Bacterial infections involving antibiotic tolerant and resistant populations represent a challenge due to their high mortality rates and hospital morbidity. The evaluation of the factors leading to difficult-to-treat and recurrent infections are highly relevant in order to generate appropriate and tailored treatment strategies. In addition, understanding the host role in the generation of such recurrent infections and the accompanying and interacting microbiota might be the key to elucidate new therapies and increase patient survival and optimal recovery.

As such, in our past CRPP grant, we have achieved important goals proposed in order to streamline the collection and analysis of patient samples within our cohorts. We have improved and further established several clinical cohorts in order to collect and evaluate patient material and clinical isolates from distinct host sources under very specific clinical conditions resulting in various publications. We established a longitudinal sampling protocol for better understanding of the pathophysiological status during the ongoing infections

In the next period of this grant, we plan to take our research several steps forward with the implementation of computer algorithms and mechanistic assays to better understand and identify factors associated with S. aureus difficult-to-treat and recurrent infections. We will take a closer look into the different properties associated with each particular clinical case and generate a comprehensive map for the identification of difficult cases of S. aureus infections where recurrent infections are most likely to occur and therefore, generate new therapeutic strategies to tackle them. We plan to evaluate the host and S. aureus response to different antibiotic regimes and times of application, clinical origin of the sample, presence of biofilms and the use of novel therapeutic approaches such as endolysins and microbiota-derived agents.

We will make use of cutting edge and highly standardized in-house techniques as well as computational algorithms to generate high throughput data and analysis for transcriptomics, proteomics and genomics in invasive infections including the use of enriched populations of bacteria derived directly from the patient material. We will continue with the collection of clinical samples for evaluation and expand our parameters to BurnSkin and MicrobiotaCOVID cohorts.

Overall, in the next period, we plan to generate an in depth understanding on how S. aureus infections establish onto a difficult-to-treat infection. Under which conditions the host (clinical and individual health characteristics) and accompanying microbiota (bacteria pathophysiological profiles) play a role in the response of S. aureus to antibiotic treatments and how it influences the generation of heterogeneity and further emergence of persisters. Finally, we aim to evaluate the feasibility of using the newly acquired knowledge along with new therapeutic alternatives for diagnostics and treatments.