What are the differences between aerobic and anaerobic toxic effects of sulfonamides on Escherichia coli?

Environmental Toxicology and Pharmacology 41 (2016) 251–258

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What are the differences between aerobic and anaerobic toxic effects of sulfonamides on Escherichia coli? Mengnan Qin a , Zhifen Lin a,b,c,∗ , Dali Wang a , Xi Long a , Min Zheng a , Yanling Qiu b a State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China b Shanghai Key Lab of Chemical Assessment and Substainability, Shanghai, China c Collaborative Innovation Center for Regional Environmental Quality, Beijing, China

a r t i c l e

i n f o

Article history: Received 11 September 2015 Received in revised form 14 December 2015 Accepted 16 December 2015 Available online 23 December 2015 Keywords: Sulfonamides Anaerobic and aerobic toxicity Toxic difference Total binding energy

a b s t r a c t Bacteria in the environment face the threat of antibiotics. However, most studies investigating the toxicity and toxicity mechanisms of antibiotics have been conducted on microorganisms in aerobic conditions, while studies examining the anaerobic toxicity and toxicity mechanisms of antibiotics are still limited. In this study, we determined the aerobic and anaerobic toxicities of sulfonamides (SAs) on Escherichia coli. Next, a comparison of the aerobic and anaerobic toxicities indicated that the SAs could be divided into three groups: Group I: log(1/EC50-anaerobic ) > log(1/EC50-aerobic ) (EC50-anaerobic /EC50-aerobic , the median effective concentration under anaerobic/aerobic conditions), Group II: log(1/EC50-anaerobic ) ≈ log(1/EC50-aerobic ), and Group III: log(1/EC50-anaerobic ) < log(1/EC50-aerobic ). Furthermore, this division was not based on the reactive oxygen species (ROS) level or the interaction energy (Ebinding ) value, which represents the affinity between SAs and dihydropteroate synthase (dhps) but rather on the total binding energy. Furthermore, SAs with greatly similar structures were categorized into different groups. This deep insight into the difference between aerobic and anaerobic toxicities will benefit environmental science, and the results of this study will serve as a reference for the risk assessment of chemicals in the environment. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Antibiotics have been widely used in human, livestock and agriculture since penicillin was formally introduced for clinical treatment in 1942 (Barton, 2000). However, the extensive use of antibiotics, especially their misuse, has resulted in a serious environmental accumulation of antibiotics (Zhou et al., 2007), which has caused harm to organisms, including bacteria, algae, plants, fish and mammals (Yu et al., 2011). Particularly, the interactions between antibiotics and microorganisms have aroused

Abbreviations: SAs, sulfonamides; EC50-anaerobic , the median effective concentration under anaerobic conditions; EC50-aerobic , the median effective concentration under aerobic conditions; ROS, reactive oxygen species; Ebinding , interaction energy; dhps, dihydropteroate synthase; QSAR, quantitative structure–activity relations; Dow, the pH-dependent n-octanol–water distribution ratio; SCP, sulfachloropyridazine; SD, sulfadiazine; SIX, sulfisoxazole; SM, sulfameter; SMM, sulfamonomethoxine; SMP, sulfamethoxypyridazine; SMR, sulfamerazine; SMZ, sulfamethazine; SQ, sulfaquinoxaline; SPY, sulfapyridine; DMSO, dimethyl sulfoxide; OD, optical density; DCFH2-DA, 2 ,7 -dichlorodihydrofluorescein diacetate. ∗ Corresponding author at: State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China. E-mail address: [email protected] (Z. Lin). http://dx.doi.org/10.1016/j.etap.2015.12.013 1382-6689/© 2015 Elsevier B.V. All rights reserved.

wide concerns because microorganisms are the target organisms of antibiotics. However, the microorganisms in the environment live in aerobic and/or anaerobic conditions, and different growth conditions may result in different biological activities. Therefore, the biological activities of antibiotics in microorganisms should be studied not only under aerobic conditions but also under anaerobic conditions. Until now, most studies on the biological activities of antibiotics in microorganisms have been performed only under aerobic conditions. For example, the toxicity of sulfonamides (SAs) to Photobacterium phosphoreum was observed with EC50 values of the chronic toxicity ranging from 2.5 to 40.2 mg/L, while the EC50 values of the acute toxicity ranged from 14.3 to 299.7 mg/L (Zou et al., 2012). All these toxic effects resulted from the drug-target interaction, and the mechanisms of these toxic effects were revealed accordingly. For instance, SAs have been proven to act as competitive inhibitors to dhps and to inhibit the biosynthesis of folic acid through this mechanism (Fig. 1) (Bell and Roblin Jr, 1942; Brown, 1962; Sköld, 2000). In addition to the drug-target interaction, researchers proposed that a common mechanism of action of antibiotics existed, and it was found that antibiotics stimulate the production of highly deleterious reactive oxygen species (ROS) in bacteria and, therefore, ultimately contribute to cell death (Kohanski et al., 2007).

