Title:	GEARA: The Global Environmental Antibiotic Resistance Atlas
DNr:	NAISS 2025/5-97
Project Type:	NAISS Medium Compute
Principal Investigator:	Johan Bengtsson-Palme <johan.bengtsson.palme@chalmers.se>
Affiliation:	Chalmers tekniska högskola
Duration:	2025-02-25 – 2025-09-01
Classification:	10610 10606
Homepage:	https://microbiology.se
Keywords:

Abstract

Antibiotic resistance (AR) represents one of the most pressing challenges to modern medicine, threatening to reverse many of the medical advances achieved over the last century. While traditional research has primarily focused on clinical aspects of antibiotic resistance, there is growing recognition of the environment's crucial role as both a reservoir and dissemination route for antibiotic-resistant bacteria. This shifting perspective has led both the scientific community and intergovernmental organizations to advocate for a one-health approach to studying AR, which integrates clinical, animal, and environmental perspectives while examining their interconnections. Environmental monitoring and risk assessment of antibiotic resistance genes (ARGs) are essential for identifying high-risk environments that may significantly impact human health. Currently, ARG abundance data are scattered across various databases, publications, and repositories, making comprehensive analyses challenging. Additionally, while extensive environmental metagenomic data exists in public databases, most samples have not been evaluated for their antimicrobial resistance load. This knowledge gap hinders evidence-based policy decisions and targeted interventions. Our project aims to address these limitations by developing a comprehensive understanding of AR distribution in various environments through large-scale analysis of publicly available metagenomic data. The project will implement a two-phase screening approach of public metagenomes. The initial phase will analyse 30,000 metagenomes from European countries to establish and validate our computational pipeline and preliminary models. The second phase will expand the analysis to include 320,000 additional metagenomes from the rest of the world, creating a comprehensive dataset of environmental AR. This extensive dataset will be integrated into a unified database that enables systematic analysis of ARG distribution patterns. By leveraging this large-scale data integration, we will develop predictive models capable of: (1) estimating ARG content and abundance in unsampled environments, and (2) identifying sampling gaps in current environmental AR data. All data and analytical tools generated through this project will be made freely available through a web-based platform, establishing a new centralized resource for the scientific community. This comprehensive database will serve as a tool for both researchers and policy makers, providing unprecedented access to both curated datasets and predictive modeling results. The database will not only reveal patterns of environmental AR distribution but also facilitate future research by providing a unified resource for addressing novel biological questions about antibiotic resistance in environmental settings.