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<p>The production rate of Polyethylene (PE) has increased to a concerning level, necessitating effective strategies to reduce its environmental impacts. This study investigates the role of novel laccase-like multicopper oxidase (LMCO1) in oxidative degradation of PE through a comprehensive approach, including machine learning analysis, recombinant expression, and physio-chemical assays. To this end, the publicly available RNA-seq data related to <i>Rhodococcus opacus</i> R7 cultured with PE was mined using several attribute weighting algorithms to explore the discriminative role of LMCO1 in PE degradation. Further, the recombinant LMCO1 was expressed and evaluated for substantial degrading impact on the PE films. Oxidative degradation of PE samples was evaluated by measuring weight loss rate and assessing water contact angle, confirmed by Fourier transform infrared spectroscopy and scanning electron microscopy analysis. The findings revealed that Cu²⁺ enhanced the activity of LMCO1 in the crude enzyme extract by 490%, with peak activity occurring at pH 8 and 60&#xa0;°C (optimal temperature). The PE degradation experiments over 72&#xa0;h indicated that a 14.28% weight loss rate was in LDPE as well as decreasing water contact angle to 76.73°. Fourier transform infrared spectroscopy analysis revealed the existence of various polar functional groups on the PE surface, including carbonyl, carboxyl, and hydroxyl groups. Significant damage to the PE surface, including cracks, pitting, and roughness, as well as internal aspects such as structural weakening and material degradation was identified through scanning electron microscopy. This study demonstrated, for the first time, the high potential of LMCO1 in PE films and particles’ oxidative degradation efficiently without the need for prior treatment and presented LMCO1 as a promising environmentally friendly catalyst through machine learning analysis and heterologous expression.</p>

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Heterologous expression and characterization of Rhodococcus opacus R7 laccase-like multicopper oxidase (LMCO1) enzyme for polyethylene biodegradation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

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  • Mahboobeh Pishan,
  • Sima Sazegari,
  • Ali Niazi,
  • Marjan Majdinasab,
  • Zahra Zinati,
  • Mohammad Hadi Eskandari

摘要

The production rate of Polyethylene (PE) has increased to a concerning level, necessitating effective strategies to reduce its environmental impacts. This study investigates the role of novel laccase-like multicopper oxidase (LMCO1) in oxidative degradation of PE through a comprehensive approach, including machine learning analysis, recombinant expression, and physio-chemical assays. To this end, the publicly available RNA-seq data related to Rhodococcus opacus R7 cultured with PE was mined using several attribute weighting algorithms to explore the discriminative role of LMCO1 in PE degradation. Further, the recombinant LMCO1 was expressed and evaluated for substantial degrading impact on the PE films. Oxidative degradation of PE samples was evaluated by measuring weight loss rate and assessing water contact angle, confirmed by Fourier transform infrared spectroscopy and scanning electron microscopy analysis. The findings revealed that Cu²⁺ enhanced the activity of LMCO1 in the crude enzyme extract by 490%, with peak activity occurring at pH 8 and 60 °C (optimal temperature). The PE degradation experiments over 72 h indicated that a 14.28% weight loss rate was in LDPE as well as decreasing water contact angle to 76.73°. Fourier transform infrared spectroscopy analysis revealed the existence of various polar functional groups on the PE surface, including carbonyl, carboxyl, and hydroxyl groups. Significant damage to the PE surface, including cracks, pitting, and roughness, as well as internal aspects such as structural weakening and material degradation was identified through scanning electron microscopy. This study demonstrated, for the first time, the high potential of LMCO1 in PE films and particles’ oxidative degradation efficiently without the need for prior treatment and presented LMCO1 as a promising environmentally friendly catalyst through machine learning analysis and heterologous expression.