A cross-sectional study of the role of epithelial cell injury in kidney transplant outcomes

. 2025 May 22 ; 10 (10) : . [epub] 20250415

Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection

Typ dokumentu časopisecké články, pozorovací studie

Perzistentní odkaz   https://www.medvik.cz/link/pmid40232852

BACKGROUND: Expression of acute kidney injury-associated (AKI-associated) transcripts in kidney transplants may reflect recent injury and accumulation of epithelial cells in "failed repair" states. We hypothesized that the phenomenon of failed repair could be associated with deterioration and failure in kidney transplants. METHODS: We defined injury-induced transcriptome states in 4,502 kidney transplant biopsies injury-induced gene sets and classifiers previously developed in transplants. RESULTS: In principal component analysis (PCA), PC1 correlated with both acute and chronic kidney injury and related inflammation and PC2 with time posttransplant. Positive PC3 was a dimension that correlated with epithelial remodeling pathways and anticorrelated with inflammation. Both PC1 and PC3 correlated with reduced survival, with PC1 effects strongly increasing over time whereas PC3 effects were independent of time. In this model, we studied the expression of 12 "new" gene sets annotated in single-nucleus RNA-sequencing studies of epithelial cells with failed repair in native kidneys. The new gene sets reflecting epithelial-mesenchymal transition correlated with injury PC1 and PC3, lower estimated glomerular filtration rate, higher donor age, and future failure as strongly as any gene sets previously derived in transplants and were independent of nephron segment of origin and graft rejection. CONCLUSION: These results suggest 2 dimensions in the kidney transplant response to injury: PC1, AKI-induced changes, failed repair, and inflammation; and PC3, a response involving epithelial remodeling without inflammation. Increasing kidney age amplifies PC1 and PC3. TRIAL REGISTRATION: INTERCOMEX (ClinicalTrials.gov NCT01299168); Trifecta-Kidney (ClinicalTrials.gov NCT04239703). FUNDING: Genome Canada; Natera, Inc.; and Thermo Fisher Scientific.

Alberta Transplant Applied Genomics Centre and

Charité Universitätsmedizin Berlin Berlin Germany

Cleveland Clinic Foundation Cleveland Ohio USA

Hannover Medical School Hannover Germany

Henry Ford Transplant Institute Detroit Michigan USA

Institute for Experimental and Clinical Medicine Prague Czech Republic

Intermountain Transplant Services Murray Utah USA

Johns Hopkins University School of Medicine Baltimore Maryland USA

Manchester Royal Infirmary Manchester United Kingdom

Medical University of Białystok Białystok Poland

Medical University of Gdańsk Gdańsk Poland

Medical University of Vienna Vienna Austria

Medical University of Wrocław Wrocław Poland

Montefiore Medical Center Bronx New York USA

PinnacleHealth Transplant Associates UPMC Harrisburg Pennsylvania USA

Pomeranian Medical University Szczecin Poland

Silesian Medical University Katowice Poland

St Paul's Hospital Vancouver British Columbia Canada

Tampa General Hospital Tampa Florida USA

The Royal Melbourne Hospital Parkville Victoria Australia

University Hospital Cleveland Medical Center Cleveland Ohio USA

University Hospital Merkur Zagreb Croatia

University Hospital No 1 Bydgoszcz Poland

University Hospital Zurich Zurich Switzerland

University of Alabama at Birmingham Birmingham Alabama USA

University of Alberta Edmonton Alberta Canada

University of Ljubljana Ljubljana Slovenia

University of Maryland Baltimore Maryland USA

University of Minnesota Minneapolis Minnesota USA

University of Washington Seattle Washington USA

University of Wisconsin Madison Wisconsin USA

Vilnius University Hospital Santaros Klinikos Vilnius Lithuania

Virginia Commonwealth University Richmond Virginia USA

Warsaw Medical University Warsaw Poland

Washington University at St Louis St Louis Missouri USA

Wojewodzki Hospital Poznan Poland

Zobrazit více v PubMed

Einecke G, et al. Factors associated with kidney graft survival in pure antibody-mediated rejection at the time of indication biopsy: importance of parenchymal injury but not disease activity. Am J Transplant. 2021;21(4):1391–1401. doi: 10.1111/ajt.16161. PubMed DOI

