Discovering the Achilles heels of AMR opportunistic pathogens that find reservoir in the human gut
Supervisor: Nassos Typas
Location: European Molecular Biology Laboratory
This project examines how different gut microbiomes affect antibiotic-resistant urinary tract bacteria (UPEC). Using competitive fitness tests, The Doctoral Candidate will identify which bacterial communities and nutrients reduce the fitness of resistant UPEC strains. This project aims to understand the molecular mechanisms behind these fitness costs and demonstrate how specific bacterial strains or molecules could prevent resistant UPEC colonization in different microbiome environments, including mouse models.
Fitness effect of resistance mutations within microbiomes
Supervisor: Isabel Gordo & Karina Xavier
Location: Gulbenkian Institute for Molecular Medicine
This project studies how antibiotic resistance affects E. coli fitness in the gut. The Doctoral Candidate will examine competition between resistant and non-resistant bacteria across different microbial environments, analyze how bacteria adapt to resistance costs over time, and investigate the role of bacterial communication (quorum sending signaling) in these processes. The project aims to improve understanding of resistance dynamics and inform microbiome management strategies.
Inducible resistance elements in microbiome species as a low fitness cost reservoir of AMR
Supervisor: André Mateus
Location: Umeå University
This project examines how gut bacteria activate resistance proteins in response to drugs. The Doctoral Candidate will identify which bacterial proteins become more active with drug exposure, investigate drug interactions and antagonisms, and assess how common these resistance mechanisms are across bacterial species. This project aims to map drug-resistance relationships and could improve treatments that protect beneficial gut bacteria while targeting pathogens.
Tracing the evolution and dissemination of antimicrobial resistance through the analysis of HGT and bacterial strains
Supervisor: Peer Bork
Location: European Molecular Biology Laboratory
This project studies how antibiotic resistance genes spread through bacterial populations via vertical (parent to offspring) and horizontal (between strains) transfer. The Doctoral Candidate will track gene movement between species and environments, identify resistance reservoirs, and analyze co-evolutionary patterns. By mapping transfer timelines and spread patterns, the project aims to inform strategies against antibiotic resistance using evolutionary biology and computational analysis.
Uncovering drivers of conjugation-mediated antibiotic resistance gene spread in Enterobacteriaceae
Supervisor: Ana Rita Brochado & Lisa Maier
Location: University of Tübingen
This project examines how antibiotic resistance genes transfer between gut bacteria through plasmid conjugation. Using different culture environments and high-throughput screening, the Doctoral Candidate will identify factors affecting transfer rates and substances that influence conjugation. This project aims to understand how the broader microbiome affects gene transfer and validate findings in mouse models to help prevent resistance spread in humans and animals.
Mechanisms and diagnostics of heteroresistance
Supervisor: Dan I. Andersson
Location: Uppsala University
This project studies how bacterial subpopulations develop increased antibiotic resistance through gene variations and mutations. The Doctoral Candidate will examine how different pathogens achieve heteroresistance, investigate common resistance mechanisms across bacteria types, and develop diagnostic tests for clinical use. The project aims to understand bacterial adaptation and create practical tools for detecting heteroresistance.
Mechanistic models of response to antibiotics in systems of interacting bacteria
Supervisor: Fernanda Pinheiro
Location: Human Technopole
This project examines how bacterial communities interact and respond to antibiotics collectively. Using growth experiments and molecular analysis, the Doctoral Candidate will model how environmental changes affect microbial communities’ survival and resistance development. The project aims to improve treatment strategies by understanding bacterial behavior in complex community settings rather than studying species in isolation.
Collateral susceptibility effects of antineoplastic drug exposure on antibiotic resistance
Supervisor: Pål Jarle Johnsen
Location: The University of Tromsø – The Arctic University of Norway
This research project examines how cancer drugs affect bacterial evolution and antibiotic resistance. The Doctoral Candidate will study how bacteria respond genetically and phenotypically to antineoplastic drugs, identifying patterns of cross-resistance and sensitivity between cancer drugs and antibiotics. The project aims to find drug combinations that reduce treatment failure in cancer patients with infections by leveraging collateral sensitivity—cases where resistance to one drug makes bacteria more vulnerable to another.
Drug combinations targeting beta-lactamase-resistant pathogens
Supervisor: Viktória Lázár
Location: Biological Research Centre of the Hungarian Academy of Sciences
This research project explores drug combinations that could reverse beta-lactam resistance in harmful gut bacteria like E. coli and Salmonella. The Doctoral Candidate will identify drug pairs that work against resistant pathogens while minimizing impact on beneficial gut bacteria. The project aims to develop sustainable treatment strategies that both fight current infections and reduce antibiotic resistance, potentially changing how we treat high-risk clinical infections.
Identifying drug combination for preventing the evolution of resistance
Supervisor: Nathalie Balaban
Location: Hebrew University of Jerusalem
This research project develops tools and theories to evaluate how antibiotic combinations affect bacterial survival and resistance evolution. The Doctoral Candidate will create high-throughput methods to test drug combinations and predict how bacterial cell variations influence resistance development. Using measurements of bacterial recovery time and tolerance levels, they’ll identify factors that control resistance evolution speed. The project aims to create a framework for predicting resistance development under drug combinations, potentially improving clinical antibiotic use by tailoring treatments to specific bacterial strains.