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sulfamethoxypyridazine (SMP), sulfamerazine (SMR), sulfamethazine (SMZ), sulfaquinoxaline (SQ), and sulfapyridine (SPY) were purchased from Sigma Co. Ltd and used without further purification (purity ≥98.0%). Escherichia coli MG1655 was used as the test organism and was obtained from Biovector Inc. Co. Ltd. (Beijing). The organism was reconstituted and maintained on agar slants at 4 ◦ C. 2.2. Toxicity assessment

Fig. 1. Mechanism of action of SA: DHPPP—7,8-dihydro-6-hydroxymethylpterindhps—dihydropteroate synthase, PABA—p-aminobenzoic pyrophosphate, acid, DP—dihydropteridine, DF—dihydrofolate, dhfr—dihydrofolate reductase, TF—tetrahydrofolic acid.

However, little is known about the toxicities of antibiotics on microorganisms under anaerobic conditions because maintaining anaerobic conditions during toxicity experiments is difficult. Furthermore, contradictory results have been reported regarding the comparison of the toxic effects of antibiotics under anaerobic and aerobic conditions. For instance, Kohanski et al. (2007) compared the toxicity of antibiotics on E. coli between aerobic and anaerobic conditions, and the results indicated that bacteriostatic antibiotics (chloramphenicol, spectinomycin, tetracycline, and the macrolides erythromycin and rifamycin) had the same toxic effects under aerobic and anaerobic conditions, but bactericidal antibiotics (norfloxacin, ampicillin and kanamycin) had higher bactericidal potential under aerobic conditions than under anaerobic conditions. However, Keren et al. (2013) and Liu and Imlay (2013) disagreed on that conclusion. They found no difference between the toxic effects of the bactericidal antibiotics on E. coli under anaerobic and aerobic conditions. It is well-known that the molecular docking approach is a promising method for providing information on the interaction of chemicals and their target proteins (Lin et al., 2003; Wang et al., 2001). Furthermore, studies on the quantitative structure–activity relations (QSAR) models-based mechanism have been further developed by the introduction of the protein target receptor interaction energy (Ebinding ) factor. For example, Gao et al. (2012b) and Wang et al. (2012) found that the toxic effects of the chemicals were highly correlated with their Ebinding values. Furthermore, we previously constructed the QSAR with Ebinding and successfully revealed the differences between the acute and chronic toxicity mechanisms of SAs (Zou et al., 2012). Therefore, the QSAR with Ebinding or ROS (if present) may be a valuable method for determining the differences between the aerobic and anaerobic toxic mechanisms of SAs. This study used SAs as the test chemicals, which are widely distributed in the environment and their residual levels were as high as 776 ng/L (Gao et al., 2012a; Tang et al., 2014). The study employed also E. coli as the test organism, which lives under both aerobic and anaerobic conditions. The purposes of this study were as follows: (1) to determine the toxicity of SAs on E. coli under aerobic and anaerobic conditions; (2) to analyze the factors that influence the aerobic and anaerobic toxicities of SAs with the QSAR models based on the parameters of Ebinding , logDow (Dow, the pH-dependent noctanol–water distribution ratio) and/or ROS; (3) to compare the differences between the aerobic and anaerobic toxicities of SAs and then explore the factors influencing these differences. 2. Materials and methods 2.1. Chemicals and organisms All SAs, namely sulfachloropyridazine (SCP), sulfadiazine (SD), sulfisoxazole (SIX), sulfameter (SM), sulfamonomethoxine (SMM),

