Literature DB >> 15307835

Kidney transplant rejection and tissue injury by gene profiling of biopsies and peripheral blood lymphocytes.

Stuart M Flechner1, Sunil M Kurian, Steven R Head, Starlette M Sharp, Thomas C Whisenant, Jie Zhang, Jeffrey D Chismar, Steve Horvath, Tony Mondala, Timothy Gilmartin, Daniel J Cook, Steven A Kay, John R Walker, Daniel R Salomon.   

Abstract

A major challenge for kidney transplantation is balancing the need for immunosuppression to prevent rejection, while minimizing drug-induced toxicities. We used DNA microarrays (HG-U95Av2 GeneChips, Affymetrix) to determine gene expression profiles for kidney biopsies and peripheral blood lymphocytes (PBLs) in transplant patients including normal donor kidneys, well-functioning transplants without rejection, kidneys undergoing acute rejection, and transplants with renal dysfunction without rejection. We developed a data analysis schema based on expression signal determination, class comparison and prediction, hierarchical clustering, statistical power analysis and real-time quantitative PCR validation. We identified distinct gene expression signatures for both biopsies and PBLs that correlated significantly with each of the different classes of transplant patients. This is the most complete report to date using commercial arrays to identify unique expression signatures in transplant biopsies distinguishing acute rejection, acute dysfunction without rejection and well-functioning transplants with no rejection history. We demonstrate for the first time the successful application of high density DNA chip analysis of PBL as a diagnostic tool for transplantation. The significance of these results, if validated in a multicenter prospective trial, would be the establishment of a metric based on gene expression signatures for monitoring the immune status and immunosuppression of transplanted patients. Copyright 2004 Blackwell Munksgaard

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Year:  2004        PMID: 15307835      PMCID: PMC2041877          DOI: 10.1111/j.1600-6143.2004.00526.x

Source DB:  PubMed          Journal:  Am J Transplant        ISSN: 1600-6135            Impact factor:   8.086


  26 in total

1.  Microarrays: how many do you need?

Authors:  Alexander Zien; Juliane Fluck; Ralf Zimmer; Thomas Lengauer
Journal:  J Comput Biol       Date:  2003       Impact factor: 1.479

Review 2.  The Banff classification of renal allograft pathology: where do we go from here?

Authors:  L Racusen; D Rayner; K Trpkov; S Olsen; K Solez
Journal:  Transplant Proc       Date:  1996-02       Impact factor: 1.066

Review 3.  Protocol biopsies in renal transplantation: research tool or clinically useful?

Authors:  D N Rush; P Nickerson; J R Jeffery; R M McKenna; P C Grimm; J Gough
Journal:  Curr Opin Nephrol Hypertens       Date:  1998-11       Impact factor: 2.894

4.  Gene expression analysis in human renal allograft biopsy samples using high-density oligoarray technology.

Authors:  E Akalin; R C Hendrix; R G Polavarapu; T C Pearson; J F Neylan; C P Larsen; F G Lakkis
Journal:  Transplantation       Date:  2001-09-15       Impact factor: 4.939

5.  Molecular analyses of human renal allografts: differential intragraft gene expression during rejection.

Authors:  M Suthanthiran
Journal:  Kidney Int Suppl       Date:  1997-03       Impact factor: 10.545

6.  The intragraft gene activation of markers reflecting T-cell-activation and -cytotoxicity analyzed by quantitative RT-PCR in renal transplantation.

Authors:  J Strehlau; M Pavlakis; M Lipman; W Maslinski; M Shapiro; T B Strom
Journal:  Clin Nephrol       Date:  1996-07       Impact factor: 0.975

7.  Beneficial effects of treatment of early subclinical rejection: a randomized study.

Authors:  D Rush; P Nickerson; J Gough; R McKenna; P Grimm; M Cheang; K Trpkov; K Solez; J Jeffery
Journal:  J Am Soc Nephrol       Date:  1998-11       Impact factor: 10.121

8.  Expression monitoring by hybridization to high-density oligonucleotide arrays.

Authors:  D J Lockhart; H Dong; M C Byrne; M T Follettie; M V Gallo; M S Chee; M Mittmann; C Wang; M Kobayashi; H Horton; E L Brown
Journal:  Nat Biotechnol       Date:  1996-12       Impact factor: 54.908

9.  Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection.

