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Zhang Y, Li Y, Wang N, Ma X, Sun J, Wang X, Wang J. Joint action of six-component mixtures based on concentration response curves morphological parameter in acute and long-term toxicity assay. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2025; 113:104595. [PMID: 39613123 DOI: 10.1016/j.etap.2024.104595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2024] [Revised: 11/20/2024] [Accepted: 11/24/2024] [Indexed: 12/01/2024]
Abstract
Previous studies found that the multi-component mixtures with hormesis concentration-response curves (CRCs) were divided into three types according to the combined toxicity analysis of the segment-based method and σ2(k∙ECx) (the variance of k∙ECx). In this study, the acute and long-term toxicity of six pollutants and 12 six-component mixtures were assessed using microplate toxicity analyses (MTA). The functional relationship between σ2(k·ECx) and effect ratio (ERx) was determined by means of the independent action (IA) and the ER model to systematically investigate the correlation between mixture types in acute and long-term toxicity. The results indicated that across the entire concentration range, the mixture type of acute toxicity was consistent with short time exposure (0.25 h) measured in the long-term toxicity experiment. In the inhibition effect range, the types of mixtures of acute toxicity remained consistent with the chronic toxicity (exposure for 24 h) in 11 of the 12 mixtures. This study clarified the changes in the joint action of multi-component mixtures on Aliivibrio fischeri in terms of acute and long-term toxicity. The chronic toxicity of the mixtures can be predicted from the acute toxicity results, which provides a theoretical basis for the biological toxicity evaluation of multi-component mixtures.
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Affiliation(s)
- Yujiao Zhang
- College of Architecture and Civil Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China.
| | - Yajiao Li
- College of Architecture and Civil Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China.
| | - Na Wang
- School of Environmental Science and Engineering, Shaanxi University of Science and Technology, Xi'an, Shaanxi Province 710021, China.
| | - Xiaoyan Ma
- Key Laboratory of Northwest Water Resource, Environment and Ecology, MOE; Engineering Technology Research Center for Wastewater Treatment and Reuse; Key Laboratory of Environmental Engineering, Shaanxi Province; Xi'an University of Architecture and Technology, Xi'an, Shaanxi 710055, China.
| | - Jiajing Sun
- College of Architecture and Civil Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China.
| | - Xiaochang Wang
- Key Laboratory of Northwest Water Resource, Environment and Ecology, MOE; Engineering Technology Research Center for Wastewater Treatment and Reuse; Key Laboratory of Environmental Engineering, Shaanxi Province; Xi'an University of Architecture and Technology, Xi'an, Shaanxi 710055, China.
| | - Jiaxuan Wang
- College of Architecture and Civil Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China.
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Tao MT, Ding TT, Wang ZJ, Gu ZW, Liu SS. Prediction of toxicity and identification of key components for complex mixtures containing hormetic components. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 957:177733. [PMID: 39626415 DOI: 10.1016/j.scitotenv.2024.177733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Revised: 11/04/2024] [Accepted: 11/21/2024] [Indexed: 12/21/2024]
Abstract
Mixtures containing hormetic components are likely to induce hormesis. However, due to the presence of stimulatory effects, predicting the toxicity of such mixtures and identifying their key components face challenges. This study investigated the complex relationship between the stimulatory effects of individual components and their mixtures, focusing on predicting mixture toxicity and identifying key components influencing this toxicity. Sixteen chemicals, commonly found in disinfectants and hand sanitizers, were selected to construct a complex mixture system containing hormetic components. Using Vibrio qinghaiensis sp.-Q67 as an indicator organism, the study employed microplate toxicity tests to collect toxicity data for individual chemicals and their mixtures. The independent action (IA) and back-propagation neural network (BPNN) methods were utilized to predict mixture toxicity, while global sensitivity analysis (GSA) identified key components affecting toxicity. Results revealed that six of the sixteen chemicals exhibited time-dependent hormesis. However, when combined into mixtures, the stimulatory effects observed in individual components tended to diminish or disappear, leading to higher overall toxicity, likely due to synergism. Traditional models like the IA significantly underestimated mixture toxicity, whereas the BPNN model demonstrated superior predictive performance. GSA identified five key components, and changes in the levels of some non-toxic components significantly altered the toxicity of the mixtures. Moreover, increasing the levels of certain key components could either increase or decrease the mixture's toxicity, making the strategy of reducing their concentration to control mixture toxicity ineffective. This study revealed the potential of neural networks in predicting the toxicity of mixtures containing hormetic components and the possible characteristics of the effects of key components on mixture toxicity.
