Aquatic ecotoxicity of an antidepressant, sertraline hydrochloride, on microbial communities

Science of the Total Environment 654 (2019) 129–134

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Aquatic ecotoxicity of an antidepressant, sertraline hydrochloride, on microbial communities Zhaopeng Yang a, Tao Lu b, Youchao Zhu b, Qi Zhang b, Zhigao Zhou b, Xiangliang Pan b, Haifeng Qian a,b,⁎ a b

Xinjiang Key Laboratory of Environmental Pollution and Bioremediation, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China College of Environment, Zhejiang University of Technology, Hangzhou 310032, PR China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Effects of sertraline on the microbial communities were investigated in a microcosm. • Sertraline lowered the photosynthetic efficiency and N availability. • Sertraline indirectly caused the disruption of microbial ecological balance. • Sertraline contamination carries a potential ecological risk.

a r t i c l e

i n f o

Article history: Received 25 September 2018 Received in revised form 7 November 2018 Accepted 10 November 2018 Available online 12 November 2018 Editor: Shuzhen Zhang Keywords: Sertraline hydrochloride Microbial community Ecotoxicity Antidepressants Cyanobacteria

a b s t r a c t Sertraline hydrochloride (Ser-HCl), a widely used antidepressant, becomes an aquatic contaminant via metabolic excretion and improper disposal; however, it is unknown how Ser-HCl affects aquatic microbial communities. The present study investigated the effects of Ser on the structures of aquatic microbial communities via highthroughput sequencing analyses. Ser-HCl treatment inhibited the growth of two model algae (the green alga, Chlorella vulgaris, and the cyanobacterium, Microcystis aeruginosa) and decreased the chlorophyll a (Chl-a) concentration in the microcosm to reduce the photosynthetic efficiency. High-throughput sequencing analyses showed that exposure to Ser-HCl disturbed the balance of cyanobacteria species by stimulating the growth of specific cyanobacteria. Among eukaryotes, the richness as well as the diversity indices were significantly enhanced after 5 days of Ser-HCl treatment but sharply decreased with exposure time. Nucleariida occupied an absolute majority (97.83%) within the eukaryotes, implicating that Ser-HCl disturbed the ecological equilibrium in microcosms. Ser-HCl will continue to be an environmental contaminant due to its wide usage and production. Our current study clarified the potential ecological risk of Ser-HCl to aquatic microorganisms. These findings suggest that more attention should be given to the negative effects of these bioactive pollutants on aquatic environments. © 2018 Published by Elsevier B.V.

1. Introduction ⁎ Corresponding author at: Xinjiang Key Laboratory of Environmental Pollution and Bioremediation, Chinese Academy of Sciences, Urumqi 830011, PR China. E-mail address: [email protected] (H. Qian).

https://doi.org/10.1016/j.scitotenv.2018.11.164 0048-9697/© 2018 Published by Elsevier B.V.

Bioactive compounds such as antidepressants, which are commonly prescribed psychotropic drugs, can contaminate the aquatic environment by improper disposal, discarding of expired drugs, and metabolic

