ABSTRACT
Polychlorinated biphenyls (PCBs) are toxic pollutants that are distributed worldwide. Indigenous microorganisms are believed to contribute significantly to their degradation. To better understand how environmental microorganisms degrade toxic pollutants, such as PCBs, we identified the indigenous bacteria involved in the natural attenuation of PCBs and PCB congener profiles in sediments from harbors on Awaji Island, Hyogo, Japan. High-resolution gas chromatography and high-resolution mass spectrometry (HRGC/HRMS) were used to analyze the PCB congener profiles. The sediment samples contained 2,200–3,800 pg PCBs/g dry, while the sediment sample with 100 pg PCBs/g dry served as a control harbor. The profiles of PCB congener composition in each homolog group using cosine theta (cos θ) analysis suggested that the polluted harbor with 2,200–3,800 pg PCBs/g dry was primarily affected by multiple commercial PCB products. Furthermore, the main congeners in the polluted harbor exhibited a positional preference that was not observed in the control harbor or in commercial PCB products; this suggests the possibility of microbial degradation rather than merely the presence of multiple commercial PCB products. Additionally, 16S ribosomal RNA (rRNA) next-generation sequencing (NGS) analysis revealed similar alpha-diversity but different bacterial community compositions between the two harbors, particularly in the sediments, with the presence of PCB-dechlorinating Dehalococcoidaceae species in the polluted harbor sediments. Polymerase chain reaction (PCR) amplification revealed the presence of PCB-dechlorinating bacteria, namely Dehalogenimonas sp., only in the polluted harbor. These findings suggest that anaerobic PCB-dechlorinating bacteria such as Dehalogenimonas sp. play a key role in the natural attenuation of PCBs by dechlorination in polluted harbors. Overall, we not only provided insight into the biodegradation process by indigenous microorganisms and the potential for the natural attenuation of PCBs in polluted environments but also demonstrated that the combination of PCB congener analysis using HRGC/HRMS and microbial analysis using environmental DNA is a powerful tool for assessing PCB pollution and indicating natural attenuation in the environment.
INTRODUCTION
Polychlorinated biphenyls (PCBs) are persistent organic pollutants listed under the Stockholm Convention because of their environmental persistence, bioaccumulation, toxicity to humans and wildlife, and widespread distribution. Formerly used in various applications, such as electric insulating oil, lubricating oil, heating media, plasticizers, paint, and pressure-sensitive copying paper, their production, transport, and use are now banned. However, PCBs remain detectable worldwide in soil, sediments, biota, water, and air. A survey conducted in 2021 by Japan’s Ministry of the Environment reported PCB concentrations averaging 100 pg/L in water, 4,900 pg/g dry sediment, and 71 pg/m3 in the atmosphere (Ministry of the Environment Government of Japan, 2023). This survey also revealed higher PCB concentrations in fish (average of 13,000 pg/g wet) than in sediment, indicating bioaccumulation. Combined with their high toxicity (Van den Berg et al., 2006; IARC, 2013), PCBs pose environmental and health risks at low concentrations. This is also the case for contaminated soils and the related accumulation in livestock (Weber et al., 2018a; Petrlik et al., 2022).
PCBs are chlorinated hydrocarbons derived from biphenyls in which one or more hydrogen atoms are replaced with chlorine atoms. Based on the chlorine substitution in the biphenyl structure, 209 theoretically possible congeners can be categorized into 10 homologous groups based on the total number of chlorine substituents. Commercially, PCBs were typically used as mixtures, such as Kanechlor (KC), the predominant commercial PCB product produced and used in Japan. Commercial products have specific profiles of homologous groups and congeners (Takasuga et al., 1995; Ministry of the Environment, 2011). The composition profiles of PCB homologous groups have been employed to identify the sources of PCB pollution in environmental samples (Honda et al., 2008), whereas those of PCB congeners have been used to detect specific PCB congeners that are degraded by environmental bacteria (Imamoglu et al., 2004; Mattes et al., 2018). Therefore, the analysis of PCBs using gas chromatography-mass spectrometry (GC-MS) is valuable for assessing the current extent of PCB contamination but does not help predict future changes in PCB levels.
In natural environments, PCBs undergo biodegradation under aerobic and anaerobic conditions (Borja et al., 2005; Sharma et al., 2017). Anaerobic bacteria can reductively dechlorinate PCBs, converting highly chlorinated PCBs into low-chlorinated congeners that are more susceptible to aerobic degradation (Quensen et al., 1988). Several bacterial species are capable of dechlorinating PCBs and have been used as biomarkers for PCB dechlorination. The Dehalococcoides genus and related species such as Dehalobium chlorocoercia DF-1, bacterium ortho-17 (o-17), and Dehalogenimonas strains are commonly used as indicators of PCB-dechlorinating bacteria (Fagervold et al., 2005; Hiraishi et al., 2005; Xu et al., 2012; Wang and He, 2013a; Liang et al., 2014). In addition, dechlorination of PCBs in environments containing Dehalobacter sp. has been reported, although this bacterium has not yet been isolated (Yoshida et al., 2009; Wang and He, 2013b). Certain strains of Desulfitobacterium dechlorinate PCB hydroxides (Wiegel et al., 1999). However, the presence of these bacterial species in contaminated soils remains unknown. In contrast, aerobic bacteria can oxidatively degrade PCBs via the biphenyl catabolic pathway, resulting in PCB mineralization. Biphenyl dioxygenase, the first enzyme in the biphenyl catabolic pathway, is used as an indicator of aerobic PCB degradation (Pieper, 2005; Correa et al., 2010).
In this study, we evaluated the natural attenuation of PCBs in the sediments of two harbors located on Awaji Island, Hyogo Prefecture, using a combination of PCB congener analysis by high-resolution gas chromatography and high-resolution mass spectrometry (HRGC/HRMS) and microbial analysis using environmental DNA extracted from sediment samples.
MATERIALS AND METHODS
CHEMICALS
BP-WD, MBP-CG, MBP-70, and MBP-170 (Wellington Laboratories Inc., Ontario, Canada) were used as calibration standard solutions, clean-up solutions, and syringe spikes, respectively. Acetone, copper powder, n-hexane, and sodium chloride were purchased from Wako Pure Chemical Industries Ltd. (Osaka, Japan). Anhydrous sodium sulfate was purchased from Kanto Chemical Co. (Tokyo, Japan). A 12-mL Si/44% H2SO4/Si solid-phase extraction (SPE) tube was purchased from Merck (Darmstadt, Germany).
