RESUMEN
Tightly interlinked feedback regulators control the dynamics of intracellular responses elicited by the activation of signal transduction pathways. Interferon alpha (IFNα) orchestrates antiviral responses in hepatocytes, yet mechanisms that define pathway sensitization in response to prestimulation with different IFNα doses remained unresolved. We establish, based on quantitative measurements obtained for the hepatoma cell line Huh7.5, an ordinary differential equation model for IFNα signal transduction that comprises the feedback regulators STAT1, STAT2, IRF9, USP18, SOCS1, SOCS3, and IRF2. The model-based analysis shows that, mediated by the signaling proteins STAT2 and IRF9, prestimulation with a low IFNα dose hypersensitizes the pathway. In contrast, prestimulation with a high dose of IFNα leads to a dose-dependent desensitization, mediated by the negative regulators USP18 and SOCS1 that act at the receptor. The analysis of basal protein abundance in primary human hepatocytes reveals high heterogeneity in patient-specific amounts of STAT1, STAT2, IRF9, and USP18. The mathematical modeling approach shows that the basal amount of USP18 determines patient-specific pathway desensitization, while the abundance of STAT2 predicts the patient-specific IFNα signal response.
Asunto(s)
Retroalimentación Fisiológica/efectos de los fármacos , Hepatocitos/metabolismo , Interferón-alfa/farmacología , Factor de Transcripción STAT1/metabolismo , Factor de Transcripción STAT2/metabolismo , Transducción de Señal/efectos de los fármacos , Línea Celular Tumoral , Regulación de la Expresión Génica/efectos de los fármacos , Regulación de la Expresión Génica/genética , Hepatocitos/efectos de los fármacos , Humanos , Factor 2 Regulador del Interferón/genética , Factor 2 Regulador del Interferón/metabolismo , Subunidad gamma del Factor 3 de Genes Estimulados por el Interferón/genética , Subunidad gamma del Factor 3 de Genes Estimulados por el Interferón/metabolismo , Modelos Teóricos , ARN Interferente Pequeño , Factor de Transcripción STAT1/genética , Factor de Transcripción STAT2/genética , Transducción de Señal/genética , Programas Informáticos , Proteína 1 Supresora de la Señalización de Citocinas/genética , Proteína 1 Supresora de la Señalización de Citocinas/metabolismo , Proteína 3 Supresora de la Señalización de Citocinas/genética , Proteína 3 Supresora de la Señalización de Citocinas/metabolismo , Ubiquitina Tiolesterasa/genética , Ubiquitina Tiolesterasa/metabolismoRESUMEN
Breakdown of synthesis, excretion and detoxification defines liver failure. Post-hepatectomy liver failure (PHLF) is specific for liver resection and a rightfully feared complication due to high lethality and limited therapeutic success. Individual cytokine and growth factor profiles may represent potent predictive markers for recovery of liver function. We aimed to investigate these profiles in post-hepatectomy regeneration. This study combined a time-dependent cytokine and growth factor profiling dataset of a training (30 patients) and a validation (14 patients) cohorts undergoing major liver resection with statistical and predictive models identifying individual pathway signatures. 2319 associations were tested. Primary hepatocytes isolated from patient tissue samples were stimulated and their proliferation was analysed through DNA content assay. Common expression trajectories of cytokines and growth factors with strong correlation to PHLF, morbidity and mortality were identified despite highly individual perioperative dynamics. Especially, dynamics of EGF, HGF, and PLGF were associated with mortality. PLGF was additionally associated with PHLF and complications. A global association-network was calculated and validated to investigate interdependence of cytokines and growth factors with clinical attributes. Preoperative cytokine and growth factor signatures were identified allowing prediction of mortality following major liver resection by regression modelling. Proliferation analysis of corresponding primary human hepatocytes showed associations of individual regenerative potential with clinical outcome. Prediction of PHLF was possible on as early as first postoperative day (POD1) with AUC above 0.75. Prediction of PHLF and mortality is possible on POD1 with liquid-biopsy based risk profiling. Further utilization of these models would allow tailoring of interventional strategies according to individual profiles.