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excretion (Lindberg et al., 2005; Minagh et al., 2009). Pharmacological pollutants are generally metabolically stable and are therefore resistant to degradation by microorganisms during sewage treatment (Glassmeyer et al., 2005; Segura et al., 2013). Once these stable and highly bioactive pollutants are discharged into aquatic environments such as rivers and lakes, aquatic organisms could be exposed to low doses of drug contaminants over long periods (Zhu et al., 2018). Commonly used antidepressants (such as amitriptyline, fluoxetine, and sertraline) have been detected in urban sewage and other environmental water samples (Kostich et al., 2014; Lajeunesse et al., 2012; Wu et al., 2015). Sertraline hydrochloride (Ser-HCl), a selective serotonin reuptake inhibitor, has been widely used in recent years for the treatment of depressive disorders (Schultz et al., 2010). It was detected at trace levels in aquatic samples (0.77–120 ng/L) throughout China, Europe and North America (Kostich et al., 2014; Lajeunesse et al., 2012; Unceta et al., 2007; Yuan et al., 2013) and might result in adverse effects on aquatic organisms. Although Ser-HCl was detected in low concentrations (ng/L) in surface waters and effluents, an understanding of its impact on aquatic microbial ecology is lacking. Toxicological effects of Ser-HCl on some single aquatic organisms, growth suppression effects on Pseudokirchneriella subcapitata and Simulium vittatum (43–475 μg L−1), stress-related, gene-transcriptional inhibition in Danio rerio (25–250 μg L−1), and even reproduction toxicity in Pimephales promelas (0.47–53.4 μg L−1), were reported in several studies (Christensen et al., 2007; Minagh et al., 2009; Overmyer et al., 2010; Valenti et al., 2009; Woodman et al., 2016). Aquatic microorganisms play important roles in the environment and are indicators of the condition of ecosystems (Zhang et al., 2018; Lu et al., 2018a). The impact on the aquatic microbial community will be magnified through the food chain, thereby calling for ecological risk assessment. Some studies reported the influence of external contaminants, such as heavy metals (Borymski et al., 2018; Liu et al., 2018), pesticides (Castro-Gutierrez et al., 2018; Chen et al., 2017), plastic (Qian et al., 2018) and nanoparticles (Das et al., 2012) on the taxonomy or function of microbial communities. Although Ser-HCl was demonstrated to negatively affect various microorganisms (Johnson et al., 2007), it is still unknown how Ser-HCl affects microbial communities. In the present study, we aim to investigate how Ser-HCl affects the microbial communities in aquatic environments by high-throughput sequencing analyses and evaluate the potential hazard of Ser-HCl to aquatic ecology. The main goals of this study were as follows: 1) to analyze the effects of Ser-HCl on algal growth and water quality; and 2) to analyze if chronic Ser-HCl can affect biological components in aquatic microbial ecosystems. To answer these questions, we set up aquatic microcosms that contain a modified BG-11 medium and natural plankton communities obtained from Lake Taihu. 2. Materials and methods 2.1. Microalgae culturing and Ser-HCl exposure Axenic strains of the green alga Chlorella vulgaris (FACHB-24) and the cyanobacterium Microcystis aeruginosa (FACHB-905), were obtained from the Institute of Hydrobiology at the Chinese Academy of Sciences (Wuhan, China). The algae were cultivated in modified BG-11 medium in an artificial greenhouse at 25 ± 0.5 °C under cool-white fluorescent light (46 μmol m−2 s−1) with a 12 h:12 h light: dark cycle and were agitated three times a day. The chemical composition of the medium is shown in Table S1. The tested species were maintained at exponential growth in batch cultures in 250-mL Erlenmeyer glass flasks containing 150 mL of modified BG-11 medium. After inoculation, SerHCl was added to the flasks at initial concentrations of 25–200 μg L−1. The algal cells yield was measured every 24 h using a spectrophotometer at 680 nm (OD680). Standard curves of each alga used for calculating cells yield from absorbance were produced using a standardized algal

