PM.20.001 – Exploiting molecular and clinical heterogeneity to personalize sepsis care

Route: Personalised medicine: the individual at the centre

Cluster question: 096 How can we improve diagnostics, treatment, and vaccines for immunodeficiencies and infectious diseases?

Sepsis is a life-threatening syndrome caused by a dysregulated response to infection that leads to organ dysfunction. The global burden is high, with an estimated annual incidence of >30 million cases and 6 million deaths. Despite attempts to unravel the pathogenesis, the exact mechanisms underlying the clinical response remain largely unknown and treatment remains limited to source control and supportive care, rather than targeting molecular derangements. Strategies using a ‘one-size-fits-all’ approach failed due to extreme clinical heterogeneity. Given the septic cascade that leads to organ dysfunction and death is initiated by activated leukocytes disrupting endothelial function, we hypothesize that variations in the evolving molecular response in leukocytes and their effect on endothelium drives clinical heterogeneity. Unraveling this will improve diagnosis, predict prognosis and therapeutic response, and aid the development of personalized treatments. For this, the molecular signature of leukocytes and blood plasma will be analyzed at multiple time-points after hospitalization and associated with effects of these leukocytes and plasma on endothelial signaling and function. Features governed by infection per se will be dissected by comparing findings obtained in septic patients to lipopolysaccharide-challenged volunteers. Artificial intelligence will be used to analyze the nature and dynamics of developing patterns in the molecular signature in association with the clinical and functional response, assessed by advanced wearables to reveal characteristics associated with different clinical responses, from swift deterioration to rapid recovery. These features will be used to identify drugable targets that will be validated for their efficacy to prevent organ dysfunction in pre-clinical sepsis models. By this unique systematic analysis of the evolving molecular response in relation to functional outcome over time, we expect to generate key insight that is essential to advance the translational sepsis field.

Keywords

drug discovery, Endothelium, leukocyte, machine learning, sepsis

Submitter

Organisation University Medical Center Groningen (UMCG)
Name Dr. H.R. (Hjalmar) Bouma
E-mail h.r.bouma@umcg.nl
Website https://www.rug.nl/staff/h.r.bouma/cv