SITE DESCRIPTION AND SAMPLE COLLECTION
In October 2019, two harbors located in Hyogo, Japan, were sampled (Fig. 1). Sediment samples were collected from Sites A, B, and C in Harbor X (34-20.5’ N, 134-53.5’ E) and Site D in nearby Harbor Y (34-21.2’ N, 134-53.4’ E) using an Ekman–Birge dredge. Four samples, each weighing approximately 250 g, were collected from the upper layer of the sediment at a depth of roughly 5–10 cm. These samples were placed in sterilized centrifuge tubes for DNA extraction and analysis or in clean glass bottles pre-washed with acetone for PCB extraction and analysis. The samples were then stored at 4°C until further analysis. Two water samples were collected from Sites B and D and filtered through a Sterivex filter (Merck, Darmstadt, Germany) with a pore size of 0.22 µm. The bacteria trapped on the filter were stored at −30°C until DNA extraction.
Fig. 1 Locations of sampling sites in Awaji Island in Hyogo prefecture
PCB extraction from the sediments was conducted as described by Asaoka et al. (2019) with minor modifications. The weighed wet sediment samples (approximately 10 g) were spiked with 20 μL of MBP-CG (50 ng mL−1). PCBs were extracted from the spiked sediments by shaking extraction (10 min), ultrasonic extraction (10 min) with 25 mL of acetone, and separation from the sample by centrifugation (2,500 rpm for 10 min) performed twice. The same extraction procedure was performed using 40 mL of n-hexane.
The extract was transferred to a separatory funnel containing 300 mL of purified water and 15 g of sodium chloride and shaken for 10 min. After shaking, the mixture was left to stand for 10 min until the n-hexane phase was completely separated from the water. The n-hexane extract was transferred to a glass beaker. This extraction procedure was repeated with 50 mL of n-hexane, after which the two n-hexane extracts were combined. The combined extracts were dehydrated using anhydrous sodium sulfate and concentrated to 2–3 mL using a rotary evaporator.
Subsequently, 1–2 g of anhydrous sodium sulfate was added to a pretreatment cartridge (12-mL Si/44% H2SO4/Si SPE tube), and the cartridge was conditioned with 20 mL of n-hexane. The extract was loaded onto a cartridge, and the PCBs were eluted with 50 mL of n-hexane. The purified PCBs in the extracts were concentrated to 3–4 mL using a rotary evaporator. Activated copper powder was added to the extract, transferred to a spit glass, and concentrated under a nitrogen gas flow. The extract volume was then adjusted to 0.1 mL with hexane and added to 10 μL of MBP-70 (50 ng mL−1) and MBP-170 (50 ng mL−1). The PCB concentrations in the extracted samples were analyzed using a gas chromatography system (6890 N; Agilent) with a high-resolution mass spectrometer (JMS-800D; JEOL, Tokyo).
PCB CONGENER ANALYSIS
The PCB concentrations in the extracts were analyzed based on previous studies (Haga et al., 2018) with minor modifications. A gas chromatograph coupled with a high-resolution mass spectrometer was used to determine PCB concentrations in the sediment samples. An HT8-PCB column (60 m × 0.25 mm i.d.; SGE Analytical Science, Victoria, Australia) was used for the analysis. The column oven temperature was controlled at 120°C and increased to 180°C at 20°C min−1. Subsequently, the temperature was increased to 260°C at 2°C min−1 before reaching 300°C at 5°C min−1, and held for 4 min. The 2-μL sample was injected in the splitless mode, with He as the carrier gas at a flow rate of 1.0 mL min−1. The inlet, ion source, and interface temperatures were maintained at 280°C. Electron ionization mass spectra were recorded at 38 eV with an ionization current of 400 μA and a detector voltage of 300 V. The resolution was maintained above 10,000. As described previously, peaks were assigned to all PCB congeners (Matsumura et al., 2002). PCB concentrations were determined using the isotope dilution method.
The composition profiles of PCB homologous groups and congeners were compared using cosine theta (cos θ) analysis, i.e., a measure of similarity between two datasets, as described in previous reports (DeCaprio et al., 2005; Saba and Boehma, 2011; Mattes et al., 2018). The cos θ value varies from 0 (no correlation) to 1 (complete correlation). The analysis was conducted using R software (version 4.3.2) (R Core Team R., 2024). Before the cos θ analysis, each homolog was normalized to represent 100% of the total homologs detected in the sediment samples. In contrast, each congener was normalized to represent 100% of the total congeners detected in the sediment samples or 100% of the sum of the congeners within each homolog group. Kanechlor datasets (KC-300–KC-600) provided in the PCB manual (Ministry of the Environment, 2011) were used as references for commercial PCB products.
GENOMIC DNA EXTRACTION
DNA was extracted from the sediment samples (approximately 0.25 g) and Sterivex filters using the DNeasy PowerSoil Kit (QIAGEN, Hilden, Germany) and PowerWater Sterivex Kit (QIAGEN), respectively, following the manufacturer’s instructions. The concentration of DNA extracts was measured using a Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA) and stored at −30°C until further analysis.
16S RIBOSOMAL RNA (rRNA) GENE-NEXT-GENERATION SEQUENCING (NGS) ANALYSIS
The bacterial communities in the sediment and water samples from Sites B and D in Harbors X and Y, respectively, were analyzed using amplicon sequencing of the extracted genomic DNA. Amplicon sequencing and data processing were performed at Bioengineering Lab. Co., Ltd. (Kanagawa, Japan). Amplicon sequencing targeting partial sequences in the hypervariable V3–V4 region of the bacterial 16S rRNA gene was conducted using an Illumina MiSeq platform (San Diego, CA, USA). Sequence data were analyzed using the QIIME2 software package version 2019.011.
