Spearman’s ρ matrix was generated using the R package corr.plot v.0.92 . Measurements below the LoQ were not included in the calculation. A p-value < 0.01 was considered significant. All statistical analyses were performed using R v.4.1.0 .
Inclusivity in global research
Additional information regarding the ethical, cultural, and scientific considerations specific to inclusivity in global research is included in S1 Checklist.
Results and discussion
16S rRNA gene amplicon sequencing analysis of influent and treated effluent wastewater samples
16S rRNA gene amplicon sequencing analysis was performed to determine the presence and relative abundance of the targeted potentially pathogenic bacterial groups in the analyzed samples. In this study, 31,845–46,464 sequences of the 16S rRNA gene were obtained per sample from the influent wastewater and treated effluent samples following analysis using mothur. The relative 16S rRNA gene abundances of the genera Aeromonas, Arcobacter, Klebsiella, and Mycobacterium in the representative wastewater influent and treated effluent samples are summarized in Fig 1. The SILVA reference database (release 138.1) used in this study reflected the division of the genus Arcobacter into six Arcobacteraceae genera: Aliarcobacter (not defined in the reference database), Pseudarcobacter, Malaciobacter, Halarcobacter, Poseidonibacter, and Arcobacter (sensu stricto), as proposed by Pérez-Cataluña et al. . However, On et al.  later proposed that these genera be considered a single genus Arcobacter based on phenotypic, genetic, and phylogenomic data. The genera Aeromonas, Arcobacter, and Mycobacterium were detected in all analyzed influent wastewater and treated effluent samples. The relative abundance of the genus Arcobacter (the sum of the relative abundances of the five above-mentioned Arcobacteraceae genera in the reference database) was markedly high (≥ 5.0%) in seven out of the nine analyzed samples, indicating the presence of Arcobacter as a dominant prokaryotic genus in most of the analyzed wastewater influent samples. The relatively high abundance of the genus Arcobacter in the influent wastewater prokaryotic communities is in good agreement with the previously reported 16S rRNA gene-targeted amplicon sequencing data [8–10]. Although the reason for the particularly high abundance of the genus Arcobacter in various influent wastewater samples is still under debate, a compelling explanation is their ability to grow in hydrogen sulfide-rich and oxygen-limited sewer pipe environments . The relative abundances of the genus Mycobacterium in the effluent samples were consistently higher than those in the influent wastewater samples. The genus Klebsiella, which includes the KpSC, was extremely low in abundance or undetectable in most of the samples analyzed. This may have been caused by the insufficient variability of the 16S rRNA gene hypervariable region V4 to distinguish the Enterobacteriaceae genera .
The dominant Aeromonas, Arcobacter, Pseudarcobacter, and Mycobacterium OTUs with relative abundances greater than 1.0% in at least one of the analyzed samples are summarized in Fig 2. The genera Aeromonas, Arcobacter, and Mycobacterium were highly dominated by a few OTUs. The data suggest the presence of specific Aeromonas, Arcobacter, Pseudarcobacter, and Mycobacterium OTUs capable of occupying ecological niches in WTF environments. The metabolic flexibility of these OTUs might allow them to become dominant in highly dynamic/disturbed WTF environments . The dominant Aeromonas, Arcobacter, and Mycobacterium OTUs (i.e., OTU00002, OTU00014, OTU00016, and OTU00075) have a high 16S rRNA gene sequence similarity (98.0–99.6%) to known biosafety level (BSL) 1 or 2 pathogenic bacterial species defined by the Japanese Society for Bacteriology  (Table 3). Pseudarcobacter OTU00006 showed relatively low 16S rRNA gene sequence similarity (94.9%) to pathogenic Arcobacter species (i.e., Ar. butzleri [BSL 1] and Ar. cryaerophilus [BSL 1]), but the sequence showed high similarity (98.0%) to Ar. defluvii with virulence traits . The habitat preference of the dominant Aeromonas, Arcobacter, Pseudarcobacter, and Mycobacterium OTUs, as indicated by the ProkAtlas analysis, is summarized in Table 3. The data suggest that most of these OTUs can adapt to natural aquatic environments (e.g., fresh, ground, marine, and lake water), in addition to WTF environments. Therefore, the release of these potentially pathogenic OTUs into the environment without appropriate reduction may pose a potential threat to human health.
PCR primers for quantitative detection of Aeromonas
16S rRNA gene-targeted PCR primers for the amplification of Aeromonas sequences have been reported previously [70, 71]. However, our analysis revealed that the in silico coverage of previously reported Aeromonas-specific primer sets A16SF/A16SR and Aer66f/Aer613r in the SILVA reference database (release 138.1) (sequence collection: RefNR) was relatively low (66.9% [658/983 sequences] and 69.0% [644/933 sequences] of coverage at 0 mismatch, respectively) (S2 Table). Therefore, for a more comprehensive detection of environmental Aeromonas, we designed a new PCR primer set (Aero581F/Aero848R), which showed a slightly improved in silico coverage of 74.3% (765/1030 sequences) at 0 mismatches in the reference database (sequence collection: RefNR). In addition, the new PCR primer set was shown to cover 32 out of the 35 eligible 16S rRNA gene sequences of Aeromonas type strains in the reference database (sequence collection: LTP) without any mismatches. Therefore, the new primer set is expected to detect a broader range of environmental Aeromonas than the previously reported primer sets. Moreover, the relatively short amplicon size of Aero581F/Aero848R (268 bp) is advantageous to ensure efficient amplification. The LoD and LoQ values of the newly developed Aeromonas qPCR assay were 1 and 10 copies reaction−1, respectively. Therefore, although previously reported PCR primer sets have been successfully applied to the monitoring of environmental Aeromonas [71, 72], the new qPCR assay described herein is an effective alternative option for the quantitative detection of environmental Aeromonas.
