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Jiang S, Liang Y, Shi S, Wu C, Shi Z. Improving predictions and understanding of primary and ultimate biodegradation rates with machine learning models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 904:166623. [PMID: 37652371 DOI: 10.1016/j.scitotenv.2023.166623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 08/08/2023] [Accepted: 08/25/2023] [Indexed: 09/02/2023]
Abstract
This study aimed to develop machine learning based quantitative structure biodegradability relationship (QSBR) models for predicting primary and ultimate biodegradation rates of organic chemicals, which are essential parameters for environmental risk assessment. For this purpose, experimental primary and ultimate biodegradation rates of high consistency were compiled for 173 organic compounds. A significant number of descriptors were calculated with a collection of quantum/computational chemistry software and tools to achieve comprehensive representation and interpretability. Following a pre-screening process, multiple QSBR models were developed for both primary and ultimate endpoints using three algorithms: extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR). Furthermore, a unified QSBR model was constructed using the knowledge transfer technique and XGBoost. Results demonstrated that all QSBR models developed in this study had good performance. Particularly, SVM models exhibited high level of goodness of fit (coefficient of determination on the training set of 0.973 for primary and 0.980 for ultimate), robustness (leave-one-out cross-validated coefficient of 0.953 for primary and 0.967 for ultimate), and external predictive ability (external explained variance of 0.947 for primary and 0.958 for ultimate). The knowledge transfer technique enhanced model performance by learning from properties of two biodegradation endpoints. Williams plots were used to visualize the application domains of the models. Through SHapley Additive exPlanations (SHAP) analysis, this study identified key features affecting biodegradation rates. Notably, MDEO-12, APC2D1_C_O, and other features contributed to primary biodegradation, while AATS0v, AATS2v, and others inhibited it. For ultimate biodegradation, features like No. of Rotatable Bonds, APC2D1_C_O, and minHBa were contributors, while C1SP3, Halogen Ratio, GGI4, and others hindered the process. Also, the study quantified the contributions of each feature in predictions for individual chemicals. This research provides valuable tools for predicting both primary and ultimate biodegradation rates while offering insights into the mechanisms.
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Affiliation(s)
- Shan Jiang
- School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China
| | - Yuzhen Liang
- School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China.
| | - Songlin Shi
- School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China
| | - Chunya Wu
- School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China
| | - Zhenqing Shi
- School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China
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Davenport R, Curtis‐Jackson P, Dalkmann P, Davies J, Fenner K, Hand L, McDonough K, Ott A, Ortega‐Calvo JJ, Parsons JR, Schäffer A, Sweetlove C, Trapp S, Wang N, Redman A. Scientific concepts and methods for moving persistence assessments into the 21st century. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2022; 18:1454-1487. [PMID: 34989108 PMCID: PMC9790601 DOI: 10.1002/ieam.4575] [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] [Received: 02/11/2021] [Revised: 09/29/2021] [Accepted: 12/06/2021] [Indexed: 05/19/2023]
Abstract
The evaluation of a chemical substance's persistence is key to understanding its environmental fate, exposure concentration, and, ultimately, environmental risk. Traditional biodegradation test methods were developed many years ago for soluble, nonvolatile, single-constituent test substances, which do not represent the wide range of manufactured chemical substances. In addition, the Organisation for Economic Co-operation and Development (OECD) screening and simulation test methods do not fully reflect the environmental conditions into which substances are released and, therefore, estimates of chemical degradation half-lives can be very uncertain and may misrepresent real environmental processes. In this paper, we address the challenges and limitations facing current test methods and the scientific advances that are helping to both understand and provide solutions to them. Some of these advancements include the following: (1) robust methods that provide a deeper understanding of microbial composition, diversity, and abundance to ensure consistency and/or interpret variability between tests; (2) benchmarking tools and reference substances that aid in persistence evaluations through comparison against substances with well-quantified degradation profiles; (3) analytical methods that allow quantification for parent and metabolites at environmentally relevant concentrations, and inform on test substance bioavailability, biochemical pathways, rates of primary versus overall degradation, and rates of metabolite formation and decay; (4) modeling tools that predict the likelihood of microbial biotransformation, as well as biochemical pathways; and (5) modeling approaches that allow for derivation of more generally applicable biotransformation rate constants, by accounting for physical and/or chemical processes and test system design when evaluating test data. We also identify that, while such advancements could improve the certainty and accuracy of persistence assessments, the mechanisms and processes by which they are translated into regulatory practice and development of new OECD test guidelines need improving and accelerating. Where uncertainty remains, holistic weight of evidence approaches may be required to accurately assess the persistence of chemicals. Integr Environ Assess Manag 2022;18:1454-1487. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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Affiliation(s)
