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Author Spotlight: Understanding and Detecting Environmental Antimicrobial Resistance by Combining Culture-Based Techniques and Genomics
Published on: July 19, 2024
Antimicrobial resistance dashboard application for mapping environmental occurrence and resistant pathogens
Robert D Stedtfeld1, Maggie R Williams1, Umama Fakher1
1Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA.
A new Antibiotic Resistance (AR) Dashboard app maps antibiotic resistance genes (ARG) and bacteria (ARB) globally. This tool helps track AR spread, identify hotspots, and monitor pollution by analyzing environmental and clinical data.
Area of Science:
- Environmental Science
- Microbiology
- Public Health
Background:
- Antibiotic resistance (AR) poses a significant global health threat, driven by the spread of antibiotic resistance genes (ARG) and bacteria (ARB) in both environmental and clinical settings.
- Effective monitoring and data analysis are crucial for understanding and combating the increasing prevalence of AR.
Purpose of the Study:
- To develop and introduce a novel Antibiotic Resistance (AR) Dashboard application for comprehensive geospatial mapping and analysis of AR data.
- To create a centralized database integrating diverse AR studies for regional and global-scale insights into ARGs and ARB occurrence and transmission.
Main Methods:
- The AR Dashboard application was initially populated using data from qPCR ARG array testing on surface waters, wastewater treatment facility influents, and clinical isolates.
- Data from previously published studies, including river, park soil, and swine farm samples, were integrated to broaden the database scope.
- The application facilitates the collection and geospatial mapping of AR studies, reported occurrence, and antibiograms, with downloadable data for offline analysis.
Main Results:
- The integrated database enables identification of AR hotspots, tracking of AR spread and transmission pathways.
- It allows for quantification of environmental and human factors influencing ARG-harboring organisms and differentiation of natural versus anthropogenic ARGs.
- The system can cluster ARGs, compare connections across environments and hosts, and identify potential proxy genes for monitoring pollution.
Conclusions:
- The AR Dashboard provides a powerful, scalable tool for researchers and public health officials to understand and manage antibiotic resistance.
- Its ability to integrate diverse data sets and provide actionable insights supports targeted interventions and pollution monitoring efforts.
- User engagement through beta version testing and feedback is encouraged to enhance the application's functionalities and utility.
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