Literature DB >> 33492376

Molecular Diversity of Clinically Stable Human Kidney Allografts.

Dmitry Rychkov1,2, Swastika Sur1, Marina Sirota2,3, Minnie M Sarwal1.   

Abstract

Importance: Clinical decision and immunosuppression dosing in kidney transplantation rely on transplant biopsy tissue histology even though histology has low specificity, sensitivity, and reproducibility for rejection diagnosis. The inclusion of stable allografts in mechanistic and clinical studies is vital to provide a normal, noninjured comparative group for all interrogative studies on understanding allograft injury. Objective: To refine the definition of a stable allograft as one that is clinically, histologically, and molecularly quiescent using publicly available transcriptomics data. Design, Setting, and Participants: In this prognostic study, the National Center for Biotechnology Information Gene Expression Omnibus was used to search for microarray gene expression data from kidney transplant tissues, resulting in 38 studies from January 1, 2017, to December 31, 2018. The diagnostic annotations included 510 acute rejection (AR) samples, 1154 histologically stable (hSTA) samples, and 609 normal samples. Raw fluorescence intensity data were downloaded and preprocessed followed by data set merging and batch correction. Main Outcomes and Measures: The primary measure was area under the receiver operating characteristics curve from a set of feature selected genes and cell types for distinguishing AR from normal kidney tissue.
Results: Within the 28 data sets, the feature selection procedure identified a set of 6 genes (KLF4, CENPJ, KLF2, PPP1R15A, FOSB, TNFAIP3) (area under the curve [AUC], 0.98) and 5 immune cell types (CD4+ T-cell central memory [Tcm], CD4+ T-cell effector memory [Tem], CD8+ Tem, natural killer [NK] cells, and Type 1 T helper [TH1] cells) (AUC, 0.92) that were combined into 1 composite Instability Score (InstaScore) (AUC, 0.99). The InstaScore was applied to the hSTA samples: 626 of 1154 (54%) were found to be immune quiescent and redefined as histologically and molecularly stable (hSTA/mSTA); 528 of 1154 (46%) were found to have molecular evidence of rejection (hSTA/mAR) and should not have been classified as stable allografts. The validation on an independent cohort of 6 months of protocol biopsy samples in December 2019 showed that hSTA/mAR samples had a significant change in graft function (r = 0.52, P < .001) and graft loss at 5-year follow-up (r = 0.17). A drop by 10 mL/min/1.73m2 in estimated glomerular filtration rate was estimated as a threshold in allograft transitioning from hSTA/mSTA to hSTA/mAR. Conclusions and Relevance: The results of this prognostic study suggest that the InstaScore could provide an important adjunct for comprehensive and highly quantitative phenotyping of protocol kidney transplant biopsy samples and could be integrated into clinical care for accurate estimation of subsequent patient clinical outcomes.

Entities:  

Year:  2021        PMID: 33492376      PMCID: PMC7835722          DOI: 10.1001/jamanetworkopen.2020.35048

Source DB:  PubMed          Journal:  JAMA Netw Open        ISSN: 2574-3805


  63 in total

1.  Non-HLA antibodies to immunogenic epitopes predict the evolution of chronic renal allograft injury.

Authors:  Tara K Sigdel; Li Li; Tim Q Tran; Purvesh Khatri; Maarten Naesens; Poonam Sansanwal; Hong Dai; Szu-chuan Hsieh; Minnie M Sarwal
Journal:  J Am Soc Nephrol       Date:  2012-02-02       Impact factor: 10.121

2.  Molecular diagnosis of T cell-mediated rejection in human kidney transplant biopsies.

Authors:  J Reeve; J Sellarés; M Mengel; B Sis; A Skene; L Hidalgo; D G de Freitas; K S Famulski; P F Halloran
Journal:  Am J Transplant       Date:  2013-01-28       Impact factor: 8.086

Review 3.  Lymphocyte Function in Human Acute Kidney Injury.

