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Kar S, Pathakoti K, Tchounwou PB, Leszczynska D, Leszczynski J. Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli: Mechanistic understanding through In Vitro and In Silico studies. CHEMOSPHERE 2021; 264:128428. [PMID: 33022504 PMCID: PMC7919734 DOI: 10.1016/j.chemosphere.2020.128428] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/23/2020] [Accepted: 09/21/2020] [Indexed: 05/25/2023]
Abstract
The toxic effect of eight metal oxide nanoparticles (MONPs) on Escherichia coli was experimentally evaluated following standard bioassay protocols. The obtained cytotoxicity ranking of these studied MONPs is Er2O3, Gd2O3, CeO2, Co2O3, Mn2O3, Co3O4, Fe3O4/WO3 (in descending order). The computed EC50 values from experimental data suggested that Er2O3 and Gd2O3 were the most acutely toxic MONPs to E. coli. To identify the mechanism of toxicity of these 8 MONPs along with 17 other MONPs from our previous study, we employed seven classifications and machine learning (ML) algorithms including linear discriminant analysis (LDA), naïve bayes (NB), multinomial logistic regression (MLogitR), sequential minimal optimization (SMO), AdaBoost, J48, and random forest (RF). We also employed 1st and 2nd generation periodic table descriptors developed by us (without any sophisticated computing facilities) along with experimentally analyzed Zeta-potential, to model the cytotoxicity of these MONPs. Based on qualitative validation metrics, the LDA model appeared to be the best among the 7 tested models. The core environment of metal defined by the ratio of the number of core electrons to the number of valence electrons and the electronegativity count of oxygen showed a positive impact on toxicity. The identified properties were important for understanding the mechanisms of nanotoxicity and for predicting the potential environmental risk associated with MONPs exposure. The developed models can be utilized for environmental risk assessment of any untested MONP to E. coli, thereby providing a scientific basis for the design and preparation of safe nanomaterials.
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Affiliation(s)
- Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA
| | - Kavitha Pathakoti
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA; RCMI Center for Environmental Health, Department of Biology, Jackson State University, Jackson, MS, 39217, USA
| | - Paul B Tchounwou
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA; RCMI Center for Environmental Health, Department of Biology, Jackson State University, Jackson, MS, 39217, USA
| | - Danuta Leszczynska
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA; Department of Civil and Environmental Engineering, Jackson State University, Jackson, MS, 39217, USA
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA.
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Ramchandran V, Gernand JM. A dose-response-recovery clustering algorithm for categorizing carbon nanotube variants into toxicologically distinct groups. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.comtox.2019.02.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Forest V, Hochepied JF, Pourchez J. Importance of Choosing Relevant Biological End Points To Predict Nanoparticle Toxicity with Computational Approaches for Human Health Risk Assessment. Chem Res Toxicol 2019; 32:1320-1326. [PMID: 31243983 DOI: 10.1021/acs.chemrestox.9b00022] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Because it is impossible to assess in vitro or in vivo the toxicity of all nanoparticles available on the market on a case-by-case basis, computational approaches have been proposed as useful alternatives to predict in silico the hazard potential of engineered nanoparticles. Despite promising results, a major issue associated with these mathematical models lies in the a priori choice of the physicochemical descriptors and the biological end points. We performed a thorough bibliographic survey on the biological end points used for nanotoxicology purposes and compared them between experimental and computational approaches. They were found to be disparate: while conventional in vitro nanotoxicology assays usually investigate a large array of biological effects using eukaryotic cells (cytotoxicity, pro-inflammatory response, oxidative stress, genotoxicity), computational studies mostly focus on cell viability and also include studies on prokaryotic cells. We may thus wonder the relevance of building complex mathematical models able to predict accurately a biological end point if this latter is not the most relevant to support human health risk assessment. The choice of biological end points clearly deserves to be more carefully discussed. This could bridge the gap between experimental and computational nanotoxicology studies and allow in silico predictive models to reach their full potential.
