Modeling and optimization of chlorpyrifos and glyphosate biodegradation using RSM and ANN: Elucidating their degradation pathways by GC-MS based metabolomics

Muneer Ahmad Malla; Anamika Dubey; Ashwani Kumar; Shweta Yadav; Sheena Kumari

doi:10.1016/j.ecoenv.2023.114628

Modeling and optimization of chlorpyrifos and glyphosate biodegradation using RSM and ANN: Elucidating their degradation pathways by GC-MS based metabolomics

Ecotoxicol Environ Saf. 2023 Mar 1:252:114628. doi: 10.1016/j.ecoenv.2023.114628. Epub 2023 Feb 10.

Authors

Muneer Ahmad Malla¹, Anamika Dubey², Ashwani Kumar³, Shweta Yadav⁴, Sheena Kumari⁵

Affiliations

¹ Department of Zoology, Dr. Harisingh Gour University (A Central University), Sagar 470003, Madhya Pradesh, India; Metagenomics and Secretomics Research Laboratory, Department of Botany, Dr. Harisingh Gour University (A Central University), Sagar 470003, Madhya Pradesh, India.
² Metagenomics and Secretomics Research Laboratory, Department of Botany, Dr. Harisingh Gour University (A Central University), Sagar 470003, Madhya Pradesh, India.
³ Metagenomics and Secretomics Research Laboratory, Department of Botany, Dr. Harisingh Gour University (A Central University), Sagar 470003, Madhya Pradesh, India; Metagenomics and Secretomics Research Laboratory, Department of Botany, University of Allahabad (A Central University), Prayagraj 211002, Uttar Pradesh, India. Electronic address: ashwaniiitd@hotmail.com.
⁴ Department of Zoology, Dr. Harisingh Gour University (A Central University), Sagar 470003, Madhya Pradesh, India.
⁵ Institute for Water and Wastewater Technology, Durban University of Technology, Durban 4001, South Africa.

PMID: 36774796
DOI: 10.1016/j.ecoenv.2023.114628

Abstract

Ongoing and extensive use of pesticides negatively impact the environment and human health. Microbe-based remediation bears importance as it is an eco-friendly and cost-effective technique. The present study investigated chlorpyrifos (CHL) and glyphosate (GLY) degrading potential of Bacillus cereus AKAD 3-1, isolated from the soybean rhizosphere. Optimization and validation of different process variables were carried out by response surface methodology (RSM) and artificial neural network (ANN). Critical parameters which affect the degradation process are initial pesticide concentration, pH, and inoculum size. At optimum conditions, the bacterial strain demonstrated 94.52% and 83.58% removal of chlorpyrifos and glyphosate, respectively. Both Central-composite design (CCD-RSM) and ANN approaches proved to perform well in modeling and optimizing the growth conditions. The optimum ANN-GA model resulted in R² ≥ 0.99 for chlorpyrifos and glyphosate, while in the case of RSM, the obtained R² value was 0.96 and 0.95, respectively. Results indicated that the process variables significantly (p < 0.05) impact chlorpyrifos and glyphosate biodegradation. Moreover, the predicted RSM model had a "lack of fit p-value" of "0.8849" and "0.2502" for chlorpyrifos and glyphosate, respectively. GC-MS analysis revealed that the strain first converted chlorpyrifos into 3,5,6-trichloro pyridin-2-ol & O, O-diethyl O-hydrogen phosphorothiate. Later, these intermediate metabolites were broken and completely mineralized into non-toxic by-products. Similarly, glyphosate was first converted into 2-(methylamino) acetic acid and amino-oxyphosphonic acid, which were further mineralized without any toxic by-products. Taken together, the results of this study clarify the biodegradation pathways and highlights the promising potential of B. cereus AKAD 3-1 in the bioremediation of chlorpyrifos and glyphosate-polluted environments.

Keywords: ANN; Bacillus cereus AKAD 3-1; GC-MS; Pesticide biodegradation; RSM.

MeSH terms

Amino Acids
Biodegradation, Environmental
Chlorpyrifos* / metabolism
Gas Chromatography-Mass Spectrometry
Glyphosate
Neural Networks, Computer
Pesticides* / metabolism

Substances

Amino Acids
Chlorpyrifos
Pesticides