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Sun M, Sun Q, Li T, Ren X, Xu Q, Sun Z, Duan J. Silica nanoparticles induce liver lipid metabolism disorder via ACSL4-mediated ferroptosis. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 359:124590. [PMID: 39043312 DOI: 10.1016/j.envpol.2024.124590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 07/11/2024] [Accepted: 07/20/2024] [Indexed: 07/25/2024]
Abstract
The disease burden of non-alcoholic fatty liver disease (NAFLD) is increasing worldwide. Emerging evidence has revealed that silica nanoparticles (SiNPs) could disorder the liver lipid metabolism and cause hepatotoxicity, but the underlying mechanism remains unknown. The purpose of this study is to elucidate the molecular mechanism of hepatic lipid metabolism disorder caused by SiNPs, and to reveal the role of ferroptosis in SiNPs-induced hepatotoxicity. To explore the phenotypic changes in liver, the wild-type C57BL/6J mice were exposed to different doses of SiNPs (5, 10, 20 mg/kg·bw) with or without melatonin (20 mg/kg·bw). SiNPs accelerated hepatic oxidative stress and promoted pathological injury and lipid accumulation, resulting in NAFLD development. Melatonin significantly inhibited the oxidative damage caused by SiNPs. Then, the hepatocytes were treated with SiNPs, the ferroptosis inducer and inhibitor, respectively. In vitro, SiNPs (25 μg/mL) generated mitochondrial and intracellular Fe2+ accumulation and lipid peroxidation repair ability impairment, decreased the activity of GPX4 through ACSL4/p38 MAPK signaling pathway, resulting in ferroptosis of hepatocytes. Notably, Erastin (the ferroptosis activator, 5 μM) increased the sensitivity of hepatocytes to ferroptosis. Ferrostatin-1 (Fer-1, the ferroptosis inhibitor, 5 μM) restored GPX4 activity and protected against deterioration of lipid hydroperoxides (LOOHs) to salvage SiNPs-induced cytotoxicity. Finally, the liver tissue conditional ACSL4 knockout (cKO) mice and ACSL4-KO hepatocytes were adopted to further identify the role of the ACSL4-mediated ferroptosis on SiNPs-induced NAFLD development. The results displayed ACSL4 knockout could down-regulate the lipid peroxidation and ferroptosis, ultimately rescuing the progression of NAFLD. In summary, our data indicated that ACSL4/p38 MAPK/GPX4-mediated ferroptosis was a novel and critical mechanism of SiNPs-induced NAFLD.
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Affiliation(s)
- Mengqi Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Qinglin Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Tianyu Li
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Xiaoke Ren
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Qing Xu
- Core Facilities for Electrophysiology, Core Facilities Center, Capital Medical University, Beijing, 100069, PR China
| | - Zhiwei Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Junchao Duan
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China.
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Chen D, Liu Y, Liu Y, Zhao K, Zhang T, Gao Y, Wang Q, Song B, Hao G. ChemFREE: a one-stop comprehensive platform for ecological and environmental risk evaluation of chemicals in one health world. Nucleic Acids Res 2024; 52:W450-W460. [PMID: 38832633 PMCID: PMC11223831 DOI: 10.1093/nar/gkae446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/03/2024] [Accepted: 05/11/2024] [Indexed: 06/05/2024] Open
Abstract
Addressing health and safety crises stemming from various environmental and ecological issues is a core focus of One Health (OH), which aims to balance and optimize the health of humans, animals, and the environment. While many chemicals contribute significantly to our quality of life when properly used, others pose environmental and ecological health risks. Recently, assessing the ecological and environmental risks associated with chemicals has gained increasing significance in the OH world. In silico models may address time-consuming and costly challenges, and fill gaps in situations where no experimental data is available. However, despite their significant contributions, these assessment models are not web-integrated, leading to user inconvenience. In this study, we developed a one-stop comprehensive web platform for freely evaluating the eco-environmental risk of chemicals, named ChemFREE (Chemical Formula Risk Evaluation of Eco-environment, available in http://chemfree.agroda.cn/chemfree/). Inputting SMILES string of chemicals, users will obtain the assessment outputs of ecological and environmental risk, etc. A performance evaluation of 2935 external chemicals revealed that most classification models achieved an accuracy rate above 0.816. Additionally, the $Q_{F1}^2$ metric for regression models ranges from 0.618 to 0.898. Therefore, it will facilitate the eco-environmental risk evaluation of chemicals in the OH world.
