Lung Transplant donor: prediction, evaluation, and mechanism

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): Primary graft dysfunction (PGD) is the most common cause of morbidity and mortality after lung transplantation. Recent data indicate participation of a few specific pathways in acute lung injury models and post-transplant PGD. A better understanding of these specific pathways would offer mechanistic clues to PGD pathogenesis and potentially stimulate investigation of novel therapeutic avenues. In addition, identification o specific, individualized risk factors for PGD might allow future personalized therapy. Our preliminary data indicate that among the top genes identified as having the largest fold differences in PGD compared to control, ANGPT2 a key mediator of vascular inflammation/ permeability demonstrated a 7-fold increase in donors pre- procurement which further increased an additional 10-fold post reperfusion. Given the up-regulation of ANGPT2 gene expression in our transplant cohort prior to harvest, there appears to be unmeasured injury to the lung despite normal physiologic measurements which needs to be further characterized. The long-term objective of our line of research is to understand the mechanism of PGD in human lung transplantation in order to identify strategies to identify donors at risk, prevent recipient death and potentially expand the donor pool through better donor selection and use of ex vivo lung perfusion (EVLP) strategies. Our approach is to use gene expression in donor lung biopsies to predict PGD, and to examine specific inflammatory, innate immunity and vascular permeability pathways involved in PGD of transplanted donors and untransplantable donors placed on EVLP. The central hypotheses are that lung injury occurring in the donor lung prior to reperfusion can be evaluated by gene expression methods to determine PGD risk, identify "low risk" organs that would have been discarded that can be transplanted, and understand common mechanisms of PGD and recovery on EVLP. Completion of this project will provide the candidate with advanced training and critical experience in cohort study design and conduct; tailoring genetic analysis at the gene, expression and pathway level; and applying advanced bioinformatic and computational techniques for pathways analysis and risk prediction. The candidate has assembled a rich mentoring committee spanning expertise in patient-oriented research, molecular and genetic epidemiology, genomics, bioinformatics, and molecular biology. In addition, he is taking advantage of Penn's outstanding educational opportunities through a Master's in Clinical Epidemiology. The proposal maps a clear plan to allow the candidate to become an independent clinical investigator in patient-oriented translational research.