A pre-culture of E. coli was grown for 6 h (logarithmic growth period) in Luria-Bertani medium and was used as the inoculum (approximately 1000 CFU/mL) in both aerobic and anaerobic toxicity test. The SAs solutions with different concentrations were first dissolved in dimethyl sulfoxide (DMSO, Aladdin Co. Ltd) and then prepared in 1% NaCl, the final concentration of the DMSO in each well was less than 0.32% which had no adverse effect on the cell growth (Kim et al., 2007). The concentration gradients designed for the toxicity test were provided in Table S1 of the Supporting information. The chemicals were then mixed with the bacteria into the 96-well plates, and the plates were incubated at 37 ◦ C for 24 h. For the anaerobic experiments, before inoculation, dissolved oxygen was removed from the anaerobic medium and SA solutions by nitrogen-blow for 30 min and then they were equilibrated in the Thermo 1029 Forma Anaerobic System (Thermo Co., Ltd., USA). All other steps were the same as those under aerobic conditions but were performed in the anaerobic system. Finally, the 96-well plates containing chemicals and inoculum were placed in the anaerobic chamber for 24 h at 37 ◦ C. Optical density (OD) was used to indicate the growth of E. coli and was determined using a Spectrophotometer (Thermo Co., Ltd., USA). The median effective concentration (EC50 ), i.e., the concentration of a chemical that inhibits 50% of the optical density, was chosen as an indicator of toxicity. This measurement can be calculated based on the decrease in optical density using a probit model (Lin et al., 2005). 2.3. ROS determination The Reactive Oxygen Species Assay Kit based on 2 ,7 dichlorodihydrofluorescein diacetate (DCFH2 -DA) oxidation was used to determine the ROS generation (Cui et al., 2012) (Beyotime Institute of Biotechnology, China). The DCFH2 was reported to be an indicator for many kinds of ROS, such as H2 O2 and HO• (Gomes et al., 2005). In this study, DCFH2 -DA was diluted 1000 times with serum-free medium to a final concentration of 10 ␮mol/L. Then, cells exposed to antibiotics for 24 h were collected and suspended in the DCFH2 -DA solution for incubation for 20 min at 37 ◦ C. Afterwards, the cells were washed three times to remove any excessive DCFH2 -DA, and the ROS level was detected by the Flow Cytometer (Accuri, USA). 2.4. Molecular docking The molecular docking analysis was performed with Discovery Studio 3.1 (Accelrys Software Inc., San Diego, CA). The threedimensional structures of the chemicals (ligands) used in the molecular docking analysis were generated using Chemoffice 2014. The crystal structure of dhps (EC.2.5.1.15, 1AJ0. pdb) (Achari, 1997) was obtained from the protein data bank (http://www.pdb.org). Force field and energy minimization were applied to the receptor and the ligands, respectively, to guarantee reliable docking before performing the molecular docking analysis. The molecular docking analysis was performed using the CDOCKER protocol section of Discovery Studio. At the end of docking, the results were obtained

M. Qin et al. / Environmental Toxicology and Pharmacology 41 (2016) 251–258

from the output files. The CDOCKER interaction energy (Ebinding ) value between the ligand and the receptor was computed, and the highest score indicated the most favorable binding, which was selected for further investigation. The amino acid residues involved in the binding processes were also viewed in the ligand interaction diagrams. 2.5. Statistical analyses Statistical analyses were performed using ORIGIN 8.1 software (OriginLab Inc.) and SPSS 13.0 software (SPSS Inc.). The coefficient of determination (R2 ), standard deviation (S), F ratio, and P value were taken into consideration when testing the quality of the regression. 3. Results 3.1. Comparison of the toxic effects for SAs on E. coli under aerobic and anaerobic conditions Fig. 2 shows that the log(1/EC50-aerobic ) of the tested SAs on E. coli ranged from 4.53 to 5.23, while the log(1/EC50-anaerobic ) ranged from 4.07 to 5.47. There were no differences between the toxic effects of the bacteriostatic antibiotics on E. coli under anaerobic and aerobic conditions, as was found in the study by Kohanski et al. (2007). However, obvious differences were observed if the data were divided into three groups as follows: Group I: the log(1/EC50-anaerobic ) is greater than the log(1/EC50-aerobic ), which included SIX, SCP and SQ; Group II: the log(1/EC50-anaerobic ) is similar to the log(1/EC50-aerobic ), which included SD, SMM and SM; and Group III: the log(1/EC50-aerobic ) is greater than the log(1/EC50-anaerobic ), which included SMR, SMZ, SMP and SPY. 3.2. Comparison of the dose–response curves of the SAs on E. coli under aerobic and anaerobic conditions To further demonstrate the differences between Group I, Group II and Group III, we also analyzed the linear segments of the dose–response curves of SAs on E. coli under aerobic and anaerobic conditions. The complete dose–response curves are shown in Fig. S1 (see Supporting information). Fig. 3 shows that the aerobic dose–response curves of Group I, which includes SIX, SCP and SQ, were on the right side of the corresponding anaerobic dose–response curves, which was consistent with the result shown in Fig. 2 of log(1/EC50-anaerobic ) > log(1/EC50-aerobic ). However, the