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Affiliation(s)
- Dongyu Chen
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
| | - Yingwei Liu
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, P. R. China
| | - Yang Liu
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, P. R. China
| | - Kejun Zhao
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, P. R. China
| | - Tianhan Zhang
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, P. R. China
| | - Yangyang Gao
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
| | - Qi Wang
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, P. R. China
| | - Baoan Song
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
| | - Gefei Hao
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
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Zhang Y, Xie L, Zhang D, Xu X, Xu L. Application of Machine Learning Methods to Predict the Air Half-Lives of Persistent Organic Pollutants. Molecules 2023; 28:7457. [PMID: 38005179 PMCID: PMC10673120 DOI: 10.3390/molecules28227457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023] Open
Abstract
Persistent organic pollutants (POPs) are ubiquitous and bioaccumulative, posing potential and long-term threats to human health and the ecological environment. Quantitative structure-activity relationship (QSAR) studies play a guiding role in analyzing the toxicity and environmental fate of different organic pollutants. In the current work, five molecular descriptors are utilized to construct QSAR models for predicting the mean and maximum air half-lives of POPs, including specifically the energy of the highest occupied molecular orbital (HOMO_Energy_DMol3), a component of the dipole moment along the z-axis (Dipole_Z), fragment contribution to SAscore (SAscore_Fragments), subgraph counts (SC_3_P), and structural information content (SIC). The QSAR models were achieved through the application of three machine learning methods: partial least squares (PLS), multiple linear regression (MLR), and genetic function approximation (GFA). The determination coefficients (R2) and relative errors (RE) for the mean air half-life of each model are 0.916 and 3.489% (PLS), 0.939 and 5.048% (MLR), 0.938 and 5.131% (GFA), respectively. Similarly, the determination coefficients (R2) and RE for the maximum air half-life of each model are 0.915 and 5.629% (PLS), 0.940 and 10.090% (MLR), 0.939 and 11.172% (GFA), respectively. Furthermore, the mechanisms that elucidate the significant factors impacting the air half-lives of POPs have been explored. The three regression models show good predictive and extrapolation abilities for POPs within the application domain.
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Affiliation(s)
| | | | | | - Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China; (Y.Z.); (D.Z.)
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China; (Y.Z.); (D.Z.)
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Toropova AP, Toropov AA, Lombardo A, Lavado G, Benfenati E. Paradox of 'ideal correlations': improved model for air half-life of persistent organic pollutants. ENVIRONMENTAL TECHNOLOGY 2022; 43:2510-2515. [PMID: 33502960 DOI: 10.1080/09593330.2021.1882588] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 01/22/2021] [Indexed: 06/12/2023]
Abstract
The persistence of organic pollutants is an important environmental property due to the extended possibility to have an impact of corresponding substances. In many cases, the experimental values of the thousands of contaminants are missing. The object of the study is novel computational modelling for air pollutions. Quantitative structure-property relationship (QSPR) for air half-life has been built using the Monte Carlo method with applying the index of ideality of correlation (IIC). The basis of the predictive model of air half-life is the representation of the molecular structure by simplifying molecular input-line entry system (SMILES) and numerical data on the above endpoint (expressed by hours) converted to a decimal logarithm. The statistical quality of the model has been checked up with different validation metrics and is quite good. Paradoxically, the improvement of the statistical quality via the IIC for the validation set is done in detriment to the training set. The new model has performed better than those obtained previously on the same set of compounds, for the prediction of new compounds in the validation set. Some semi-quantitative indicators for the mechanistic interpretation of the model are suggested.
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Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Anna Lombardo
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Giovanna Lavado
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
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5
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Wang H, Wang Z, Chen J, Liu W. Graph Attention Network Model with Defined Applicability Domains for Screening PBT Chemicals. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:6774-6785. [PMID: 35475611 DOI: 10.1021/acs.est.2c00765] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In silico models for screening environmentally persistent, bio-accumulative, and toxic (PBT) substances are necessary for sound management of chemicals. Due to the complex structure-activity landscapes (SALs) on the PBT attributes, previous models for screening PBT chemicals lack either applicability domain (AD) characterizations or interpretability, restricting their applications. Herein, graph attention networks (GATs), a novel neural network architecture, were introduced to construct models for screening PBT chemicals. Results show that the GAT model not only outperformed those in previous studies but also exhibited interpretability since it optimizes attention weight parameters (PAW) that indicate contributions of each atom to the PBT attributes. An AD characterization termed ADFP-AC, which considers both molecular fingerprint (FP) similarities and compounds at activity cliffs (ACs) of SALs, was proposed to describe the ADs, which further assured the performance of the GAT model. Eight previously unidentified classes of compounds were identified as PBT chemicals from the Inventory of Existing Chemical Substances in China. The GAT model together with the ADFP-AC characterization may serve as efficient tools for screening PBT chemicals, and the modeling methodology can be applied to other physicochemical, environmental, behavioral, and toxicological parameters of chemicals that are necessary for their risk assessment and management.
