Peptidome profiling for the immunological stratification in sepsis: a proof of concept study

Abstract

Sepsis constitutes a major cause of death in intensive care units. Patients present a dysregulated response to infection, which progresses through different pro/anti-inflammatory phases. The lack of tools to monitor their response constrains the therapeutic approaches. Here, we evaluated a test based on plasma protein fingerprints acquired by MALDI-TOF-mass spectrometry and supervised/unsupervised algorithms to discriminate the different immunological stages of sepsis in lipopolysaccharide-induced murine models with encouraging results. Moreover, our predictive models through machine learning algorithms were able to discriminate the different groups with a sensitivity of up to 95.7% and a specificity of 90.9% depending on the selected peaks number. Potential individual biomarkers associated with each phase were also analysed. Our data reveal the potential of plasma peptidome analysis by MALDI-TOF-mass spectrometry as a highly relevant strategy for sepsis patient stratification that could contribute to therapeutic decisions, depending on the immunological phase that the patient is undergoing.