Literature Review: Genomic Profiling for Monitoring Antimicrobial Resistance in Environmental Wastewater

Title

Literature Review: Genomic Profiling for Monitoring Antimicrobial Resistance in Environmental Wastewater

Author

1. Prakash Chheatry, Student, Nagaland University, India
2. Manimala P, Professor, Nagaland University, India

Abstract

Both human and environmental health are seriously jeopardised by the rise of antimicrobial resistance (AMR).  The monitoring of antimicrobial resistance (AMR) in populations has become increasingly dependent on wastewater-based surveillance.  One promising comprehensive high-throughput approach to identifying and measuring resistance genes is metagenomics, which entails sequencing and analysing genetic material from environmental samples.  Nevertheless, its efficacy is constrained by methodological obstacles such inconsistent bioinformatics, low-abundance gene detection, and environmental heterogeneity.  Findings from important research on AMR monitoring using metagenomics in wastewater are synthesised in this review.  It emphasises new developments such as coupled qPCR-metagenomic methods for higher sensitivity and specificity, better bioinformatics pipelines, and synthetic DNA standards.  Machine learning for pattern identification and worldwide surveillance networks for coordinated monitoring are two options proposed in the study to fill up the gaps in standardisation and data interpretation.  In order to make metagenomic AMR monitoring in wastewater settings more accurate and scalable, it is essential to establish uniform procedures and to increase the worldwide surveillance infrastructure.

Keywords

Conclusion

Metagenomics holds significant promise for wastewater-based AMR surveillance, providing a comprehensive and nontargeted approach to monitoring resistance genes. However, challenges related to low-abundance gene detection, bioinformatics consistency, and environmental variability remain unresolved. Recent advances, such as the use of synthetic reference standards and machine learning-based data analysis, could address these limitations and improve the reliability and scalability of metagenomic surveillance. Combining qPCR and metagenomics, establishing standardized protocols, and expanding global surveillance networks will be critical steps toward effective AMR monitoring and management.

Author Contrubution

The author independently managed the study's conception, design, data acquisition, analysis, and manuscript drafting.

Funding

No grants from public, commercial, or non-profit funding agencies supported the research, authorship, or publication of this article.

Conflict of Interest

There are no conflicts of interest declared by the authors.

Data Sharing Statement

Not applicable    

Software And Tools Use

Not applicable

Acknowledgements

My gratitude goes to those who assisted in this study and manuscript preparation, and to the anonymous reviewers for their constructive insights.