1
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Rivenbark KJ, Nikkhah H, Wang M, Beykal B, Phillips TD. Toxicity of representative organophosphate, organochlorine, phenylurea, dinitroaniline, carbamate, and viologen pesticides to the growth and survival of H. vulgaris, L. minor, and C. elegans. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:21781-21796. [PMID: 38396181 DOI: 10.1007/s11356-024-32444-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 02/08/2024] [Indexed: 02/25/2024]
Abstract
Pesticides are commonly found in the environment and pose a risk to target and non-target species; therefore, employing a set of bioassays to rapidly assess the toxicity of these chemicals to diverse species is crucial. The toxicity of nine individual pesticides from organophosphate, organochlorine, phenylurea, dinitroaniline, carbamate, and viologen chemical classes and a mixture of all the compounds were tested in three bioassays (Hydra vulgaris, Lemna minor, and Caenorhabditis elegans) that represent plant, aquatic, and soil-dwelling species, respectively. Multiple endpoints related to growth and survival were measured for each model, and EC10 and EC50 values were derived for each endpoint to identify sensitivity patterns according to chemical classes and target organisms. L. minor had the lowest EC10 and EC50 values for seven and five of the individual pesticides, respectively. L. minor was also one to two orders of magnitude more sensitive to the mixture compared to H. vulgaris and C. elegans, where EC50 values were calculated to be 0.00042, 0.0014, and 0.038 mM, respectively. H. vulgaris was the most sensitive species to the remaining individual pesticides, and C. elegans consistently ranked the least sensitive to all tested compounds. When comparing the EC50 values across all pesticides, the endpoints of L. minor were correlated with each other while the endpoints measured in H. vulgaris and C. elegans were clustered together. While there was no apparent relationship between the chemical class of pesticide and toxicity, the compounds were more closely clustered based on target organisms (herbicide vs insecticide). The results of this study demonstrate that the combination of these plant, soil, and aquatic specie can serve as representative indicators of pesticide pollution in environmental samples.
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Affiliation(s)
- Kelly J Rivenbark
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, TX, USA
- Department of Veterinary Physiology and Pharmacology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Hasan Nikkhah
- Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT, USA
- Center for Clean Energy Engineering, University of Connecticut, Storrs, CT, USA
| | - Meichen Wang
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, TX, USA
- Department of Veterinary Physiology and Pharmacology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Burcu Beykal
- Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT, USA
- Center for Clean Energy Engineering, University of Connecticut, Storrs, CT, USA
| | - Timothy D Phillips
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, TX, USA.
- Department of Veterinary Physiology and Pharmacology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA.
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2
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Aghayev Z, Szafran AT, Tran A, Ganesh HS, Stossi F, Zhou L, Mancini MA, Pistikopoulos EN, Beykal B. Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data. Chem Eng Sci 2023; 281:119086. [PMID: 37637227 PMCID: PMC10448728 DOI: 10.1016/j.ces.2023.119086] [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] [Indexed: 08/29/2023]
Abstract
Humans are continuously exposed to a variety of toxicants and chemicals which is exacerbated during and after environmental catastrophes such as floods, earthquakes, and hurricanes. The hazardous chemical mixtures generated during these events threaten the health and safety of humans and other living organisms. This necessitates the development of rapid decision-making tools to facilitate mitigating the adverse effects of exposure on the key modulators of the endocrine system, such as the estrogen receptor alpha (ERα), for example. The mechanistic stages of the estrogenic transcriptional activity can be measured with high content/high throughput microscopy-based biosensor assays at the single-cell level, which generates millions of object-based minable data points. By combining computational modeling and experimental analysis, we built a highly accurate data-driven classification framework to assess the endocrine disrupting potential of environmental compounds. The effects of these compounds on the ERα pathway are predicted as being receptor agonists or antagonists using the principal component analysis (PCA) projections of high throughput, high content image analysis descriptors. The framework also combines rigorous preprocessing steps and nonlinear machine learning algorithms, such as the Support Vector Machines and Random Forest classifiers, to develop highly accurate mathematical representations of the separation between ERα agonists and antagonists. The results show that Support Vector Machines classify the unseen chemicals correctly with more than 96% accuracy using the proposed framework, where the preprocessing and the PCA steps play a key role in suppressing experimental noise and unraveling hidden patterns in the dataset.
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Affiliation(s)
- Zahir Aghayev
- Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT
- Center for Clean Energy Engineering, University of Connecticut, Storrs, CT
| | - Adam T. Szafran
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX
| | - Anh Tran
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX
- Texas A&M Energy Institute, Texas A&M University, College Station, TX
| | - Hari S. Ganesh
- Discipline of Chemical Engineering, Indian Institute of Technology Gandhinagar, Palaj, Gandhinagar, Gujarat - 382055, India
| | - Fabio Stossi
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX
| | - Lan Zhou
- Department of Statistics, Texas A&M University, College Station, TX
| | - Michael A. Mancini
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX
| | - Efstratios N. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX
- Texas A&M Energy Institute, Texas A&M University, College Station, TX
| | - Burcu Beykal
- Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT
- Center for Clean Energy Engineering, University of Connecticut, Storrs, CT
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3
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Guo Q, Chen Y. The Effects of Visual Complexity and Task Difficulty on the Comprehensive Cognitive Efficiency of Cluster Separation Tasks. Behav Sci (Basel) 2023; 13:827. [PMID: 37887477 PMCID: PMC10604666 DOI: 10.3390/bs13100827] [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: 05/09/2023] [Revised: 09/24/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023] Open
Abstract
Cluster separation is required to perform multi-class visual statistics tasks and plays an essential role in information processing in visualization. This cognition behavioral study investigated the cluster separation task and the effects of visual complexity and task difficulty. A total of 32 college students (18 men and 14 women, with ages ranging from 18 to 25 years; mean = 21.2, SD = 3.9) participated in this study. The observers' average response accuracy, reaction time, mental effort, and comprehensive cognitive efficiency were measured as functions of three levels of visual complexity and task difficulty. The levels of visual complexity and task difficulty were quantified via an optimized complexity evaluation method and discrimination judgment task, respectively. The results showed that visual complexity and task difficulty significantly influenced comprehensive cognitive efficiency. Moreover, a strong interaction was observed between the effects of visual complexity and task difficulty. However, there was no positive linear relationship between the mental effort and the complexity level. Furthermore, two-dimensional color × shape redundant coding showed higher cognitive efficiency at low task difficulty levels. In contrast, the one-dimensional color encoding approach showed higher cognitive efficiency at increased task difficulty levels. The findings of this study provide valuable insights into designing more efficient and user-friendly visualization in the future.