The R package (version 4.3.2) was used to analyze the alpha- and beta-diversity of the taxonomic profiles at the family level of bacteria in the water and sediment samples from Sites B and D (R Core Team, 2024). The alpha-diversity of the microbial community was determined using the Simpson (Simpson, 1949) and Shannon (Shannon, 1997) indices, whereas the beta-diversity was assessed using the Bray–Curtis dissimilarity (Bray and Curtis, 1957).
DETECTION OF THE PCB-DECHLORINATING BACTERIA BY PCR AMPLIFICATION
PCB-dechlorinating bacteria (genera Dehalococcoides, Dehalobacter, and Desulfitobacterium) were detected by polymerase chain reaction (PCR) amplification using genomic DNA extracted from sediments at Sites A–D. The primer sets Dhc, Dre, and Dsb were used to detect Dehalococcoides, Dehalobacter, and Desulfitobacterium, respectively (Table S1) (Smits et al., 2004; Yan et al., 2009). The PCR amplification reaction was performed in a total volume of 20 μL. The reaction mixtures contained 2 μL of 10× Buffer, 2 μL of 2 mM deoxynucleotide triphosphates (dNTPs), 0.4 μL of 10 μM of each primer, 1 unit of Blend Taq Plus polymerase (TOYOBO, Osaka, Japan), and 3–5 ng of the extracted genomic DNA (1st PCR). The following thermal cycling program was used for amplification: initial denaturation at 94°C for 2 min; 30 cycles of denaturation at 98°C for 10 s; annealing at 54°C for 30 s; extension at 72°C for 1 min; and final extension at 72°C for 2 min. The same PCR amplification reactions were repeated using the 1st PCR product as a template (2nd PCR). The PCR products were electrophoresed on a 2% agarose gel. Discrete DNA fragments of the predicted size were excised and purified using a NucleoSpin Gel and PCR clean-up kit (Macherey-Nagel, Düren, Germany). The purified DNA fragments were sequenced by FASMAC (Kanagawa, Japan).
RESULTS AND DISCUSSION
ESTIMATION OF THE MAJOR PCB CONTAMINATION SOURCES IN HARBORS BASED ON PCB CONGENER ANALYSIS
Sediment samples were collected from three locations (Sites A–C) in Harbor X (34-20.5’ N, 134-53.5’ E) and one location (Site D) in nearby Harbor Y (34-21.2’ N, 134-53.4’ E) (Fig. 1). The total PCB concentration at Sites A–C varied from 2,200 to 3,800 pg PCBs/g dry, with an overall average of 2,900 pg PCBs/g dry (Table 1); this indicates that Harbor X was contaminated with PCBs and is referred to as the polluted harbor. These concentrations are also consistent with the range of PCB concentrations found in the environment in Japan, as reported by the Ministry of the Environment, Government of Japan, in 2023, indicating that the PCB concentrations in Harbor X are commonly found in the Japanese environment. In contrast, Site D in Harbor Y had a PCB concentration of 100 pg PCBs/g dry weight, which was approximately 29-fold lower than the average value at Sites A–C. Therefore, Harbor Y served as a low-contamination control area (referred to as the control harbor).
Table 1. PCB concentrations in sediments at Sites A–D
| Locations | Concentration[pg/g-dry] |
|---|
| Site A | 3,800 |
| Site B | 2,700 |
| Site C | 2,200 |
| Site D | 100 |
The sources of PCBs in the environment can be estimated by comparing the composition of PCB homologs with those in commercial products, which are the main sources of contamination (Honda et al., 2008). Figure 2 shows the PCB homolog composition profiles of the polluted harbor (n = 3; Sites A–C) and known Kanechlors (KC-300–KC-600). The average homolog composition profile in the polluted harbor showed high concentrations of trichlorinated biphenyls (Tri-CBs), tetrachlorinated biphenyls (Tetra-CBs), pentachlorinated biphenyls (Penta-CBs), and hexachlorinated biphenyls (Hexa-CBs), similar to each profile at Sites A–C in the polluted harbor (Fig. S1). The cos θ analysis of the profiles showed that the homolog composition data at Sites A–C were closest to that at KC-500 (each cos θ was within 0.824–0.836) (Table S2), suggesting the possibility that PCB contaminations at Sites A–C were mainly influenced by KC-500. However, when examining the PCB congener composition in the total congeners at Sites A–C, the cos θ to KC-500 ratio fell between 0.598 and 0.660, which was substantially lower than the homolog composition (0.824 to 0.836) (Table S2). This discrepancy indicates that the homolog composition does not accurately reflect the source of PCB contamination. The inaccuracy in representing the PCB contamination source through homolog composition can be attributed to the possibility that less-chlorinated congeners are more susceptible to water solubility, volatility, and biodegradation, resulting in these less-chlorinated congeners diminishing more rapidly than their highly chlorinated congeners. In contrast, the homolog composition profile at Site D could not be analyzed because of the extremely low PCB detection concentrations. Nevertheless, the PCB congener composition at Site D demonstrated the highest cos θ value for KC-300 (0.869), with noticeably lower cos θ values for the other Kanechlors.