Quantitative detection and reduction of E. coli and potentially pathogenic bacterial groups in existing WTFs
The abundances of selected potentially pathogenic bacterial groups of Aeromonas, Arcobacter, KpSC, and Mycobacterium in the influent wastewater and treated effluent of nine WTFs were determined using qPCR assays. All target bacterial groups were detected with varying concentrations in all influent wastewater samples. The abundances of Aeromonas, Arcobacter, and Mycobacterium could be quantified in all analyzed effluent samples. In contrast, KpSC was not detected or was below the LoQ (i.e., 3.1 × 101 cells mL−1) in 17 out of 34 analyzed effluent samples. The geometric mean abundances of E. coli (determined using the Compact Dry “Nissui” EC method) and potentially pathogenic bacterial groups (determined using qPCR assays) in the influent wastewater and treated effluent of the WTFs are summarized in Fig 3. The average LRVs of E. coli and potentially pathogenic bacterial groups in the WTFs are summarized in Fig 4.
Error bars indicate the maximum and minimum abundances. Horizontal dashed lines indicate the limit of quantification (LoQ) for each quantitative real-time polymerase chain reaction assay. *, KpSC abundances below the LoQ are indicated by the value of LoQ/2.
Error bars indicate the standard deviation. *, p < 0.01 according to Student’s t-test with the Benjamini–Hochberg correction.
Quantitative data on E. coli.
The geometric mean abundances of E. coli in the influent wastewater and treated effluent samples were 3.2 × 103 to 6.5 × 104 and 1.1 × 101 to 1.5 × 103 (CFU mL−1), respectively (Fig 3A). The geometric mean E. coli abundances in the treated effluents were slightly or markedly higher than the WHO’s strictest guideline value for unrestricted irrigation (1,000 CFU 100 mL−1) . In contrast, the geometric mean E. coli abundances in the treated effluent of seven WTFs were below the WHO’s guideline value for restricted agricultural irrigation for processed food crops (10,000 CFU 100 mL−1) . Although the treated effluent from Bangkok’s domestic WTFs is used for street washing, irrigation of green belts, cleaning of temples, etc., without the application of a disinfection process , the E. coli abundance data suggest that the hygienic safety of the treated effluent, especially that from centralized domestic WTF G, should be rigorously evaluated. Notably, fecal indicators, including E. coli, have not been used in any effluent quality standards in Thailand . The average LRVs of E. coli calculated from the obtained CFU counts ranged from 1.05 to 3.71 (Fig 4A). A significant difference in average E. coli LRVs was observed only between WTF G (1.05) and WTFs D (3.71, p = 0.0019) or E (3.71, p = 0.0019). The LRVs were consistent with those of several previously reported WTFs [1, 74, 75]. The markedly high geometric mean E. coli abundance found in the effluent of centralized domestic WTF G (i.e., 1.5 × 103 CFU mL−1) is likely attributed to the low LRV (1.05).
Quantitative data on Aeromonas.
The geometric mean abundance of Aeromonas in the influent wastewater samples was 1.2 × 104 to 2.4 × 105 cells mL−1, whereas that in the treated effluent was 2.3 × 102 to 5.2 × 103 cells mL−1 (Fig 3B). The relatively high Aeromonas abundances in the influent wastewater and treated effluent are consistent with those reported in previous studies. Martone-Rocha et al. detected Aeromonas in the raw sewage influent of a Brazilian sanitary sewage stabilization pond treatment system, with abundance fluctuations ranging from <3 to 3.0 × 109 most probable number 100 mL−1 . Moreover, in a Moroccan basin wastewater treatment system, the average Aeromonas abundances in the influent wastewater and treated effluent were 3.3 × 104 and 2.4 × 103 CFU mL−1, respectively . The average LRVs of Aeromonas in the nine WTFs analyzed in this study ranged from 0.77 to 2.57 (Fig 4B), and no significant difference was found in the average LRVs (p = 0.015–0.90). Most of the average LRVs of Aeromonas were almost comparable with those of a Brazilian sanitary sewage stabilization pond (2.57)  and a Moroccan basin wastewater treatment system (1.14) .
Quantitative data on Arcobacter.
Among the quantified potentially pathogenic bacterial groups, particularly high abundances of Arcobacter were confirmed in the analyzed influent wastewater and treated effluent samples (Fig 3C). The geometric mean abundances of Arcobacter in the influent wastewater ranged from 1.0 × 105 to 4.5 × 106 cells mL−1. The geometric mean abundances in the treated effluent ranged from 2.0 × 103 to 5.5 × 104 cells mL−1. The primer set Arco605F/Arco688R  used in this study was developed for the quantitative detection of members of the genus Arcobacter as proposed by On et al. . Highly abundant Arcobacter populations in wastewater treatment environments have also been reported in previous research. Yang et al.  reported that the 23S rRNA gene abundances of Arcobacter in the influent and treated effluent of a rural domestic WTF with a membrane bioreactor located in Beijing, China were 1.01 × 1011 and 1.45 × 108 copies L−1, respectively (equivalent to 2.02 × 107 and 2.90 × 104 cells mL−1, respectively). Fluorescence in situ hybridization-based quantification by Kristensen et al.  showed that the range of Arcobacter abundance in the influent wastewater of 14 full-scale Danish municipal WTFs was 4.2 × 105 to 3.2 × 107 cells mL−1, whereas that in the treated effluent was 3.5 × 104 to 5.1 × 106 cells mL−1. Ghaju Shrestha et al.  reported the 16S rRNA gene copy numbers of Arcobacter in two WTFs in southern Arizona using conventional activated sludge and biological trickling filter processes, respectively. The average abundances of the influent Arcobacter 16S rRNA gene in the WTFs were 7.9 and 8.5 log10 copies L−1 (equivalent to 1.6 × 104 and 6.3 × 104 cells mL−1, respectively), whereas those in the treated effluent were 6.4 and 6.1 log10 copies L−1 (equivalent to 5.0 × 102 and 2.5 × 102 cells mL−1, respectively). The observed average LRVs of Arcobacter in the nine WTFs analyzed in this study were between 1.00 and 3.06 (Fig 4C). The maximum average LRV of 3.06 was observed in WTF C, which is significantly higher than those observed at WTFs A (1.25, p = 0.0049) and G (1.00, p = 0.0023). The observed average LRVs were comparable to or slightly lower than those of a full-scale Danish municipal WTF with an enhanced biological phosphorus removal process (LRV: 2.40) , two WTFs in southern Arizona (1.7 [conventional activated sludge process] and 2.3 [biological trickling filter process]) , and a rural Chinese domestic WTF with a membrane bioreactor (2.84) .