culture and a hemocytometer (Lu et al., 2018b; Qian et al., 2016). Initial OD680 of each algal culture was adjusted to 0.04, i.e., the initial algal cell density of C. vulgaris and M. aeruginosa were 7.9 × 105 and 2.1 × 105 cells mL−1, respectively. 2.2. Aquatic microcosm preparation and Ser-HCl exposure Water samples were collected in Meiliang Bay (30°55′–31°32′ N; 119°52′–120°36′ E) of Lake Taihu in Nov 2017 and then passed through a 0.22-μm filter membrane to filter the lake water before transferring all the species in the freshwater, including microorganism and a few zooplankton, into the medium. The purpose of filtering is to replace the lake water with modified BG-11 medium to prepare the microcosm. The freshly prepared microcosm was maintained in the same conditions as the algal cultivation mentioned above. Three control microcosms (Con1, Con2, and Con3) and three other microcosms with 50 μg L−1 Ser-HCl (initial concentration) (Ser1, Ser 2, Ser 3) were harvested for the physiological and high-throughput sequencing analysis. 2.3. The detection of chlorophyll, phycocyanin, pH, electrical conductivity, nitrate nitrogen, and total phosphorus Chlorophyll a (Chl-a) content was measured as previously described (Inskeep and Bloom, 1985) in microcosms exposed to Ser-HCl. In parallel, phycocyanin was extracted in a sodium phosphate buffer and measured by spectrophotometry according to the methodology detailed in Silveira et al. (2007). Water pH was measured using a pH meter (FE20, Mettler Toledo®, Columbus, USA). Electrical conductivity (EC) was measured using an EC meter (InPro 7100i/12/120, Mettler Toledo®, Columbus, USA). Nitrate nitrogen (NO3−-N) and total phosphorus (TP) concentrations were measured in filtered lake water according to the methods described in Song et al. (2017a). 2.4. DNA extraction and sequencing The total nucleic acid content of filter-retained aquatic microorganisms was extracted using the DNeasy Tissue Reagents (Qiagen, Valencia, CA, USA), following the manufacturer's protocol. Specific regions of the 16S rRNA and 18S rRNA gene sequences were amplified using primers with barcodes. Sample was amplified in triplicate and the combined replicate samples were cleaned using a DNA Gel Extraction kit (Axygen, Inc., USA). The PCR products were sequenced on an Illumina MiSeq platform (San Diego, CA, USA). 2.5. OTU cluster and species annotation Once determined, 16S and 18S rRNA gene sequences with ≥97% similarity were assigned to the same operational taxonomic units (OTUs). A representative sequence for each OTU was screened for further annotation. For each representative sequence, the RDP classifier (Version 11.4, https://github.com/rdpstaff/RDPTools) algorithm was used to annotate taxonomic information from the Silva_128 16S rRNA database (http:// www.arb-silva.de/) and Silva_128 18S rRNA database (http://www. arb-silva.de/). OTU abundance information was normalized using a standard number of sequences, which was the number of sequences in the sample with the fewest sequences. 2.6. Statistical analysis Alpha diversity is applied in analyzing complexity of species diversity for a sample through 4 indices, including Chao1, Shannon, Simpson, ACE, which were calculated with QIIME (Version 1.9.1). Cluster analysis was preceded by principal component analysis (PCA), which was applied using the FactoMineR package and the ggplot2 package in R software (Version 3.2.2) to lower the dimensions of the original variables. Data are presented as the means ± standard errors of the mean

Z. Yang et al. / Science of the Total Environment 654 (2019) 129–134

with Ser-HCl, indicating that the growth of cyanobacteria in this microcosm was not suppressed (Fig. 2b). There is no significant difference in pH and EC values between the control group and the Ser-HCl-treated group (Fig. 2c, d), suggesting that the water's physical qualities were not changed during Ser-HCl treatment. The contents of NO3−-N appeared to gradually decline over the 15 days, indicating that the microbial community continually consumed N during culture (Song et al., 2017b). The Ser-HCl-treated group, however, showed a higher NO3−-N concentration than the control group (Fig. 2e), which contributed to the inhibitory effect of SerHCl on algal growth, as well as other microbes in the microcosms. There is an obvious decline of TP content in the control and treated groups in the first 5 days, but it appeared to stabilize at a low level after 5 days of growth and showed no significant difference between the Ser-HCl-treated and the control groups (Fig. 2f). These findings indicate that nutrient concentrations significantly decreased over time as a consequence of continued algae and microorganism growth in the presence of Ser-HCl; however, by some physiological parameters, growth in the treated group may not be as robust as in the control group. The higher NO3−-N concentration in the Ser-HCl-treated group also indicated that the microorganism biomass might be decreased. Similarly, the decreased Chl-a and relatively stable phycocyanin content (both compared to control) with Ser-HCl treatment implies that Ser-HCl contamination may be not conducive to the growth of green algae, whereas cyanobacteria are not affected.