Fig. 2 Profiles of PCB homologs grouped by the number of chlorine atoms in KC-300 (a), KC-400 (b), KC-500 (c), and KC-600 (d); the average PCB homolog data of Sites A-C (n = 3) in the polluted harbor (e)
To eliminate the effects of homolog composition on congener composition, we employed cos θ analysis to examine the PCB congener composition in each homolog group, but not in the total congeners, at Sites A–D. The congener compositions in the homolog groups and the major congeners at Sites A–C were similar to each other but differed from those at Site D (Tables S3 and S4); this suggests that Sites A–C can be considered equivalent polluted sites, a conclusion further supported by cos θ analysis of the homolog and congener compositions in the total, as mentioned above (Table S2). We then compared the congener compositions in the homolog groups of the polluted harbor (average of data from Sites A–C) and the control harbor with those of the Kanechlors (Table 2). Analysis of the Di-CBs, Tri-CBs, and Tetra-CBs data revealed that the average PCB congener composition in the homolog groups in the polluted harbor showed a higher cos θ value for KC-300 and/or KC-400 compared with KC-500 and KC-600. Conversely, the examination of the hexa-CBs and Hepta-CBs data indicated that the average PCB congener composition in the homolog groups in the polluted harbor exhibited a higher cos θ value for KC-600 than for the other Kanechlors. Notably, the analysis of the Penta-CBs data revealed that the average PCB congener composition in the homolog groups in the polluted harbor did not display a high cos θ value for any of the Kanechlors. This dissimilarity can be attributed to differences in the major congeners, especially PCB-126, found in the polluted harbor compared with KC-500 (Table 3). In contrast, the PCB composition in the homolog groups in the control harbor showed the highest cos θ value for KC-300 when analyzed using data excluding Penta-CBs, whose chromatograms made accurate quantification challenging due to the extremely low concentrations of PCBs detected (Fig. S2). These results suggest that the polluted harbors were primarily affected by a combination of different Kanechlors, specifically KC-300, KC-400, and KC-600, rather than by any single Kanechlor. In contrast, the control harbor was mainly affected by KC-300. The presence of KC-300, KC-400, and KC-500 might be related to the historical inflow of electric drains, whereas KC-600 could be related to ship-bottom paints typically encountered in PCB-polluted harbors. Interestingly, in the examination of Di-CBs, the PCB congener composition in homolog groups in the control harbor exhibited a higher cos θ value for that in the polluted harbor than KC-300, but this was not the case when examining Tri-CBs and Tetra-CBs. The disparity in Tri-CBs and Tetra-CBs congener compositions between the control and polluted harbors may be attributed to differences in multiple Kanechlors and/or dehalogenation/degradation processes, potentially carried out by indigenous microorganisms. This rationale is more likely than common factors such as solubility in water, volatility, and UV-induced dehalogenation.
Table 2. Cos
θ analysis of the PCB congener composition in each homolog in the control and polluted harbors
| Homolog groups | In the control harbora) | In the polluted harborb) |
|---|
| KC-300 | KC-400 | KC-500 | KC-600 | Site D | KC-300 | KC-400 | KC-500 | KC-600 |
|---|
| Mo-CBs | —c) | — | — | — | 0.761 | — | — | — | — |
| Di-CBs | 0.868 | 0.828 | 0.849 | 0.811 | 0.989 | 0.843 | 0.820 | 0.810 | 0.759 |
| Tri-CBs | 0.989 | 0.966 | 0.981 | 0.951 | 0.873 | 0.826 | 0.853 | 0.848 | 0.737 |
| Tetra-CBs | 0.978 | 0.977 | 0.781 | 0.924 | 0.863 | 0.845 | 0.889 | 0.590 | 0.778 |
| Penta-CBs | 0.763 | 0.790 | 0.826 | 0.541 | 0.734 | 0.702 | 0.722 | 0.716 | 0.510 |
| Hexa-CBs | — | — | — | — | — | 0.777 | 0.621 | 0.810 | 0.824 |
| Hepta-CBs | — | — | — | — | — | — | — | 0.660 | 0.943 |
a) PCB congener composition data from Site D in the control harbor were used as data for the control harbor.
b) The average PCB congener composition data of Sites A–C in the polluted harbor were used as data for the polluted harbor (
n = 3).
Table 3. Composition percentages of major congeners in the harbors and Kanechlors
| IUPAC No. | Congeners | Composition % in all PCBs |
|---|
| Control harbor | Polluted harbor | KC-300 | KC-400 | KC-500 | KC-600 |
|---|
| #153 | 2,2’,4,4’,5,5’- | —a) | 7.1 | — | 0.2 | 5.3 | 8.5 |
| #28 | 2,4,4’- | 16.0 | 5.6 | 7.9 | 3.0 | 0.3 | 0.2 |
| #126 | 3,3’,4,4’,5- | — | 4.6 | — | — | — | — |
| #66 | 2,3’,4,4’- | — | 4.1 | 2.9 | 6.2 | 0.6 | 0.1 |
| #118 | 2,3’,4,4’,5- | — | 3.5 | 0.2 | 2.3 | 6.8 | — |
| #149/#139 | 2,2’,3,4’,5’,6- /2,2’,3,4’,4’,6- | — | 3.5 | 0.1 | 0.3 | 6.3 | 10.0 |
| #99 | 2,2’,4,4’,5- | — | 3.0 | 0.2 | 1.7 | 2.4 | 0.1 |
| #31 | 2,4’,5- | 16.5 | 3.0 | 8.6 | 4.3 | 0.3 | 0.2 |
| #90/#101 | 2,2’,3,4’,5-/2,2’,4,5,5’- | — | 2.9 | 0.4 | 2.7 | 9.3 | 2.3 |
| #138 | 2,2’,3,4,4’,5’- | — | 2.6 | — | 0.4 | 6.5 | 4.4 |
| #182/#187 | 2,2’,3,4,4’,5,6’/2,2’,3,4’,5,5’,6- | — | 2.6 | — | — | 0.4 | 6.0 |
| #70 | 2,3’,4’,5- | — | 2.4 | 2.7 | 6.7 | 2.3 | 0.2 |
| #180 | 2,2’,3,4,4’,5,5’- | — | 2.4 | — | — | 1.0 | 11.0 |
| #20/#33 | 2,3,3’-/2,3’,4’- | 14.3 | 2.4 | 6.6 | 1.8 | 0.2 | 0.1 |
| #120/#110 | 2,3’,4,5,5’-/2,3,3’,4’,6- | — | 2.2 | 0.3 | 3.0 | 8.7 | 0.8 |
| #43/#49 | 2,2’,3,5-/2,2’,4,5’- | — | 2.1 | 2.9 | 4.8 | 0.7 | 0.1 |
| #74 | 2,4,4’,5- | — | 1.7 | 1.7 | 3.9 | 0.5 | 0.1 |
| #52/#69 | 2,2’,5,5’-/2,3’,4,6- | — | 1.7 | 3.9 | 7.7 | 4.7 | 0.3 |