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Affiliation(s)
- Qi Guo
- School of Art Design and Media, East China University of Science and Technology, Shanghai 200030, China;
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4
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Aghayev Z, Walker GF, Iseri F, Ali M, Szafran AT, Stossi F, Mancini MA, Pistikopoulos EN, Beykal B. Binary Classification of the Endocrine Disrupting Chemicals by Artificial Neural Networks. ESCAPE. EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING 2023; 52:2631-2636. [PMID: 37575176 PMCID: PMC10413412 DOI: 10.1016/b978-0-443-15274-0.50418-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
We develop a machine learning framework that integrates high content/high throughput image analysis and artificial neural networks (ANNs) to model the separation between chemical compounds based on their estrogenic receptor activity. Natural and man-made chemicals have the potential to disrupt the endocrine system by interfering with hormone actions in people and wildlife. Although numerous studies have revealed new knowledge on the mechanism through which these compounds interfere with various hormone receptors, it is still a very challenging task to comprehensively evaluate the endocrine disrupting potential of all existing chemicals and their mixtures by pure in vitro or in vivo approaches. Machine learning offers a unique advantage in the rapid evaluation of chemical toxicity through learning the underlying patterns in the experimental biological activity data. Motivated by this, we train and test ANN classifiers for modeling the activity of estrogen receptor-α agonists and antagonists at the single-cell level by using high throughput/high content microscopy descriptors. Our framework preprocesses the experimental data by cleaning, scaling, and feature engineering where only the middle 50% of the values from each sample with detectable receptor-DNA binding is considered in the dataset. Principal component analysis is also used to minimize the effects of experimental noise in modeling where these projected features are used in classification model building. The results show that our ANN-based nonlinear data-driven framework classifies the benchmark agonist and antagonist chemicals with 98.41% accuracy.
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Affiliation(s)
- Zahir Aghayev
- Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USA
- Center for Clean Energy Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - George F Walker
- Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USA
- Center for Clean Energy Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Funda Iseri
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Moustafa Ali
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Adam T Szafran
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Fabio Stossi
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
- GCC Center for Advanced Microscopy and Image Informatics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Michael A Mancini
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
- GCC Center for Advanced Microscopy and Image Informatics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Efstratios N Pistikopoulos
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Burcu Beykal
- Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USA
- Center for Clean Energy Engineering, University of Connecticut, Storrs, CT 06269, USA
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5
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Cordova AC, Klaren WD, Ford LC, Grimm FA, Baker ES, Zhou YH, Wright FA, Rusyn I. Integrative Chemical-Biological Grouping of Complex High Production Volume Substances from Lower Olefin Manufacturing Streams. TOXICS 2023; 11:586. [PMID: 37505552 PMCID: PMC10385386 DOI: 10.3390/toxics11070586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 06/24/2023] [Accepted: 07/03/2023] [Indexed: 07/29/2023]
Abstract
Human cell-based test methods can be used to evaluate potential hazards of mixtures and products of petroleum refining ("unknown or variable composition, complex reaction products, or biological materials" substances, UVCBs). Analyses of bioactivity and detailed chemical characterization of petroleum UVCBs were used separately for grouping these substances; a combination of the approaches has not been undertaken. Therefore, we used a case example of representative high production volume categories of petroleum UVCBs, 25 lower olefin substances from low benzene naphtha and resin oils categories, to determine whether existing manufacturing-based category grouping can be supported. We collected two types of data: nontarget ion mobility spectrometry-mass spectrometry of both neat substances and their organic extracts and in vitro bioactivity of the organic extracts in five human cell types: umbilical vein endothelial cells and induced pluripotent stem cell-derived hepatocytes, endothelial cells, neurons, and cardiomyocytes. We found that while similarity in composition and bioactivity can be observed for some substances, existing categories are largely heterogeneous. Strong relationships between composition and bioactivity were observed, and individual constituents that determine these associations were identified. Overall, this study showed a promising approach that combines chemical composition and bioactivity data to better characterize the variability within manufacturing categories of petroleum UVCBs.