(SEM) and tested for statistical significance by analysis of variance (ANOVA) followed by the Dunnett's post hoc test using the StatView 5.0 program. 3. Results and discussion 3.1. Growth inhibition of C. vulgaris and M. aeruginosa after Ser-HCl exposure Growth influence analysis was first carried out to identify the SerHCl concentration that inhibited the growth of C. vulgaris and M. aeruginosa, which are model green algae and cyanobacterium, respectively, in toxicology tests. Ser-HCl concentrations of 50 μg L−1 slightly inhibited C. vulgaris and M. aeruginosa cell growth by b10% after 7 days of treatment (Fig. 1). With increasing concentrations of Ser-HCl, the cell growth decreased 27% for the two algae at 100 μg L−1; and 57.5% and 69.2% in C. vulgaris and M. aeruginosa, respectively, after 7 days at 200 μg L−1 Ser-HCl treatment (Fig. 1). Here we concluded that the two algae had similar sensitivity to Ser-HCl. Since C. vulgaris and M. aeruginosa are eukaryotic and prokaryotic algae, respectively, we speculated that the toxicity of sertraline was generalized across kingdoms. Previous studies showed that the inhibitory effect of Ser-HCl on algal growth is related to algal species; for example, the 96-h half maximal inhibitory concentrations (IC50) of sertraline in Pseudokirchneriella subcapitata and Scenedesmus acutus were 12.1 μg L−1 and 98.9 μg L−1, respectively (Johnson et al., 2007), and the 96h IC50 value of sertraline for C. vulgaris (764 μg L−1) was much higher than in the present study (232 μg L−1). This may be due to different culture conditions and initial algal densities. For further studies on microorganism communities, a concentration of 50 μg L−1 initial Ser-HCl was selected as it was the lowest tested concentration affecting algal growth over the 7-day exposure used in this study.

3.3. Bacterial community changes Principal component analysis (PCA) showed that the community structure in the Ser-HCl group was different from the community structure in the control group, especially after 15 days of treatment (Fig. 3a), indicating that the Ser-HCl (50 μg L−1) in microcosms had a strong effect on the microbial community structure. Species richness on two indices (Chao1 and abundance-based coverage (ACE)) did not change significantly after Ser-HCl treatment, while two diversity indices (i.e., the Shannon and Simpson indices) increased (Table 1), suggesting that Ser-HCl did not affect the total microbial richness but altered the community structure, possibly by differentially affecting the growth of bacteria within the community. To display the variation of microbial communities after Ser-HCl treatment, we analyzed the mean relative abundance (RA) of the 16S rRNA gene of the main bacterial orders in the microcosm. After 5 days of culture, bacterial communities in the control group were mainly composed of Methylophilaceae (53.30%), Subsection III (8.91%), Pseudomonadales (7.21%), Rhodobacterales (7.15%), Sphingobacteriales (6.52%) and Subsection I (6.48%). Additionally, Subsection I and Subsection III both belong to cyanobacteria. The RA of Methylophilales on the 10th day (10.52%) was lower than that on the

3.2. Effects of Ser-HCl on microcosms (chlorophyll, algal albumin, pH, EC, NO3−-N, total P) The contents of photosynthetic pigments, such as Chl-a and phycocyanin, can be used to indicate the capacity of algal photosynthesis (Xie et al., 2015; Qian et al., 2016). Exposure to 50 μg L−1 Ser-HCl for 3 day decreased Chl-a concentrations by ~21% compared to controls, indicating that Ser-HCl displayed toxic effects on the phytoplankton communities and potentially affected the biomass of algae (Fig. 2a). Chl-a concentration decreased by 14.9% to 28.8% over the range of 5–15 days of Ser-HCl treatment, suggesting that this antidepressant has a long-term negative impact on algal growth. Phycocyanin is one of the characteristic chromoproteins in cyanobacteria (Hodges et al., 2018). It did not change significantly during the 15-day treatment

