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Macrophages exert the primary cellular immune response. Pathogen components like bacterial lipopolysaccharides (LPS) stimulate macrophage migration, phagocytotic activity and cytokine expression. Previously, we identified the poly(A)+ RNA interactome of RAW 264.7 macrophages. Of the 402 RNA-binding proteins (RBPs), 32 were classified as unique in macrophages, including nineteen not reported to interact with nucleic acids before. Remarkably, P23 a HSP90 co-chaperone, also known as cytosolic prostaglandin E2 synthase (PTGES3), exhibited differential poly(A)+ RNA binding in untreated and LPS-induced macrophages. To identify mRNAs bound by P23 and to elucidate potential regulatory RBP functions in macrophages, we immunoprecipitated P23 from cytoplasmic extracts of cross-linked untreated and LPS-induced cells. RNAseq revealed that enrichment of 44 mRNAs was reduced in response to LPS. Kif15 mRNA, which encodes kinesin family member 15 (KIF15), a motor protein implicated in cytoskeletal reorganization and cell mobility was selected for further analysis. Noteworthy, phagocytic activity of LPS-induced macrophages was enhanced by P23 depletion. Specifically, in untreated RAW 264.7 macrophages, decreased P23 results in Kif15 mRNA destabilization, diminished KIF15 expression and accelerated macrophage migration. We show that the unexpected RBP function of P23 contributes to the regulation of macrophage phagocytotic activity and migration.
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.