Quantitative data on KpSC.
The geometric mean abundances of KpSC in the influent wastewater and treated effluent are shown in (Fig 3D). The geometric mean abundances of KpSC in the influent wastewater ranged from 3.6 × 102 to 4.3 × 104 cells mL−1. The geometric mean abundances of KpSC in the treated effluent were comparatively lower than that of the other quantified potentially pathogenic bacterial groups: the geometric mean abundances ranged from < 3.1 × 101 (below the LoQ) to 1.6 × 102 cells mL−1. Since the highly specific qPCR assay for KpSC quantification was not available prior to the report by Barbier et al. , reliable data on KpSC abundance in the environment had not been obtained. To the best of our knowledge, this is the first report on the KpSC abundances in the influent wastewater and treated effluent of existing WTFs obtained using the specific qPCR assay. The observed average LRVs of KpSC in the analyzed WTFs ranged from 1.35 to 3.11 (Fig 4D). The maximum LRV of 3.11 was observed in WTF C, which was significantly higher than those of WTFs A (1.35, p = 0.0082), B (1.51, p = 0.0088), and G (1.54, p = 0.0088).
Quantitative data on Mycobacterium.
The geometric mean abundances of Mycobacterium in the influent wastewater and treated effluent of the existing WTFs are shown in Fig 3E. In contrast to the other quantified potentially pathogenic bacterial groups, similar abundance levels were confirmed in numerous analyzed influent wastewater and treated effluent samples. The maximum and minimum geometric mean abundances of Mycobacterium in the influent wastewater were 5.5 × 104 and 6.9 × 103 cells mL−1, respectively. The maximum and minimum geometric mean abundances of Mycobacterium in the treated effluent were 5.5 × 104 and 1.1 × 103 cells mL−1, respectively. The Mycobacterium abundances of influent wastewater and treated effluent samples were higher than those of a WTF located in Paris that used a combined process of physical-chemical decantation for suspended matter and phosphorus removal, followed by biofiltration for organic matter and nitrogen removal . The average Mycobacterium 16S rRNA gene abundance in the untreated influent sewage of the WTF in Paris was 5.5 × 105 copies L−1 (equivalent to 5.5 × 102 cells mL−1), whereas the Mycobacterium 16S rRNA gene was not detected in the treated effluent. The LRVs of Mycobacterium in the WTFs analyzed in this study are shown in Fig 4E. The LRVs of Mycobacterium were lower than that of other quantified potentially pathogenic bacterial groups: the average LRVs ranged from −0.67 to 1.57. The negative average LRVs of Mycobacterium suggested the growth of Mycobacterium during wastewater treatment in the WTFs. The growth of Mycobacterium in the WTFs analyzed in this study is not surprising as Mycobacterium has been reported as an abundant bacterial genus in the activated sludge of many biological WTFs [76, 77]. Moreover, the growth of Mycobacterium in various WTFs, including those investigated in this study, might be due to their ability to degrade cholesterol, a major component of sewage . The highest average LRV of 1.57 was observed in WTF C, which was significantly higher than those observed in WTFs B (−0.018, p = 0.0076), D (−0.67, p = 0.00049), E (−0.088, p = 0.0076), and G (−0.002, p = 0.0076). The second highest average LRV was observed in WTF A (0.87), which was significantly higher than that of WTF D (−0.67, p = 0.0076). The average LRVs observed in all WTFs investigated in this study were lower than that of a WTF in Paris using a consecutive biofiltration process (average LRV: 3.2) .
Summary of the obtained quantitative data on E. coli and potentially pathogenic bacterial groups.
In this study, one or more average LRVs of Aeromonas, Arcobacter, and KpSC were confirmed in all or most of the WTFs analyzed. In contrast, the analyzed WTFs did not effectively remove the genus Mycobacterium. Despite differences in the treatment scale, treatment system, and operating environment, most analyzed WTFs showed a similar reduction potential for the quantified potentially pathogenic bacterial groups. The significant differences in the LRVs of Arcobacter, KpSC, and Mycobacterium observed between some WTFs may be attributed to the significant differences in the water quality parameters of the influent wastewater and treated effluent, as discussed later in this paper. The changes in the water quality parameters may be influenced by the configuration and operating conditions of the WTFs. Among the various treatment systems analyzed in this study, the combined system of an anaerobic filter bed and contact aeration used in rural community WTF C showed the highest average LRVs for all potentially pathogenic bacterial groups quantified. The data suggest that the combined system is an appropriate option for reducing the health risks caused by Aeromonas, Arcobacter, KpSC, and Mycobacterium in wastewater. In contrast, the successful quantification of Aeromonas, Arcobacter, KpSC, and Mycobacterium in treated effluents suggests the need for disinfection prior to discharge into the environment and reuse for human purposes.
Correlations between potentially pathogenic bacterial groups and E. coli
Correlations between the abundances of E. coli and the quantified potentially pathogenic bacterial groups are shown in Figs 5 and 6. Based on the Spearman’s ρ values, there was a significant positive correlation between the abundances of E. coli, Aeromonas, Arcobacter, and KpSC (ρ = 0.62–0.85, p = 0.00) (Fig 5). In addition, a linear positive correlation was found between the abundances of E. coli and Aeromonas, Arcobacter, or KpSC (coefficient of determination R2 = 0.526–0.695) (Fig 6A–6C). These data suggest the applicability of E. coli as a bacterial indicator of Aeromonas, Arcobacter, and KpSC contamination levels in wastewater-related samples. Correlations between the abundances of E. coli and Aeromonas or Arcobacter have also been reported in some non-wastewater environments. Solaiman et al. reported a positive correlation between the abundances of Aeromonas and E. coli in reclaimed water sources and non-tidal freshwater rivers/creeks, whereas no correlation was found in tidal brackish water and irrigation ponds . A positive correlation between E. coli and Arcobacter abundances in surface water samples from canals and the Chao Phraya River in Bangkok, Thailand, was also confirmed in our previous report . The insignificant correlation between E. coli and Mycobacterium abundances (Figs 5 and 6D) also indicates the possible importance of independent monitoring of Mycobacterium.