| | | | - Philipp Dalkmann
- Bayer AG, Crop Science Division, Environmental SafetyMonheimGermany
| | | | - Kathrin Fenner
- Eawag, Swiss Federal Institute of Aquatic Science and TechnologyDübendorfSwitzerland
- Department of ChemistryUniversity of ZürichZürichSwitzerland
| | - Laurence Hand
- Syngenta, Product Safety, Jealott's Hill International Research CentreBracknellUK
| | | | - Amelie Ott
- School of EngineeringNewcastle UniversityNewcastle upon TyneUK
- European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC)BrusselsBelgium
| | - Jose Julio Ortega‐Calvo
- Instituto de Recursos Naturales y Agrobiología de SevillaConsejo Superior de Investigaciones CientíficasSevillaSpain
| | - John R. Parsons
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
| | - Andreas Schäffer
- RWTH Aachen University, Institute for Environmental ResearchAachenGermany
| | - Cyril Sweetlove
- L'Oréal Research & InnovationEnvironmental Research DepartmentAulnay‐sous‐BoisFrance
| | - Stefan Trapp
- Department of Environmental EngineeringTechnical University of DenmarkBygningstorvetLyngbyDenmark
| | - Neil Wang
- Total Marketing & ServicesParis la DéfenseFrance
| | - Aaron Redman
- ExxonMobil Petroleum and ChemicalMachelenBelgium
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Huang K, Zhang H. Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:12755-12764. [PMID: 35973069 DOI: 10.1021/acs.est.2c01764] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Machine learning (ML) is viewed as a promising tool for the prediction of aerobic biodegradation, one of the most important elimination pathways of organic chemicals from the environment. However, available models only have small datasets (<3200 records), make binary classification predictions, evaluate ready biodegradability, and do not incorporate experimental conditions (e.g., system setup and reaction time). This study addressed all these limitations by first compiling a large database of 12,750 records, considering both ready and inherent biodegradation under different conditions, and then developing regression and classification models using different chemical representations and ML algorithms. The best regression model (R2 = 0.54 and root mean square error of 0.25) and classification model (the prediction accuracy from 85.1%) achieved very good performance. The model interpretation indicated that the models correctly captured the effects of chemical substructures, following the order of C═O > O═C-O > OH > CH3 > halogen > branching > N > 6-member ring. The consideration of chemical speciation based on pKa and α notations did not affect the regression model performance but significantly improved the classification model performance (the accuracy increased to 87.6%). The models also showed large applicability domains and provided reasonable predictions for more than 98% of over 850,000 environmentally relevant chemicals in the Distributed Structure-Searchable Toxicity database. These robust, trustable models were finally made widely accessible through two free online predictors with graphical user interface.
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Affiliation(s)
- Kuan Huang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Huichun Zhang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
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Bhaduri D, Sihi D, Bhowmik A, Verma BC, Munda S, Dari B. A review on effective soil health bio-indicators for ecosystem restoration and sustainability. Front Microbiol 2022; 13:938481. [PMID: 36060788 PMCID: PMC9428492 DOI: 10.3389/fmicb.2022.938481] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 06/29/2022] [Indexed: 11/30/2022] Open
Abstract
Preventing degradation, facilitating restoration, and maintaining soil health is fundamental for achieving ecosystem stability and resilience. A healthy soil ecosystem is supported by favorable components in the soil that promote biological productivity and provide ecosystem services. Bio-indicators of soil health are measurable properties that define the biotic components in soil and could potentially be used as a metric in determining soil functionality over a wide range of ecological conditions. However, it has been a challenge to determine effective bio-indicators of soil health due to its temporal and spatial resolutions at ecosystem levels. The objective of this review is to compile a set of effective bio-indicators for developing a better understanding of ecosystem restoration capabilities. It addresses a set of potential bio-indicators including microbial biomass, respiration, enzymatic activity, molecular gene markers, microbial metabolic substances, and microbial community analysis that have been responsive to a wide range of ecosystem functions in agricultural soils, mine deposited soil, heavy metal contaminated soil, desert soil, radioactive polluted soil, pesticide polluted soil, and wetland soils. The importance of ecosystem restoration in the United Nations Sustainable Development Goals was also discussed. This review identifies key management strategies that can help in ecosystem restoration and maintain ecosystem stability.