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aerobic dose–response curves of Group II, which includes SD, SMM and SM, were almost the same as the corresponding anaerobic dose–response curves, which was consistent with the result shown in Fig. 2 of log(1/EC50-anaerobic ) ≈ log(1/EC50-aerobic ). Furthermore, the aerobic dose–response curves of Group III, which includes SMR, SMZ, SMP and SPY, were on the left side of the corresponding anaerobic dose–response curves, which was also consistent with the result shown in Fig. 2 of log(1/EC50-anaerobic ) < log(1/EC50-aerobic ). Therefore, this categorization based on the aerobic and anaerobic dose–response curves exactly agrees with that based on the log(1/EC50-anaerobic ) and log(1/EC50-aerobic ) values, which indicates that the categorization presented in Fig. 2 is appropriate. 3.3. Comparison of the toxicity factors for the SAs on E. coli under aerobic and anaerobic conditions It has long been recognized that after SAs are transported into the cell, they inhibit the activity of dhps during bacterial biosynthetic processes (Hitchings, 1973); thus, our previous studies indicated that logDow (Wells, 2007) and Ebinding have vital roles in the toxic effects of SAs (Jiang et al., 2011; Zou et al., 2012). In this study, log(1/EC50-aerobic ) and log(1/EC50-anaerobic ) were constructed with Ebinding and logDow values (Table 1) as follows:



log

1



EC50−aerobic

= 6.107 + 0.035 Ebinding − 0.208 log Dow

(1)

n = 10, R2 = 0.814, P = 0.003, F = 15.358, S = 0.107

 log



1 EC50−anaerobic

= 6.657 + 0.057 Ebinding − 0.690 log Dow (2)

N = 10, R2 = 0.902, P = 0.000, F = 32.082, S = 0.192 The significant correlation of Eq. (1) (R2 = 0.814) and Eq. (2) (R2 = 0.902) indicated that the Ebinding and logDow values were key contributors to both log(1/EC50-aerobic ) and log(1/EC50-anaerobic ). However, it has been reported that antibiotics may stimulate cells to produce ROS, which can only be formed in an oxygenrich environment (Dwyer et al., 2009), to trigger injury. Therefore, we have compared the growth inhibition of SAs and ROS level with/without NAC. Results indicated that ROS may contribute to the toxicity of antibiotics under aerobic conditions (shown in Fig. S2, see Supporting information), and the introduction of ROS into log(1/EC50-aerobic ) produces Eq. (3):



log

1 EC50−aerobic



= 4.819 − 0.258 log Dow + 0.009ROS

(3)

n = 10, R2 = 0.774, P = 0.005, F = 12.008, S = 0.118 The correlation of Eq. (3) (R2 = 0.774) indicated that ROS level was a contributor to the log(1/EC50-aerobic ) in addition to the logDow and Ebinding values, as shown in Eq. (1). 4. Discussion 4.1. The effect of the ROS on the aerobic and anaerobic toxicities of SAs

Fig. 2. log(1/EC50 ) of the SAs on E. coli obtained under aerobic and anaerobic conditions: , the toxicity data obtained under aerobic conditions (log(1/EC50-aerobic )); , the toxicity data obtained under anaerobic conditions (log(1/EC50-anaerobic )).

ROS production has been reported as the common mechanism of antibiotics ability to kill cells under aerobic conditions. Therefore, we determined the production of ROS stimulated by SAs at their EC50-aerobic concentrations (Table 1). The good correlation of Eq. (3) indicated that ROS level was the key contributor to the aerobic toxicity of SAs. However, Table 1 shows

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Fig. 3. The dose–response curves of the SAs on E. coli under aerobic and anaerobic conditions: dose–response curves obtained under anaerobic conditions.

that there was no apparent regularity of ROS level among the three groups of SAs. Thus, ROS level may not be the true factor that caused SAs divided into three groups because it influenced only the aerobic toxicities but not the anaerobic toxicities.

