Literature DB >> 24610929

Urinary cell mRNA profiles and differential diagnosis of acute kidney graft dysfunction.

Marie Matignon1, Ruchuang Ding2, Darshana M Dadhania3, Franco B Mueller2, Choli Hartono3, Catherine Snopkowski2, Carol Li2, John R Lee3, Daniel Sjoberg4, Surya V Seshan5, Vijay K Sharma2, Hua Yang2, Bakr Nour6, Andrew J Vickers4, Manikkam Suthanthiran3, Thangamani Muthukumar7.   

Abstract

Noninvasive tests to differentiate the basis for acute dysfunction of the kidney allograft are preferable to invasive allograft biopsies. We measured absolute levels of 26 prespecified mRNAs in urine samples collected from kidney graft recipients at the time of for-cause biopsy for acute allograft dysfunction and investigated whether differential diagnosis of acute graft dysfunction is feasible using urinary cell mRNA profiles. We profiled 52 urine samples from 52 patients with biopsy specimens indicating acute rejection (26 acute T cell-mediated rejection and 26 acute antibody-mediated rejection) and 32 urine samples from 32 patients with acute tubular injury without acute rejection. A stepwise quadratic discriminant analysis of mRNA measures identified a linear combination of mRNAs for CD3ε, CD105, TLR4, CD14, complement factor B, and vimentin that distinguishes acute rejection from acute tubular injury; 10-fold cross-validation of the six-gene signature yielded an estimate of the area under the curve of 0.92 (95% confidence interval, 0.86 to 0.98). In a decision analysis, the six-gene signature yielded the highest net benefit across a range of reasonable threshold probabilities for biopsy. Next, among patients diagnosed with acute rejection, a similar statistical approach identified a linear combination of mRNAs for CD3ε, CD105, CD14, CD46, and 18S rRNA that distinguishes T cell-mediated rejection from antibody-mediated rejection, with a cross-validated estimate of the area under the curve of 0.81 (95% confidence interval, 0.68 to 0.93). Incorporation of these urinary cell mRNA signatures in clinical decisions may reduce the number of biopsies in patients with acute dysfunction of the kidney allograft.
Copyright © 2014 by the American Society of Nephrology.

Entities:  

Keywords:  acute allograft rejection; mRNA; renal dysfunction

Mesh:

Substances:

Year:  2014        PMID: 24610929      PMCID: PMC4073438          DOI: 10.1681/ASN.2013080900

Source DB:  PubMed          Journal:  J Am Soc Nephrol        ISSN: 1046-6673            Impact factor:   10.121


  16 in total

1.  Noninvasive diagnosis of renal-allograft rejection by measurement of messenger RNA for perforin and granzyme B in urine.

Authors:  B Li; C Hartono; R Ding; V K Sharma; R Ramaswamy; B Qian; D Serur; J Mouradian; J E Schwartz; M Suthanthiran
Journal:  N Engl J Med       Date:  2001-03-29       Impact factor: 91.245

2.  Messenger RNA for FOXP3 in the urine of renal-allograft recipients.

Authors:  Thangamani Muthukumar; Darshana Dadhania; Ruchuang Ding; Catherine Snopkowski; Rubina Naqvi; Jun B Lee; Choli Hartono; Baogui Li; Vijay K Sharma; Surya V Seshan; Sandip Kapur; Wayne W Hancock; Joseph E Schwartz; Manikkam Suthanthiran
Journal:  N Engl J Med       Date:  2005-12-01       Impact factor: 91.245

3.  Decision curve analysis: a discussion.

Authors:  Ewout W Steyerberg; Andrew J Vickers
Journal:  Med Decis Making       Date:  2008 Jan-Feb       Impact factor: 2.583

4.  Diagnostic contribution of renal allograft biopsies at various intervals after transplantation.

Authors:  S P Kon; J Templar; S M Dodd; C J Rudge; M J Raftery
Journal:  Transplantation       Date:  1997-02-27       Impact factor: 4.939

5.  The clinical usefulness of the renal allograft biopsy in the cyclosporine era: a prospective study.

Authors:  M Pascual; H Vallhonrat; A B Cosimi; N Tolkoff-Rubin; R B Colvin; F L Delmonico; D S Ko; D A Schoenfeld; W W Williams
Journal:  Transplantation       Date:  1999-03-15       Impact factor: 4.939

Review 6.  Clinical role of the renal transplant biopsy.

