Final Report Summary - RESISTOME (Towards an individualised therapy and prevention of multi-drug resistant disease)
It is common knowledge that one of the best ways to fight cancer is to catch it in the early stages, when it's more treatable. Very much alike, the prognosis of infectious diseases can be dramatically improved by the administration of an early and targeted antimicrobial therapy. Thus, early diagnosis is one of the most powerful weapons in the battlefield of multidrug-resistant infections. However despite the clinical need, microbiological diagnostics has experienced little fundamental change over the years. Bacterial species identification and resistance testing still predominantly relies on culture-dependent methods. As a consequence clinical microbiology is still labor-intensive and slow. Gene-detection-based molecular methods did not have the dramatic impact on routine diagnostic microbiology that many predicted. Yet, there is clearly a need for clinical microbiology to change. In the frame of the RESISTOME project, we profiled the resistance phenotype as well as the genotype of more than 350 clinical isolates of problematic Gram-negative bacteria. By applying machine learning in genome-wide association studies we identified sequence variations as well as gene expression variations that precisely predict resistance on a molecular basis. The acquired extensive knowledge on the nature, variability and distribution of genetic resistance determinants in Gram-negative opportunistic pathogens led to the establishment of a high density molecular resistance detection tool that is currently tested on a plethora of clinical multi-drug resistant bacterial isolates. The identification of a resistance genotype and the matching phenotype will provide the basis for therapeutic advice regarding antibiotic treatment. At the same time, it would provide information on the epidemiologic behavior of this specific isolate. Since cost, morbidity and mortality in hospitalized patients are significantly lower when reports on the resistance profile are communicated more rapidly, molecular resistance profiling will have a great impact on infection management and multi-drug resistance surveillance detection infrastructures.