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Affiliation(s)
- Debarati Bhaduri
- ICAR-National Rice Research Institute, Cuttack, India
- *Correspondence: Debarati Bhaduri
| | - Debjani Sihi
- Department of Environmental Sciences, Emory University, Atlanta, GA, United States
| | - Arnab Bhowmik
- Department of Natural Resources and Environmental Design, North Carolina Agricultural and Technical State University, Greensboro, NC, United States
- Arnab Bhowmik
| | - Bibhash C. Verma
- Central Rainfed Upland Rice Research Station (ICAR-NRRI), Hazaribagh, India
| | | | - Biswanath Dari
- Agriculture and Natural Resources, Cooperative Extension at North Carolina Agricultural and Technical State University, Greensboro, NC, United States
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Rich SL, Zumstein MT, Helbling DE. Identifying Functional Groups that Determine Rates of Micropollutant Biotransformations Performed by Wastewater Microbial Communities. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:984-994. [PMID: 34939795 DOI: 10.1021/acs.est.1c06429] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The goal of this research was to identify functional groups that determine rates of micropollutant (MP) biotransformations performed by wastewater microbial communities. To meet this goal, we performed a series of incubation experiments seeded with four independent wastewater microbial communities and spiked them with a mixture of 40 structurally diverse MPs. We collected samples over time and used high-resolution mass spectrometry to estimate biotransformation rate constants for each MP in each experiment and to propose structures of 46 biotransformation products. We then developed random forest models to classify the biotransformation rate constants based on the presence of specific functional groups or observed biotransformations. We extracted classification importance metrics from each random forest model and compared them across wastewater microbial communities. Our analysis revealed 30 functional groups that we define as either biotransformation promoters, biotransformation inhibitors, structural features that can be biotransformed based on uncharacterized features of the wastewater microbial community, or structural features that are not rate-determining. Our experimental data and analysis provide novel insights into MP biotransformations that can be used to more accurately predict MP biotransformations or to inform the design of new chemical products that may be more readily biodegradable during wastewater treatment.
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Affiliation(s)
- Stephanie L Rich
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Michael T Zumstein
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14853, United States
- Division of Environmental Geosciences, Centre for Microbiology and Environmental Systems Science, University of Vienna, Wien 1090 Austria
| | - Damian E Helbling
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14853, United States
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Ahmad S, Cui D, Zhong G, Liu J. Microbial Technologies Employed for Biodegradation of Neonicotinoids in the Agroecosystem. Front Microbiol 2021; 12:759439. [PMID: 34925268 PMCID: PMC8675359 DOI: 10.3389/fmicb.2021.759439] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 10/25/2021] [Indexed: 11/13/2022] Open
Abstract
Neonicotinoids are synthetic pesticides widely used for the control of various pests in agriculture throughout the world. They mainly attack the nicotinic acetylcholine receptors, generate nervous stimulation, receptor clot, paralysis and finally cause death. They are low volatile, highly soluble and have a long half-life in soil and water. Due to their extensive use, the environmental residues have immensely increased in the last two decades and caused many hazardous effects on non-target organisms, including humans. Hence, for the protection of the environment and diversity of living organism's the degradation of neonicotinoids has received widespread attention. Compared to the other methods, biological methods are considered cost-effective, eco-friendly and most efficient. In particular, the use of microbial species makes the degradation of xenobiotics more accessible fast and active due to their smaller size. Since this degradation also converts xenobiotics into less toxic substances, the various metabolic pathways for the microbial degradation of neonicotinoids have been systematically discussed. Additionally, different enzymes, genes, plasmids and proteins are also investigated here. At last, this review highlights the implementation of innovative tools, databases, multi-omics strategies and immobilization techniques of microbial cells to detect and degrade neonicotinoids in the environment.