Authors:  C Li; W H Wong
Journal:  Proc Natl Acad Sci U S A       Date:  2001-01-02       Impact factor: 11.205

10.  The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data.

Authors:  David M Mutch; Alvin Berger; Robert Mansourian; Andreas Rytz; Matthew-Alan Roberts
Journal:  BMC Bioinformatics       Date:  2002-06-21       Impact factor: 3.169

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  101 in total

1.  A molecular classifier for predicting future graft loss in late kidney transplant biopsies.

Authors:  Gunilla Einecke; Jeff Reeve; Banu Sis; Michael Mengel; Luis Hidalgo; Konrad S Famulski; Arthur Matas; Bert Kasiske; Bruce Kaplan; Philip F Halloran
Journal:  J Clin Invest       Date:  2010-05-24       Impact factor: 14.808

Review 2.  Novel insights into lung transplant rejection by microarray analysis.

Authors:  Jeffrey D Lande; Jagadish Patil; Na Li; Todd R Berryman; Richard A King; Marshall I Hertz
Journal:  Proc Am Thorac Soc       Date:  2007-01

3.  Biological mechanism analysis of acute renal allograft rejection: integrated of mRNA and microRNA expression profiles.

Authors:  Shi-Ming Huang; Xia Zhao; Xue-Mei Zhao; Xiao-Ying Wang; Shan-Shan Li; Yu-Hui Zhu
Journal:  Int J Clin Exp Med       Date:  2014-12-15

4.  MicroRNA expression profiles predictive of human renal allograft status.

Authors:  Dany Anglicheau; Vijay K Sharma; Ruchuang Ding; Aurélie Hummel; Catherine Snopkowski; Darshana Dadhania; Surya V Seshan; Manikkam Suthanthiran
Journal:  Proc Natl Acad Sci U S A       Date:  2009-03-16       Impact factor: 11.205

5.  Villitis of unknown etiology is associated with a distinct pattern of chemokine up-regulation in the feto-maternal and placental compartments: implications for conjoint maternal allograft rejection and maternal anti-fetal graft-versus-host disease.

Authors:  Mi Jeong Kim; Roberto Romero; Chong Jai Kim; Adi L Tarca; Sovantha Chhauy; Christopher LaJeunesse; Deug-Chan Lee; Sorin Draghici; Francesca Gotsch; Juan Pedro Kusanovic; Sonia S Hassan; Jung-Sun Kim
Journal:  J Immunol       Date:  2009-03-15       Impact factor: 5.422

6.  Methods to increase reproducibility in differential gene expression via meta-analysis.

Authors:  Timothy E Sweeney; Winston A Haynes; Francesco Vallania; John P Ioannidis; Purvesh Khatri
Journal:  Nucleic Acids Res       Date:  2016-09-14       Impact factor: 16.971

7.  Molecular mechanisms of chronic kidney transplant rejection via large-scale proteogenomic analysis of tissue biopsies.

Authors:  Aleksey Nakorchevsky; Johannes A Hewel; Sunil M Kurian; Tony S Mondala; Daniel Campbell; Steve R Head; Christopher L Marsh; John R Yates; Daniel R Salomon
Journal:  J Am Soc Nephrol       Date:  2010-01-21       Impact factor: 10.121

Review 8.  Renal transplant rejection markers.

Authors:  Wilfried Gwinner
Journal:  World J Urol       Date:  2007-09-05       Impact factor: 4.226

9.  Identification of Candidate Biomarkers for Transplant Rejection from Transcriptome Data: A Systematic Review.

Authors:  Sheyla Velasques Paladini; Graziela Hünning Pinto; Rodrigo Haas Bueno; Raquel Calloni; Mariana Recamonde-Mendoza
Journal:  Mol Diagn Ther       Date:  2019-08       Impact factor: 4.074

10.  Identification of a peripheral blood transcriptional biomarker panel associated with operational renal allograft tolerance.

Authors:  Sophie Brouard; Elaine Mansfield; Christophe Braud; Li Li; Magali Giral; Szu-chuan Hsieh; Dominique Baeten; Meixia Zhang; Joanna Ashton-Chess; Cécile Braudeau; Frank Hsieh; Alexandre Dupont; Annaik Pallier; Anne Moreau; Stéphanie Louis; Catherine Ruiz; Oscar Salvatierra; Jean-Paul Soulillou; Minnie Sarwal
Journal:  Proc Natl Acad Sci U S A       Date:  2007-09-14       Impact factor: 11.205

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