Spearman’s rank correlation coefficients are shown only for p < 0.01 with the Benjamini–Hochberg correction. CODCr: chemical oxygen demand concentration determined by the potassium dichromate method (mg L−1), BOD: biochemical oxygen demand concentration (mg L−1), TN: total nitrogen concentration (mg L−1), NH4+-N: ammonium-nitrogen concentration (mg L−1), NO2−-N: nitrite-nitrogen concentration (mg L−1), NO3−-N: nitrate-nitrogen concentration (mg L−1), SS: suspended solids concentration (mg L−1), VSS: volatile suspended solids concentration (mg L−1), ORP: oxidation-reduction potential (mV), DO: dissolved oxygen concentration (mg L−1), Temp: water temperature (°C), E. coli: Escherichia coli abundance (log10 colony-forming units mL−1), Aeromonas: Aeromonas abundance (log10 cells mL−1), Arcobacter: Arcobacter abundance (log10 cells mL−1), KpSC: Klebsiella pneumoniae species complex abundance (log10 cells mL−1), and Mycobacterium: Mycobacterium abundance (log10 cells mL−1).
Correlation between abundances of potentially pathogenic bacterial groups and water quality parameters
The water quality parameters of the influent wastewater and treated effluent of the WTFs and the removal rates (%) of CODCr, BOD, TN, SS, and VSS are summarized in S3 Table. The Spearman’s ρ values between the quantified potentially pathogenic bacterial groups and the water quality parameters are shown in Fig 5. The Spearman’s ρ values indicated the presence of positive or negative correlations between the abundances of potentially pathogenic bacterial groups and several water quality parameters. Aeromonas and Arcobacter abundances were positively correlated with the CODCr, BOD, TN, NH4+-N, SS, and VSS concentrations (ρ = 0.36–0.75, p = 0.00) but negatively correlated with the NO3−-N concentration and ORP (ρ = −0.58 to −0.36, p = 0.00). Similarly, KpSC abundance was positively correlated with the CODCr, BOD, TN, NH4+-N, SS, and VSS concentrations (ρ = 0.47–0.74, p = 0.00), whereas it was negatively correlated only with the NO3−-N concentration (ρ = −0.64, p = 0.00). Mycobacterium abundance was significantly positively correlated with CODCr, TN, NO2−-N, SS, and VSS (ρ = 0.39–047, p = 0.00), but no significant negative correlation was found with the other water quality parameters (p = 0.10 and 0.04 for the DO concentration and water temperature, respectively).
Pathogen reduction in WTFs is achieved by a combination of physical, chemical, and biological mechanisms . The correlation between the four quantified potentially pathogenic bacterial groups with the SS and VSS concentrations suggests that the adsorption of these bacterial cells to the activated sludge and their subsequent removal by the excess sludge is one of the mechanisms to reduce the abundance of these potentially pathogenic bacterial groups . Radomski et al. also reported that the Mycobacterium reduction in a WTF was related to the behaviors of insoluble compounds . However, relatively high geometric mean abundances of Aeromonas, Arcobacter, and Mycobacterium (≥ 102 cells mL−1) were observed in the analyzed effluent samples with relatively low average SS and VSS concentrations (2–19 and 1–16 mg L−1, respectively). These data suggest that a fraction of these potentially pathogenic bacterial cells do not flocculate and attach well to the sludge flocs, which is consistent with the report by Kristensen et al. , who studied the abundance and reduction of Arcobacter in full-scale municipal WTFs.
The correlations of Aeromonas, Arcobacter, and KpSC abundances with CODCr and BOD concentrations may reflect their robust growth/survival potential under organic matter-rich wastewater conditions. In contrast, relatively low or insignificant correlations were found between Mycobacterium abundance and CODCr or BOD concentrations, respectively. This may be explained by the superior growth/survival potential of Mycobacterium under oligotrophic conditions .
Based on the correlations between the concentrations of NO3−-N, NH4+-N, and ORP with the abundances of the potentially pathogenic bacterial groups, Aeromonas, Arcobacter, and KpSC are more susceptible to oxidative conditions, where NO3−-N can be accumulated by the biological nitrification of NH4+-N, than Mycobacterium. Therefore, improving the nitrification capacity of WTFs may be effective in removing Aeromonas, Arcobacter, and KpSC. The observed correlation between the TN concentration and abundance of potentially pathogenic bacterial groups, including Mycobacterium, might be caused by the enhanced survival potential of the potentially pathogenic bacterial groups in the presence of the available TN . Considering this correlation, the enhancement or introduction of a biological denitrification process in WTFs could ensure further reduction of Aeromonas, Arcobacter, and KpSC, as well as the effective reduction of Mycobacterium. Tomioka et al.  reported a negative correlation between Arcobacter abundance and NO3−-N concentration or ORP, and a positive correlation between Arcobacter abundance and TN or NH4+-N concentrations in surface water samples from canals and the Chao Phraya River in Bangkok, Thailand. In our study, a significant correlation between the NO2−-N concentration and quantified bacterial group abundance was found only in Mycobacterium. This correlation suggests the superior growth/survival capacity of Mycobacterium under nitrite-accumulating conditions, where incomplete nitrification occurs by insufficient oxygen supply. In fact, adaptation to transient and prolonged oxygen deprivation has been demonstrated in saprophytic and pathogenic Mycobacterium species .