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Affiliation(s)
- Sajjad Ahmad
- Key Laboratory of Integrated Pest Management of Crop in South China, Ministry of Agriculture and Rural Affairs, Key Laboratory of Natural Pesticide and Chemical Biology, Ministry of Education, South China Agricultural University, Guangzhou, China
| | - Dongming Cui
- Key Laboratory of Integrated Pest Management of Crop in South China, Ministry of Agriculture and Rural Affairs, Key Laboratory of Natural Pesticide and Chemical Biology, Ministry of Education, South China Agricultural University, Guangzhou, China
| | - Guohua Zhong
- Key Laboratory of Integrated Pest Management of Crop in South China, Ministry of Agriculture and Rural Affairs, Key Laboratory of Natural Pesticide and Chemical Biology, Ministry of Education, South China Agricultural University, Guangzhou, China
| | - Jie Liu
- Key Laboratory of Integrated Pest Management of Crop in South China, Ministry of Agriculture and Rural Affairs, Key Laboratory of Natural Pesticide and Chemical Biology, Ministry of Education, South China Agricultural University, Guangzhou, China
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7
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Fenner K, Elsner M, Lueders T, McLachlan MS, Wackett LP, Zimmermann M, Drewes JE. Methodological Advances to Study Contaminant Biotransformation: New Prospects for Understanding and Reducing Environmental Persistence? ACS ES&T WATER 2021; 1:1541-1554. [PMID: 34278380 PMCID: PMC8276273 DOI: 10.1021/acsestwater.1c00025] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 06/11/2021] [Accepted: 06/11/2021] [Indexed: 05/14/2023]
Abstract
Complex microbial communities in environmental systems play a key role in the detoxification of chemical contaminants by transforming them into less active metabolites or by complete mineralization. Biotransformation, i.e., transformation by microbes, is well understood for a number of priority pollutants, but a similar level of understanding is lacking for many emerging contaminants encountered at low concentrations and in complex mixtures across natural and engineered systems. Any advanced approaches aiming to reduce environmental exposure to such contaminants (e.g., novel engineered biological water treatment systems, design of readily degradable chemicals, or improved regulatory assessment strategies to determine contaminant persistence a priori) will depend on understanding the causal links among contaminant removal, the key driving agents of biotransformation at low concentrations (i.e., relevant microbes and their metabolic activities), and how their presence and activity depend on environmental conditions. In this Perspective, we present the current understanding and recent methodological advances that can help to identify such links, even in complex environmental microbiomes and for contaminants present at low concentrations in complex chemical mixtures. We discuss the ensuing insights into contaminant biotransformation across varying environments and conditions and ask how much closer we have come to designing improved approaches to reducing environmental exposure to contaminants.
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Affiliation(s)
- Kathrin Fenner
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092 Zürich, Switzerland
- Department of Chemistry, University of Zürich, 8057 Zürich, Switzerland
| | - Martin Elsner
- Chair of Analytical Chemistry and Water Chemistry, Technical University of Munich, 85748 Garching, Germany
| | - Tillmann Lueders
- Chair of Ecological Microbiology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, 95448 Bayreuth, Germany
| | - Michael S McLachlan
- Department of Environmental Science (ACES), Stockholm University, 106 91 Stockholm, Sweden
| | - Lawrence P Wackett
- Biotechnology Institute, University of Minnesota, Saint Paul, Minnesota 55108, United States
| | - Michael Zimmermann
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Jörg E Drewes
- Chair of Urban Water Systems Engineering, Technical University of Munich, 85748 Garching, Germany
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Rios‐Miguel AB, Jetten MSM, Welte CU. Effect of concentration and hydraulic reaction time on the removal of pharmaceutical compounds in a membrane bioreactor inoculated with activated sludge. Microb Biotechnol 2021; 14:1707-1721. [PMID: 34132479 PMCID: PMC8313272 DOI: 10.1111/1751-7915.13837] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 04/16/2021] [Accepted: 05/10/2021] [Indexed: 01/04/2023] Open
Abstract
