Konrad Buscher1,2, Barbara Heitplatz3, Veerle van Marck3, Jian Song4,5, Sophie Loismann4,5, Rebecca Rixen6, Birte Hüchtmann6, Sunil Kurian7, Erik Ehinger8, Dennis Wolf2,9, Klaus Ley2, Hermann Pavenstädt6, Stefan Reuter6. 1. Division of General Internal Medicine, Nephrology and Rheumatology, Department of Medicine D, University Hospital of Muenster, Muenster, Germany konrad.buscher@ukmuenster.de. 2. Division of Inflammation Biology, La Jolla Institute for Immunology, La Jolla, California. 3. Institute of Pathology, University Hospital Muenster, Muenster, Germany. 4. Institute of Physiological Chemistry and Pathobiochemistry, University of Muenster, Muenster, Germany. 5. Cells-in-Motion Cluster of Excellence, University of Muenster, Muenster, Germany. 6. Division of General Internal Medicine, Nephrology and Rheumatology, Department of Medicine D, University Hospital of Muenster, Muenster, Germany. 7. Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, California. 8. Division of Signaling and Gene Expression, La Jolla Institute for Immunology, La Jolla, California. 9. Department of Cardiology and Angiology I, University Heart Center, and Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Abstract
BACKGROUND: In transplant medicine, clinical decision making largely relies on histology of biopsy specimens. However, histology suffers from low specificity, sensitivity, and reproducibility, leading to suboptimal stratification of patients. We developed a histology-independent immune framework of kidney graft homeostasis and rejection. METHODS: We applied tailored RNA deconvolution for leukocyte enumeration and coregulated gene network analysis to published bulk human kidney transplant RNA transcriptomes as input for unsupervised, high-dimensional phenotype clustering. We used framework-based graft survival analysis to identify a biomarker that was subsequently characterized in independent transplant biopsy specimens. RESULTS: We found seven immune phenotypes that confirm known rejection types and uncovered novel signatures. The molecular phenotypes allow for improved graft survival analysis compared with histology, and identify a high-risk group in nonrejecting transplants. Two fibrosis-related phenotypes with distinct immune features emerged with reduced graft survival. We identified lysyl oxidase-like 2 (LOXL2)-expressing peritubular CD68+ macrophages as a framework-derived biomarker of impaired allograft function. These cells precede graft fibrosis, as demonstrated in longitudinal biopsy specimens, and may be clinically useful as a biomarker for early fibrogenesis. CONCLUSIONS: This study provides a comprehensive, data-driven atlas of human kidney transplant phenotypes and demonstrates its utility to identify novel clinical biomarkers.
BACKGROUND: In transplant medicine, clinical decision making largely relies on histology of biopsy specimens. However, histology suffers from low specificity, sensitivity, and reproducibility, leading to suboptimal stratification of patients. We developed a histology-independent immune framework of kidney graft homeostasis and rejection. METHODS: We applied tailored RNA deconvolution for leukocyte enumeration and coregulated gene network analysis to published bulk human kidney transplant RNA transcriptomes as input for unsupervised, high-dimensional phenotype clustering. We used framework-based graft survival analysis to identify a biomarker that was subsequently characterized in independent transplant biopsy specimens. RESULTS: We found seven immune phenotypes that confirm known rejection types and uncovered novel signatures. The molecular phenotypes allow for improved graft survival analysis compared with histology, and identify a high-risk group in nonrejecting transplants. Two fibrosis-related phenotypes with distinct immune features emerged with reduced graft survival. We identified lysyl oxidase-like 2 (LOXL2)-expressing peritubular CD68+ macrophages as a framework-derived biomarker of impaired allograft function. These cells precede graft fibrosis, as demonstrated in longitudinal biopsy specimens, and may be clinically useful as a biomarker for early fibrogenesis. CONCLUSIONS: This study provides a comprehensive, data-driven atlas of human kidney transplant phenotypes and demonstrates its utility to identify novel clinical biomarkers.
Authors: Joris A H de Groot; Patrick M M Bossuyt; Johannes B Reitsma; Anne W S Rutjes; Nandini Dendukuri; Kristel J M Janssen; Karel G M Moons Journal: BMJ Date: 2011-08-02
Authors: E Guillén-Gómez; I Dasilva; I Silva; Y Arce; C Facundo; E Ars; A Breda; A Ortiz; L Guirado; J A Ballarín; M M Díaz-Encarnación Journal: Am J Transplant Date: 2016-09-15 Impact factor: 8.086
Authors: Brian J Nankivell; Meena Shingde; Karen L Keung; Caroline L-S Fung; Richard J Borrows; Philip J O'Connell; Jeremy R Chapman Journal: Am J Transplant Date: 2018-01-03 Impact factor: 8.086
Authors: M Haas; A Loupy; C Lefaucheur; C Roufosse; D Glotz; D Seron; B J Nankivell; P F Halloran; R B Colvin; Enver Akalin; N Alachkar; S Bagnasco; Y Bouatou; J U Becker; L D Cornell; J P Duong van Huyen; I W Gibson; Edward S Kraus; R B Mannon; M Naesens; V Nickeleit; P Nickerson; D L Segev; H K Singh; M Stegall; P Randhawa; L Racusen; K Solez; M Mengel Journal: Am J Transplant Date: 2018-01-21 Impact factor: 8.086
Authors: Christoph Kuppe; Mahmoud M Ibrahim; Jennifer Kranz; Xiaoting Zhang; Susanne Ziegler; Javier Perales-Patón; Jitske Jansen; Katharina C Reimer; James R Smith; Ross Dobie; John R Wilson-Kanamori; Maurice Halder; Yaoxian Xu; Nazanin Kabgani; Nadine Kaesler; Martin Klaus; Lukas Gernhold; Victor G Puelles; Tobias B Huber; Peter Boor; Sylvia Menzel; Remco M Hoogenboezem; Eric M J Bindels; Joachim Steffens; Jürgen Floege; Rebekka K Schneider; Julio Saez-Rodriguez; Neil C Henderson; Rafael Kramann Journal: Nature Date: 2020-11-11 Impact factor: 49.962