High-Throughput Shotgun Metagenomics of Microbial Footprints Uncovers a Cocktail of Noxious Antibiotic Resistance Genes in the Winam Gulf of Lake Victoria, Kenya
2024

Antibiotic Resistance Genes in Lake Victoria's Winam Gulf

Sample size: 130 publication 10 minutes Evidence: high

Author Information

Author(s): Khatiebi Sandra, Kiprotich Kelvin, Onyando Zedekiah, Mwaura John, Wekesa Clabe, Chi Celestine N., Mulambalah Chrispinus, Okoth Patrick

Primary Institution: Masinde Muliro University of Science and Technology

Hypothesis

The study aims to identify antibiotic resistance genes in the Winam Gulf of Lake Victoria using a shotgun metagenomics approach.

Conclusion

The study found a diverse array of antibiotic-resistant genes in the Winam Gulf, indicating potential risks to human health and aquatic biodiversity.

Supporting Evidence

  • The study identified 21 antibiotic-resistant genes in the sediments and water samples.
  • Proteobacteria was the dominant phylum with the highest abundance of antibiotic resistance genes.
  • Phenotypic resistance was observed against multiple antibiotics including vancomycin and tetracycline.
  • Antibiotic resistance genes were linked to various environmental pollutants in the Gulf.
  • The findings suggest that the Gulf may serve as a reservoir for antibiotic resistance.
  • High-throughput sequencing provided a comprehensive view of the microbial community.
  • KEGG pathway analysis revealed mechanisms of antibiotic resistance.
  • The study highlights the need for monitoring antibiotic resistance in aquatic ecosystems.

Takeaway

Scientists looked at water and sediment from Lake Victoria and found many germs that can resist antibiotics, which could be bad for people and fish.

Methodology

The study used shotgun metagenomics to analyze genomic DNA from water and sediment samples, followed by phenotypic antibiotic resistance assessment using disk diffusion.

Limitations

The study may not account for all environmental factors influencing antibiotic resistance in the Gulf.

Digital Object Identifier (DOI)

10.1155/jotm/7857069

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