Pharmaceuticals are often not fully removed in wastewater treatment plants (WWTPs) and are thus being detected at trace levels in water bodies all over the world posing a risk to numerous organisms. These organic micropollutants (OMPs) reach WWTPs at concentrations sometimes too low to serve as growth substrate for microorganisms; thus, co-metabolism is thought to be the main conversion mechanism. In this study, the microbial removal of six pharmaceuticals was investigated in a membrane bioreactor at increasing concentrations (4-800 nM) of the compounds and using three different hydraulic retention times (HRT; 1, 3.5 and 5 days). The bioreactor was inoculated with activated sludge from a municipal WWTP and fed with ammonium, acetate and methanol as main growth substrates to mimic co-metabolism. Each pharmaceutical had a different average removal efficiency: acetaminophen (100%) > fluoxetine (50%) > metoprolol (25%) > diclofenac (20%) > metformin (15%) > carbamazepine (10%). Higher pharmaceutical influent concentrations proportionally increased the removal rate of each compound, but surprisingly not the removal percentage. Furthermore, only metformin removal improved to 80-100% when HRT or biomass concentration was increased. Microbial community changes were followed with 16S rRNA gene amplicon sequencing in response to the increment of pharmaceutical concentration: Nitrospirae and Planctomycetes 16S rRNA relative gene abundance decreased, whereas Acidobacteria and Bacteroidetes increased. Remarkably, the Dokdonella genus, previously implicated in acetaminophen metabolism, showed a 30-fold increase in abundance at the highest concentration of pharmaceuticals applied. Taken together, these results suggest that the incomplete removal of most pharmaceutical compounds in WWTPs is dependent on neither concentration nor reaction time. Accordingly, we propose a chemical equilibrium or a growth substrate limitation as the responsible mechanisms of the incomplete removal. Finally, Dokdonella could be the main acetaminophen degrader under activated sludge conditions, and non-antibiotic pharmaceuticals might still be toxic to relevant WWTP bacteria.
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Affiliation(s)
- Ana B. Rios‐Miguel
- Department of MicrobiologyInstitute for Water and Wetland ResearchRadboud UniversityHeyendaalseweg 135Nijmegen6525 AJThe Netherlands
| | - Mike S. M. Jetten
- Department of MicrobiologyInstitute for Water and Wetland ResearchRadboud UniversityHeyendaalseweg 135Nijmegen6525 AJThe Netherlands
- Soehngen Institute of Anaerobic MicrobiologyRadboud UniversityHeyendaalseweg 135Nijmegen6525 AJThe Netherlands
| | - Cornelia U. Welte
- Department of MicrobiologyInstitute for Water and Wetland ResearchRadboud UniversityHeyendaalseweg 135Nijmegen6525 AJThe Netherlands
- Soehngen Institute of Anaerobic MicrobiologyRadboud UniversityHeyendaalseweg 135Nijmegen6525 AJThe Netherlands
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9
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Singh AK, Bilal M, Iqbal HMN, Raj A. Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 770:144561. [PMID: 33736422 DOI: 10.1016/j.scitotenv.2020.144561] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 12/13/2020] [Accepted: 12/13/2020] [Indexed: 02/05/2023]
Abstract
The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.
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Affiliation(s)
- Anil Kumar Singh
- Environmental Microbiology Laboratory, Environmental Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Muhammad Bilal
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian 223003, China
| | - Hafiz M N Iqbal
- Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey 64849, Mexico.
| | - Abhay Raj
- Environmental Microbiology Laboratory, Environmental Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
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10
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Tang W, Li Y, Yu Y, Wang Z, Xu T, Chen J, Lin J, Li X. Development of models predicting biodegradation rate rating with multiple linear regression and support vector machine algorithms. CHEMOSPHERE 2020; 253:126666. [PMID: 32289603 DOI: 10.1016/j.chemosphere.2020.126666] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 03/27/2020] [Accepted: 03/30/2020] [Indexed: 06/11/2023]
Abstract
Biodegradation is a significant process for removing organic chemicals from water, soil and sediment environments, and therefore biodegradability is critical to evaluate the environmental persistence of organic chemicals. In this study, based on a dataset with 171 compounds, four quantitative structure-activity relationship (QSAR) models were developed for predicting primary and ultimate biodegradation rate rating with multiple linear regression (MLR) and support vector machine (SVM) algorithms. Two MLR models were built with a dataset with carbon atom number ≤9, and two SVM models were built with a dataset with carbon atom number >9. In the MLR models, nArX (number of X on aromatic ring) is the most important descriptor governing primary and ultimate biodegradation of organic chemicals. For the SVM models, determination coefficient (R2) values, cross-validated coefficients (Q2LOO) and external validation coefficient (Q2ext) values are over 0.9, indicating the SVM models have satisfactory goodness-of-fit, robustness and external predictive abilities. The applicability domains of these models were visualized by the Williams plot. The developed models can be used as effective tools to predict biodegradability of organic chemicals.
