Unveiling the Microbiome Landscape: A Metagenomic Study of Bacterial Diversity, Antibiotic Resistance, and Virulence Factors in the Sediments of the River Ganga, India.
Ajaya Kumar RoutPartha Sarathi TripathySangita DixitDibyajyoti Uttameswar BeheraBhaskar BeheraBasanta Kumar DasBijay Kumar BeheraPublished in: Antibiotics (Basel, Switzerland) (2023)
The global rise in antibiotic resistance, fueled by indiscriminate antibiotic usage in medicine, aquaculture, agriculture, and the food industry, presents a significant public health challenge. Urban wastewater and sewage treatment plants have become key sources of antibiotic resistance proliferation. The present study focuses on the river Ganges in India, which is heavily impacted by human activities and serves as a potential hotspot for the spread of antibiotic resistance. We conducted a metagenomic analysis of sediment samples from six distinct locations along the river to assess the prevalence and diversity of antibiotic resistance genes (ARGs) within the microbial ecosystem. The metagenomic analysis revealed the predominance of Proteobacteria across regions of the river Ganges. The antimicrobial resistance (AMR) genes and virulence factors were determined by various databases. In addition to this, KEGG and COG analysis revealed important pathways related to AMR. The outcomes highlight noticeable regional differences in the prevalence of AMR genes. The findings suggest that enhancing health and sanitation infrastructure could play a crucial role in mitigating the global impact of AMR. This research contributes vital insights into the environmental aspects of antibiotic resistance, highlighting the importance of targeted public health interventions in the fight against AMR.
Keyphrases
- antibiotic resistance genes
- antimicrobial resistance
- public health
- microbial community
- wastewater treatment
- anaerobic digestion
- human health
- water quality
- heavy metals
- escherichia coli
- climate change
- healthcare
- drinking water
- single cell
- pseudomonas aeruginosa
- risk factors
- genome wide
- endothelial cells
- signaling pathway
- mental health
- risk assessment
- type diabetes
- machine learning
- cancer therapy
- cystic fibrosis
- dna methylation
- drug delivery
- insulin resistance
- organic matter
- artificial intelligence
- adipose tissue
- genome wide analysis