Overall, our data indicate that changes in several water quality parameters affect the abundances of the potentially pathogenic bacterial groups of Aeromonas, Arcobacter, KpSC, and Mycobacterium. The data also suggest that WTFs with a superior removal capacity of organic matter, nitrogen compounds (caused by biological nitrification and subsequent denitrification), and suspended solids can effectively remove the potentially pathogenic bacteria. Of note, rural community WTF C with a high average removal rate of CODCr (93%), BOD (94%), TN (77%), SS (98%), and VSS (98%) (S3 Table) showed the highest average LRVs of Aeromonas (2.57), Arcobacter (3.06), KpSC (3.11), and Mycobacterium (1.57). Also, although similar levels of Aeromonas, Arcobacter, and KpSC reduction were confirmed in WTFs C and D (p = 0.015–0.61), the average LRV of Mycobacterium in the combined anaerobic filter bed and contact aeration system-based WTF C (1.56) was significantly higher than that of the contact aeration system-based WTF D (−0.67, p = 0.00049) (Fig 4). The effective Mycobacterium reduction by WTF C might be because of efficient TN removal (average removal rate: 77%) via the anaerobic filter bed process in which biological denitrification occurs effectively. This study has some limitations. Conventional qPCR assays cannot distinguish between viable and non-viable cells, which pose a low health risk. Therefore, further viability-qPCR assay-based surveys  may provide additional insights into the development and appropriate management of WTFs to produce safe and hygienic water. In addition, the correlation between antibiotic-resistant bacteria, antibiotic-resistance genes, and water quality parameters should be further investigated to comprehensively reduce health risks associated with the discharge and use of treated wastewater . Furthermore, certain Aeromonas, Arcobacter, and Mycobacterium members can be highly resistant to chlorine disinfection, which is the most cost-effective disinfection method . Therefore, the efficacy of different disinfection methods against these potentially pathogenic bacterial groups warrants further investigation.
In this study, we successfully determined the abundance of potentially pathogenic bacterial groups Aeromonas, Arcobacter, KpSC, and Mycobacterium in the influent wastewater and treated effluents of nine existing WTFs located in Japan and Thailand using qPCR assays. One or more average LRVs of Aeromonas, Arcobacter, and KpSC were confirmed in most of the WTFs analyzed. However, owing to the relatively high geometric mean abundances of Aeromonas, Arcobacter, and Mycobacterium (≥ 104 cells mL−1) in the influent wastewater and the relatively low LRV of Mycobacterium (ranging from −0.67 to 1.57) in the existing WTFs, high geometric mean abundances of Aeromonas, Arcobacter, and Mycobacterium (≥ 102 cells mL−1) were detected in all treated effluent samples analyzed. The LRVs of Arcobacter, KpSC, and Mycobacterium in some WTFs differed significantly. A linear, positive correlation was found between the abundances of E. coli and Aeromonas, Arcobacter, or KpSC, suggesting the applicability of E. coli as a bacterial indicator to predict the contamination levels of Aeromonas, Arcobacter, and KpSC. Finally, Spearman’s ρ values suggested that changes in several water quality parameters (CODCr, BOD, TN, NH4+-N, NO2−-N, NO3−-N, SS, VSS, and ORP) affected the abundance of the potentially pathogenic bacterial groups. The data obtained in this study provide new insights into the establishment and proper administration of WTFs to effectively generate safe hygienic water.
S1 Checklist. Additional information regarding the ethical, cultural, and scientific considerations specific to inclusivity in global research.
S1 Text. Detailed methods of quantitative real-time polymerase chain reaction (qPCR) assays.
S1 Table. Sequences of synthetic standard DNA fragments used for quantitative real-time polymerase chain reaction assays.
S2 Table. In silico coverage and specificity of primer sets targeting the 16S rRNA gene of the genus Aeromonas.
We thank the Drainage and Sewage Department, Bangkok Metropolitan Administration, Bangkok; the Environmental Preservation Division, Tokushima City, Tokushima Prefecture; the Environment Division, Naka-cho, Tokushima Prefecture; and the owners of the Johkasou systems for the permission to conduct wastewater sampling. We also thank M. Phumrakchart, M. Sadsara, Y. Inoue, S. Ito, S. Arima, and Y. Saito for supporting our research.
- 1. Hube S, Wu B. Mitigation of emerging pollutants and pathogens in decentralized wastewater treatment processes: A review. Sci. Total Environ. 2021;779:146545. pmid:33752021
- 2. Levine AD, Asano T. Recovering sustainable water from wastewater. Environ. Sci. Technol. 2004;38(11):201A–208A. pmid:15224722
- 3. Jones ER, van Vliet MTH, Qadir M, Bierkens MFP. Country-level and gridded estimates of wastewater production, collection, treatment and reuse. Earth Syst. Sci. Data 2021;13(2):237–254.
- 4. Naidoo S, Olaniran AO. Treated wastewater effluent as a source of microbial pollution of surface water resources. Int. J. Environ. Res. Public Health 2014;11(1):249–270. pmid:24366046
- 5. Sun Y-X, Wu Q-Y, Hu H-Y, Tian J. Effect of ammonia on the formation of THMs and HAAs in secondary effluent chlorination. Chemosphere 2009(5);76:631–637. pmid:19482329
- 6. Delanka-Pedige HMK, Munasinghe-Arachchige SP, Zhang Y, Nirmalakhandan N. Bacteria and virus reduction in secondary treatment: Potential for minimizing post disinfectant demand. Water Res. 2020;177:115802. pmid:32311576
- 7. Cai L, Zhang T. Detecting human bacterial pathogens in wastewater treatment plants by a high-throughput shotgun sequencing technique. Environ. Sci. Technol. 2013;47(10):5433–5441. pmid:23594284
- 8. Numberger D, Ganzert L, Zoccarato L, Mühldorfer K, Sauer S, Grossart H-P, et al. Characterization of bacterial communities in wastewater with enhanced taxonomic resolution by full-length 16S rRNA sequencing. Sci. Rep. 2019;9:9673. pmid:31273307
- 9. Kristensen JM, Nierychlo M, Albertsen M, Nielsen PH. Bacteria from the genus Arcobacter are abundant in effluent from wastewater treatment plants. Appl. Environ. Microbiol. 2020;86(9):e03044–19. pmid:32111585
- 10. Galagoda R, Chanto M, Takemura Y, Tomioka N, Syutsubo K, Honda R, et al. Quantitative 16S rRNA gene amplicon sequencing for comprehensive pathogenic bacterial tracking in a municipal wastewater treatment plant. ACS EST Water 2023;3(4), 923–933.
