Assessment of antibiotic resistance genes in soils polluted by chemical and technogenic ways with poly-aromatic hydrocarbons and heavy metals


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Wong M. H., Minkina T., Vasilchenko N., Sushkova S., Delegan Y., Ranjan A., ...More

ENVIRONMENTAL RESEARCH, vol.252, 2024 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 252
  • Publication Date: 2024
  • Doi Number: 10.1016/j.envres.2024.118949
  • Journal Name: ENVIRONMENTAL RESEARCH
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Chemical Abstracts Core, Communication Abstracts, Computer & Applied Sciences, EMBASE, Environment Index, Geobase, Greenfile, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

Anthropogenic activities are leaving lots of chemical footprints on the soil. It alters the physiochemical characteristics of the soil thereby modifying the natural soil microbiome. The prevalence of antimicrobial-resistance microbes in polluted soil has gained attention due to its obvious public health risks. This study focused on assessing the prevalence and distribution of antibiotic-resistance genes in polluted soil ecosystems impacted by industrial enterprises in southern Russia. Metagenomic analysis was conducted on soil samples collected from polluted sites using various approaches, and the prevalence of antibiotic-resistance genes was investigated. The results revealed that efflux-encoding pump sequences were the most widely represented group of genes, while genes whose products replaced antibiotic targets were less represented. The level of soil contamination increased, and there was an increase in the total number of antibiotic-resistance genes in proteobacteria, but a decrease in actinobacteria. The study proposed an optimal mechanism for processing metagenomic data in polluted soil ecosystems, which involves mapping raw reads by the KMA method, followed by a detailed study of specific genes. The study's conclusions provide valuable insights into the prevalence and distribution of antibioticresistance genes in polluted soils and have been illustrated in heat maps.