(a)

Chlorella vulgaris

(b)

Microcystis aeruginosa

15

Cell number (×105)

Cell number (×106)

8

131

6

4

2

Con 10

5

0

0 1

2

3

4

5

Time (days)

6

7

1

2

3

4

5

6

7

Time (days)

Fig. 1. Chlorella vulgaris (a) and Microcystis aeruginosa (b) cell growth after sertraline hydrochloride treatments at concentrations of 25–200 μg L−1.

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(a)

Con Ser

1.5

*

*

*

1.0 * 0.5

*

*

11

(b)

(c)

*

10

6 5 4 3 2 1

9 8 7 6

0

650 600 550

*

500 450 400

0d 1d 3d 5d 7d 10d 13d 15d

30

Content of total phosphorus (m

(d)

)

700

Content of NO3--N (m

Electrical conductivity ( s m)

)

0.0

8 7

pH

2.0

Content of phycocyanin (m

Content of Chl a (m

)

)

132

(e) *

*

25 20 15

0d 1d 3d 5d 7d 10d 13d 15d

0.6

(f)

0.4

0.2

0.0

0d 1d 3d 5d 7d 10d 13d 15d

Fig. 2. Variations in the physical and chemical characteristics of the water samples in the microcosms with no added sertraline hydrochloride (Con) and microcosms with 50 μg L−1 sertraline hydrochloride (Ser) for 7 days. (a) Chl-a content; (b) phycocyanin content; (c) pH value; (d) electrical conductivity; (e) NO− 3 -N content; (f) total phosphorus content. The asterisks represent statistically significant differences between the control group and the sertraline hydrochloride treated group (p b 0.05).

fifth day (58.76%), while the RA of Sphingobacteriales was significantly increased. On the 15th day, Subsection III, whose relative abundance was 54.53%, was dominant in the control group. After 5 days of Ser-HCl treatment, the RA of Methylophilales (64.21%) was significantly higher than the control culture, while the RA of Subsection III and Rhodobacterales was only 5.02% and 6.81%, respectively, lower than those in the control. A similar change in Subsection III still existed after 10 days of exposure and was larger on the 15th day. In total, after 15 days of culture, Ser-HCl inhibited the growth of Subsection III but increased the RA of Methylophilales. Due to the favorable medium conditions, the RA of cyanobacteria continued to increase over 15 days of culture (BG-11 medium favors cyanobacteria). As a result, the proportion of heterotrophic bacteria also changed following the producer. There was no obvious difference in the RA of cyanobacteria between the control and Ser-HCl-treated groups, which was consistent with similar increases in phycocyanin content (Fig. 2a). The RA of these two categories of cyanobacteria

(a)

showed little change compared to the control group in initial phases of treatment, however, Subsection I became dominant over Subsection III after 10 and 15 days of treatment, showing that Ser-HCl differentially affected cyanobacteria species and thus disturbed their balance. It has been suggested that Methylophilaceae are important in microcystin degradation (Mou et al., 2013), and its RA in the Ser-HCl group was always higher than in the control group, implicating that exposure to Ser-HCl may promote the generation of microcystin in the microcosms. 3.4. Eukaryotic community changes Fig. 4 shows the results of PCA, which indicate that the eukaryotic community structure in the Ser-HCl group (5 days) was different from the community structure in the control; however, the differences between the two groups seemed to decrease at 10 and 15 days (Fig. 4a), indicating that Ser-HCl (50 μg L−1) treatment might affect the eukaryotic microbial community more at the initial cultivation stage. After

