A pharmacoproteomic landscape of organotypic intervention responses in Gram-negative sepsis

Abstract

AbstractSepsis is the major cause of mortality across intensive care units globally, yet details of accompanying pathological molecular events remains unclear. This knowledge gap has resulted in ineffective development of sepsis-specific biomarkers and therapies, and suboptimal treatment regimens to prevent or reverse organ damage. Here, we used pharmacoproteomics to score treatment effects in a murine Escherichia coli sepsis model based on changes in the organ, cell, and plasma proteome landscapes. A combination of pathophysiological read-outs and time-resolved proteome maps of organs and blood enabled us to define time-dependent and organotypic proteotypes of dysfunction and damage upon administration of several combinations of the broad-spectrum beta-lactam antibiotic meropenem (Mem) and/or the immunomodulatory glucocorticoid methylprednisolone (Gcc). Three distinct response patterns were identified, defined as intervention-specific reversions, non-reversions, and specific intervention-induced effects, which depended on the underlying proteotype and varied significantly across organs. In the later stages of the disease, Gcc enhanced some positive treatment effects of Mem with superior reduction of the inflammatory response in the kidneys and partial restoration of sepsis-induced metabolic dysfunction. Unexpectedly, Mem introduced sepsis-independent perturbations in the mitochondrial proteome that were to some degree counteracted by Gcc. In summary, this study provides a pharmacoproteomic resource describing the time-resolved septic organ failure landscape across organs and blood, coupled to a novel scoring strategy that captures unintended secondary drug effects as an important criterion to consider when assessing therapeutic efficacy. Such information is critical for quantitative, objective, and organotypic assessment of benefits and unintended effects of candidate treatments in relationship to dosing, timing, and potential synergistic combinations in murine sepsis models.