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Affiliation(s)
- Alexandra C Cordova
- Interdisciplinary Faculty of Toxicology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
- Department of Veterinary Physiology and Pharmacology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
| | - William D Klaren
- Interdisciplinary Faculty of Toxicology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
- Department of Veterinary Physiology and Pharmacology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Lucie C Ford
- Interdisciplinary Faculty of Toxicology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
- Department of Veterinary Physiology and Pharmacology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Fabian A Grimm
- Interdisciplinary Faculty of Toxicology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
- Department of Veterinary Physiology and Pharmacology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Erin S Baker
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yi-Hui Zhou
- Departments of Statistics and Biological Sciences and Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27606, USA
| | - Fred A Wright
- Departments of Statistics and Biological Sciences and Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27606, USA
| | - Ivan Rusyn
- Interdisciplinary Faculty of Toxicology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
- Department of Veterinary Physiology and Pharmacology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
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6
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Smeltz MG, Clifton MS, Henderson WM, McMillan L, Wetmore BA. Targeted Per- and Polyfluoroalkyl substances (PFAS) assessments for high throughput screening: Analytical and testing considerations to inform a PFAS stock quality evaluation framework. Toxicol Appl Pharmacol 2023; 459:116355. [PMID: 36535553 PMCID: PMC10367912 DOI: 10.1016/j.taap.2022.116355] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 11/25/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022]
Abstract
Per- and polyfluoroalkyl substances (PFAS) represent a large chemical class lacking hazard, toxicokinetic, and exposure information. To accelerate PFAS hazard evaluation, new approach methodologies (NAMs) comprised of in vitro high-throughput toxicity screening, toxicokinetic data, and computational modeling are being employed in read across strategies to evaluate the larger PFAS landscape. A critical consideration to ensure robust evaluations is a parallel assessment of the quality of the screening stock solutions, where dimethyl sulfoxide (DMSO) is often the diluent of choice. Challenged by the lack of commercially available reference standards for many of the selected PFAS and reliance on mass spectrometry approaches for such an evaluation, we developed a high-throughput framework to evaluate the quality of screening stocks for 205 PFAS selected for these NAM efforts. Using mass spectrometry coupled with either liquid or gas chromatography, a quality scoring system was developed that incorporated observations during mass spectral examination to provide a simple pass or fail notation. Informational flags were used to further describe findings regarding parent analyte presence through accurate mass identification, evidence of contaminants and/or degradation, or further describe characteristics such as isomer presence. Across the PFAS-DMSO stocks tested, 148 unique PFAS received passing quality scores to allow for further in vitro testing whereas 57 received a failing score primarily due to detection issues or confounding effects of DMSO. Principle component analysis indicated vapor pressure and Henry's Law Constant as top indicators for a failed quality score for those analyzed by gas chromatography. Three PFAS in the hexafluoropropylene oxide family failed due to degradation in DMSO. As the PFAS evaluated spanned over 20 different structural categories, additional commentary describes analytical observations across specific groups related to PFAS stock composition, detection, stability, and methodologic considerations that will be useful for informing future analytical assessment and downstream HTS efforts. The high-throughput stock quality scoring workflow presented holds value as a tool to evaluate chemical presence and quality efficiently and for informing data inclusion in PFAS or other NAM screening efforts.
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Affiliation(s)
- Marci G Smeltz
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC 27711, United States of America
| | - M Scott Clifton
- Center for Environmental Measurement and Modeling, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC 27711, United States of America
| | - W Matthew Henderson
- Center for Environmental Measurement and Modeling, Office of Research and Development, United States Environmental Protection Agency, Athens, GA 23605, United States of America
| | - Larry McMillan
- National Caucus and Center on Black Aged, Inc, Durham, NC, United States of America
| | - Barbara A Wetmore
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC 27711, United States of America.
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7
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Roman-Hubers AT, Cordova AC, Barrow MP, Rusyn I. Analytical chemistry solutions to hazard evaluation of petroleum refining products. Regul Toxicol Pharmacol 2023; 137:105310. [PMID: 36473579 PMCID: PMC9771979 DOI: 10.1016/j.yrtph.2022.105310] [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: 07/31/2022] [Revised: 11/26/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
Products of petroleum refining are substances that are both complex and variable. These substances are produced and distributed in high volumes; therefore, they are heavily scrutinized in terms of their potential hazards and risks. Because of inherent compositional complexity and variability, unique challenges exist in terms of their registration and evaluation. Continued dialogue between the industry and the decision-makers has revolved around the most appropriate approach to fill data gaps and ensure safe use of these substances. One of the challenging topics has been the extent of chemical compositional characterization of products of petroleum refining that may be necessary for substance identification and hazard evaluation. There are several novel analytical methods that can be used for comprehensive characterization of petroleum substances and identification of most abundant constituents. However, translation of the advances in analytical chemistry to regulatory decision-making has not been as evident. Therefore, the goal of this review is to bridge the divide between the science of chemical characterization of petroleum and the needs and expectations of the decision-makers. Collectively, mutual appreciation of the regulatory guidance and the realities of what information these new methods can deliver should facilitate the path forward in ensuring safety of the products of petroleum refining.
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Affiliation(s)
- Alina T Roman-Hubers
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA
| | - Alexandra C Cordova
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA
| | - Mark P Barrow
- Department of Chemistry, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - Ivan Rusyn
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA.
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8
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House JS, Grimm FA, Klaren WD, Dalzell A, Kuchi S, Zhang SD, Lenz K, Boogaard PJ, Ketelslegers HB, Gant TW, Rusyn I, Wright FA. Grouping of UVCB substances with dose-response transcriptomics data from human cell-based assays. ALTEX 2022; 39:388–404. [PMID: 35288757 PMCID: PMC9344966 DOI: 10.14573/altex.2107051] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 02/22/2022] [Indexed: 12/18/2022]
Abstract
The application of in vitro biological assays as new approach methodologies (NAMs) to support grouping of UVCB (unknown or variable composition, complex reaction products, and biological materials) substances has recently been demonstrated. In addition to cell-based phenotyping as NAMs, in vitro transcriptomic profiling is used to gain deeper mechanistic understanding of biological responses to chemicals and to support grouping and read-across. However, the value of gene expression profiling for characterizing complex substances like UVCBs has not been explored. Using 141 petroleum substance extracts, we performed dose-response transcriptomic profiling in human induced pluripotent stem cell (iPSC)-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, as well as cell lines MCF7 and A375. The goal was to determine whether transcriptomic data can be used to group these UVCBs and to further characterize the molecular basis for in vitro biological responses. We found distinct transcriptional responses for petroleum substances by manufacturing class. Pathway enrichment informed interpretation of effects of substances and UVCB petroleum-class. Transcriptional activity was strongly correlated with concentration of polycyclic aromatic compounds (PAC), especially in iPSC-derived hepatocytes. Supervised analysis using transcriptomics, alone or in combination with bioactivity data collected on these same substances/cells, suggest that transcriptomics data provide useful mechanistic information, but only modest additional value for grouping. Overall, these results further demonstrate the value of NAMs for grouping of UVCBs, identify informative cell lines, and provide data that could be used for justifying selection of substances for further testing that may be required for registration.