Authors:  Sophie Weller; Matt Varrier; Marlies Ostermann
Journal:  Nephron       Date:  2017-06-30       Impact factor: 2.847

Review 4.  The critical role of Krüppel-like factors in kidney disease.

Authors:  Sandeep K Mallipattu; Chelsea C Estrada; John C He
Journal:  Am J Physiol Renal Physiol       Date:  2016-11-16

5.  Expression of protective genes in human renal allografts: a regulatory response to injury associated with graft rejection.

Authors:  Yingyos Avihingsanon; Naili Ma; Eva Csizmadia; Candace Wang; Martha Pavlakis; Mauricio Giraldo; Terry B Strom; Miguel P Soares; Christiane Ferran
Journal:  Transplantation       Date:  2002-04-15       Impact factor: 4.939

6.  NCBI GEO: archive for functional genomics data sets--update.

Authors:  Tanya Barrett; Stephen E Wilhite; Pierre Ledoux; Carlos Evangelista; Irene F Kim; Maxim Tomashevsky; Kimberly A Marshall; Katherine H Phillippy; Patti M Sherman; Michelle Holko; Andrey Yefanov; Hyeseung Lee; Naigong Zhang; Cynthia L Robertson; Nadezhda Serova; Sean Davis; Alexandra Soboleva
Journal:  Nucleic Acids Res       Date:  2012-11-27       Impact factor: 16.971

7.  Gene expression profiling in acute allograft rejection: challenging the immunologic constant of rejection hypothesis.

Authors:  Tara L Spivey; Lorenzo Uccellini; Maria Libera Ascierto; Gabriele Zoppoli; Valeria De Giorgi; Lucia Gemma Delogu; Alyson M Engle; Jaime M Thomas; Ena Wang; Francesco M Marincola; Davide Bedognetti
Journal:  J Transl Med       Date:  2011-10-12       Impact factor: 5.531

8.  A common gene signature across multiple studies relate biomarkers and functional regulation in tolerance to renal allograft.

Authors:  Daniel Baron; Gérard Ramstein; Mélanie Chesneau; Yann Echasseriau; Annaick Pallier; Chloé Paul; Nicolas Degauque; Maria P Hernandez-Fuentes; Alberto Sanchez-Fueyo; Kenneth A Newell; Magali Giral; Jean-Paul Soulillou; Rémi Houlgatte; Sophie Brouard
Journal:  Kidney Int       Date:  2015-01-28       Impact factor: 10.612

9.  STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.

Authors:  Damian Szklarczyk; Annika L Gable; David Lyon; Alexander Junge; Stefan Wyder; Jaime Huerta-Cepas; Milan Simonovic; Nadezhda T Doncheva; John H Morris; Peer Bork; Lars J Jensen; Christian von Mering
Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

10.  Accurate and fast feature selection workflow for high-dimensional omics data.

Authors:  Yasset Perez-Riverol; Max Kuhn; Juan Antonio Vizcaíno; Marc-Phillip Hitz; Enrique Audain
Journal:  PLoS One       Date:  2017-12-20       Impact factor: 3.240

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  2 in total

1.  Integrated Analysis of Prognostic Genes Associated With Ischemia-Reperfusion Injury in Renal Transplantation.

Authors:  Di Zhang; Yicun Wang; Song Zeng; Min Zhang; Xin Zhang; Yuxuan Wang; Zijian Zhang; Xi Wang; Xiaopeng Hu
Journal:  Front Immunol       Date:  2021-09-07       Impact factor: 7.561

2.  Through the Looking Glass: Unraveling the Stage-Shift of Acute Rejection in Renal Allografts.

Authors:  Reuben D Sarwal; Wanzin Yazar; Nicholas Titzler; Jeremy Wong; Chih-Hung Lai; Christopher Chin; Danielle Krieger; Jeff Stoll; Francisco Dias Lourenco; Minnie M Sarwal; Srinka Ghosh
Journal:  J Clin Med       Date:  2022-02-09       Impact factor: 4.241

  2 in total

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