Thangamani Muthukumar1, Kemal M Akat2, Hua Yang1, Joseph E Schwartz1,3, Carol Li1, Heejung Bang4, Iddo Z Ben-Dov2, John R Lee1, David Ikle5, Anthony J Demetris6, Thomas Tuschl2, Manikkam Suthanthiran1. 1. Division of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine and Department of Transplantation Medicine, New York Presbyterian-Weill Cornell Medicine, New York, NY. 2. Laboratory of RNA Molecular Biology, The Rockefeller University, New York, NY. 3. Department of Psychiatry and Behavioral Science, Stony Brook University, Stony Brook, NY. 4. Division of Biostatistics, Department of Public Health Sciences, University of California at Davis, Davis, CA. 5. Rho Federal Systems, Chapel Hill, NC. 6. Division of Transplantation Pathology, Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA.
Abstract
BACKGROUND: Acute rejection (AR) and recurrent hepatitis C virus (R-HCV) are significant complications in liver allograft recipients. Noninvasive diagnosis of intragraft pathologies may improve their management. METHODS: We performed small RNA sequencing and microRNA (miRNA) microarray profiling of RNA from sera matched to liver allograft biopsies from patients with nonimmune, nonviral (NINV) native liver disease. Absolute levels of informative miRNAs in 91 sera matched to 91 liver allograft biopsies were quantified using customized real-time quantitative PCR (RT-qPCR) assays: 30 biopsy-matched sera from 26 unique NINV patients and 61 biopsy-matched sera from 41 unique R-HCV patients. The association between biopsy diagnosis and miRNA abundance was analyzed by logistic regression and calculating the area under the receiver operating characteristic curve. RESULTS: Nine miRNAs-miR-22, miR-34a, miR-122, miR-148a, miR-192, miR-193b, miR-194, miR-210, and miR-885-5p-were identified by both sRNA-seq and TLDA to be associated with NINV-AR. Logistic regression analysis of absolute levels of miRNAs and goodness-of-fit of predictors identified a linear combination of miR-34a + miR-210 (P < 0.0001) as the best statistical model and miR-122 + miR-210 (P < 0.0001) as the best model that included miR-122. A different linear combination of miR-34a + miR-210 (P < 0.0001) was the best model for discriminating NINV-AR from R-HCV with intragraft inflammation, and miR-34a + miR-122 (P < 0.0001) was the best model for discriminating NINV-AR from R-HCV with intragraft fibrosis. CONCLUSIONS: Circulating levels of miRNAs, quantified using customized RT-qPCR assays, may offer a rapid and noninvasive means of diagnosing AR in human liver allografts and for discriminating AR from intragraft inflammation or fibrosis due to R-HCV.
BACKGROUND: Acute rejection (AR) and recurrent hepatitis C virus (R-HCV) are significant complications in liver allograft recipients. Noninvasive diagnosis of intragraft pathologies may improve their management. METHODS: We performed small RNA sequencing and microRNA (miRNA) microarray profiling of RNA from sera matched to liver allograft biopsies from patients with nonimmune, nonviral (NINV) native liver disease. Absolute levels of informative miRNAs in 91 sera matched to 91 liver allograft biopsies were quantified using customized real-time quantitative PCR (RT-qPCR) assays: 30 biopsy-matched sera from 26 unique NINV patients and 61 biopsy-matched sera from 41 unique R-HCV patients. The association between biopsy diagnosis and miRNA abundance was analyzed by logistic regression and calculating the area under the receiver operating characteristic curve. RESULTS: Nine miRNAs-miR-22, miR-34a, miR-122, miR-148a, miR-192, miR-193b, miR-194, miR-210, and miR-885-5p-were identified by both sRNA-seq and TLDA to be associated with NINV-AR. Logistic regression analysis of absolute levels of miRNAs and goodness-of-fit of predictors identified a linear combination of miR-34a + miR-210 (P < 0.0001) as the best statistical model and miR-122 + miR-210 (P < 0.0001) as the best model that included miR-122. A different linear combination of miR-34a + miR-210 (P < 0.0001) was the best model for discriminating NINV-AR from R-HCV with intragraft inflammation, and miR-34a + miR-122 (P < 0.0001) was the best model for discriminating NINV-AR from R-HCV with intragraft fibrosis. CONCLUSIONS: Circulating levels of miRNAs, quantified using customized RT-qPCR assays, may offer a rapid and noninvasive means of diagnosing AR in human liver allografts and for discriminating AR from intragraft inflammation or fibrosis due to R-HCV.