, the dose–response curves obtained under aerobic conditions;

, the

4.2. The effect of the Ebinding value on the aerobic and anaerobic toxicity of SAs The Ebinding value was calculated to represent the affinity between SAs and the dhps (Table 1). Eqs. (1) and (2) showed that the

Table 1 Toxicity results under aerobic and anaerobic conditions, physicochemical properties and docking results of test chemicals. Aerobic inhibition% at EC50-anaerobic

log(1/EC50-aerobic )

log(1/EC50-anaerobic )

Ebinding (kcal/mol)

pKa b

logDowc

ROS%d

Groups

−1.01 −1.07 −1.20

10.71 17.63 12.68

5.12 5.09 5.22

5.48 5.47 5.45

−30.61 −31.66 −33.85

5.10 6.00 5.50

−0.99 −0.82 −0.77

8.90 12.40 13.00

I

9.12 × 10−6 4.37 × 10−6 1.70 × 10−5

−3.43 −1.45 −5.88

40.08 43.42 57.95

4.92 5.23 4.85

5.04 5.36 4.77

−37.64 −33.18 −34.62

6.37 5.90 6.80

−0.89 −0.53 −0.066

−3.60 19.80 2.07

II

127-79-7 57-68-1

2.70 × 10−5 6.92 × 10−5

−10.14 −27.22

73.24 91.41

4.82 4.53

4.57 4.16

−37.69 −39.34

7.06 7.40

−0.18 0.71

80-35-3 144-83-2

−5

−7.31 −30.89

95.40 96.83

4.92 4.79

4.66 4.07

−33.43 −36.30

7.20 8.40

Sulfonamides

CAS number

EC50-anaerobic (mol/L)

SIX SCP SQ

127-69-5 80-32-0 59-40-5

3.31 × 10−6 3.39 × 10−6 3.55 × 10−6

SD SMM SM

68-35-9 1220-83-3 651-06-9

SMR SMZ SMP SPY a b c d

2.19 × 10 8.51 × 10−5

Total binding energya (kcal/L)*10−4

Total binding energy = EC50-anaerobic *Ebinding . Gleaned from literatures. LogDow was calculated by the formula: logDow = logKow − log(1 + 10pH−pKa ) ROS = ROSaerobic EC50 − ROScontrol .

0.066 0.33

8.00 4.20 −3.50 −5.27

III

Table 2 Properties of the chemicals with similar structures and their interactions with dhps. Sulfonamides

Structures

EC50-anaerobic (mol/L)

Total binding energy (kcal/L)*10−4

logDow

The same interactions between SAs and the dhps (Fig. 4)

The different interactions between SAs and the dhps (Fig. 4)

Groups

Cation-␲ bond with LYS 211, ARG 63; hydrogen bond with ARG 63, ARG 255

Hydrogen bond with THR 62

II

SD

9.12 × 10−6

−3.43

−37.64

−0.89

SMR

2.70 × 10−5

−10.14

−37.69

−0.18

Cation-␲ bond with ARG 255

III

SMZ

6.92 × 10−5

−27.22

−39.34

0.71

Cation-␲ bond with ARG 255, hydrogen bond with THR 62

III

SD

9.12 × 10−6

−3.43

−37.64

−0.89

Hydrogen bond with ARG 63

II

SPY

8.51 × 10−5

−30.89

−36.30

0.33

–

III

Cation-␲ bond with LYS 211, ARG 63; hydrogen bond with ARG 255, THR 62

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Ebinding (kcal/mol)

255

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Ebinding value was a key contributor to both the aerobic and anaerobic toxicities of SAs. However, the Ebinding value only indicated the energy for a single SA molecule to bind to dhps, not the energy for all SAs to bind in real solutions. Therefore, the Ebinding value is not the main factor that divides the SAs into three groups. 4.3. The effect of total binding energy on aerobic and anaerobic toxicities of SAs Afterwards, to determine the reason that SAs were divided into three groups, the inhibition rate (Inhibition%) of the SAs on E. coli at their EC50-anaerobic concentrations was obtained under aerobic conditions, and the results are listed in Table 1. The results showed that SIX, SCP and SQ (Group I) at their EC50-anaerobic concentrations (the chemical at this concentration can inhibit 50% of the OD under anaerobic conditions) only inhibited less than 20% of the OD under aerobic conditions (10.71–17.63%, Table 1). SD, SM and SMM (Group II) at their EC50-anaerobic concentrations inhibited approximately 50% of the OD under aerobic conditions (43.42–57.95%, Table 1), and SMR, SMP, SMZ and SPY (Group III) at their EC50-anaerobic concentrations inhibited approximately 90% of the OD under aerobic conditions (73.24–96.84%, Table 1).