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Affiliation(s)
- Haobo Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Zhongyu Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Wenjia Liu
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
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6
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Wang F, Liang Q, Ma Y, Sun M, Li T, Lin L, Sun Z, Duan J. Silica nanoparticles induce pyroptosis and cardiac hypertrophy via ROS/NLRP3/Caspase-1 pathway. Free Radic Biol Med 2022; 182:171-181. [PMID: 35219847 DOI: 10.1016/j.freeradbiomed.2022.02.027] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/21/2022] [Accepted: 02/22/2022] [Indexed: 12/11/2022]
Abstract
Growing literatures suggest that silica nanoparticles (SiNPs) exposure is correlated with adverse cardiovascular effects. Cardiac hypertrophy is one of the most common risk factors for heart failure. However, whether SiNPs involved in cardiac hypertrophy and the underlying mechanisms was remained unexploited. Our study aimed to investigate the molecular mechanisms of SiNPs on pyroptosis and cardiac hypertrophy. The in vivo results found that SiNPs induced ultrastructural change and histopathological damage, accompanied by oxidative damage occurred and increased levels of inflammatory factors (IL-18 and IL-1β) in heart tissue. In addition, SiNPs could upregulate the expressions of cardiac hypertrophy-related special marker including ANP, BNP, β-MHC, it also elevated the pyroptosis-related protein, such as NLRP3, Cleaved-Caspase-1, GSDMD, IL-18 and Cleaved-IL-1β in vivo. For in vitro study, SiNPs increased the intracellular ROS generation and activated the NLRP3/Caspase-1/GSDMD signaling pathway in cardiomyocytes. Whereas, the NADPH oxidase (NOX) inhibitor VAS2870 had effectively inhibited the ROS level and suppressed the expression of NLRP3, ASC, Pro-Caspase-1, Cleaved-Caspase-1, N-GSDMD, IL-18, Cleaved-IL-1β, ANP, BNP and β-MHC. Moreover, transfected with si-NLRP3 or adopted with Caspase-1 inhibitor VX-765 in cardiomyocytes showed an inhibitory effect on SiNPs-induced pyroptosis and cardiac hypertrophy. In summary, our results demonstrated that SiNPs could trigger pyroptosis and cardiac hypertrophy via ROS/NLRP3/Caspase-1 signaling pathway.
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Affiliation(s)
- Fenghong Wang
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China; Sinopharm North Hospital, Baotou, 014040, PR China
| | - Qingqing Liang
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Yuexiao Ma
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Mengqi Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Tianyu Li
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Lisen Lin
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Zhiwei Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China.
| | - Junchao Duan
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China.
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7
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Hitabatuma A, Wang P, Su X, Ma M. Metal-Organic Frameworks-Based Sensors for Food Safety. Foods 2022; 11:foods11030382. [PMID: 35159532 PMCID: PMC8833942 DOI: 10.3390/foods11030382] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 01/11/2022] [Accepted: 01/21/2022] [Indexed: 01/07/2023] Open
Abstract
Food contains a variety of poisonous and harmful substances that have an impact on human health. Therefore, food safety is a worldwide public concern. Food detection approaches must ensure the safety of food at every step of the food supply chain by monitoring and evaluating all hazards from every single step of food production. Therefore, early detection and determination of trace-level contaminants in food are one of the most crucial measures for ensuring food safety and safeguarding consumers’ health. In recent years, various methods have been introduced for food safety analysis, including classical methods and biomolecules-based sensing methods. However, most of these methods are laboratory-dependent, time-consuming, costly, and require well-trained technicians. To overcome such problems, developing rapid, simple, accurate, low-cost, and portable food sensing techniques is essential. Metal-organic frameworks (MOFs), a type of porous materials that present high porosity, abundant functional groups, and tunable physical and chemical properties, demonstrates promise in large-number applications. In this regard, MOF-based sensing techniques provide a novel approach in rapid and efficient sensing of pathogenic bacteria, heavy metals, food illegal additives, toxins, persistent organic pollutants (POPs), veterinary drugs, and pesticide residues. This review focused on the rapid screening of MOF-based sensors for food safety analysis. Challenges and future perspectives of MOF-based sensors were discussed. MOF-based sensing techniques would be useful tools for food safety evaluation owing to their portability, affordability, reliability, sensibility, and stability. The present review focused on research published up to 7 years ago. We believe that this work will help readers understand the effects of food hazard exposure, the effects on humans, and the use of MOFs in the detection and sensing of food hazards.
