Project description:Sepsis is the major cause of mortality across intensive care units globally, yet details of accompanying pathological molecular events are unclear. This has resulted in ineffective development of sepsis-specific biomarkers and therapies, and suboptimal treatment regimens in preventing and reversing organ damage. Here, we used pharmacoproteomics to score treatment effects in a preclinical 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 the definition of time-dependent and organotypic proteotypes of dysfunction and damage that was used to guide the administration of several combinations of beta-lactam antibiotic meropenem and immunomodulatory glucocorticoid methylprednisolone. The proteome-based scoring strategy revealed three distinct response patterns defined as intervention-specific reversions, non-reversions, and specific intervention-induced effects, revealing that the intervention effects depended on the underlying proteotype and varied significantly between organs. In the later stages of the disease, administration of glucocorticoids accentuated some of the positive effects exerted by Mem leading to superior reduction of the inflammatory response in the kidneys and partial restoration of the metabolic dysfunction instigated by sepsis. Unexpectedly, antibiotics introduced sepsis-independent perturbations in the mitochondrial proteome that was to some degree counteracted by glucocorticoids. In summary, this study provides a pharmacoproteomic resource describing the time-resolved septic organ failure landscape across organs and the blood compartment, and a novel scoring strategy that captures unintended secondary drug effects as an important criterion to consider while assessing therapeutic efficacy. Such information is critically important to enable a quantitative, objective and organotypic assessment of treatment benefits and unintended effects, drug synergies of candidate treatments as well as the effect of dose and time in murine sepsis models.

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A pairwise cytokine code explains the organism-wide response to sepsis [Spatial transcriptomics by Array-seq]

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