Antimicrobial resistance (AMR) is a global epidemic causing more than 700.000 deaths per year and a projected 10 million deaths annually by 2050. AMR is associated with increased morbidity, longer hospitalization and a marked increase in the associated medical costs. Due to the misuse and abuse of antibiotics, we are in dire needs of systematic strategies to rationalize the use of antibiotics. Bacterial culture-based antibiograms are the staple diagnostic method of AMR but this approach presents important drawbacks, such as a long turnaround time, low sensitivity, and the requirement to have, at least to a certain degree, prior information on the putative causative bacteria. Moreover, fastidious growing bacteria are difficult to detect, and some bacteria do not grow in laboratory conditions and cannot be detected. Altogether, these factors lead to misdiagnose and inefficient antibiotic treatments, which favor the emergence of resistances, infection dissemination and eventually patient death. Hence, it is critical to develop novel methods to identify bacteria and the associated resistances to provide optimized antibiotic therapies in a timely manner. While several systems, such as amplification-based approaches or short-read DNA sequencing, are currently being developed, they are limited to specific pathogens and genes and require costly equipment. Herein, we the ResisTest project has developed a fast, high-sensitive, and cost effective AMR detection approach that allows the enrichment of bacterial RNA in the sample. This approach can be combined with subsequent real-time identification of the pathogenic strains and resistance genes, significantly shortening the time to diagnose. This disruptive approach could represent a significant breakthrough in the fight against AMR, allowing an early and precise recognition of the antibiotics of choice, hence limiting the use of wide-spectrum antibiotics, reducing hospitalization time and costs and improving patient comfort and survival rates.