- 11. Nnadozie CF, Kumari S, Bux F. Status of pathogens, antibiotic resistance genes and antibiotic residues in wastewater treatment systems. Rev. Environ. Sci. Biotechnol. 2017;16:491–515.
- 12. Yang B, Kong X, Cui B, Jin D, Deng Y, Zhuang X, et al. Impact of rural domestic wastewater irrigation on the physicochemical and microbiological properties of pakchoi and soil. Water 2015;7(5):1825–1839.
- 13. Fernández-Bravo A, Figueras MJ. An update on the genus Aeromonas: Taxonomy, epidemiology, and pathogenicity. Microorganisms 2020;8(1):129. pmid:31963469
- 14. Janda JM, Abbott SL. The genus Aeromonas: Taxonomy, pathogenicity, and infection. Clin. Microbiol. Rev. 2010;23(1):35–73. pmid:20065325
- 15. Lamy B, Kodjo A, colBVH Study Group, Laurent F. Prospective nationwide study of Aeromonas infections in France. J. Clin. Microbiol. 2009;47(4):1234–1237. pmid:19244464
- 16. Chen P-L, Lamy B, Ko W-C. Aeromonas dhakensis, an increasingly recognized human pathogen. Front. Microbiol. 2016;7:793. pmid:27303382
- 17. Boussaid A, Baleux B, Hassani L, Lesne J. Aeromonas species in stabilization ponds in the arid region of Marrakesh, Morocco, and relation to fecal-pollution and climatic factors. Microb. Ecol. 1991;21:11–20. pmid:24194198
- 18. Martone-Rocha S, Piveli RP, Matté GR, Dória MC, Dropa M, Morita M, et al. Dynamics of Aeromonas species isolated from wastewater treatment system. J. Water Health 2010;8(4):703–711. pmid:20705981
- 19. Uljanovas D, Gölz G, Brückner V, Grineviciene A, Tamuleviciene E, Alter T, et al. Prevalence, antimicrobial susceptibility and virulence gene profiles of Arcobacter species isolated from human stool samples, foods of animal origin, ready-to-eat salad mixes and environmental water. Gut Pathog. 2021;13:76. pmid:34930425
- 20. Ferreira S, Queiroz JA, Oleastro M, Domingues FC. Insights in the pathogenesis and resistance of Arcobacter: A review. Crit. Rev. Microbiol. 2016;42(3):364–383. pmid:25806423
- 21. Ghaju Shrestha R, Tanaka Y, Haramoto E. A review on the prevalence of Arcobacter in aquatic environments. Water 2022;14(8):1266
- 22. Ghaju Shrestha R, Sherchan SP, Kitajima M, Tanaka Y, Gerba CP, Haramoto E. Reduction of Arcobacter at two conventional wastewater treatment plants in southern Arizona, USA. Pathogens 2019;8(4):175. pmid:31581714
- 23. Matsen JM, Spindler JA, Blosser RO. Characterization of Klebsiella isolates from natural receiving waters and comparison with human isolates. Appl. Microbiol. 1974;28(4):672–678. pmid:4607526
- 24. Struve C, Krogfelt KA. Pathogenic potential of environmental Klebsiella pneumoniae isolates. Environ. Microbiol. 2004;6(6):584–590. pmid:15142246
- 25. Knittel MD, Seidler RJ, Eby C, Cabe LM. Colonization of the botanical environment by Klebsiella isolates of pathogenic origin. Appl. Environ. Microbiol. 1977;34(5):557–563. pmid:337900
- 26. Wyres KL, Lam MMC, Holt KE Population genomics of Klebsiella pneumoniae. Nat. Rev. Microbiol. 2020;18:344–359. pmid:32055025
- 27. Barbier E, Rodrigues C, Depret G, Passet V, Gal L, Piveteau P, et al. The ZKIR assay, a real-time PCR method for the detection of Klebsiella pneumoniae and closely related species in environmental samples. Appl. Environ. Microbiol. 2020;86(7):e02711–19. pmid:32005732
- 28. Shannon KE, Lee DY, Trevors JT, Beaudette LA. Application of real-time quantitative PCR for the detection of selected bacterial pathogens during municipal wastewater treatment. Sci. Total Environ. 2007;382(1):121–129. pmid:17462712
- 29. Johansen MD, Herrmann J-L, Kremer L. Non-tuberculous mycobacteria and the rise of Mycobacterium abscessus. Nat. Rev. Microbiol. 2020;18:392–407. pmid:32086501
- 30. To K, Cao R, Yegiazaryan A, Owens J, Venketaraman V. General overview of nontuberculous Mycobacteria opportunistic pathogens: Mycobacterium avium and Mycobacterium abscessus. J. Clin. Med. 2020;9(8):2541. pmid:32781595
- 31. Alexander KJ, Furlong JL, Baron JL, Rihs JD, Stephenson D, Perry JD, et al. Evaluation of a new culture medium for isolation of nontuberculous mycobacteria from environmental water samples. PLoS One 2021;16(3):e0247166. pmid:33657154
- 32. Radomski N, Lucas FS, Moilleron R, Cambau E, Haenn S, Moulin L. Development of a real-time qPCR method for detection and enumeration of Mycobacterium spp. in surface water. Appl. Environ. Microbiol. 2010;76(21):7348–7351. pmid:20851986
- 33. Radomski N, Betelli L, Moilleron R, Haenn S, Moulin L, Cambau E, et al. Mycobacterium behavior in wastewater treatment plant, a bacterial model distinct from Escherichia coli and Enterococci. Environ. Sci. Technol. 2011;45(12):5380–5386. pmid:21591688
- 34. World Health Organization. WHO guidelines for the safe use of wastewater, excreta and greywater—Volume 4 Excreta and greywater use in agriculture. Geneva: WHO press; 2006. https://www.who.int/publications/i/item/9241546859
- 35. Rock CM, Brassill N, Dery JL, Carr D, McLain JE, Bright KR, et al. Review of water quality criteria for water reuse and risk-based implications for irrigated produce under the FDA Food Safety Modernization Act, produce safety rule. Environ Res. 2019;172:616–629. pmid:30878733
- 36. Schellenberg T, Subramanian V, Ganeshan G, Tompkins D, Pradeep R. Wastewater discharge standards in the evolving context of urban sustainability–The case of India. Front. Environ. Sci. 2020;8:30.