Authors:  Winfred W Williams; Diana Taheri; Nina Tolkoff-Rubin; Robert B Colvin
Journal:  Nat Rev Nephrol       Date:  2012-01-10       Impact factor: 28.314

7.  Urinary neutrophil gelatinase-associated lipocalin accurately detects acute allograft rejection among other causes of acute kidney injury in renal allograft recipients.

Authors:  Nils Heyne; Stephan Kemmner; Christian Schneider; Silvio Nadalin; Alfred Königsrainer; Hans-Ulrich Häring
Journal:  Transplantation       Date:  2012-06-27       Impact factor: 4.939

8.  International variation in the interpretation of renal transplant biopsies: report of the CERTPAP Project.

Authors:  P N Furness; N Taub
Journal:  Kidney Int       Date:  2001-11       Impact factor: 10.612

9.  Decision curve analysis: a novel method for evaluating prediction models.

Authors:  Andrew J Vickers; Elena B Elkin
Journal:  Med Decis Making       Date:  2006 Nov-Dec       Impact factor: 2.583

10.  Importance of allograft biopsy in renal transplant recipients: correlation between clinical and histological diagnosis.

Authors:  I A Al-Awwa; S Hariharan; M R First
Journal:  Am J Kidney Dis       Date:  1998-06       Impact factor: 8.860

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  18 in total

Review 1.  Role for urinary biomarkers in diagnosis of acute rejection in the transplanted kidney.

Authors:  Basma Merhi; George Bayliss; Reginald Y Gohh
Journal:  World J Transplant       Date:  2015-12-24

Review 2.  Extracellular vesicles in renal disease.

Authors:  Diana Karpman; Anne-Lie Ståhl; Ida Arvidsson
Journal:  Nat Rev Nephrol       Date:  2017-07-24       Impact factor: 28.314

Review 3.  Moving Biomarkers toward Clinical Implementation in Kidney Transplantation.

Authors:  Madhav C Menon; Barbara Murphy; Peter S Heeger
Journal:  J Am Soc Nephrol       Date:  2017-01-06       Impact factor: 10.121

4.  Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use.

Authors:  Kathleen F Kerr; Marshall D Brown; Kehao Zhu; Holly Janes
Journal:  J Clin Oncol       Date:  2016-05-31       Impact factor: 44.544

5.  Urinary cell transcriptomics and acute rejection in human kidney allografts.

Authors:  Akanksha Verma; Thangamani Muthukumar; Hua Yang; Michelle Lubetzky; Michael F Cassidy; John R Lee; Darshana M Dadhania; Catherine Snopkowski; Divya Shankaranarayanan; Steven P Salvatore; Vijay K Sharma; Jenny Z Xiang; Iwijn De Vlaminck; Surya V Seshan; Franco B Mueller; Karsten Suhre; Olivier Elemento; Manikkam Suthanthiran
Journal:  JCI Insight       Date:  2020-02-27

6.  Urinary C-X-C Motif Chemokine 10 Independently Improves the Noninvasive Diagnosis of Antibody-Mediated Kidney Allograft Rejection.

Authors:  Marion Rabant; Lucile Amrouche; Xavier Lebreton; Florence Aulagnon; Aurélien Benon; Virginia Sauvaget; Raja Bonifay; Lise Morin; Anne Scemla; Marianne Delville; Frank Martinez; Marc Olivier Timsit; Jean-Paul Duong Van Huyen; Christophe Legendre; Fabiola Terzi; Dany Anglicheau
Journal:  J Am Soc Nephrol       Date:  2015-05-06       Impact factor: 10.121

7.  Urine CXCL10/IP-10 Fingers Ongoing Antibody-Mediated Kidney Graft Rejection.

Authors:  Robert L Fairchild; Manikkam Suthanthiran
Journal:  J Am Soc Nephrol       Date:  2015-05-06       Impact factor: 10.121

Review 8.  Genomic and proteomic fingerprints of acute rejection in peripheral blood and urine.

Authors:  Song Ong; Roslyn B Mannon
Journal:  Transplant Rev (Orlando)       Date:  2014-12-10       Impact factor: 3.943

9.  Assessing the Clinical Impact of Risk Models for Opting Out of Treatment.

Authors:  Kathleen F Kerr; Marshall D Brown; Tracey L Marsh; Holly Janes
Journal:  Med Decis Making       Date:  2019-01-16       Impact factor: 2.583

Review 10.  The Use of Genomics and Pathway Analysis in Our Understanding and Prediction of Clinical Renal Transplant Injury.

Authors:  Madhav C Menon; Karen L Keung; Barbara Murphy; Philip J OʼConnell
Journal:  Transplantation       Date:  2016-07       Impact factor: 4.939

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