(b) 100 90

Relative abundance (%)

80

Others Pseudomonadales

70

Xanthomonadales 60

Rhodospirillales Rhizobiales

50

Caulobacterales 40

Rhodobacterales

30

SubsectionI Sphingobacteriales

20

SubsectionIII

10

Methylophilales

15 dS er

on 15 dC

10 dS er

on 10 dC

5d Se r

5d C on

0

Fig. 3. PCA plot (a) and relative abundance of 16S rRNA gene of main prokaryotic orders (b) of the bacterial communities after 5 days, 10 days and 15 days of exposure to 50 μg L−1 sertraline hydrochloride (5dSer, 10dSer, 15dSer) and their respective control (5dCon, 10dCon, 15dCon).

Z. Yang et al. / Science of the Total Environment 654 (2019) 129–134 Table 1 Alpha diversity in prokaryotic communities after 5 days, 10 days and 15 days of exposure to 50 μg L−1 sertraline hydrochloride (5dSer, 10dSer, 15dSer) and their respective control (5dCon, 10dCon, 15dCon). Different letters represent significant differences within one index (p b 0.05). n = 3. Sample

ACE

Chao1 a

5dCon 5dSer 10dCon 10dSer 15dCon 15dSer

91.2 85.3a 87.2a 88.8a 82.9a 85.7a

a

90.7 86.3a 85.8a 90.6a 80.8a 84.9a

Shannon a

2.9 2.3b 3.8c 4.2d 2.9a 3.9c

Table 2 Alpha diversity in eukaryotic communities after 5 days, 10 days and 15 days of exposure to 50 μg L−1 sertraline hydrochloride (5dSer, 10dSer, 15dSer) and their respective control (5dCon, 10dCon, 15dCon). Different letters represent significant differences within one index (p b 0.05). n = 3.

Simpson

Sample

a

0.69 0.57b 0.88c 0.92c 0.70a 0.89c

ACE

Chao1 a

5dCon 5dSer 10dCon 10dSer 15dCon 15dSer

a

48.7 61.9bc 59.8ac 31.1d 47.0a 31.8d

49.5 63.1bc 60.0ac 30.1d 46.1a 31.8d

Shannon a

2.2 4.0b 0.8c 0.2d 1.6e 0.4d

Simpson 0.63a 0.89b 0.23c 0.04d 0.41e 0.11d

organisms, including aquatic microbes and zooplankton. Ser-HCl could therefore be toxic to such aquatic microorganisms and change the microbial community structure. Fifty micrograms/liter Ser-HCl had a sublethal effect on many species (such as C. vulgaris), which might survive Ser-HCl treatment but suffered heavily under persistent SerHCl exposure (Kreke and Dietrich, 2008). As a result, the metabolic state (reduced photosynthetic efficiency, N availability or the ability to withstand predators or competitors, etc.) of surviving individuals would inevitably change and alter the relationship between them (allelopathy, parasitism, mutualism, etc.) and indirectly cause the disruption of microbial ecological balance (Song et al., 2017a). Changes in competitive relationships can result in the survival advantage of some microbes such as Methylophilaceae (Fig. 3b) and Nucleariida (Fig. 4b), and SerHCl may accumulate in these dominant organisms and then continue to accumulate in their predators along the food chain. Previous studies have reported that antidepressants, such as fluoxetine and norfluoxetine, can accumulate in biological tissues in mussels, gammarid and Oryzias latipes (Chen et al., 2015; Franzellitti et al., 2014; Nakamura et al., 2008). We thus postulated that Ser-HCl could also accumulate in aquatic organisms that prey on plankton that have a survival advantage under chronic Ser-HCl pollution. This phenomenon carries a potential ecological risk and possibly threatens human health. Therefore, the government should appeal to reduce improper disposal and recycle expired drugs to reduce the potential hazards. The present study only focused on one kind of antidepressant within 15 days exposure (i.e., Ser-HCl), and there is a lack of relevant research on long-term effects, the compound effect of multiple antidepressants on aquatic organisms, as well as their combined toxicology with other pollutants, such as pesticides which have different mechanisms of action in aquatic environments.

