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Pesticides often feature structurally complex chemical architectures, incorporating halogen groups and multiple aromatic rings. These characteristics confer high chemical stability, rendering many pesticides resistant to natural degradation processes. This resistance poses significant environmental concerns, as persistent pesticide residues can accumulate in ecosystems and affect non-target organisms.Despite the inherent stability of many pesticides, certain microorganisms possess the metabolic...
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Development of Sulfidogenic Sludge from Marine Sediments and Trichloroethylene Reduction in an Upflow Anaerobic Sludge Blanket Reactor
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Decoding Microbial Reductive Dechlorination of 209 Polychlorinated Biphenyl Congeners through Experiment-Aided

Shanquan Wang1,2, Haozheng He1, Shangwei Zhang1,2

  • 1School of Environmental Science and Engineering, Environmental Microbiomics Research Center, Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-Sen University, Guangzhou 510275, The People's Republic of China.

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This study reveals how molecular properties dictate microbial reductive dechlorination of polychlorinated biphenyls (PCBs). An integrated approach accurately predicts PCB congener dechlorination pathways and reactivity, aiding bioremediation strategies.

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dechlorination pathwaysmachine learningpolychlorinated biphenylsquantum chemistryreactivity

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Area of Science:

  • Environmental Science
  • Microbiology
  • Computational Chemistry

Background:

  • Polychlorinated biphenyls (PCBs) are persistent global pollutants with complex structures hindering remediation.
  • Understanding microbial conversion pathways is crucial for effective PCB bioremediation.

Purpose of the Study:

  • To elucidate reductive dechlorination pathways and reactivity for all 209 PCB congeners.
  • To develop predictive models for PCB microbial degradation.
  • To provide mechanistic insights into PCB bioremediation.

Main Methods:

  • High-throughput enzymatic assays and quantum chemical calculations.
  • Machine learning (XGBoost) models incorporating electronic, steric, and physicochemical descriptors.
  • Hirshfeld charge analysis and empirical steric effect data.

Main Results:

  • Achieved 98.3% accuracy in predicting dechlorination pathways across diverse microbial cultures.
  • Identified steric effect-corrected Hirshfeld charge and PCB solubility as key factors controlling dechlorination.
  • Predicted 11 of 12 dioxin-like PCBs are susceptible to microbial reductive dechlorination.

Conclusions:

  • This integrative framework provides the first comprehensive view of microbial PCB dechlorination.
  • Molecular properties fundamentally dictate halogen removal and microbial respiration.
  • Findings offer a roadmap for designing microbiome-based bioremediation strategies for PCBs.