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Affiliation(s)
- Weihao Tang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Yanying Li
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Yang Yu
- Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment (MEE), Beijing, 100029, China
| | - Zhongyu Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Tong Xu
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Jun Lin
- Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment (MEE), Beijing, 100029, China
| | - Xuehua Li
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China.
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11
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Fenner K, Screpanti C, Renold P, Rouchdi M, Vogler B, Rich S. Comparison of Small Molecule Biotransformation Half-Lives between Activated Sludge and Soil: Opportunities for Read-Across? ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:3148-3158. [PMID: 32062976 DOI: 10.1021/acs.est.9b05104] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Compartment-specific degradation half-lives are essential pieces of information in the regulatory risk assessment of synthetic chemicals. However, their measurement according to regulatory testing guidelines is laborious and costly. Despite the obvious ecological and economic benefits of knowing environmental degradability as early as possible, its consideration in the early phases of rational chemical design is therefore challenging. Here, we explore the possibility to use half-lives determined in highly time- and work-efficient biotransformation experiments with activated sludge and mixtures of chemicals to predict soil half-lives from regulatory simulation studies. We experimentally determined half-lives for 52 structurally diverse agrochemical active ingredients in batch reactors with three concentrations of the same activated sludge. We then developed bi- and multivariate models for predicting half-lives in soil by regressing the experimentally determined half-lives in activated sludge against average soil half-lives of the same chemicals extracted from regulatory data. The models differed in how we accounted for sorption-related bioavailability differences in soil and activated sludge. The best-performing models exhibited good coefficients of determination (R2 of around 0.8) and low average errors (<factor of 3 in half-life predictions) and were robust in cross-validation. From a practical perspective, these results suggest that it may indeed be possible to read across from half-lives determined in highly efficient biotransformation experiments in activated sludge to soil half-lives, which are obtained from much more work- and resource-intense regulatory studies, and that these predictions are clearly superior to predictions based on the output of BIOWIN, a publicly available quantitative structure-biodegradation relationship (QSBR) model. From a theoretical perspective, these results suggest that soil and activated sludge microbial communities, although certainly different in terms of taxonomic composition, may be functionally similar with respect to the enzymatic transformation of environmentally relevant concentrations of a diverse range of chemical compounds.
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Affiliation(s)
- Kathrin Fenner
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092 Zürich, Switzerland
- Department of Chemistry, University of Zürich, 8057 Zürich, Switzerland
| | - Claudio Screpanti
- Chemical Research, Syngenta Crop Protection AG, Schaffhauserstrasse 101, CH-4332 Stein, Switzerland
| | - Peter Renold
- Chemical Research, Syngenta Crop Protection AG, Schaffhauserstrasse 101, CH-4332 Stein, Switzerland
| | - Marwa Rouchdi
- Chemical Research, Syngenta Crop Protection AG, Schaffhauserstrasse 101, CH-4332 Stein, Switzerland
| | - Bernadette Vogler
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
| | - Stephanie Rich
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
- Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
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Nolte TM, Chen G, van Schayk CS, Pinto-Gil K, Hendriks AJ, Peijnenburg WJGM, Ragas AMJ. Disentanglement of the chemical, physical, and biological processes aids the development of quantitative structure-biodegradation relationships for aerobic wastewater treatment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 708:133863. [PMID: 31771845 DOI: 10.1016/j.scitotenv.2019.133863] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 07/16/2019] [Accepted: 08/08/2019] [Indexed: 04/15/2023]
Abstract
Attenuation of organic compounds in sewage treatment plants (STPs) is affected by a complex interplay between chemical (e.g. ionization, hydrolysis), physical (e.g. sorption, volatilization), and biological (e.g. biodegradation, microbial acclimation) processes. These effects should be accounted for individually, in order to develop predictive cheminformatics tools for STPs. Using measured data from 70 STPs in the Netherlands for 69 chemicals (pharmaceuticals, herbicides, etc.), we highlighted the influences of 1) chemical ionization, 2) sorption to sludge, and 3) acclimation of the microbial consortia on the primary removal of chemicals. We used semi-empirical corrections for each of these influences to deduce biodegradation rate constants upon which quantitative structure-biodegradation relationships (QSBRs) were developed. As shown by a global QSBR, biodegradation in STPs generally relates to structural complexity, size, energetics, and charge distribution. Statistics of the global QSBR were reasonable, being R2training=0.69 (training set of 51 compounds) and R2validation=0.50 (validation set of 18 compounds). Class-specific QSBRs utilized electronic properties potentially relating to rate-limiting enzymatic steps. For class-specific QSBRs, values of R2 of in between 0.7 and 0.8 were obtained. With caution, environmental risk assessment methodologies may apply these models to estimate biodegradation rates for 'data-poor' compounds. The approach also highlights 'meta data' on STP operational parameters needed to develop QSBRs of better predictability in the future.
