Tracking and Mitigating Antimicrobial Resistance from Livestock Manure in Agricultural Soils: A One-Health Genomic Approach

Context & Project Idea:

The widespread application of conventional livestock manure to agricultural lands poses a significant One-Health challenge by acting as a reservoir for antimicrobial resistance (AMR). Based on baseline studies in high-density farming areas, this project aims to understand how fertilization practices alter the soil microbiota and drive the environmental dissemination of antimicrobials and antimicrobial resistance genes (ARGs). We aim to track specific antimicrobial residues and quantify the abundance of critical ARGs in fertilized soils. By mapping the co-occurrence of these ARGs with specific soil microbial taxa, our objective is to trace dissemination pathways from the animal gut to the soil and develop targeted, counteractive soil management strategies.

What We Are Looking For:

We are seeking to join an existing, multidisciplinary consortium as a project partner. We aim to collaborate with soil ecologists, environmental chemists, and agronomists to provide the crucial veterinary microbiome, AMR profiling, and bioinformatics analyses required to map the full lifecycle of agricultural AMR.

Our Expertise:

Representing the Veterinary Research Institute (ELGO-DIMITRA), we bring multidisciplinary expertise bridging veterinary medicine, microbiology, and advanced data science. We possess foundational knowledge as veterinary scientists focusing on the epidemiology and transmission dynamics of antimicrobial-resistant pathogens within the livestock industry. We have extensive hands-on experience utilizing Next-Generation Sequencing (NGS) technologies, including Whole Genome Sequencing (WGS), metagenomics, and 16S rRNA sequencing for microbiome analysis. We are proficient in utilizing diverse sequencing platforms, including Illumina and Oxford Nanopore (MinION), for biomarker discovery and genomic characterization. We bring robust computational skills, specializing in the management and analysis of large-scale "omics" data using Python, R, and high-performance computing (HPC) environments. We develop and apply AI and machine learning models to classify biological data and predict pathogen behavior based on genetic profiles. Our past research and involvement in European (COST) networks are strongly grounded in the One-Health approach, mitigating risks that cross between animal production and public health.