| #8/#5 | 2,4’-/2,3- | — | 1.5 | 4.7 | 0.4 | 0.2 | 0.2 |
| #37 | 3,4,4’- | 2.9 | 1.5 | 2.3 | 0.6 | 0.2 | — |
| #98/#95 | 2,2’,3,4’,6’-/2,2’,3,5’,6- | — | 1.5 | 0.2 | 1.0 | 3.3 | 0.8 |
| #48/#47 | 2,2’,4,5-/2,2’,4,4’- | — | 1.5 | 2.6 | 3.8 | 0.2 | 0.1 |
| #32 | 2,4’,6- | 6.4 | 1.2 | 2.6 | 0.8 | 0.1 | 0.1 |
| #22 | 2,3,4’- | 6.7 | 1.2 | 3.7 | 1.1 | 0.2 | 0.1 |
| #18 | 2,2’,5- | 17.0 | 1.2 | 11.0 | 3.3 | 0.4 | 0.3 |
| #151 | 2,2’,3,5,5’,6- | — | 0.9 | — | — | 0.9 | 3.0 |
| #17 | 2,2’,4- | 6.6 | 0.9 | 3.6 | 0.8 | 0.1 | 0.1 |
| #44 | 2,2’,3,5’- | — | 0.9 | 4.1 | 6.8 | 1.6 | 0.2 |
| #194 | 2,2’,3,3’,4,4’,5,5’- | — | 0.9 | — | — | 0.1 | 3.0 |
| #174 | 2,2’,3,3’,4,5,6’- | — | 0.8 | — | — | 0.5 | 5.5 |
| #170 | 2,2’,3,3’,4,4’,5- | — | 0.8 | — | — | 0.8 | 3.6 |
| #64 | 2,3,4’,6- | — | 0.8 | 2.1 | 4.1 | 0.4 | 0.1 |
| #26 | 2,3’,5- | 2.4 | 0.6 | 1.7 | 0.3 | 0.1 | — |
| #135 | 2,2’,3,3’,5,6’- | — | 0.6 | — | 0.2 | 5.3 | 8.5 |
| #87/#115 | 2,2’,3,4,5’-/2,3,4,4’,6- | — | 0.6 | 0.2 | 1.5 | 3.8 | 0.2 |
| #16 | 2,2’,3- | 6.5 | 0.4 | 4.5 | 0.9 | 0.2 | 0.1 |
Note: The top 10 composition percentages of major congeners in the control harbor (Site D; n = 1), polluted harbor (Sites A–C; n = 3), and Kanechlors are indicated in bold.
To determine whether degradation/dehalogenation occurred, we compared the composition percentages of the major congeners in the control harbor, polluted harbor, and Kanechlors (Table 3). The PCB congeners with the highest percentages of total PCBs in the polluted harbor differed significantly from the Kanechlors. In the polluted harbor, PCB congeners with chlorine atoms in both para positions (4 and 4’) on the biphenyl backbone were found at higher percentages, whereas PCB congeners with chlorine atoms in both ortho positions (2 and 2’) tended to have lower percentages. This positional preference for PCB congeners was not observed in any Kanechlors; this suggests that degradation and/or dehalogenation may have occurred in the polluted harbor rather than simply the presence of multiple Kanechlors. Notably, PCB-126 was detected only in the polluted harbor (Table 3 and Fig. S2). PCB-126 is a marker congener of the thermal PCB pattern (Takasuga et al., 1995), although other related congeners such as PCB-77 and PCB-169 were only marginally detected or not detected at all, as indicated in Fig. S3, Fig. S4, and Table S5. PCB-126 can also be formed by dechlorination from technical PCB mixtures (Weber et al., 2018b). The UV-dehalogenation pathway for increased PCB-126 formation has been documented in bioindicators for the atmospheric entry of PCBs into vegetation (Weber et al., 2018b); however, the levels of PCB-126 formed by dechlorination from technical mixtures were still considerably lower than those of PCB-118 (Weber et al., 2018b). Considering the high share of PCB-126 of the Penta-CBs in the sediments in a concentration similar to that of PCB-118, the largest share of PCB-126 stems from thermal sources. The congener pattern of the Penta-CBs closely resembled that of the incinerated fly ash, except for PCB-118 (Fig. S3), which mainly originated from commercial. A similar conclusion was made by Ogura et al. (2002) on the source contribution of individual PCB congeners in sediments from different locations, estimating that 80% of PCB 126 stem from incineration.
In addition, we compared the average PCB congener composition in each homolog group in the polluted harbor with that in the control harbor to eliminate common factors, such as water solubility, volatility, and UV-dehalogenation (Fig. 3). The proportions of PCB congeners (#4, #16, #17, #18, #44, #45, and #53) with chlorine at both ortho positions (2 and 2’) in the polluted harbor were lower than those in the control harbor, suggesting the possibility that PCB congeners with chlorine at the 2,2’-position were preferably degraded or dehalogenated in the polluted harbor. Conversely, the proportions of PCB congeners (#15, #28, #37, #60, #66, and #74) with chlorine in both para positions (4 and 4’) in the polluted harbor were higher than those in the control harbor, suggesting that the 4,4’-substituted congeners were less likely to be degraded and accumulated. The positional specificity of the abundance of PCB congeners in the polluted harbor, which differed from that in the control harbor, suggests that microbial degradation/dehalogenation is more probable as the primary cause, particularly for Tri-CBs and Tetra-CBs, rather than common factors.
Fig. 3 Difference between the PCB congener composition profile in each homolog at Site D and the average of Sites A-C. Only congeners representing 1% of the total fraction are presented
We compared the bacterial communities at the family level at Site B in the polluted harbor and at Site D in the control harbor in the sea and sediments. We used NGS to investigate the impact of PCBs on environmental bacteria. We found that the alpha-diversity indices of the bacterial communities in polluted and control harbors were similar for sea and sediment samples (Table S6). However, the Bray–Curtis dissimilarity index revealed differences in the bacterial community compositions between the two harbors, particularly in the sediments (Table 4). Notably, we found the presence of Dehalococcoidaceae only in the sediment at Site B in the polluted harbor, accounting for 0.19% of the total family. This family is renowned for its capacity to dechlorinate PCBs, despite accounting for less than 1% of the total bacterial population (Holoman et al., 1998; Watts et al., 2001).
