Kenneth R Williams1,2, Christopher M Colangelo3, Lin Hou4, Lisa Chung1, Justin M Belcher5, Thomas Abbott1, Isaac E Hall6, Hongyu Zhao7, Lloyd G Cantley5, Chirag R Parikh5,8. 1. W.M. Keck Foundation Biotechnology Laboratory, Yale University School of Medicine, New Haven, USA. 2. Molecular Biophysics and Biochemistry, Yale University School of Medicine, New Haven, USA. 3. Primary Ion, Old Lyme, USA. 4. Center for Statistical Science, Tsinghua University, Beijing, China. 5. Internal Medicine, Yale University School of Medicine, New Haven, USA. 6. Division of Nephrology, Hypertension & Renal Transplantation, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, USA. 7. Epidemiology & Public Health, Yale University School of Medicine, New Haven, USA. 8. Program of Applied Translational Research, Yale University School of Medicine, New Haven, USA.
Abstract
PURPOSE: Development of delayed graft function (DGF) following kidney transplant is associated with poor outcomes. An ability to rapidly identify patients with DGF versus those with immediate graft function (IGF) may facilitate the treatment of DGF and the research needed to improve prognosis. The purpose of this study was to use a Targeted Urine Proteome Assay to identify protein biomarkers of delayed recovery from kidney transplant. EXPERIMENTAL DESIGN: Potential biomarkers were identified using the Targeted Urine Proteome (MRM) Assay to interrogate the relative DGF/IGF levels of expression of 167 proteins in urine taken 12-18 h after kidney implantation from 21 DGF, 15 SGF (slow graft function), and 16 IGF patients. An iterative Random Forest analysis approach evaluated the relative importance of each biomarker, which was then used to identify an optimum biomarker panel that provided the maximum sensitivity and specificity with the least number of biomarkers. CONCLUSIONS AND CLINICAL RELEVANCE: Four proteins were identified that together distinguished DGF with a sensitivity of 77.4%, specificity of 82.6%, and AUC of 0.891. This panel represents an important step toward identifying DGF at an early stage so that more effective treatments can be developed to improve long-term graft outcomes.
PURPOSE: Development of delayed graft function (DGF) following kidney transplant is associated with poor outcomes. An ability to rapidly identify patients with DGF versus those with immediate graft function (IGF) may facilitate the treatment of DGF and the research needed to improve prognosis. The purpose of this study was to use a Targeted Urine Proteome Assay to identify protein biomarkers of delayed recovery from kidney transplant. EXPERIMENTAL DESIGN: Potential biomarkers were identified using the Targeted Urine Proteome (MRM) Assay to interrogate the relative DGF/IGF levels of expression of 167 proteins in urine taken 12-18 h after kidney implantation from 21 DGF, 15 SGF (slow graft function), and 16 IGF patients. An iterative Random Forest analysis approach evaluated the relative importance of each biomarker, which was then used to identify an optimum biomarker panel that provided the maximum sensitivity and specificity with the least number of biomarkers. CONCLUSIONS AND CLINICAL RELEVANCE: Four proteins were identified that together distinguished DGF with a sensitivity of 77.4%, specificity of 82.6%, and AUC of 0.891. This panel represents an important step toward identifying DGF at an early stage so that more effective treatments can be developed to improve long-term graft outcomes.
Authors: M Pérez Fontán; A Rodríquez-Carmona; P Bouza; T García Falcón; J Moncalián; J Oliver; F Valdés Journal: Transplantation Date: 1996-07-15 Impact factor: 4.939
Authors: Chris F Taylor; Norman W Paton; Kathryn S Lilley; Pierre-Alain Binz; Randall K Julian; Andrew R Jones; Weimin Zhu; Rolf Apweiler; Ruedi Aebersold; Eric W Deutsch; Michael J Dunn; Albert J R Heck; Alexander Leitner; Marcus Macht; Matthias Mann; Lennart Martens; Thomas A Neubert; Scott D Patterson; Peipei Ping; Sean L Seymour; Puneet Souda; Akira Tsugita; Joel Vandekerckhove; Thomas M Vondriska; Julian P Whitelegge; Marc R Wilkins; Ioannnis Xenarios; John R Yates; Henning Hermjakob Journal: Nat Biotechnol Date: 2007-08 Impact factor: 54.908
Authors: R Hirschberg; J Kopple; P Lipsett; E Benjamin; J Minei; T Albertson; M Munger; M Metzler; G Zaloga; M Murray; S Lowry; J Conger; W McKeown; M O'shea; R Baughman; K Wood; M Haupt; R Kaiser; H Simms; D Warnock; W Summer; R Hintz; B Myers; K Haenftling; W Capra Journal: Kidney Int Date: 1999-06 Impact factor: 10.612
Authors: Christopher M Colangelo; Mark Shifman; Kei-Hoi Cheung; Kathryn L Stone; Nicholas J Carriero; Erol E Gulcicek; TuKiet T Lam; Terence Wu; Robert D Bjornson; Can Bruce; Angus C Nairn; Jesse Rinehart; Perry L Miller; Kenneth R Williams Journal: Genomics Proteomics Bioinformatics Date: 2015-02-21 Impact factor: 7.691
Authors: Steven A Carr; Susan E Abbatiello; Bradley L Ackermann; Christoph Borchers; Bruno Domon; Eric W Deutsch; Russell P Grant; Andrew N Hoofnagle; Ruth Hüttenhain; John M Koomen; Daniel C Liebler; Tao Liu; Brendan MacLean; D R Mani; Elizabeth Mansfield; Hendrik Neubert; Amanda G Paulovich; Lukas Reiter; Olga Vitek; Ruedi Aebersold; Leigh Anderson; Robert Bethem; Josip Blonder; Emily Boja; Julianne Botelho; Michael Boyne; Ralph A Bradshaw; Alma L Burlingame; Daniel Chan; Hasmik Keshishian; Eric Kuhn; Christopher Kinsinger; Jerry S H Lee; Sang-Won Lee; Robert Moritz; Juan Oses-Prieto; Nader Rifai; James Ritchie; Henry Rodriguez; Pothur R Srinivas; R Reid Townsend; Jennifer Van Eyk; Gordon Whiteley; Arun Wiita; Susan Weintraub Journal: Mol Cell Proteomics Date: 2014-01-17 Impact factor: 5.911
Authors: Neta Gotlieb; Amirhossein Azhie; Divya Sharma; Ashley Spann; Nan-Ji Suo; Jason Tran; Ani Orchanian-Cheff; Bo Wang; Anna Goldenberg; Michael Chassé; Heloise Cardinal; Joseph Paul Cohen; Andrea Lodi; Melanie Dieude; Mamatha Bhat Journal: NPJ Digit Med Date: 2022-07-11