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Affiliation(s)
- John S House
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.,Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, RTP, NC, USA
| | - Fabian A Grimm
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
| | - William D Klaren
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA.,current address: ToxStrategies, Inc., Asheville, NC, USA
| | - Abigail Dalzell
- Public Health England, Centre for Radiation, Chemical and Environmental Hazards, Harwell Science Campus, Oxon, UK
| | - Srikeerthana Kuchi
- Northern Ireland Centre for Stratified Medicine, Ulster University, L/Derry, Northern Ireland, UK.,current address: MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, UK
| | - Shu-Dong Zhang
- Northern Ireland Centre for Stratified Medicine, Ulster University, L/Derry, Northern Ireland, UK
| | - Klaus Lenz
- SYNCOM Forschungs und Entwicklungsberatung GmbH, Ganderkesee, Germany
| | | | | | - Timothy W Gant
- Public Health England, Centre for Radiation, Chemical and Environmental Hazards, Harwell Science Campus, Oxon, UK
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
| | - Fred A Wright
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
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9
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Lai A, Clark AM, Escher BI, Fernandez M, McEwen LR, Tian Z, Wang Z, Schymanski EL. The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs). ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:7448-7466. [PMID: 35533312 PMCID: PMC9228065 DOI: 10.1021/acs.est.2c00321] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Substances of unknown or variable composition, complex reaction products, or biological materials (UVCBs) are over 70 000 "complex" chemical mixtures produced and used at significant levels worldwide. Due to their unknown or variable composition, applying chemical assessments originally developed for individual compounds to UVCBs is challenging, which impedes sound management of these substances. Across the analytical sciences, toxicology, cheminformatics, and regulatory practice, new approaches addressing specific aspects of UVCB assessment are being developed, albeit in a fragmented manner. This review attempts to convey the "big picture" of the state of the art in dealing with UVCBs by holistically examining UVCB characterization and chemical identity representation, as well as hazard, exposure, and risk assessment. Overall, information gaps on chemical identities underpin the fundamental challenges concerning UVCBs, and better reporting and substance characterization efforts are needed to support subsequent chemical assessments. To this end, an information level scheme for improved UVCB data collection and management within databases is proposed. The development of UVCB testing shows early progress, in line with three main methods: whole substance, known constituents, and fraction profiling. For toxicity assessment, one option is a whole-mixture testing approach. If the identities of (many) constituents are known, grouping, read across, and mixture toxicity modeling represent complementary approaches to overcome data gaps in toxicity assessment. This review highlights continued needs for concerted efforts from all stakeholders to ensure proper assessment and sound management of UVCBs.
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Affiliation(s)
- Adelene Lai
- Luxembourg
Centre for Systems Biomedicine (LCSB), University
of Luxembourg, 6 avenue du Swing, 4367 Belvaux, Luxembourg
- Institute
for Inorganic and Analytical Chemistry, Friedrich-Schiller University, Lessing Strasse 8, 07743 Jena, Germany
| | - Alex M. Clark
- Collaborative
Drug Discovery Inc., 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
| | - Beate I. Escher
- Helmholtz
Centre for Environmental Research GmbH—UFZ, Permoserstraße 15, 04318 Leipzig, Germany
- Environmental
Toxicology, Center for Applied Geosciences, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
| | - Marc Fernandez
- Environment
and Climate Change Canada, 401 Burrard Street, Vancouver, British Columbia V6C 3R2, Canada
| | - Leah R. McEwen
- Cornell
University, Ithaca, New York 14850, United States
- International
Union of Pure and Applied Chemistry, Research Triangle Park, North Carolina 27709, United States
| | - Zhenyu Tian
- Department
of Chemistry and Chemical Biology, Department of Marine and Environmental
Sciences, Northeastern University, Boston, Massachusetts 02115, United States
| | - Zhanyun Wang
- Empa—Swiss
Federal Laboratories for Materials Science and Technology, Technology
and Society Laboratory, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland
- Chair
of Ecological Systems Design, Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Emma L. Schymanski
- Luxembourg
Centre for Systems Biomedicine (LCSB), University
of Luxembourg, 6 avenue du Swing, 4367 Belvaux, Luxembourg
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10
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Roman-Hubers AT, Cordova AC, Rohde AM, Chiu WA, McDonald TJ, Wright FA, Dodds JN, Baker ES, Rusyn I. Characterization of Compositional Variability in Petroleum Substances. FUEL (LONDON, ENGLAND) 2022; 317:123547. [PMID: 35250041 PMCID: PMC8896784 DOI: 10.1016/j.fuel.2022.123547] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
In the process of registration of substances of Unknown or Variable Composition, Complex Reaction Products or Biological Materials (UVCBs), information sufficient to enable substance identification must be provided. Substance identification for UVCBs formed through petroleum refining is particularly challenging due to their chemical complexity, as well as variability in refining process conditions and composition of the feedstocks. This study aimed to characterize compositional variability of petroleum UVCBs both within and across product categories. We utilized ion mobility spectrometry (IMS)-MS as a technique to evaluate detailed chemical composition of independent production cycle-derived samples of 6 petroleum products from 3 manufacturing categories (heavy aromatic, hydrotreated light paraffinic, and hydrotreated heavy paraffinic). Atmospheric pressure photoionization and drift tube IMS-MS were used to identify structurally related compounds and quantified between- and within-product variability. In addition, we determined both individual molecules and hydrocarbon blocks that were most variable in samples from different production cycles. We found that detailed chemical compositional data on petroleum UVCBs obtained from IMS-MS can provide the information necessary for hazard and risk characterization in terms of quantifying the variability of the products in a manufacturing category, as well as in subsequent production cycles of the same product.