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Affiliation(s)
- Meng-Ting Tao
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Ting-Ting Ding
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Ze-Jun Wang
- National and Local Joint Engineering Laboratory of Municipal Sewage Resource Utilization Technology, School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, PR China
| | - Zhong-Wei Gu
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China
| | - Shu-Shen Liu
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China.
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Ding TT, Wang ZJ, Tao MT, Gu ZW, Chen RJ, Xu YQ, Liu SS. An innovative mixture sampling strategy with uniform design: Application to global sensitivity analysis of mixture toxicity. ENVIRONMENT INTERNATIONAL 2024; 191:108968. [PMID: 39213918 DOI: 10.1016/j.envint.2024.108968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 07/24/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024]
Abstract
Global sensitivity analysis combined with quantitative high-throughput screening (GSA-qHTS) uses random starting points of the trajectories in mixture design, which may lead to potential contingency and a lack of representativeness. Moreover, a scenario in which all factor levels were at stimulatory effects was not considered, thereby hindering a comprehensive understanding of GSA-qHTS. Accordingly, this study innovatively introduced an optimised experimental design, uniform design (UD), to generate non-random and representative sample points with smaller uniformity deviation as starting points of multiple trajectories. By combining UD with the previously optimised one-factor-at-a-time (OAT) method, a novel mixture design method was developed (UD-OAT). The single toxicity tests showed that three pyridinium and five imidazolium ionic liquids (ILs) exerted stimulatory effects on Vibrio qinghaiensis sp.-Q67; thus, four stimulatory effective concentrations of each IL were selected as factor levels. The UD-OAT generated 108 mixture samples with equal frequency and without repetition. High-throughput microplate toxicity analysis revealed that all 108 mixtures exhibited inhibitory effects. Among these, type B mixtures exhibited increasing toxicities that subsequently decreased, unlike type C mixtures, which consistently increased over time. GSA successfully identified three of the eight ILs as important factors influencing the toxicities of the mixtures. When individual ILs produced stimulatory effects, mixtures containing two to three ILs exhibited either stimulatory effects or none. In contrast, mixtures containing five to eight ILs exhibited inhibitory effects, while those containing four ILs showed a transition from stimulatory to inhibitory effects. This study provides a novel mixture design method for studying mixture toxicity and fills the application gap of GSA-qHTS. The phenomenon of individuals being beneficial while mixtures can be harmful challenges traditional mixture risk assessments.
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Affiliation(s)
- Ting-Ting Ding
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China
| | - Ze-Jun Wang
- National and Local Joint Engineering Laboratory of Municipal Sewage Resource Utilization Technology, School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, PR China
| | - Meng-Ting Tao
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Zhong-Wei Gu
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China
| | - Ru-Jun Chen
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Ya-Qian Xu
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, PR China
| | - Shu-Shen Liu
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China.
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Liang L, Qin L, Liu Y, Mo L, Dai J, Wang D. Key Component Analysis of the Time Toxicity Interaction of Five Antibiotics to Q67. TOXICS 2024; 12:521. [PMID: 39058173 PMCID: PMC11281310 DOI: 10.3390/toxics12070521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024]
Abstract
Antibiotics are considered as persistent emerging contaminants. The phenomenon of mixed exposure to the environment is a common occurrence causing serious harm to human health and the environment. Therefore, we employed enrofloxacin (ENR), chlortetracycline (CTC), methotrexate (TMP), chloramphenicol (CMP), and erythromycin (ETM) in this study. Nine treatments were designed using the uniform design concentration ratio (UDCR) method to systematically determine the toxicity of individual contaminants and their mixtures on Vibrio qinghaiensis sp.-Q67 through the time-dependent microplate toxicity assay. The combinatorial index (CI) method and the dose reduction index (DRI) were used to analyze the toxic interactions of the mixtures and the magnitude of the contribution of each component to the toxic interactions. The results showed that the toxicities of ENR, CTC, TMR, CMP, and ETM and their mixtures were time-dependent, with toxic effects being enhanced except when exposure time was prolonged. The types of toxic interactions in the ENR-CTC-TMR-CMP-ETM mixtures were found to be correlated with the proportion of each component's concentration, where the proportion of the components exerted the most significant influence. Through DRI extrapolation, it was determined that the primary components of the mixture exhibited a pronounced dependency on time. Specifically, at the 4 h mark, TMP emerged as the predominant component, gradually giving way to ENR as time advanced. Upon analyzing the frequency of mixture interactions under specified effects, the additive effect appeared most frequently (66.6%), while the antagonist effect appeared the least frequently (15.9%) among the nine rays.