Table 4. Bray–Curtis similarity indices of the bacterial community in the sea and sediments of Sites B (polluted harbor) and D (control harbor)
| Groups | Bray–Curtis similarity index |
|---|
| Sea at Site B vs. Sea at Site D | 0.316 |
| Sediment at Site B vs. Sediment at Site D | 0.615 |
To analyze anaerobic bacteria using environmental DNA, PCB-dechlorinating bacteria were detected by PCR amplification using DNA extracted from sediments at Sites A–D. We focused on Dehalococcoides, Dehalobacter, and Desulfitobacterium as biomarkers of PCB dechlorination (Fig. 4). Through second PCR using 16S rDNA-specific primer sets for the Dehalococcoides group, we detected bands of the expected size (200 bp) at all sites. However, the PCR products closest to Dehalogenimonas sp. were found only at Sites A–C in the polluted harbor; this suggests that Dehalogenimonas sp. only existed in polluted harbors, which is supported by the fact that Dehalococcoidaceae was detected by NGS analysis. Bands corresponding to the Dehalobacter group, which were not detected by NGS, were detected at all sites, whereas bands corresponding to the Desulfitobacterium group were not detected at any site. These results suggest that Dehalogenimonas sp. may play a more significant role in PCB dechlorination than Dehalobacter, even when they co-exist. Both Dehalogenimonas and Dehalobacter have been detected in PCB-polluted sediments (Wang and He, 2013a; Mattes et al., 2018; Qi et al., 2020; Cao et al., 2022). Recently, Xu et al. (2024) reported that the abundance of these bacteria increased during the attenuation of PCBs in sewage sludge and was positively correlated with PCB dechlorination. In addition, they showed that the cell density of Dehalogenimonas was higher than that of Dehalobacter. Therefore, Dehalogenimonas sp. likely contributes to PCB dechlorination. Unfortunately, we could not detect reductive dehalogenase by PCR amplification using some of the primer sets designed by Waller et al. (2005) (data not shown); this may be because they were below the detection limit or were novel enzymes.
Fig. 4 Detection of PCB-dechlorinating bacteria using agarose gel electrophoresis. The second PCR was conducted using the 16S rDNA-specific primer sets for the Dehalococcoides group (a). The first PCR was conducted using the 16S rDNA-specific primer sets for the Dehalobacter group (b). The second PCR was conducted using the 16S rDNA-specific primer sets for the Desulfitobacterium group (c). Lanes: M, markers; 1, Site A; 2, Site B; 3, Site C; and 4, Site D
Enrichment cultures were conducted using biphenyl, benzoate, or chlorobenzoate as the sole carbon source in either inorganic salt or sea salt medium to obtain aerobic bacteria capable of degrading PCBs; this resulted in the isolation of 220 strains from each of the examined bottom sediments. However, no degradation activity was observed among these strains. These results suggest that aerobic bacteria capable of degrading PCBs, which facilitate the detoxification of these compounds, are virtually absent at all sites, indicating that natural attenuation may not be effective.
CONCLUSION
In this study, we evaluated the natural attenuation of PCBs in sediments from two harbors in Hyogo, Japan, where such contamination is typically found in the Japanese environment. We showed that cos θ analysis of PCB congener composition within each homolog group, but not in the total congener composition, can be used to assess current pollution levels impacted by multiple commercial PCB products and/or microbial degradation, which cannot be precisely estimated by PCB homolog composition analysis. Additionally, the main congeners in the polluted harbor had a positional preference that was not observed in either the control harbor or Kanechlors, strongly suggesting that microbial degradation/dehalogenation reactions may have occurred in the polluted harbor. To identify the key contributors to the natural attenuation process, we conducted NGS and PCR analyses of PCB-dechlorinating bacteria. NGS analysis revealed similar alpha-diversity but different bacterial community compositions between the two harbors, particularly in the sediments, with PCB-dechlorinating Dehalococcoidaceae in the polluted harbor sediment. PCR detection of PCB-dechlorinating bacteria suggested that anaerobic PCB-dechlorinating bacteria, particularly Dehalogenimonas sp., likely contribute to PCB dechlorination. Overall, our results not only provide insight into the biodegradation process by indigenous microorganisms and the potential for natural attenuation of PCBs in polluted environments but also demonstrate that the combination of PCB congener analysis using HRGC/HRMS and microbial analysis using environmental DNA is a powerful tool for assessing PCB pollution in the environment.
ACKNOWLEDGEMENTS
We would like to express our gratitude to Dr. Ryouji Iwakiri from the National Environmental Research and Training Institute for his assistance in obtaining the ash data. We also extend our thanks to Shunichiro Nagano and Koji Kamenaga from the Kyoto University of Advanced Science for their valuable support during this study. This study was supported by JSPS Grants-in-Aid (KAKENHI) for Scientific Research (C) (No. 15K00590), a Research Grant for Joint Usage/Research (No. 301007), the Biosignal Research Center, Kobe University, the Environmental Research and Technology Development Fund (5-1602), and Japan–Korea joint research Work on POPs and related substances of the Ministry of the Environment, Japan. We thank Editage (www.editage.jp) for its English language editing.
SUPPLEMENTARY MATERIAL
Fig. S1, Profiles of PCBs homologs grouped by the number of chlorine atoms in Site A (a), Site B (b), and Site C (c) in the polluted harbor; Fig. S2, Chromatograms of Penta-CBs in the sediment samples at Site A-D and BP-WD; Fig. S3, Chromatograms of Tetra-CBs in the sediment samples at Site A-D and BP-WD; Fig. S4, Chromatograms of Hexa-CBs in the sediment samples at Site A-D and BP-WD; Fig. S5, Chromatograms of fly ash (a) and Site C in the polluted harbor (b). Data on fly ash was analyzed at the National Environmental Research and Training Institute, as referenced in NIES CRM No. 24 Fly Ash II; Table S1, PCR primer used in this study; Table S2, The cos θ analyses of the PCBs homolog and congener compositions in total at Sites A-D with those of Kanechlors; Table S3, Cos θ analysis of the PCB congener composition in each homolog at Site A-D with those of Kanechlors; Table S4, Composition percentages of major congeners at Site A-D; Table S5, The congener compositions in the total congers and in each homolog group at Site A-D; Table S6. Family diversity indices for the bacterial community in the sea and the sediments of Sites B (in the polluted harbor) and D (in the control harbor).
This material is available on the Website at https://doi.org/10.5985/emcr.20240028.