The different inhibitions of SAs on E. coli under aerobic conditions presented in Table 1 may be due to differences in energy consumption because microorganisms always consume energy to fight against chemical stress. Therefore, the total binding energy values were calculated (EC50-anaerobic *Ebinding ) and are listed in Table 1. The results demonstrated that SIX, SCP and SQ (Group I), which only inhibited less than 20% of OD under aerobic conditions (10.71–17.63%), required the most energy (−1.20 to −1.01)*10−4 kcal/L. Furthermore, SMR, SMZ, SMP, SPY (Group III) required the least energy, and the total energy needed by SD, SMM, SM (Group II) was in between that of the other two groups. Thus, the different total binding energies needed by the different SAs to bind to dhps may be the real reason for their division into three groups. 4.4. The effect of the structures of SAs on their aerobic and anaerobic toxicities It seems that there are some “outlier SAs” in the three groups. For instance, some SAs with similar structures were listed in different groups (Table 2). Despite the similar structures of SD (no

Fig. 4. Interaction of SD, SMR, SMZ and SPY with dhps: the solid lines represent c-␲ bonds, the dotted lines represent hydrogen bonds.

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methyl group on the heterocycle ring), SMR (one methyl group on the heterocycle ring), and SMZ (two methyl groups on the heterocycle ring), SD belongs to Group II, and the other two belong to Group III. Similarly, SD has two nitrogen atoms on the heterocycle ring while SPY has only one on the heterocycle ring, but they belong to Group II and Group III, respectively. As mentioned above, the division of SAs into groups is related to the Ebinding value between the SAs and their target protein, dhps. Therefore, the “outlier SAs” can be revealed from their total binding energy with dhps in Fig. 4 and Table 2. Table 2 shows that SD, SMR and SMZ share the following interactions with dhps: one cation-␲ bond with LYS 211, one cation-␲ bond and one hydrogen bond with ARG 63, and one cation-␲ bond with ARG 255. However, they also have the unique interactions with dhps (Fig. 4 and Table 2). SD has one hydrogen bond with THR 62, SMR has one cation-␲ bond with ARG 255, and SMZ has one cation-␲ bond with ARG 255 and one hydrogen bond with THR 62. Therefore, the interactions of SD, SMR and SMZ with dhps are almost the same, and therefore, their Ebinding values are only slightly different (−37.64, −37.69, −39.34 kcal/mol, respectively). However, as is seen from Table 2, the total binding energies of SD, SMR and SMZ (−3.43 × 10−4 , −10.14 × 10−4 , and −27.22 × 10−4 kcal/L, respectively) are significantly different even though they have almost the same Ebinding values. Also, further analysis indicated that the significant differences in the total binding energy of these SAs were caused by their different EC50-anaerobic values because the total binding energy was calculated by the following equation:

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5. Conclusions We cannot concretely conclude whether there are any differences between the aerobic and anaerobic toxicities of SAs on E. coli because the experimental results are complicated. Thus, SAs can be divided into three groups, log(1/EC50-anaerobic ) > log(1/EC50-aerobic ), and log(1/EC50-anaerobic ) ≈ log(1/EC50-aerobic ), log(1/EC50-anaerobic ) < log(1/EC50-aerobic ). However, this division is neither based on the ROS level, nor on the Ebinding value, but is rather based on the total binding energy needed by the SAs to bind to dhps at their EC50-anaerobic concentrations. Furthermore, SAs with similar structures (Table 2) were listed in different groups, which meant that the differences in their structures may greatly influence the differences between their aerobic and anaerobic toxicities. The complexity of the results of this study indicates that it is not accurate to use the results for aerobic toxicity, which is relatively easy to determine, to perform ecological risk assessments of chemicals without considering anaerobic toxicity, which may have great impact on ecological systems. Therefore, we suggest that anaerobic toxicity should be considered in addition to the aerobic toxicity for the ecological risk assessment of chemicals in the real environment, even though anaerobic toxicity is more difficult to determine. Conflict of interest The authors declare that they have no competing interests. Transparency document

Total binding energy = EC50−anaerobic ∗ Ebinding .