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Affiliation(s)
- Luyi Liang
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541006, China; (L.L.); (L.Q.); (Y.L.); (J.D.)
| | - Litang Qin
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541006, China; (L.L.); (L.Q.); (Y.L.); (J.D.)
- Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area, Guilin University of Technology, Guilin 541006, China
| | - Yongan Liu
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541006, China; (L.L.); (L.Q.); (Y.L.); (J.D.)
| | - Lingyun Mo
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541006, China; (L.L.); (L.Q.); (Y.L.); (J.D.)
- Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area, Guilin University of Technology, Guilin 541006, China
- Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin 541006, China
| | - Junfeng Dai
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541006, China; (L.L.); (L.Q.); (Y.L.); (J.D.)
- Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area, Guilin University of Technology, Guilin 541006, China
| | - Dunqiu Wang
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541006, China; (L.L.); (L.Q.); (Y.L.); (J.D.)
- Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin 541006, China
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Tao MT, Sun X, Ding TT, Xu YQ, Liu SS. Screening for frequently detected quaternary ammonium mixture systems in waters based on frequent itemset mining and prediction of their toxicity. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 280:116581. [PMID: 38875820 DOI: 10.1016/j.ecoenv.2024.116581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 06/04/2024] [Accepted: 06/09/2024] [Indexed: 06/16/2024]
Abstract
Screening and prioritizing research on frequently detected mixture systems in the environment is of great significance, as conducting toxicity testing on all mixtures is impractical. Therefore, the frequent itemset mining (FIM) was introduced and applied in this paper to identify variables that commonly co-occur in a dataset. Based on the dataset of the quaternary ammonium compounds (QACs) in the water environment, the four frequent QAC mixture systems with detection rate ≥ 35 % were found, including [BDMM]+Cl--[BTMM]+Cl- (M1), [BDMM]+Cl--[BHMM]+Cl- (M2), [BTMM]+Cl- -[BHMM]+Cl- (M3), and [BDMM]+Cl--[BTMM]+Cl--[BHMM]+Cl- (M4). [BDMM]+Cl-, [BTMM]+Cl-, and [BHMM]+Cl- are benzyl dodecyl dimethyl ammonium chloride, benzyl tetradecyl dimethyl ammonium chloride, and benzyl hexadecyl dimethyl ammonium chloride, respectively. Then, the toxicity of the representative mixture rays and components for the four frequently detected mixture systems was tested using Vibrio qinghaiensis sp.-Q67 (Q67) as a luminescent indicator organism at 0.25 and 12 h. The toxicity of the mixtures was predicted using concentration addition (CA) and independent action (IA) models. It was shown that both the components and the representative mixture rays for the four frequently detected mixture systems exhibited obvious acute and chronic toxicity to Q67, and their median effective concentrations (EC50) were below 7 mg/L. Both CA and IA models predicted the toxicity of the four mixture systems well. However, the CA model had a better predictive ability for the toxicity of the M3 and M4 mixtures than IA at 12 h.
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Affiliation(s)
- Meng-Ting Tao
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Xiao Sun
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Ting-Ting Ding
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Ya-Qian Xu
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, PR China
| | - Shu-Shen Liu
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China.