REFERENCES
- Asaoka, S., Umehara, A., Haga, Y., Matsumura, C., Ryosuke Yoshiki, R., Takeda, K., 2019. Persistent organic pollutants are still present in surface marine sediments from the Seto Inland Sea, Japan. Mar. Pollut. Bull. 149, 110543. doi: 10.1016/j.marpolbul.2019.110543.
- Borja, J., Taleon, D.M., Auresenia, J., Gllardo, S., 2005. Polychlorinated biphenyls and their biodegradation. Process Biochem. 40(6), 1999–2013. doi: 10.1016/j.procbio.2004.08.006.
- Bray, J.R., Curtis, J.T., 1957. An Ordination of the Upland Forest Communities of Southern Wisconsin. Ecol. Monogr. 27, 325–349. doi: 10.2307/1942268.
- Cao, S., Davis, A., Kjellerup, B.V., 2022. Presence of bacteria capable of PCB biotransformation in stormwater bioretention cells. FEMS Microbiol. Ecol. 97(12), fiab159. doi: 10.1093/femsec/fiab159.
- Correa, P.A., Lin, L., Just, C.L., Hu, D., Hornbuckle, K.C., Schnoor, J.L., Van Aken, B., 2010. The effects of individual PCB congeners on the soil bacterial community structure and the abundance of biphenyl dioxygenase genes. Environ. Int. 36(8), 901–906. doi: 10.1016/j.envint.2009.07.015.
- Fagervold, S.K., Watts, J.E., May, H.D., Sowers, K.R., 2005. Sequential Reductive Dechlorination of meta-Chlorinated Polychlorinated Biphenyl Congeners in Sediment Microcosms by Two Different Chloroflexi Phylotypes. Appl. Environ. Microbiol. 71(12), 8085–8090. doi: 10.1128/AEM.71.12.8085-8090.2005.
- Haga, Y., Suzuki, M., Matsumura, C., Okuno, T., Tsurukawa, M., Fujimori, K., Kannan, N., Weber, R., Nakano, T., 2018. Monitoring OH-PCBs in PCB transport worker’s urine as a non-invasive exposure assessment tool. Environ. Sci. Pollut. R. 25, 16446–16454. doi: 10.1007/s11356-018-1927-0.
- Hiraishi, A., Sakamaki, N., Miyakoda, H., Maruyama, T., Kato, K., Futamata, H., 2005. Estimation of “Dehalococcoides” Populations in Lake Sediment Contaminated with Low Levels of Polychlorinated Dioxins. Microbes Environ. 20(4), 216–226. doi: 10.1264/jsme2.20.216.
- Honda, T., Wada, M., Nakashima, K., 2008. Concentration and Characteristics of Polychlorinated Biphenyls in the Sediments of Sea and River in Nagasaki Prefecture, Japan. J. Health Sci. 54(4), 400–408. doi: 10.1248/jhs.54.400.
- IARC, 2013. Polychlorinated biphenyls and polybrominated biphenyls. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans IARC monographs on the evaluation of carcinogenic risks to humans Volume 107, Lyon, France.
- Imamoglu, I., Li, K., Christensen, E.R., McMullin, J.K., 2004. Sources and dechlorination of polychlorinated biphenyl congeners in the sediments of Fox River, Wisconsin. Environ. Sci. Technol. 38(9), 2574–2583. doi: 10.1021/es035165x.
- Liang, Y., Martinez, A., Hornbuckle, K.C., Mattes, T.E., 2014. Potential for Polychlorinated Biphenyl Biodegradation in Sediments from Indiana Harbor and Ship Canal. Int. Biodeterior. Biodegradation 89, 50–57. doi: 10.1016/j.ibiod.2014.01.005.
- Mattes, T.E., Ewald, J.M., Liang, Y., Martinez, A., Awad, A., Richards, P., Hornbuckle, K.C., Schnoor, J.L., 2018. PCB dechlorination hotspots and reductive dehalogenase genes in sediments from a contaminated wastewater lagoon. Environ. Sci. Pollut. Res. Int. 25(17), 16376–16388. doi: 10.1007/s11356-017-9872-x.
- Matsumura, C., Tsurukawa, M., Nakano, T., Ezaki, T., Ohashi, M., 2002. Elution order of all 209 PCBs congeners on capillary column “HT8-PCB”. J. Environ. Chem. 12(4), 855–865. doi: 10.5985/jec.12.855.
- Ministry of the Environment Government of Japan, 2011. Simple Measurement Method Manual for Trace PCB in Insulating Oil (third edition). https://www.env.go.jp/press/files/jp/17471.pdf (accessed 1 August, 2024)
- Ministry of the Environment Government of Japan, 2023. CHEMICALS IN THE ENVIRONMENT, Report on Environmental Survey and Monitoring of Chemicals in FY2021. https://www.env.go.jp/chemi/kurohon/en/http2022e/pdf/05_Chapter3.pdf (accessed 1 August, 2024)
- Ogura, I., Masunaga, S., Yoshida, K., Nakanishi, J., 2002. Identification of sources of dioxin-like PCBs in sediments of Japan by a chemical mass balance approach. Organohalogen Compd. 56, 473–476.
- Oksanen, J., Simpson, G., Blanchet, F., Kindt, R., Legendre, P., Minchin, P., O’Hara, R., Solymos, P., Stevens, M., Szoecs, E., Wagner, H., Barbour, M., Bedward, M., Bolker, B., Borcard, D., Carvalho, G., Chirico, M., De Caceres, M., Durand, S., Evangelista, H., FitzJohn, R., Friendly, M., Furneaux, B., Hannigan, G., Hill, M., Lahti, L., McGlinn, D., Ouellette, M., Ribeiro Cunha, E., Smith, T., Stier, A., Ter Braak, C., Weedon, J., 2024. vegan: Community Ecology Package. R package version 2.6-7. https://github.com/vegandevs/vegan; https://vegandevs.github.io/vegan/. (accessed 1 August, 2024)
- Petrlik, J., Bell, L., DiGangi, J., Allo’o Allo’o, S.M., Kuepouo, G., Ochola, G.O., Grechko, V., Jelinek, N., Strakova, J., Skalsky, M., Drwiega, Y.I., Hogarh, J.N., Akortia, E., Adu-Kumi, S., Teebthaisong, A., Carcamo, M., Beeler, B., Behnisch, P., Baitinger, C., Herold, C., Weber, R. 2022. Review: Monitoring of Dioxins and PCB in Eggs as Sensitive Indicator for Environmental Pollution and Contaminated Sites and Recommendations for Reducing and Controlling Releases and Exposure. Emerg. Contam. 8, 254–279. doi: 10.1016/j.emcon.2022.05.001.