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Affiliation(s)
| | | | - Xiaoou Su
- Correspondence: ; Tel.: +86-82106577
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Salmani MH, Garzegar S, Ehrampoush MH, Askarishahi M. Predicting anionic surfactant toxicity to Daphnia magna in aquatic environment: a green approach for evaluation of EC 50 values. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:50731-50746. [PMID: 33973114 DOI: 10.1007/s11356-021-14107-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 04/21/2021] [Indexed: 06/12/2023]
Abstract
The median effective concentration (EC50) is the concentration of a substance expected to produce a specific effect in 50% of the populations with a certain density under defined conditions. This parameter is expressed as an acute toxicity and is obtained via chemical toxicity testing. But, the laboratory work is time-consuming, expensive, and not eco-friendly. Therefore, to predict EC50 for new anionic surfactants, a quantitative structure-activity relationship (QSAR) tool was studied for modeling the EC50 of anionic surfactants on Daphnia magna based on the molecular descriptors. The best model (R2 = 0.901 and F = 118.077, p<0.01) included 3 variables of the number of carbons, hydrogens, and the octanol-water partition coefficient logarithm. The main contribution to the toxicity was the octanol-water partition coefficient logarithm descriptor that had a negative effect on the toxicity of surfactants. The QSAR approach exhibited good results in predicting anionic surfactants EC50, which allows the building of a simple, valid, and interpretable model that can be utilized as potential tools for rapidly predicting the lnEC50 of new or untested anionic surfactants to Daphnia magna.
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Affiliation(s)
- Mohammad Hossein Salmani
- Environmental Science & Technology Research Center, Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, I. R, Yazd, Iran
| | - Sahar Garzegar
- Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, I. R, Yazd, Iran.
| | - Mohammad Hassan Ehrampoush
- Environmental Science & Technology Research Center, Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, I. R, Yazd, Iran
| | - Mohsen Askarishahi
- Department of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, I. R, Yazd, Iran
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Adithya S, Jayaraman RS, Krishnan A, Malolan R, Gopinath KP, Arun J, Kim W, Govarthanan M. A critical review on the formation, fate and degradation of the persistent organic pollutant hexachlorocyclohexane in water systems and waste streams. CHEMOSPHERE 2021; 271:129866. [PMID: 33736213 DOI: 10.1016/j.chemosphere.2021.129866] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/23/2021] [Accepted: 02/03/2021] [Indexed: 05/05/2023]
Abstract
The environmental impacts of persistent organic pollutants (POPs) is an increasingly prominent topic in the scientific community. POPs are stable chemicals that are accumulated in living beings and can act as endocrine disruptors or carcinogens on prolonged exposure. Although efforts have been taken to minimize or ban the use of certain POPs, their use is still widespread due to their importance in several industries. As a result, it is imperative that POPs in the ecosystem are degraded efficiently and safely in order to avoid long-lasting environmental damage. This review focuses on the degradation techniques of hexachlorocyclohexane (HCH), a pollutant that has strong adverse effects on a variety of organisms. Different technologies such as adsorption, bioremediation and advanced oxidation process have been critically analyzed in this study. All 3 techniques have exhibited near complete removal of HCH under ideal conditions, and the median removal efficiency values for adsorption, bioremediation and advanced oxidation process were found to be 80%, 93% and 82% respectively. However, it must be noted that there is no ideal HCH removal technique and the selection of removal method depends on several factors. Furthermore, the fates of HCH in the environment and challenges faced by HCH degradation have also been explained in this study. The future scope for research in this field has also received attention.
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Affiliation(s)
- Srikanth Adithya
- Department of Chemical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, 603110, Tamil Nadu, India
| | - Ramesh Sai Jayaraman
- Department of Chemical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, 603110, Tamil Nadu, India
| | - Abhishek Krishnan
- Department of Chemical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, 603110, Tamil Nadu, India
| | - Rajagopal Malolan
- Department of Chemical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, 603110, Tamil Nadu, India
| | - Kannappan Panchamoorthy Gopinath
- Department of Chemical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, 603110, Tamil Nadu, India
| | - Jayaseelan Arun
- Centre for Waste Management, International Research Centre, Sathyabama Institute of Science and Technology, Jeppiaar Nagar (OMR), Chennai, 600119, Tamil Nadu, India
| | - Woong Kim
- Department of Environmental Engineering, Kyungpook National University, Daegu, South Korea
| | - Muthusamy Govarthanan
- Department of Environmental Engineering, Kyungpook National University, Daegu, South Korea.