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Affiliation(s)
- Alina T. Roman-Hubers
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843, United States
| | - Alexandra C. Cordova
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843, United States
| | - Arlean M. Rohde
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843, United States
| | - Weihsueh A. Chiu
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843, United States
| | - Thomas J. McDonald
- Departments of Environmental and Occupational Health, Texas A&M University, College Station, Texas 77843, United States
| | - Fred A. Wright
- Departments of Statistics and Biological Sciences, Raleigh, North Carolina 27695, United States
| | - James N. Dodds
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Erin S. Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843, United States
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11
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Bagozi A, Bianchini D, De Antonellis V. Multi-level and relevance-based parallel clustering of massive data streams in smart manufacturing. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.08.039] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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12
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Ganesh HS, Beykal B, Szafran AT, Stossi F, Zhou L, Mancini MA, Pistikopoulos EN. Predicting the Estrogen Receptor Activity of Environmental Chemicals by Single-Cell Image Analysis and Data-driven Modeling. ESCAPE. EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING 2021; 50:481-486. [PMID: 34355221 DOI: 10.1016/b978-0-323-88506-5.50076-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
A comprehensive evaluation of toxic chemicals and understanding their potential harm to human physiology is vital in mitigating their adverse effects following exposure from environmental emergencies. In this work, we develop data-driven classification models to facilitate rapid decision making in such catastrophic events and predict the estrogenic activity of environmental toxicants as estrogen receptor-α (ERα) agonists or antagonists. By combining high-content analysis, big-data analytics, and machine learning algorithms, we demonstrate that highly accurate classifiers can be constructed for evaluating the estrogenic potential of many chemicals. We follow a rigorous, high throughput microscopy-based high-content analysis pipeline to measure the single cell-level response of benchmark compounds with known in vivo effects on the ERα pathway. The resulting high-dimensional dataset is then pre-processed by fitting a non-central gamma probability distribution function to each feature, compound, and concentration. The characteristic parameters of the distribution, which represent the mean and the shape of the distribution, are used as features for the classification analysis via Random Forest (RF) and Support Vector Machine (SVM) algorithms. The results show that the SVM classifier can predict the estrogenic potential of benchmark chemicals with higher accuracy than the RF algorithm, which misclassifies two antagonist compounds.
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Affiliation(s)
- Hari S Ganesh
- Texas A&M Energy Institute, Texas A&M University, College Station, TX, United States of America
| | - Burcu Beykal
- Texas A&M Energy Institute, Texas A&M University, College Station, TX, United States of America
| | - Adam T Szafran
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States of America
| | - Fabio Stossi
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States of America.,GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States of America
| | - Lan Zhou
- Department of Statistics, Texas A&M University, College Station, TX, United States of America
| | - Michael A Mancini
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States of America.,GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States of America.,Texas A&M University Institute for Bioscience and Technology, Houston, TX, United States of America.,Pharmacology and Chemical Genomics, Baylor College of Medicine, Houston, TX, United States of America
| | - Efstratios N Pistikopoulos
- Texas A&M Energy Institute, Texas A&M University, College Station, TX, United States of America.,Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, United States of America
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13
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Roman-Hubers AT, Cordova AC, Aly NA, McDonald TJ, Lloyd DT, Wright FA, Baker ES, Chiu WA, Rusyn I. Data Processing Workflow to Identify Structurally Related Compounds in Petroleum Substances Using Ion Mobility Spectrometry-Mass Spectrometry. ENERGY & FUELS : AN AMERICAN CHEMICAL SOCIETY JOURNAL 2021; 35:10529-10539. [PMID: 34366560 PMCID: PMC8341389 DOI: 10.1021/acs.energyfuels.1c00892] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Ion mobility spectrometry coupled with mass spectrometry (IMS-MS) is a post-ionization separation technique that can be used for rapid multidimensional analyses of complex samples. IMS-MS offers untargeted analysis, including ion-specific conformational data derived as collisional cross section (CCS) values. Here, we combine nitrogen gas drift tube CCS (DTCCSN2) and Kendrick mass defect (KMD) analyses based on CH2 and H functional units to enable compositional analyses of petroleum substances. First, polycyclic aromatic compound standards were analyzed by IMS-MS to demonstrate how CCS assists the identification of isomeric species in homologous series. Next, we used case studies of a gasoline standard previously characterized for paraffin, isoparaffin, aromatic, naphthene, and olefinic (PIANO) compounds, and a crude oil sample to demonstrate the application of the KMD analyses and CCS filtering. Finally, we propose a workflow that enables confident molecular formula assignment to the IMS-MS-derived features in petroleum samples. Collectively, this work demonstrates how rapid untargeted IMS-MS analysis and the proposed data processing workflow can be used to provide confident compositional characterization of hydrocarbon-containing substances.
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Affiliation(s)
- Alina T. Roman-Hubers
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843, United States
| | - Alexandra C. Cordova
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843, United States
| | - Noor A. Aly
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843, United States
| | - Thomas J. McDonald
- Department of Environmental and Occupational Health, Texas A&M University, College Station, Texas 77843, United States
| | - Dillon T. Lloyd
- Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Fred A. Wright
- Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Erin S. Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Weihsueh A. Chiu
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843, United States
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843, United States
- Corresponding Author Ivan Rusyn, MD, PhD. Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77845. ; Phone: +1-979-458-9866
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14
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Orr A, Wang M, Beykal B, Ganesh HS, Hearon SE, Pistikopoulos EN, Phillips TD, Tamamis P. Combining Experimental Isotherms, Minimalistic Simulations, and a Model to Understand and Predict Chemical Adsorption onto Montmorillonite Clays. ACS OMEGA 2021; 6:14090-14103. [PMID: 34124432 PMCID: PMC8190805 DOI: 10.1021/acsomega.1c00481] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 05/11/2021] [Indexed: 05/05/2023]
Abstract
An attractive approach to minimize human and animal exposures to toxic environmental contaminants is the use of safe and effective sorbent materials to sequester them. Montmorillonite clays have been shown to tightly bind diverse toxic chemicals. Due to their promise as sorbents to mitigate chemical exposures, it is important to understand their function and rapidly screen and predict optimal clay-chemical combinations for further testing. We derived adsorption free-energy values for a structurally and physicochemically diverse set of toxic chemicals using experimental adsorption isotherms performed in the current and previous studies. We studied the diverse set of chemicals using minimalistic MD simulations and showed that their interaction energies with calcium montmorillonite clays calculated using simulation snapshots in combination with their net charge and their corresponding solvent's dielectric constant can be used as inputs to a minimalistic model to predict adsorption free energies in agreement with experiments. Additionally, experiments and computations were used to reveal structural and physicochemical properties associated with chemicals that can be adsorbed to calcium montmorillonite clay. These properties include positively charged groups, phosphine groups, halide-rich moieties, hydrogen bond donor/acceptors, and large, rigid structures. The combined experimental and computational approaches used in this study highlight the importance and potential applicability of analogous methods to study and design novel advanced sorbent systems in the future, broadening their applicability for environmental contaminants.