Ser-HCl treatment, species richness on two indices (Chao1 and ACE) increased significantly on the fifth day but decreased significantly later, and two diversity indices (the Shannon and Simpson indices) showed a similar trend (Table 2), suggesting that Ser-HCl stimulated some eukaryotic microbes at initial stages but then inhibited them over time. As shown in Fig. 4b, eukaryotic communities in the 5-day control group were mainly composed of Cryptomycota (18.04%), Nucleariida (13.82%), Rotifera (5.76%), Ascomycota (3.12%) and Basidiomycota (1.16%). The RAs of Cryptomycota, Basidiomycota and Ascomycota in the Ser-HCl-treated group were higher compared to the control group at 34.74%, 11.00% and 19.16%, respectively. Ascomycota is a division or phylum of the kingdom Fungi, together with the Basidiomycota, while members of Cryptomycota are commonly found as parasites in aquatic ecosystems and could transfer carbon source from their hosts (primary consumers) to zooplankton (grazer) and other tertiary consumers along the food chain (Gleason et al., 2012). The increase in these three fungi suggested that Ser-HCl stimulated the fungi growth at the early stages of exposure. However, the RA of these fungi phyla all decreased sharply after 10 and 15 days of Ser-HCl treatment, while Nucleariida became the dominant eukaryotic organism, with an abundance N95%. Some zooplankton, such as Rotifera or Cryptomycota, were present in the control group and almost disappeared after Ser-HCl treatment. The dominance of Nucleariida in the Ser-HCl treated group shows that Ser-HCl disturbed the ecological equilibrium in our microcosms and its presence in the natural environment has potential ecological risk. 3.5. Environmental implications Sertraline hydrochloride is a selective 5 hydroxytryptamine reuptake inhibitor, while 5 hydroxytryptamine plays roles in various

(a)

133

(b) 100 90

Relative abundance (%)

80

Others Chytridiomycota

70

Cercozoa 60

Phragmoplastophyta Ciliophora

50

Ascomycota 40

Rotifera

30

Basidiomycota Cryptomycota

20

Unclassified

10

Nucleariida

15 dS er

on 15 dC

10 dS er

on 10 dC

5d Se r

5d C on

0

Fig. 4. PCA plot (a) and relative abundance of 16S rRNA gene of main eukaryotic orders (b) of the bacterial communities after 5 days, 10 days and 15 days of exposure to 50 μg L−1 sertraline hydrochloride (5dSer, 10dSer, 15dSer) and their respective control (5dCon, 10dCon, 15dCon).