- Pieper, D.H., 2005. Aerobic degradation of polychlorinated biphenyls. Appl. Microbiol Biotechnol. 67, 170–191. doi: 10.1007/s00253-004-1810-4.
- Qiu, L., Fang, W., He, H., Liang, Z., Zhan, Y., Lu, Q., Liang, D., He, Z., Mai, B., Wang, S., 2020. Organohalide-Respiring Bacteria in Polluted Urban Rivers Employ Novel Bifunctional Reductive Dehalogenases to Dechlorinate Polychlorinated Biphenyls and Tetrachloroethene. Environ. Sci. Technol. 54(14), 8791–8800. doi: 10.1021/acs.est.0c01569. Epub 2020 Jul 3.
- Quensen, J.F. 3rd, Tiedje, J.M., Boyd, S.A., 1988. Reductive dechlorination of polychlorinated biphenyls by anaerobic microorganisms from sediments. Science 242(4879), 752–754. doi: 10.1126/science.242.4879.752.
- R Core Team R., 2024. R: A Language and Environment for Statistical Computing. https://www.R-project.org. (accessed 1 August, 2024)
- Shannon, C.E., 1997. The mathematical theory of communication. 1963. MD. Comput. 14(4), 306–317.
- Sharma, J.K., Gautam, R.K., Nanekar, S.V., Weber, R., Singh, B.K., Singh, S.K., Juwarkar, A.A. 2018. Advances and perspective in bioremediation of polychlorinated biphenyl-contaminated soils. Environ. Sci. Pollut. Res. Int. 25(17), 16355–16375. doi: 10.1007/s11356-017-8995-4.
- Simpson, E.H., 1949. Measurement of Diversity. Nature 163, 688. doi: 10.1038/163688a0.
- Saba, T., Boehma, P.D., 2011. Quantitative Polychlorinated Biphenyl (PCB) Congener and Homologue Profile. Environmental. Forensics 12(2), 134–142. doi: 10.1080/15275922.2011.572950.
- Smits, T.H., Devenoges, C., Szynalski, K., Maillard, J., Holliger, C., 2004. Development of a real-time PCR method for quantification of the three genera Dehalobacter, Dehalococcoides, and Desulfitobacterium in microbial communities. J. Microbiol. Methods. 57(3), 369–78. doi: 10.1016/j.mimet.2004.02.003.
- Takasuga, T., Inoue, T., Ohi, E., 1995. All congener specific analytical method for polychlorinated biphenyls (PCBs) with various chromatographic clean-up and HRGC/HRMS. Kankyo Kagaku (J. Environ. Chem.) 5(3), 645–675. https://www.jstage.jst.go.jp/article/jec1991/5/3/5_3_647/_pdf
- Van den Berg, M., Birnbaum, L.S., Denison, M., De Vito, M., Farland, W., Feeley, M., Fiedler, H., Hakansson, H., Hanberg, A., Haws, L., Rose, M., Safe, S., Schrenk, D., Tohyama, C., Tritscher, A., Tuomisto, J., Tysklind, M., Walker, N., Peterson, R.E., 2006. The 2005 World Health Organization reevaluation of human and Mammalian toxic equivalency factors for dioxins and dioxin-like compounds. Toxicol. Sci. 93(2), 223–241. doi: 10.1093/toxsci/kfl055.
- Waller, A.S., Krajmalnik-Brown, R., Löffler, F.E., Edwards, E.A., 2005. Multiple reductive-dehalogenase-homologous genes are simultaneously transcribed during dechlorination by Dehalococcoides-containing cultures. Appl. Environ. Microbiol. 71(12), 8257–8264. doi: 10.1128/AEM.71.12.8257-8264.2005.
- Wang, S., He, J., 2013a. Phylogenetically distinct bacteria involve extensive dechlorination of aroclor 1260 in sediment-free cultures. PLoS One 8(3), e59178. doi: 10.1371/journal.pone.0059178.
- Wang, S., He, J., 2013b. Dechlorination of commercial PCBs and other multiple halogenated compounds by a sediment-free culture containing Dehalococcoides and Dehalobacter. Environ. Sci. Technol. 47(18), 10526–10534. doi: 10.1021/es4017624.
- Weber, R., Herold. C., Hollert, H., Kamphues, J., Ungemach, L., Blepp, M., Ballschmiter, K., 2018a. Life cycle of PCBs and contamination of the environment and of food products from animal origin. Environ. Sci. Pollut. Res. Int. 25(17), 16325–16343. doi: 10.1007/s11356-018-1811-y.
- Weber, R., Gonser, S., Köhler, J., Körner, W., Herold, C., Haag, R., Krapp, M., Peichl, L., 2018b. Biomonitoring of polychlorinated biphenyls in Bavaria/Germany-long-term observations and standardization. Environ. Sci. Pollut. Res. Int. 25(17), 16344–16354. doi: 10.1007/s11356-017-1108-6.
- Xu, Y., Yu, Y., Gregory, K.B., VanBriesen, J.M., 2012. Comprehensive Assessment of Bacterial Communities and Analysis of PCB Congeners in PCB-Contaminated Sediment with Depth. J. Environ. Eng. 138(12), 1167. doi: 10.1061/(ASCE)EE.1943-7870.0000595.
- Xu, G., Zhao, S., Rogers, M.J., Chen, C., He, J., 2024. Global prevalence of organohalide-respiring bacteria dechlorinating polychlorinated biphenyls in sewage sludge. Microbiome. 12(1), 54. doi: 10.1186/s40168-024-01754-8.
- Yan, J., Rash, B.A., Rainey, F.A., Moe, W.M., 2009. Detection and Quantification of Dehalogenimonas and “Dehalococcoides” Populations via PCR-Based Protocols Targeting 16S rRNA Genes. Appl. Environ. Microbiol. 75(23), 7560–7564. doi: 10.1128/AEM.01938-09.