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10
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Sun M, Zhang J, Liang S, Du Z, Liu J, Sun Z, Duan J. Metabolomic characteristics of hepatotoxicity in rats induced by silica nanoparticles. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 208:111496. [PMID: 33099137 DOI: 10.1016/j.ecoenv.2020.111496] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 10/09/2020] [Accepted: 10/10/2020] [Indexed: 06/11/2023]
Abstract
Silica nanoparticles (SiNPs) have become one of the most widely studied nanoparticles in nanotechnology for environmental health and safety. Although many studies have devoted to evaluating the hepatotoxicity of SiNPs, it is currently impossible to predict the extent of liver lipid metabolism disorder by identifying changes in metabolites. In the present study, 40 male Sprague-Dawley (SD) rats were randomly divided into control group and 3 groups with different doses (1.8 mg/kg body weight (bw), 5.4 mg/kg bw, 16.2 mg/kg bw), receiving intratracheal instillation of SiNPs. Liver tissue was taken for lipid level analysis, and serum was used for blood biochemical analysis. Then, the metabolites changes of liver tissue in rats were systematically analyzed using 1H nuclear magnetic resonance (1H NMR) techniques in combination with multivariate statistical analysis. SiNPs induced serum alanine aminotransferase (ALT), aspartate aminotransferase (AST) and triglyceride (TG) elevation in treated groups; TG and low-density lipoprotein cholesterol (LDL-C) were significantly higher in SiNPs-treated groups of high-dose, however high-density lipoprotein cholesterol (HDL-C) showed a declining trend in liver tissue. The orthogonal partial least squares discriminant analysis (OPLS-DA) scores plots revealed different metabolic profiles between control and high-dose group (Q2 =0.495, R2Y=0.802, p = 0.037), and a total of 11 differential metabolites. Pathway analysis indicated that SiNPs treatment mainly affected 10 metabolic pathways including purine metabolism, glucose-alanine cycle and metabolism of various amino acids such as glutamate, cysteine and aspartate (impact value>0.1, false discovery rate (FDR)< 0.05). The result indicated that exposure to SiNPs caused liver lipid metabolism disorder in rats, the biochemical criterions related to lipid metabolism changed significantly. The obviously changed metabolomics in SiNPs-treated rats mostly occurred in amino acids, organic acids and nucleosides.
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Affiliation(s)
- Mengqi Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China
| | - Jingyi Zhang
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China
| | - Shuang Liang
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China
| | - Zhou Du
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China
| | - Jiangyan Liu
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China
| | - Zhiwei Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China
| | - Junchao Duan
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China.
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Stošić B, Janković R, Stošić M, Marković D, Stanković D, Sokolović D, Veselinović AM. In silico development of anesthetics based on barbiturate and thiobarbiturate inhibition of GABAA. Comput Biol Chem 2020; 88:107318. [DOI: 10.1016/j.compbiolchem.2020.107318] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 06/07/2020] [Accepted: 06/22/2020] [Indexed: 11/25/2022]
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Zivkovic M, Zlatanovic M, Zlatanovic N, Golubović M, Veselinović AM. The Application of the Combination of Monte Carlo Optimization Method based QSAR Modeling and Molecular Docking in Drug Design and Development. Mini Rev Med Chem 2020; 20:1389-1402. [DOI: 10.2174/1389557520666200212111428] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/21/2019] [Accepted: 10/28/2019] [Indexed: 01/18/2023]
Abstract
In recent years, one of the promising approaches in the QSAR modeling Monte Carlo optimization
approach as conformation independent method, has emerged. Monte Carlo optimization has
proven to be a valuable tool in chemoinformatics, and this review presents its application in drug discovery
and design. In this review, the basic principles and important features of these methods are discussed
as well as the advantages of conformation independent optimal descriptors developed from the
molecular graph and the Simplified Molecular Input Line Entry System (SMILES) notation compared
to commonly used descriptors in QSAR modeling. This review presents the summary of obtained results
from Monte Carlo optimization-based QSAR modeling with the further addition of molecular
docking studies applied for various pharmacologically important endpoints. SMILES notation based
optimal descriptors, defined as molecular fragments, identified as main contributors to the increase/
decrease of biological activity, which are used further to design compounds with targeted activity
based on computer calculation, are presented. In this mini-review, research papers in which molecular
docking was applied as an additional method to design molecules to validate their activity further,
are summarized. These papers present a very good correlation among results obtained from Monte
Carlo optimization modeling and molecular docking studies.
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Affiliation(s)
| | | | | | - Mladjan Golubović
- Clinic for Anesthesiology and Intensive Care, Clinical Center Nis, Nis, Serbia
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