(4)

Meanwhile, Eq. (2) shows a significant correlation among the EC50-anaerobic , Ebinding and logDow values. However, the logDow values of SD, SMR and SMZ (−0.89, −0.18 and 0.71, respectively, Table 2) are significantly influenced by the slight differences in their structures, but this does not result in a large difference in their Ebinding values. Therefore, the different logDow values results in significantly different EC50-anaerobic values (9.12 × 10−6 , 2.70 × 10−5 and 6.92 × 10−5 mol/L for SD, SMR and SMZ, respectively, Table 2) which is the key contributor to the total binding energy. In conclusion, the slight difference in the structures of SAs (Table 2) results in significant differences in their logDow values but only slight differences in their Ebinding values. Furthermore, Eq. (2) shows that the logDow and Ebinding values both influence the anaerobic toxicity of SAs, so the similar Ebinding and significantly different logDow values lead to the significant differences in their EC50-anaerobic values. Furthermore, the significantly different EC50-anaerobic values give rise to the large differences in their total binding energies (Table 2) based on Eq. (4). Therefore, SAs with similar structures were categorized into different groups. In addition, Table 2 shows that, except for the one hydrogen bond between SD and ARG 63 of dhps, the other interactions of SD and SPY with dhps are the same (one cation-␲ bond with LYS 211 and ARG 63; and one hydrogen bond with ARG 255 and THR 62, Table 2); thus, they have similar Ebinding values (−37.64 and −36.30 kcal/mol, respectively, Table 2). However, the logDow values of SD and SPY was significantly different (−0.89 and 0.33, respectively, Table 2), which results in the difference in their EC50-anaerobic values (9.12 × 10−6 and 8.51 × 10−5 mol/L, respectively, Table 2) and further leads to the significant difference in their total binding energy (−3.43 × 10−4 and −30.89 × 10−4 kcal/L, respectively, Table 2). Therefore, SD and SPY are also listed in two different groups, Group II and Group III, respectively.

The Transparency document associated with this article can be found in the online version. Acknowledgments This work was funded by the Foundation of the State Key Laboratory of Pollution Control and Resource Reuse, China (PCRRY11003), the National Natural Science Foundation of China (201177092, 21377096), the “Climbing” Program of Tongji University (0400219287), the 111 Project, and the Science & Technology Commission of Shanghai Municipality (14DZ2261100). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.etap.2015.12. 013. References Achari, A., 1997. Crystal structure of the anti-bacterial sulfonamide drug target dihydropteroate synthase. Nat. Struct. Mol. Biol. 4, 490–497. Barton, M.D., 2000. Antibiotic use in animal feed and its impact on human health. Nutr. Res. Rev. 13, 279–299. Bell, P.H., Roblin Jr., R.O., 1942. Studies in chemotherapy. VII. A theory of the relation of structure to activity of sulfanilamide type compounds. J. Am. Chem. Soc. 64, 2905–2917. Brown, G.M., 1962. The biosynthesis of folic acid. II. Inhibition by sulfonamides. J. Biol. Chem. 237, 536–540. Cui, Y., Zhao, Y.Y., Tian, Y., Zhang, W., Lu, X.Y., Jiang, X.Y., 2012. The molecular mechanism of action of bactericidal gold nanoparticles on Escherichia coli. Biomaterials 33, 2327–2333. Dwyer, D.J., Kohanski, M.A., Collins, J.J., 2009. Role of reactive oxygen species in antibiotic action and resistance. Curr. Opin. Microbiol. 12, 482–489. Gao, P.P., Mao, D.Q., Luo, Y., Wang, L.M., Xu, B.J., Xu, L., 2012a. Occurrence of sulfonamide and tetracycline-resistant bacteria and resistance genes in aquaculture environment. Water Res. 46, 2355–2364. Gao, Y., Lin, Z., Chen, R., Wang, T., Liu, S., Yao, Z., Yin, D., 2012b. Using molecular docking to compare toxicity of reactive chemicals to freshwater and marine luminous bacteria. Mol. Inform. 31, 809–816.

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