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Affiliation(s)
- Asuka
A. Orr
- Artie
McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
- Texas
A&M Energy Institute, Texas A&M
University, College
Station, Texas 77843-3372, United States
| | - Meichen Wang
- Veterinary
Integrative Biosciences Department, College of Veterinary Medicine
and Biomedical Sciences, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Burcu Beykal
- Texas
A&M Energy Institute, Texas A&M
University, College
Station, Texas 77843-3372, United States
| | - Hari S. Ganesh
- Texas
A&M Energy Institute, Texas A&M
University, College
Station, Texas 77843-3372, United States
| | - Sara E. Hearon
- Veterinary
Integrative Biosciences Department, College of Veterinary Medicine
and Biomedical Sciences, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Efstratios N. Pistikopoulos
- Artie
McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
- Texas
A&M Energy Institute, Texas A&M
University, College
Station, Texas 77843-3372, United States
| | - Timothy D. Phillips
- Veterinary
Integrative Biosciences Department, College of Veterinary Medicine
and Biomedical Sciences, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Phanourios Tamamis
- Artie
McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
- Texas
A&M Energy Institute, Texas A&M
University, College
Station, Texas 77843-3372, United States
- Department
of Materials Science and Engineering, Texas
A&M University, College
Station, Texas 77843-3003, United States
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15
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Luo YS, Ferguson KC, Rusyn I, Chiu WA. In Vitro Bioavailability of the Hydrocarbon Fractions of Dimethyl Sulfoxide Extracts of Petroleum Substances. Toxicol Sci 2021; 174:168-177. [PMID: 32040194 DOI: 10.1093/toxsci/kfaa007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Determining the in vitro bioavailable concentration is a critical, yet unmet need to refine in vitro-to-in vivo extrapolation for unknown or variable composition, complex reaction product or biological material (UVCB) substances. UVCBs such as petroleum substances are commonly subjected to dimethyl sulfoxide (DMSO) extraction in order to retrieve the bioactive polycyclic aromatic compound (PAC) portion for in vitro testing. In addition to DMSO extraction, protein binding in cell culture media and dilution can all influence in vitro bioavailable concentrations of aliphatic and aromatic compounds in petroleum substances. However, these in vitro factors have not been fully characterized. In this study, we aimed to fill in these data gaps by characterizing the effects of these processes using both a defined mixture of analytical standards containing aliphatic and aromatic hydrocarbons, as well as 4 refined petroleum products as prototypical examples of UVCBs. Each substance was extracted with DMSO, and the protein binding in cell culture media was measured by using solid-phase microextraction. Semiquantitative analysis for aliphatic and aromatic compounds was achieved via gas chromatography-mass spectrometry. Our results showed that DMSO selectively extracted PACs from test substances, and that chemical profiles of PACs across molecular classes remained consistent after extraction. With respect to protein binding, chemical profiles were retained at a lower dilution (higher concentration), but a greater dilution factor (ie, lower concentration) resulted in higher protein binding in cell medium, which in turn altered the ultimate chemical profile of bioavailable PACs. Overall, this case study demonstrates that extraction procedures, protein binding in cell culture media, and dilution factors prior to in vitro testing can all contribute to determining the final bioavailable concentrations of bioactive constituents of UVCBs in vitro. Thus, in vitro-to-in vivo extrapolation for UVCBs may require greater attention to the concentration-dependent and compound-specific differences in recovery and bioavailability.
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Affiliation(s)
- Yu-Syuan Luo
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station
| | - Kyle C Ferguson
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station
| | - Ivan Rusyn
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station
| | - Weihsueh A Chiu
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station
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16
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Roman-Hubers AT, McDonald TJ, Baker ES, Chiu WA, Rusyn I. A Comparative Analysis of Analytical Techniques for Rapid Oil Spill Identification. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2021; 40:1034-1049. [PMID: 33315271 PMCID: PMC8104454 DOI: 10.1002/etc.4961] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 11/20/2020] [Accepted: 12/09/2020] [Indexed: 05/25/2023]
Abstract
The complex chemical composition of crude oils presents many challenges for rapid chemical characterization in the case of a spill. A number of approaches are currently used to "fingerprint" petroleum-derived samples. Gas chromatography coupled with mass spectrometry (GC-MS) is the most common, albeit not very rapid, technique; however, with GC-MS alone, it is difficult to resolve the complex substances in crude oils. The present study examined the potential application of ion mobility spectrometry-mass spectrometry (IMS-MS) coupled with chem-informatic analyses as an alternative high-throughput method for the chemical characterization of crude oils. We analyzed 19 crude oil samples from on- and offshore locations in the Gulf of Mexico region in the United States using both GC-MS (biomarkers, gasoline range hydrocarbons, and n-alkanes) and IMS-MS (untargeted analysis). Hierarchical clustering, principal component analysis, and nearest neighbor-based classification were used to examine sample similarity and geographical groupings. We found that direct-injection IMS-MS performed either equally or better than GC-MS in the classification of the origins of crude oils. In addition, IMS-MS greatly increased the sample analysis throughput (minutes vs hours per sample). Finally, a tabletop science-to-practice exercise, utilizing both the GC-MS and IMS-MS data, was conducted with emergency response experts from regulatory agencies and the oil industry. This activity showed that the stakeholders found the IMS-MS data to be highly informative for rapid chemical fingerprinting of complex substances in general and specifically advantageous for accurate and confident source-grouping of crude oils. Collectively, the present study shows the utility of IMS-MS as a technique for rapid fingerprinting of complex samples and demonstrates its advantages over traditional GC-MS-based analyses when used for decision-making in emergency situations. Environ Toxicol Chem 2021;40:1034-1049. © 2020 SETAC.