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4. Conclusions In the present study, we investigated the responses of microbial communities to sertraline hydrochloride treatment in a microcosm. The Ser-HCl treatment inhibited the growth of C. vulgaris and M. aeruginosa and decreased the Chl-a concentration in the microcosm, implying that Ser-HCl treatment decreases the photosynthetic efficiency of the microcosm. High-throughput sequencing analyses showed that Ser-HCl treatment did not decrease the richness of prokaryotic species but slightly enhanced the diversity, possibly by differentially stimulating bacterial growth. During 15 days of culture, Ser-HCl specifically inhibited the growth of Subsection III (cyanobacteria) but stimulated Subsection I and Methylophilaceae growth. Among eukaryotes, members of Nucleariida were the dominant organisms, implicating suggesting that Ser-HCl disturbs the ecological equilibrium in microcosms and poses a potential ecological risk. Future studies will focus on the effects of multiple antidepressants on aquatic organisms, as well as their combined toxicology with other pollutants. Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.11.164. Conflict of interest The authors declare no conflict of interests. Acknowledgments This work was financially supported by National Key Research and Development Program of China [2017YFD0200503], China Postdoctoral Science Foundation [Z84129006], CAS Pioneer Hundred Talents Program to H.F. Qian, and Xingjiang Uighur Autonomous Region Talent Project to H.F. Qian. References Borymski, S., Cycon, M., Beckmann, M., Mur, L.A.J., Piotrowska-Seget, Z., 2018. Plant species and heavy metals affect biodiversity of microbial communities associated with metal-tolerant plants in metalliferous soils. Front. Microbiol. 9, 1425. Castro-Gutierrez, V., Masis-Mora, M., Carazo-Rojas, E., Mora-Lopez, M., RodriguezRodriguez, C.E., 2018. Impact of oxytetracycline and bacterial bioaugmentation on the efficiency and microbial community structure of a pesticide-degrading biomixture. Environ. Sci. Pollut. Res. 25, 11787–11799. Chen, F.F., Gong, Z.Y., Kelly, B.C., 2015. Rapid analysis of pharmaceuticals and personal care products in fish plasma micro-aliquots using liquid chromatography tandem mass spectrometry. J. Chromatogr. A 1383, 104–111. Chen, S., Li, X.X., Lavoie, M., Jin, Y.J., Xu, J.H., Fu, Z.W., Qian, H.F., 2017. Diclofop-methyl affects microbial rhizosphere community and induces systemic acquired resistance in rice. J. Environ. Sci. 51, 352–360. Christensen, A.M., Faaborg-Andersen, S., Ingerslev, F., Baun, A., 2007. Mixture and singlesubstance toxicity of selective serotonin reuptake inhibitors toward algae and crustaceans. Environ. Toxicol. Chem. 26, 85–91. Das, P., Williams, C.J., Fulthorpe, R.R., Hoque, M.E., Metcalfe, C.D., Xenopoulos, M.A., 2012. Changes in bacterial community structure after exposure to silver nanoparticles in natural waters. Environ. Sci. Technol. 46, 9120–9128. Franzellitti, S., Buratti, S., Capolupo, M., Du, B.W., Haddad, S.P., Chambliss, C.K., Brooks, B.W., Fabbri, E., 2014. An exploratory investigation of various modes of action and potential adverse outcomes of fluoxetine in marine mussels. Aquat. Toxicol. 151, 14–26. Glassmeyer, S.T., Furlong, E.T., Kolpin, D.W., Cahill, J.D., Zaugg, S.D., Werner, S.L., Meyer, M.T., Kryak, D.D., 2005. Transport of chemical and microbial compounds from known wastewater discharges: potential for use as indicators of human fecal contamination. Environ. Sci. Technol. 39, 5157–5169. Gleason, F.H., Carney, L.T., Lilje, O., Glockling, S.L., 2012. Ecological potentials of species of Rozella (Cryptomycota). Fungal Ecol. 5, 651–656. Hodges, C.M., Wood, S.A., Puddick, J., McBride, C.G., Hamilton, D.P., 2018. Sensor manufacturer, temperature, and cyanobacteria morphology affect phycocyanin fluorescence measurements. Environ. Sci. Pollut. Res. 25, 1079–1088. Inskeep, W.P., Bloom, P.R., 1985. Extinction coefficients of chlorophyll a and b in N, Ndimethylformamide and 80% acetone. Plant Physiol. 77, 483–485. Johnson, D.J., Sanderson, H., Brain, R.A., Wilson, C.J., Solomon, K.R., 2007. Toxicity and hazard of selective serotonin reuptake inhibitor antidepressants fluoxetine, fluvoxamine, and sertraline to algae. Ecotoxicol. Environ. Saf. 67, 128–139. Kostich, M.S., Batt, A.L., Lazorchak, J.M., 2014. Concentrations of prioritized pharmaceuticals in effluents from 50 large wastewater treatment plants in the US and implications for risk estimation. Environ. Pollut. 184, 354–359.

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