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<p>Environmental pollution, particularly in aquatic ecosystems, poses a major global threat. The Ganga River, vital to millions, suffers from severe contamination despite multiple remediation efforts. Bioremediation, especially enzyme-mediated approaches, offers an eco-friendly and efficient alternative to conventional methods. This study utilizes metagenomic reads to identify bioremediating enzymes, their host microorganisms, target pollutants, and enzyme abundance at two Ganga sites: Nawabganj, Kanpur (highly polluted), and Below Farakka Bridge, West Bengal (less polluted). In-silico analysis was conducted using RemeDB for enzyme identification and MG-RAST for microbial abundance. Enzyme abundance was determined by integrating microbial profiles with the RemeDB enzyme database. Key enzymes identified include Phenylacetaldehyde dehydrogenase (plastic), Biphenyl dioxygenase (hydrocarbons), and Catechol 1,2-dioxygenase (dyes). Results indicate higher bioremediating enzyme diversity in the more polluted Nawabganj site. The findings highlight the potential of native enzymes for pollutant degradation and support further exploration for environmental cleanup strategies.</p>

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In Silico Identification of Enzymes Involved in Bioremediation from Metagenomic Sequence of River Ganga

  • 期刊论文

  • 发表日期：

    2025年11月15日

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  • Chandana V,
  • Shashi Bhushan Lal,
  • Dwijesh Chandra Mishra,
  • Anu Sharma,
  • Sanjeev Kumar,
  • Krishna Kumar Chaturvedi,
  • Bijay Kumar Behera

摘要

Environmental pollution, particularly in aquatic ecosystems, poses a major global threat. The Ganga River, vital to millions, suffers from severe contamination despite multiple remediation efforts. Bioremediation, especially enzyme-mediated approaches, offers an eco-friendly and efficient alternative to conventional methods. This study utilizes metagenomic reads to identify bioremediating enzymes, their host microorganisms, target pollutants, and enzyme abundance at two Ganga sites: Nawabganj, Kanpur (highly polluted), and Below Farakka Bridge, West Bengal (less polluted). In-silico analysis was conducted using RemeDB for enzyme identification and MG-RAST for microbial abundance. Enzyme abundance was determined by integrating microbial profiles with the RemeDB enzyme database. Key enzymes identified include Phenylacetaldehyde dehydrogenase (plastic), Biphenyl dioxygenase (hydrocarbons), and Catechol 1,2-dioxygenase (dyes). Results indicate higher bioremediating enzyme diversity in the more polluted Nawabganj site. The findings highlight the potential of native enzymes for pollutant degradation and support further exploration for environmental cleanup strategies.