Development of a novel tool for diagnosis and prevention of antimicrobial resistance

Antimicrobial resistance (AMR) poses a threat to global health.

Infections lead to longer hospital stays and increased mortality, while recurrent infections increase the risk for appearance of deadly multidrug resistant bacterial and fungal pathogens. One of the greatest challenges in AMR is the shortcoming in their diagnosis.

Classical microbiology tests are often inadequate in timely detection of infections, and thus antibiotics are prescribed in a presumptive manner.

Here we propose to develop a novel molecular approach able to diagnose bacterial and fungal infections and associated AMR integrating molecular biology, high-throughput sequencing and machine learning approaches.

We will pilot the use of mRNA degradation signatures (metadegradomics) as phenotypic molecular reporter of antimicrobial resistance and develop a cost-effective diagnosis method for their identification.

Simultaneously, we will measure patient-specific microbiomes to predict infection recurrence, understand how it modulates the appearance of AMR strains and aid on the clinical management of patients.

We will demonstrate the viability of the developed diagnosis approach utility by applying it to both intensive care unit patients and by improve our understanding of recurrent vaginosis in women.

Our work aims to dramatically accelerate AMR diagnosis (< 24h), improve patient survival and contribute to rationalizing the infection treatments.