title: Machine Perfusion of Porcine Livers with Oxygen-Carrying Solution Results in Reprogramming of Dynamic Inflammation Networks.
abstract: Background:Ex vivomachine perfusion (MP) can better preserve organs for transplantation. We have recently reported on the first application of an MP protocol in which liver allografts were fully oxygenated, under dual pressures and subnormothermic conditions, with a new hemoglobin-based oxygen carrier (HBOC) solution specifically developed forex vivoutilization. In those studies, MP improved organ function post-operatively and reduced inflammation in porcine livers. Herein, we sought to refine our knowledge regarding the impact of MP by defining dynamic networks of inflammation in both tissue and perfusate.Methods:Porcine liver allografts were preserved either with MP (n= 6) or with cold static preservation (CSP;n= 6), then transplanted orthotopically after 9 h of preservation. Fourteen inflammatory mediators were measured in both tissue and perfusate during liver preservation at multiple time points, and analyzed using Dynamic Bayesian Network (DyBN) inference to define feedback interactions, as well as Dynamic Network Analysis (DyNA) to define the time-dependent development of inflammation networks.Results:Network analyses of tissue and perfusate suggested an NLRP3 inflammasome-regulated response in both treatment groups, driven by the pro-inflammatory cytokine interleukin (IL)-18 and the anti-inflammatory mediator IL-1 receptor antagonist (IL-1RA). Both DyBN and DyNA suggested a reduced role of IL-18 and increased role of IL-1RA with MP, along with increased liver damage with CSP. DyNA also suggested divergent progression of responses over the 9 h preservation time, with CSP leading to a stable pattern of IL-18-induced liver damage and MP leading to a resolution of the pro-inflammatory response. These results were consistent with prior clinical, biochemical, and histological findings after liver transplantation.Conclusion:Our results suggest that analysis of dynamic inflammation networks in the setting of liver preservation may identify novel diagnostic and therapeutic modalities.
authors.affiliation: Department of Surgery, University of Pittsburgh, Pittsburgh PA, USA.
authors.forename: David
authors.initials: D
authors.lastname: Sadowsky
authors.affiliation: Department of Surgery, University of Pittsburgh, PittsburghPA, USA; McGowan Institute for Regenerative Medicine, University of Pittsburgh, PittsburghPA, USA.
authors.forename: Ruben
authors.initials: R
authors.lastname: Zamora
authors.affiliation: Department of Surgery, University of Pittsburgh, Pittsburgh PA, USA.
authors.forename: Derek
authors.initials: D
authors.lastname: Barclay
authors.affiliation: Department of Surgery, University of Pittsburgh, Pittsburgh PA, USA.
authors.forename: Jinling
authors.initials: J
authors.lastname: Yin
authors.affiliation: Department of Surgery, University of Pittsburgh, PittsburghPA, USA; McGowan Institute for Regenerative Medicine, University of Pittsburgh, PittsburghPA, USA; Department of Surgery, Thomas E. Starzl Transplantation Institute, PittsburghPA, USA.
authors.forename: Paulo
authors.initials: P
authors.lastname: Fontes
authors.affiliation: Department of Surgery, University of Pittsburgh, PittsburghPA, USA; McGowan Institute for Regenerative Medicine, University of Pittsburgh, PittsburghPA, USA.
authors.forename: Yoram
authors.initials: Y
authors.lastname: Vodovotz
keywords: inflammation; modeling; networks; porcine; reprogramming; transplant