- 37. Water Environmental Partnership in Asia. Outlook on water environmental management in Asia 2021. Kanagawa: Institute for Global Environmental Strategies; 2021. https://wepa-db.net/wp-content/uploads/2023/02/WEPA_outlook2021_e.pdf
- 38. Ahmed W, Huygens F, Goonetilleke A, Gardner T. Real-time PCR detection of pathogenic microorganisms in roof-harvested rainwater in Southeast Queensland, Australia. Appl. Environ. Microbiol. 2008;74(17):5490–5496. pmid:18621865
- 39. Wéry N, Lhoutellier C, Ducray F, Delgenès J-P, Godon J-J. Behaviour of pathogenic and indicator bacteria during urban wastewater treatment and sludge composting, as revealed by quantitative PCR. Water Res. 2008;42(1–2):53–62. pmid:17659319
- 40. Tomioka N, Yoochatchaval W, Takemura Y, Matsuura N, Danshita T, Srisang P, et al. Detection of potentially pathogenic Arcobacter spp. in Bangkok canals and the Chao Phraya River. J. Water Health 2021;19(4):657–670. pmid:34371501
- 41. Rodríguez S, Araujo R. Effect of environmental parameters on the inactivation of the waterborne pathogen Campylobacter in a Mediterranean river. J. Water Health 2012;10(1):100–107. pmid:22361705
- 42. Endo S, Koga S. Johkasou–Wastewater Management in a Local Municipality in Japan. Asian Development Bank Institute (ADBI) Development Case Study No. 2021–4 (October). Tokyo; 2021. https://www.adb.org/publications/johkasou-wastewater-management-japan
- 43. Talang RPN, Sirivithayapakorn S, Polruang S. Environmental impacts and cost-effectiveness of Thailand’s centralized municipal wastewater treatment plants with different nutrient removal processes. J. Clean. Prod. 2020;256:120433.
- 44. Takemura Y, Yoochatchaval W, Danshita T, Miyaoka Y, Aoki M, Tran PT, et al. A pilot-scale study of a down-flow hanging sponge reactor as a post-treatment for domestic wastewater treatment system at short hydraulic retention times. J. Water Process Eng. 2022;50:103313.
- 45. Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar , et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32(4):1363–1371. pmid:14985472
- 46. Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and "All-species Living Tree Project (LTP)" taxonomic frameworks. Nucleic Acids Res. 2014;42(D1):D643–D648. pmid:24293649
- 47. Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41(1):e1. pmid:22933715
- 48. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41(D1):D590–D596. pmid:23193283
- 49. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA 2011;108(supplement_1):4516–4522. pmid:20534432
- 50. Aoki M, Okubo K, Kusuoka R, Watari T, Syutsubo K, Yamaguchi T. Hexavalent chromium removal and prokaryotic community analysis in glass column reactor packed with aspen wood as solid organic substrate. Appl. Biochem. Biotechnol. 2022;194:1425–1441. pmid:34739702
- 51. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 2011;17(1):10–12.
- 52. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 2009;75(23):7537–7541. pmid:19801464
- 53. Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 2013;79(17):5112–5120. pmid:23793624
- 54. Westcott SL, Schloss PD. OptiClust, an improved method for assigning amplicon-based sequence data to operational taxonomic units. mSphere 2017;2(2):e00073–17. pmid:28289728
- 55. Yoon S-H, Ha S-M, Kwon S, Lim J, Kim Y, Seo H, et al. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int. J. Syst. Evol. Microbiol. 2017;67(5):1613–1617. pmid:28005526
- 56. Mise K, Iwasaki W. Environmental atlas of prokaryotes enables powerful and intuitive habitat-based analysis of community structures. iScience 2020;23(10):101624. pmid:33117966
- 57. Japanese Standards Association. JIS K 0102:2016 Testing methods for industrial wastewater. Tokyo: The Association; 2016.
- 58. Danshita T, Yoochatchaval W, Takemura Y, Miyaoka Y, Kada M, Tepjun W, et al. Performance evaluation of a down-flow hanging sponge (DHS) reactor as a decentralized domestic wastewater treatment system in tropical regions. J. Environ. Sci. Health A Tox. Hazard. Subst. Environ. Eng. 2020;55(7):847–857. pmid:32253973
- 59. Kodaka H, Mizuochi S, Teramura H, Nirazuka T, Goins D, Odumeru J, et al. Comparison of the Compact Dry EC with the most probable number method (AOAC official method 966.24) for enumeration of Escherichia coli and coliform bacteria in raw meats: Performance-Tested MethodSM 110402. J. AOAC Int. 2006;89(1):100–114.