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Affiliation(s)
- Alina T. Roman-Hubers
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, USA
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas, USA
| | - Thomas J. McDonald
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas, USA
- Department of Environmental & Occupational Health, Texas A&M University, College Station, Texas, USA
| | - Erin S. Baker
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas, USA
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina, USA
| | - Weihsueh A. Chiu
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, USA
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas, USA
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, USA
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas, USA
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17
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House JS, Grimm FA, Klaren WD, Dalzell A, Kuchi S, Zhang SD, Lenz K, Boogaard PJ, Ketelslegers HB, Gant TW, Wright FA, Rusyn I. Grouping of UVCB substances with new approach methodologies (NAMs) data. ALTEX-ALTERNATIVES TO ANIMAL EXPERIMENTATION 2020; 38:123-137. [PMID: 33086383 PMCID: PMC7900923 DOI: 10.14573/altex.2006262] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 10/07/2020] [Indexed: 11/23/2022]
Abstract
One of the most challenging areas in regulatory science is assessment of the substances known as UVCB (unknown or variable composition, complex reaction products and biological materials). Because the inherent complexity and variability of UVCBs present considerable challenges for establishing sufficient substance similarity based on chemical characteristics or other data, we hypothesized that new approach methodologies (NAMs), including in vitro test-derived biological activity signatures to characterize substance similarity, could be used to support grouping of UVCBs. We tested 141 petroleum substances as representative UVCBs in a compendium of 15 human cell types representing a variety of tissues. Petroleum substances were assayed in dilution series to derive point of departure estimates for each cell type and phenotype. Extensive quality control measures were taken to ensure that only high-confidence in vitro data were used to determine whether current groupings of these petroleum substances, based largely on the manufacturing process and physico-chemical properties, are justifiable. We found that bioactivity data-based groupings of petroleum substances were generally consistent with the manufacturing class-based categories. We also showed that these data, especially bioactivity from human induced pluripotent stem cell (iPSC)-derived and primary cells, can be used to rank substances in a manner highly concordant with their expected in vivo hazard potential based on their chemical compositional profile. Overall, this study demonstrates that NAMs can be used to inform groupings of UVCBs, to assist in identification of representative substances in each group for testing when needed, and to fill data gaps by read-across.
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Affiliation(s)
- John S House
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.,current address: Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, RTP, NC, USA
| | - Fabian A Grimm
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA.,current address: ExxonMobil Biomedical Sciences Inc., Annandale, NJ, USA
| | - William D Klaren
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA.,current address: S.C. Johnson and Son, Inc., Racine, WI, USA
| | - Abigail Dalzell
- Public Health England, Centre for Radiation, Chemical and Environmental Hazards, Harwell Science Campus, Oxon, UK
| | - Srikeerthana Kuchi
- Northern Ireland Centre for Stratified Medicine, Ulster University, L/Derry, Northern Ireland, UK.,current address: MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, UK
| | - Shu-Dong Zhang
- Northern Ireland Centre for Stratified Medicine, Ulster University, L/Derry, Northern Ireland, UK
| | - Klaus Lenz
- SYNCOM Forschungs- und Entwicklungsberatung GmbH, Ganderkesee, Germany
| | - Peter J Boogaard
- SHELL International BV, The Hague, Netherlands.,Concawe, Brussels, Belgium
| | | | - Timothy W Gant
- Public Health England, Centre for Radiation, Chemical and Environmental Hazards, Harwell Science Campus, Oxon, UK
| | - Fred A Wright
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
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18
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Beykal B, Onel M, Onel O, Pistikopoulos EN. A data-driven optimization algorithm for differential algebraic equations with numerical infeasibilities. AIChE J 2020; 66. [PMID: 32921798 DOI: 10.1002/aic.16657] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Support Vector Machines (SVMs) based optimization framework is presented for the data-driven optimization of numerically infeasible Differential Algebraic Equations (DAEs) without the full discretization of the underlying first-principles model. By formulating the stability constraint of the numerical integration of a DAE system as a supervised classification problem, we are able to demonstrate that SVMs can accurately map the boundary of numerical infeasibility. The necessity of this data-driven approach is demonstrated on a 2-dimensional motivating example, where highly accurate SVM models are trained, validated, and tested using the data collected from the numerical integration of DAEs. Furthermore, this methodology is extended and tested for a multi-dimensional case study from reaction engineering (i.e., thermal cracking of natural gas liquids). The data-driven optimization of this complex case study is explored through integrating the SVM models with a constrained global grey-box optimization algorithm, namely the ARGONAUT framework.