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Affiliation(s)
- Tom M Nolte
- Department of Environmental Science, Institute for Water and Wetland Research, Radboud University, PO Box 9010, 6500 GL Nijmegen, the Netherlands; Eidgenossische Technische Hochschule (ETH) Zurich, Laboratory of Inorganic Chemistry, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland.
| | - Guangchao Chen
- Department of Environmental Science, Institute for Water and Wetland Research, Radboud University, PO Box 9010, 6500 GL Nijmegen, the Netherlands; Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, the Netherlands
| | - Coen S van Schayk
- Department of Environmental Science, Institute for Water and Wetland Research, Radboud University, PO Box 9010, 6500 GL Nijmegen, the Netherlands
| | - Kevin Pinto-Gil
- Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, Dr. Aiguader 88, 08003 Barcelona, Spain
| | - A Jan Hendriks
- Department of Environmental Science, Institute for Water and Wetland Research, Radboud University, PO Box 9010, 6500 GL Nijmegen, the Netherlands
| | - Willie J G M Peijnenburg
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, the Netherlands; National Institute of Public Health and the Environment, PO Box 1, 3720 BA Bilthoven, the Netherlands
| | - Ad M J Ragas
- Department of Environmental Science, Institute for Water and Wetland Research, Radboud University, PO Box 9010, 6500 GL Nijmegen, the Netherlands; Department of Science, Faculty of Management, Science & Technology, Open University, Valkenburgerweg 177, 6419 AT Heerlen, the Netherlands
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Jaiswal S, Singh DK, Shukla P. Gene Editing and Systems Biology Tools for Pesticide Bioremediation: A Review. Front Microbiol 2019; 10:87. [PMID: 30853940 PMCID: PMC6396717 DOI: 10.3389/fmicb.2019.00087] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2018] [Accepted: 01/16/2019] [Indexed: 01/15/2023] Open
Abstract
Bioremediation is the degradation potential of microorganisms to dissimilate the complex chemical compounds from the surrounding environment. The genetics and biochemistry of biodegradation processes in datasets opened the way of systems biology. Systemic biology aid the study of interacting parts involved in the system. The significant keys of system biology are biodegradation network, computational biology, and omics approaches. Biodegradation network consists of all the databases and datasets which aid in assisting the degradation and deterioration potential of microorganisms for bioremediation processes. This review deciphers the bio-degradation network, i.e., the databases and datasets (UM-BBD, PAN, PTID, etc.) aiding in assisting the degradation and deterioration potential of microorganisms for bioremediation processes, computational biology and multi omics approaches like metagenomics, genomics, transcriptomics, proteomics, and metabolomics for the efficient functional gene mining and their validation for bioremediation experiments. Besides, the present review also describes the gene editing tools like CRISPR Cas, TALEN, and ZFNs which can possibly make design microbe with functional gene of interest for degradation of particular recalcitrant for improved bioremediation.
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Affiliation(s)
- Shweta Jaiswal
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, India
| | - Dileep Kumar Singh
- Soil Microbial Ecology and Environmental Toxicology Laboratory, Department of Zoology, University of Delhi, New Delhi, India
| | - Pratyoosh Shukla
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, India
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