Authors: Abraham Shaked; Michele R DesMarais; Heather Kopetskie; Sandy Feng; Jeffrey D Punch; Josh Levitsky; Jorge Reyes; Goran B Klintmalm; Anthony J Demetris; Bryna E Burrell; Allison Priore; Nancy D Bridges; Peter H Sayre Journal: Am J Transplant Date: 2018-12-31 Impact factor: 8.086
Authors: Nicholas Redshaw; Timothy Wilkes; Alexandra Whale; Simon Cowen; Jim Huggett; Carole A Foy Journal: Biotechniques Date: 2013-03 Impact factor: 1.993
Authors: Pieter Mestdagh; Nicole Hartmann; Lukas Baeriswyl; Ditte Andreasen; Nathalie Bernard; Caifu Chen; David Cheo; Petula D'Andrade; Mike DeMayo; Lucas Dennis; Stefaan Derveaux; Yun Feng; Stephanie Fulmer-Smentek; Bernhard Gerstmayer; Julia Gouffon; Chris Grimley; Eric Lader; Kathy Y Lee; Shujun Luo; Peter Mouritzen; Aishwarya Narayanan; Sunali Patel; Sabine Peiffer; Silvia Rüberg; Gary Schroth; Dave Schuster; Jonathan M Shaffer; Elliot J Shelton; Scott Silveria; Umberto Ulmanella; Vamsi Veeramachaneni; Frank Staedtler; Thomas Peters; Toumy Guettouche; Linda Wong; Jo Vandesompele Journal: Nat Methods Date: 2014-06-29 Impact factor: 28.547
Authors: Kemal Marc Akat; D'Vesharronne Moore-McGriff; Pavel Morozov; Miguel Brown; Tasos Gogakos; Joel Correa Da Rosa; Aleksandra Mihailovic; Markus Sauer; Ruiping Ji; Aarthi Ramarathnam; Hana Totary-Jain; Zev Williams; Thomas Tuschl; P Christian Schulze Journal: Proc Natl Acad Sci U S A Date: 2014-07-10 Impact factor: 11.205
Authors: Pablo Landgraf; Mirabela Rusu; Robert Sheridan; Alain Sewer; Nicola Iovino; Alexei Aravin; Sébastien Pfeffer; Amanda Rice; Alice O Kamphorst; Markus Landthaler; Carolina Lin; Nicholas D Socci; Leandro Hermida; Valerio Fulci; Sabina Chiaretti; Robin Foà; Julia Schliwka; Uta Fuchs; Astrid Novosel; Roman-Ulrich Müller; Bernhard Schermer; Ute Bissels; Jason Inman; Quang Phan; Minchen Chien; David B Weir; Ruchi Choksi; Gabriella De Vita; Daniela Frezzetti; Hans-Ingo Trompeter; Veit Hornung; Grace Teng; Gunther Hartmann; Miklos Palkovits; Roberto Di Lauro; Peter Wernet; Giuseppe Macino; Charles E Rogler; James W Nagle; Jingyue Ju; F Nina Papavasiliou; Thomas Benzing; Peter Lichter; Wayne Tam; Michael J Brownstein; Andreas Bosio; Arndt Borkhardt; James J Russo; Chris Sander; Mihaela Zavolan; Thomas Tuschl Journal: Cell Date: 2007-06-29 Impact factor: 41.582
Authors: Abraham Shaked; Bao-Li Chang; Michael R Barnes; Peter Sayre; Yun R Li; Smita Asare; Michele DesMarais; Michael V Holmes; Toumy Guettouche; Brendan J Keating Journal: Hepatology Date: 2016-10-05 Impact factor: 17.425
Authors: Joseph M Dhahbi; Stephen R Spindler; Hani Atamna; Amy Yamakawa; Dario Boffelli; Patricia Mote; David I K Martin Journal: BMC Genomics Date: 2013-05-02 Impact factor: 3.969
Authors: Markus Hafner; Neil Renwick; Thalia A Farazi; Aleksandra Mihailović; John T G Pena; Thomas Tuschl Journal: Methods Date: 2012-08-07 Impact factor: 3.608