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Affiliation(s)
- Burcu Beykal
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
- Texas A&M Energy Institute, Texas A&M University College Station Texas USA
| | - Melis Onel
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
- Texas A&M Energy Institute, Texas A&M University College Station Texas USA
| | - Onur Onel
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
- Texas A&M Energy Institute, Texas A&M University College Station Texas USA
- Department of Chemical and Biological Engineering Princeton University Princeton New Jersey USA
| | - Efstratios N. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
- Texas A&M Energy Institute, Texas A&M University College Station Texas USA
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19
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Mukherjee R, Beykal B, Szafran AT, Onel M, Stossi F, Mancini MG, Lloyd D, Wright FA, Zhou L, Mancini MA, Pistikopoulos EN. Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms. PLoS Comput Biol 2020; 16:e1008191. [PMID: 32970665 PMCID: PMC7538107 DOI: 10.1371/journal.pcbi.1008191] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 10/06/2020] [Accepted: 07/25/2020] [Indexed: 12/28/2022] Open
Abstract
Environmental toxicants affect human health in various ways. Of the thousands of chemicals present in the environment, those with adverse effects on the endocrine system are referred to as endocrine-disrupting chemicals (EDCs). Here, we focused on a subclass of EDCs that impacts the estrogen receptor (ER), a pivotal transcriptional regulator in health and disease. Estrogenic activity of compounds can be measured by many in vitro or cell-based high throughput assays that record various endpoints from large pools of cells, and increasingly at the single-cell level. To simultaneously capture multiple mechanistic ER endpoints in individual cells that are affected by EDCs, we previously developed a sensitive high throughput/high content imaging assay that is based upon a stable cell line harboring a visible multicopy ER responsive transcription unit and expressing a green fluorescent protein (GFP) fusion of ER. High content analysis generates voluminous multiplex data comprised of minable features that describe numerous mechanistic endpoints. In this study, we present a machine learning pipeline for rapid, accurate, and sensitive assessment of the endocrine-disrupting potential of benchmark chemicals based on data generated from high content analysis. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, both linear logistic regression and nonlinear Random Forest classifiers were benchmarked and evaluated for predicting the estrogenic activity of unknown compounds. Furthermore, through feature selection, data visualization, and model discrimination, the most informative features were identified for the classification of ER agonists/antagonists. The results of this data-driven study showed that highly accurate and generalized classification models with a minimum number of features can be constructed without loss of generality, where these machine learning models serve as a means for rapid mechanistic/phenotypic evaluation of the estrogenic potential of many chemicals. Chemical contaminants or toxicants pose environmental and health-related risks for exposure. The ability to rapidly understand their biological impact, specifically on a key modulator of important physiological and pathological states in the human body is essential for diagnosing and avoiding undesirable health outcomes during environmental emergencies. In this study, we use advanced data analytics for creating statistical models that can accurately predict the endocrinological activity of toxic chemicals based on high throughput/high content image analysis data. We focus on a subclass of chemicals that affect the estrogen receptor (ER), which is a pivotal transcriptional regulator in health and disease. The multidimensional imaging data of these benchmark chemicals are used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, we evaluate linear and nonlinear classifiers for predicting the estrogenic activity of unknown compounds and use feature selection, data visualization, and model discrimination methodologies to identify the most informative features for the classification of ER agonists/antagonists.
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Affiliation(s)
- Rajib Mukherjee
- Texas A&M Energy Institute, Texas A&M University, College Station, TX, United States of America
| | - Burcu Beykal
- Texas A&M Energy Institute, Texas A&M University, College Station, TX, United States of America
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, United States of America
| | - Adam T. Szafran
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States of America
| | - Melis Onel
- Texas A&M Energy Institute, Texas A&M University, College Station, TX, United States of America
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, United States of America
| | - Fabio Stossi
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States of America
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States of America
| | - Maureen G. Mancini
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States of America
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States of America
| | - Dillon Lloyd
- Bioinformatics Research Center, Center for Human Health and the Environment, Department of Statistics, North Carolina State University, Raleigh, NC, United States of America
| | - Fred A. Wright
- Bioinformatics Research Center, Center for Human Health and the Environment, Department of Statistics, North Carolina State University, Raleigh, NC, United States of America
| | - Lan Zhou
- Department of Statistics, Texas A&M University, College Station, TX, United States of America
| | - Michael A. Mancini
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States of America
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States of America
- Texas A&M University Institute for Bioscience and Technology, Houston, TX, United States of America
- Pharmacology and Chemical Genomics, Baylor College of Medicine, Houston, TX, United States of America
| | - Efstratios N. Pistikopoulos
- Texas A&M Energy Institute, Texas A&M University, College Station, TX, United States of America
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, United States of America
- * E-mail:
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20
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Chen Z, Liu Y, Wright FA, Chiu WA, Rusyn I. Rapid hazard characterization of environmental chemicals using a compendium of human cell lines from different organs. ALTEX 2020; 37:623-638. [PMID: 32521033 PMCID: PMC7941183 DOI: 10.14573/altex.2002291] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Accepted: 06/08/2020] [Indexed: 02/07/2023]
Abstract
The lack of adequate toxicity data for the vast majority of chemicals in the environment has spurred the development of new approach methodologies (NAMs). This study aimed to develop a practical high-throughput in vitro model for rapidly evaluating potential hazards of chemicals using a small number of human cells. Forty-two compounds were tested using human induced pluripotent stem cell (iPSC)-derived cells (hepatocytes, neurons, cardiomyocytes and endothelial cells), and a primary endothelial cell line. Both functional and cytotoxicity endpoints were evaluated using high-content imaging. Concentration-response was used to derive points-of-departure (POD). PODs were integrated with ToxPi and used as surrogate NAM-based PODs for risk characterization. We found chemical class-specific similarity among the chemicals tested; metal salts exhibited the highest overall bioactivity. We also observed cell type-specific patterns among classes of chemicals, indicating the ability of the proposed in vitro model to recognize effects on different cell types. Compared to available NAM datasets, such as ToxCast/Tox21 and chemical structure-based descriptors, we found that the data from the five-cell-type model was as good or even better in assigning compounds to chemical classes. Additionally, the PODs from this model performed well as a conservative surrogate for regulatory in vivo PODs and were less likely to underestimate in vivo potency and potential risk compared to other NAM-based PODs. In summary, we demonstrate the potential of this in vitro screening model to inform rapid risk-based decision-making through ranking, clustering, and assessment of both hazard and risks of diverse environmental chemicals.
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Affiliation(s)
- Zunwei Chen
- Interdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Yizhong Liu
- Interdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Fred A. Wright
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
- Departments of Statistics and Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Weihsueh A. Chiu
- Interdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Ivan Rusyn
- Interdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
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