Jonathan P Rehfuss1, Kenneth M DeSart1, Jared M Rozowsky1, Kerri A O'Malley1, Lyle L Moldawer1, Henry V Baker1, Yaqun Wang1, Rongling Wu1, Peter R Nelson1, Scott A Berceli2. 1. From the Malcom Randall Veterans Affairs Medical Center, Gainesville, FL (J.P.R., K.M.D., J.M.R., K.A.O., S.A.B.); Department of Surgery (J.P.R., K.M.D., J.M.R., K.A.O., L.L.M., S.A.B.) and Department of Molecular Genetics and Microbiology (H.V.B.), University of Florida, Gainesville; Department of Biostatistics, Rutgers University, New Brunswick, NJ (Y.W.); Center for Statistical Genetics, Pennsylvania State University, Hershey (R.W.); and Department of Surgery, University of South Florida, Tampa (P.R.N.). 2. From the Malcom Randall Veterans Affairs Medical Center, Gainesville, FL (J.P.R., K.M.D., J.M.R., K.A.O., S.A.B.); Department of Surgery (J.P.R., K.M.D., J.M.R., K.A.O., L.L.M., S.A.B.) and Department of Molecular Genetics and Microbiology (H.V.B.), University of Florida, Gainesville; Department of Biostatistics, Rutgers University, New Brunswick, NJ (Y.W.); Center for Statistical Genetics, Pennsylvania State University, Hershey (R.W.); and Department of Surgery, University of South Florida, Tampa (P.R.N.). bercesa@surgery.ufl.edu.
Abstract
BACKGROUND: Despite being the definitive treatment for lower extremity peripheral arterial disease, vein bypass grafts fail in half of all cases. Early repair mechanisms after implantation, governed largely by the immune environment, contribute significantly to long-term outcomes. The current study investigates the early response patterns of circulating monocytes as a determinant of graft outcome. METHODS: In 48 patients undergoing infrainguinal vein bypass grafting, the transcriptomes of circulating monocytes were analyzed preoperatively and at 1, 7, and 28 days post-operation. RESULTS: Dynamic clustering algorithms identified 50 independent gene response patterns. Three clusters (64 genes) were differentially expressed, with a hyperacute response pattern defining those patients with failed versus patent grafts 12 months post-operation. A second independent data set, comprised of 96 patients subjected to major trauma, confirmed the value of these 64 genes in predicting an uncomplicated versus complicated recovery. Causal network analysis identified 8 upstream elements that regulate these mediator genes, and Bayesian analysis with a priori knowledge of the biological interactions was integrated to create a functional network describing the relationships among the regulatory elements and downstream mediator genes. Linear models predicted the removal of either STAT3 (signal transducer and activator of transcription 3) or MYD88 (myeloid differentiation primary response 88) to shift mediator gene expression levels toward those seen in successful grafts. CONCLUSIONS: A novel combination of dynamic gene clustering, linear models, and Bayesian network analysis has identified a core set of regulatory genes whose manipulations could migrate vein grafts toward a more favorable remodeling phenotype.
BACKGROUND: Despite being the definitive treatment for lower extremity peripheral arterial disease, vein bypass grafts fail in half of all cases. Early repair mechanisms after implantation, governed largely by the immune environment, contribute significantly to long-term outcomes. The current study investigates the early response patterns of circulating monocytes as a determinant of graft outcome. METHODS: In 48 patients undergoing infrainguinal vein bypass grafting, the transcriptomes of circulating monocytes were analyzed preoperatively and at 1, 7, and 28 days post-operation. RESULTS: Dynamic clustering algorithms identified 50 independent gene response patterns. Three clusters (64 genes) were differentially expressed, with a hyperacute response pattern defining those patients with failed versus patent grafts 12 months post-operation. A second independent data set, comprised of 96 patients subjected to major trauma, confirmed the value of these 64 genes in predicting an uncomplicated versus complicated recovery. Causal network analysis identified 8 upstream elements that regulate these mediator genes, and Bayesian analysis with a priori knowledge of the biological interactions was integrated to create a functional network describing the relationships among the regulatory elements and downstream mediator genes. Linear models predicted the removal of either STAT3 (signal transducer and activator of transcription 3) or MYD88 (myeloid differentiation primary response 88) to shift mediator gene expression levels toward those seen in successful grafts. CONCLUSIONS: A novel combination of dynamic gene clustering, linear models, and Bayesian network analysis has identified a core set of regulatory genes whose manipulations could migrate vein grafts toward a more favorable remodeling phenotype.
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