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Affiliation(s)
- Valérie Forest
- Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet , INSERM, U 1059 Sainbiose, Centre CIS , F-42023 Saint-Etienne , France
| | - Jean-François Hochepied
- MINES ParisTech , PSL Research University , MAT - Centre des matériaux, CNRS UMR 7633 , BP 87 91003 Evry , France.,UCP, ENSTA ParisTech , Université Paris-Saclay , 828 bd des Maréchaux , 91762 Palaiseau cedex , France
| | - Jérémie Pourchez
- Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet , INSERM, U 1059 Sainbiose, Centre CIS , F-42023 Saint-Etienne , France
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Lotfipanah S, Zeinali M, Yaghmaei P. Induction of caspase-2 gene expression in carboxyl-functionalized carbon nanotube-treated human T-cell leukemia (Jurkat) cell line. Drug Chem Toxicol 2019; 44:394-399. [PMID: 31060401 DOI: 10.1080/01480545.2019.1609025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Carbon nanotubes (CNTs) have great potential as novel diagnostic or therapeutic tools in biomedicine but, cellular toxicity must be well considered before widespread application of CNTs. Many chemical agents exert their toxicity through apoptotic pathways by induction of caspase biomolecules. In the current study, effects of carboxyl-functionalized single-walled (SW) and multi-walled (MW) CNTs at a single dose of 100 µg ml-1 on the survival of Jurkat cells were examined using MTT assay. Additionally, the impacts of carboxylated CNTs on the gene expression levels of selected caspases were investigated. Jurkat cells were exposed to CNTs (100 µg ml-1 for 72 h) and then expression levels of selected caspase genes (Cas) were evaluated by qRT-PCR analysis. Housekeeping genes, β-actin, and glyceraldehyde 3-phosphate dehydrogenase (GAPDH), were used as normalization controls. The results showed only a mild decrease in the viability of Jurkat cells treated with carboxylated MWCNT. The results of qRT-PCR analysis revealed the elevated level of Cas2 mRNA in the cells treated with carboxylated MWCNT (6.08-fold) and carboxylated SWCNT (1.20-fold). The expression levels of Cas4, Cas6, Cas8, and Cas10 genes were increased not significantly compared to the control untreated cells. Our findings suggested that exposure to carboxyl-functionalized CNTs could be resulted in up-regulation of the Cas2 gene and not initiator Cas8 and Cas10 genes. In addition, it seems that carboxylated MWCNT was more potent than SWCNT in activation of Cas2 gene expression and triggering cell death signal in a manner different from intrinsic or extrinsic apoptosis pathways.
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Affiliation(s)
- Shirin Lotfipanah
- Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Majid Zeinali
- Biotechnology Research Center, Research Institute of Petroleum Industry (RIPI), Tehran, Iran
| | - Parichehreh Yaghmaei
- Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
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Varsou DD, Afantitis A, Tsoumanis A, Melagraki G, Sarimveis H, Valsami-Jones E, Lynch I. A safe-by-design tool for functionalised nanomaterials through the Enalos Nanoinformatics Cloud platform. NANOSCALE ADVANCES 2019; 1:706-718. [PMID: 36132268 PMCID: PMC9473200 DOI: 10.1039/c8na00142a] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Accepted: 10/30/2018] [Indexed: 05/16/2023]
Abstract
Multi-walled carbon nanotubes are currently used in numerous industrial applications and products, therefore fast and accurate evaluation of their biological and toxicological effects is of utmost importance. Computational methods and techniques, previously applied in the area of cheminformatics for the prediction of adverse effects of chemicals, can also be applied in the case of nanomaterials (NMs), in an effort to reduce expensive and time consuming experimental procedures. In this context, a validated and predictive nanoinformatics model has been developed for the accurate prediction of the biological and toxicological profile of decorated multi-walled carbon nanotubes. The nanoinformatics workflow was fully validated according to the OECD principles before it was released online via the Enalos Cloud platform. The web-service is a ready-to-use, user-friendly application whose purpose is to facilitate decision making, as part of a safe-by-design framework for novel carbon nanotubes.
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Affiliation(s)
- Dimitra-Danai Varsou
- Nanoinformatics Department, Novamechanics Ltd Nicosia 1065 Cyprus
- School of Chemical Engineering, National Technical University of Athens 157 80 Athens Greece
| | | | | | | | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens 157 80 Athens Greece
| | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham B15 2TT Birmingham UK
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham B15 2TT Birmingham UK
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Toropova AP, Toropov AA. Nano-QSAR in cell biology: Model of cell viability as a mathematical function of available eclectic data. J Theor Biol 2017; 416:113-118. [DOI: 10.1016/j.jtbi.2017.01.012] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 12/25/2016] [Accepted: 01/06/2017] [Indexed: 10/20/2022]
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Recent Developments in 3D QSAR and Molecular Docking Studies of Organic and Nanostructures. HANDBOOK OF COMPUTATIONAL CHEMISTRY 2017. [PMCID: PMC7123761 DOI: 10.1007/978-3-319-27282-5_54] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
The development of quantitative structure–activity relationship (QSAR) methods is going very fast for the last decades. OSAR approach already plays an important role in lead structure optimization, and nowadays, with development of big data approaches and computer power, it can even handle a huge amount of data associated with combinatorial chemistry. One of the recent developments is a three-dimensional QSAR, i.e., 3D QSAR. For the last two decades, 3D-OSAR has already been successfully applied to many datasets, especially of enzyme and receptor ligands. Moreover, quite often 3D QSAR investigations are going together with protein–ligand docking studies and this combination works synergistically. In this review, we outline recent advances in development and applications of 3D QSAR and protein–ligand docking approaches, as well as combined approaches for conventional organic compounds and for nanostructured materials, such as fullerenes and carbon nanotubes.
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