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Cheng R, Huang P, Ding TT, Gu ZW, Tao MT, Liu SS. Time-dependent hormesis transfer from five high-frequency personal care product components to mixtures. ENVIRONMENTAL RESEARCH 2024; 248:118418. [PMID: 38316386 DOI: 10.1016/j.envres.2024.118418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 01/25/2024] [Accepted: 02/02/2024] [Indexed: 02/07/2024]
Abstract
There is potential for personal care products (PCPs) components and mixtures to induce hormesis. How hormesis is related to time and transmitted from components to mixtures are not clear. In this paper, we conducted determination of components in 16 PCP products and then ran frequent itemset mining on the component data. Five high-frequency components (HFCs), betaine (BET), 1,3-butanediol (BUT), ethylenediaminetetraacetic acid disodium salt (EDTA), glycerol (GLO), and phenoxyethanol (POE), and 14 mixtures were identified. For each mixture system, one mixture ray with the actual mixture ratios in the products was selected. Time-dependent microplate toxicity analysis was used to test the luminescence inhibition toxicity of five HFCs and 14 mixture rays to Vibrio qinghaiensis sp.-Q67 at 12 concentration gradients and eight exposure times. It is showed that BET, EDTA, POE, and 13 mixture rays containing at least one J-type component showed time-dependent hormesis. Characteristic parameters used to describe hormesis revealed that the absolute value of the maximum stimulatory effect (|Emin|) generally increased with time. Notably, mixtures composed of POE and S-type components showed greater |Emin| than POE alone at the same time. Importantly, the maximum stimulatory effective concentration, NOEC/the zero effective concentration point, and EC50 remained relatively stable. Nine hormesis transmission phenomena were observed in different mixture rays. While all mixtures primarily exhibited additive action, varying degrees of synergism and antagonism were noted in binary mixtures, with no strong synergism or antagonism observed in ternary and quaternary mixtures. These findings offer valuable insights for the screening of HFCs and their mixtures, as well as the study of hormesis transmission in personal care products.
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Affiliation(s)
- Rujun Cheng
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Peng Huang
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
| | - Ting-Ting Ding
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Zhong-Wei Gu
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
| | - Meng-Ting Tao
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Shu-Shen Liu
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China.
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Tao MT, Liu SS, Ding TT, Gu ZW, Cheng RJ. Time-dependent nonmonotonic concentration-response and synergism of alkyl glycosides with different alkyl side chain to Vibrio qinghaiensis sp. -Q67. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 922:171375. [PMID: 38431162 DOI: 10.1016/j.scitotenv.2024.171375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
Alkyl glycosides (AGs), commonly used nonionic surfactants, may have toxic effects on the environmental organisms. However, the complex concentration-response patterns of AGs with varying alkyl side chains and their mixtures have not been thoroughly studied. Therefore, the luminescence inhibition toxicities of six AGs with different alkyl side chains, namely, ethyl (AG02), butyl (AG04), hexyl (AG06), octyl (AG08), decyl (AG10), and dodecyl (AG12) glucosides, were determined in Vibrio qinghaiensis sp. -Q67 (Q67) at 0.25, 3, 6, 9, and 12 h. The six AGs exhibited time- and side-chain-dependent nonmonotonic concentration- responses toward Q67. AG02, with a short side chain, presented a concentration-response curve (CRC) with two peaks after 6 h and stimulated the luminescence of Q67 at both 6 and 9 h. AG04, AG06, and AG08 showed S-shaped CRCs at five exposure time points, and their toxicities increased with the side-chain length. AG10 and AG12, with long side chains, exhibited hormesis at 9 and 12 h. Molecular docking was performed to explore the mechanism governing the possible influence of AGs on the luminescence response. The effects of AGs on Q67 could be attributed to multiple luminescence-regulatory proteins, including LuxA, LuxC, LuxD, LuxG, LuxI, and LuxR. Notably, LuxR was identified as the primary binding protein among the six AGs. Given that they may co-exist, binary mixtures of AG10 and AG12 were designed to explore their concentration-response patterns and interactions. The results revealed that all AG10-AG12 binary mixture rays showed time-dependent hormesis on Q67, similar to that shown by their individual components. The interactions of these binary mixtures were mainly characterized by low-concentration additive action and high-concentration synergism at different times.
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Affiliation(s)
- Meng-Ting Tao
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Shu-Shen Liu
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China.
| | - Ting-Ting Ding
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Zhong-Wei Gu
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China
| | - Ru-Jun Cheng
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China
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