- 60. Revelle W. psych: Procedures for Psychological, Psychometric, and Personality Research. Version 2.2.9 [software]. Northwestern University. 2022 Sep 12. https://cran.r-project.org/web/packages/psych/
- 61. Wei T, Simko V. corrplot: Visualization of a Correlation Matrix. Version 0.9.2 [software]. 2021 Nov 18. https://cran.r-project.org/web/packages/corrplot/
- 62. R Core Team. R: A language and environment for statistical computing. Version 4.1.0 [software]. R Foundation for Statistical Computing. 2021 May 18. https://www.r-project.org/
- 63. Pérez-Cataluña A, Salas-Massó N, Diéguez AL, Balboa S, Lema A, Romalde JL, et al. Revisiting the taxonomy of the genus Arcobacter: Getting order from the chaos. Front. Microbiol. 2018;9:2077. pmid:30233547
- 64. On SLW, Miller WG, Biggs PJ, Cornelius AJ, Vandamme P. Aliarcobacter, Halarcobacter, Malaciobacter, Pseudarcobacter and Poseidonibacter are later synonyms of Arcobacter: transfer of Poseidonibacter parvus, Poseidonibacter antarcticus, ’Halarcobacter arenosus’, and ’Aliarcobacter vitoriensis’ to Arcobacter as Arcobacter parvus comb. nov., Arcobacter antarcticus comb. nov., Arcobacter arenosus comb. nov. and Arcobacter vitoriensis comb. nov. Int. J. Syst. Evol. Microbiol. 2021;71(11):005133. pmid:34825881
- 65. McLellan SL, Roguet A. The unexpected habitat in sewer pipes for the propagation of microbial communities and their imprint on urban waters. Curr. Opin. Biotechnol. 2019;57:34–41. pmid:30682717
- 66. Greay TL, Gofton AW, Zahedi A, Paparini A, Linge KL, Joll CA, et al. Evaluation of 16S next-generation sequencing of hypervariable region 4 in wastewater samples: An unsuitable approach for bacterial enteric pathogen identification. Sci. Total Environ. 670:1111–1124. pmid:31018427
- 67. Chen Y-J, Leung PM, Wood JL, Bay SK, Hugenholtz P, Kessler AJ, et al. Metabolic flexibility allows bacterial habitat generalists to become dominant in a frequently disturbed ecosystem. ISME J. 2021;15:2986–3004. pmid:33941890
- 68. Japanese Society for Bacteriology [Internet]. Tokyo: Biosafety levels of pathogenic bacteria (in Japanese). [cited 2023 Feb 3]. https://jsbac.org/archive/img/bsl_level.pdf.
- 69. Levican A, Alkeskas A, Günter C, Forsythe SJ, Figueras MJ. Adherence to and invasion of human intestinal cells by Arcobacter species and their virulence genotypes. Appl. Environ. Microbiol. 2013;79(16):4951–4957. pmid:23770897
- 70. Wang G, Clark CG, Liu C, Pucknell C, Munro CK, Kruk TMAC, et al. Detection and characterization of the hemolysin genes in Aeromonas hydrophila and Aeromonas sobria by multiplex PCR. J. Clin. Microbiol. 2003;41(3):1048–1054. pmid:12624028
- 71. Yu C-P, Farrell SK, Robinson B, Chu K-H. Development and application of real-time PCR assays for quantifying total and aerolysin gene-containing Aeromonas in source, intermediate, and finished drinking water. Environ. Sci. Technol. 2008;42(4):1191–1200. pmid:18351092
- 72. Robertson BK, Harden C, Selvaraju SB, Pradhan S, Yadav JS. Molecular detection, quantification, and toxigenicity profiling of Aeromonas spp. in source- and drinking-water. Open Microbiol. J. 2014;8:32–39. pmid:24949108
- 73. Kanchanapiya P, Tantisattayakul T. Wastewater reclamation trends in Thailand. Water Sci. Technol. 2022;86(11):2878–2911. pmid:36515195
- 74. Garcia-Aljaro C, Momba M, Muniesa M. Pathogenic members of Escherichia coli & Shigella spp. Shigellosis. In: Rose JB, Jiménez-Cisneros B, editors. Water and Sanitation for the 21st Century: Health and Microbiological Aspects of Excreta and Wastewater Management (Global Water Pathogen Project). Michigan State University, UNESCO; 2017.
- 75. Wang M, Zhu J, Mao X. Removal of pathogens in onsite wastewater treatment systems: a review of design considerations and influencing factors. Water 2021;13(9):1990.
- 76. Zhang T, Shao M-F, Ye L. 454 pyrosequencing reveals bacterial diversity of activated sludge from 14 sewage treatment plants. ISME J. 2012;6:1137–1147. pmid:22170428
- 77. Guo F, Zhang T, Li B, Wang Z, Ju F, Liang Y. Mycobacterial species and their contribution to cholesterol degradation in wastewater treatment plants. Sci. Rep. 2019;9:836. pmid:30696864
- 78. Solaiman S, Allard SM, Callahan MT, Jiang C, Handy E, East C, et al. Longitudinal assessment of the dynamics of Escherichia coli, total coliforms, Enterococcus spp., and Aeromonas spp. in alternative irrigation water sources: a CONSERVE Study. Appl. Environ. Microbiol. 2020;86(20):e00342–20. pmid:32769196
- 79. Pavlik I, Ulmann V, Hubelova D, Weston RT. Nontuberculous mycobacteria as sapronoses: A review. Microorganisms 2022;10(7):1345. pmid:35889064
- 80. Berney M, Greening C, Conrad R, Jacobs WR Jr., Cook GM. An obligately aerobic soil bacterium activates fermentative hydrogen production to survive reductive stress during hypoxia. Proc. Natl. Acad. Sci. USA 2014;111(31):11479–11484. pmid:25049411
- 81. Zeng D, Chen Z, Jiang Y, Xue F, Li B. Advances and challenges in viability detection of foodborne pathogens. Front. Microbiol. 2016;7:1833. pmid:27920757
- 82. Marutescu LG, Popa M, Gheorghe-Barbu I, Barbu IC, Rodríguez-Molina D, Berglund F, et al. Wastewater treatment plants, an “escape gate” for ESCAPE pathogens. Front. Microbiol. 2023;14:1193907. pmid:37293232
- 83. Zerva I, Remmas N, Kagalou I, Melidis P, Ariantsi M, Sylaios G, et al. Effect of chlorination on microbiological quality of effluent of a full-scale wastewater treatment plant. Life 2021;11(1):68. pmid:33477775