Halloran PF, et al. Archetypal analysis of injury in kidney transplant biopsies identifies two classes of early AKI. Front Med (Lausanne) 2022;9:817324. doi: 10.3389/fmed.2022.817324. PubMed DOI PMC

Famulski KS, et al. Transcriptome analysis reveals heterogeneity in the injury response of kidney transplants. Am J Transplant. 2007;7(11):2483–2495. doi: 10.1111/j.1600-6143.2007.01980.x. PubMed DOI

Famulski KS, et al. Molecular phenotypes of acute kidney injury in kidney transplants. J Am Soc Nephrol. 2012;23(5):948–958. doi: 10.1681/ASN.2011090887. PubMed DOI PMC

Einecke G, et al. Expression of B cell and immunoglobulin transcripts is a feature of inflammation in late allografts. Am J Transplant. 2008;8(7):1434–1443. doi: 10.1111/j.1600-6143.2008.02232.x. PubMed DOI

Mengel M, et al. Molecular correlates of scarring in kidney transplants: the emergence of mast cell transcripts. Am J Transplant. 2009;9(1):169–178. doi: 10.1111/j.1600-6143.2008.02462.x. PubMed DOI

Halloran PF, et al. Discovering novel injury features in kidney transplant biopsies associated with TCMR and donor aging. Am J Transplant. 2021;21(5):1725–1739. doi: 10.1111/ajt.16374. PubMed DOI

Einecke G, et al. A molecular classifier for predicting future graft loss in late kidney transplant biopsies. J Clin Invest. 2010;120(6):1862–1872. doi: 10.1172/JCI41789. PubMed DOI PMC

Halloran PF, et al. Molecular phenotype of kidney transplant indication biopsies with inflammation in scarred areas. Am J Transplant. 2019;19(5):1356–1370. doi: 10.1111/ajt.15178. PubMed DOI

Famulski KS, et al. Kidney transplants with progressing chronic diseases express high levels of acute kidney injury transcripts. Am J Transplant. 2013;13(3):634–644. doi: 10.1111/ajt.12080. PubMed DOI

Madill-Thomsen KS, et al. Relating molecular T cell-mediated rejection activity in kidney transplant biopsies to time and to histologic tubulitis and atrophy-fibrosis. Transplantation. 2023;107(5):1102–1114. doi: 10.1097/TP.0000000000004396. PubMed DOI PMC

Halloran PF, et al. The molecular phenotype of kidney transplants: insights from the MMDx project. Transplantation. 2024;108(1):45–71. doi: 10.1097/TP.0000000000004624. PubMed DOI PMC

Kirita Y, et al. Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury. Proc Natl Acad Sci U S A. 2020;117(27):15874–15883. doi: 10.1073/pnas.2005477117. PubMed DOI PMC

Hinze C, et al. Single-cell transcriptomics reveals common epithelial response patterns in human acute kidney injury. Genome Med. 2022;14(1):103. doi: 10.1186/s13073-022-01108-9. PubMed DOI PMC

Wen Y, et al. Analysis of the human kidney transcriptome and plasma proteome identifies markers of proximal tubule maladaptation to injury. Sci Transl Med. 2023;15(726):eade7287. doi: 10.1126/scitranslmed.ade7287. PubMed DOI PMC

Hinze C, et al. Epithelial cell states associated with kidney and allograft injury. Nat Rev Nephrol. 2024;20(7):447–459. doi: 10.1038/s41581-024-00834-0. PubMed DOI PMC

Muto Y, et al. Epigenetic reprogramming driving successful and failed repair in acute kidney injury. Sci Adv. 2024;10(32):eado2849. doi: 10.1126/sciadv.ado2849. PubMed DOI PMC

Ledru N, et al. Predicting proximal tubule failed repair drivers through regularized regression analysis of single cell multiomic sequencing. Nat Commun. 2024;15(1):1291. doi: 10.1038/s41467-024-45706-0. PubMed DOI PMC

Yang L, et al. Epithelial cell cycle arrest in G2/M mediates kidney fibrosis after injury. Nat Med. 2010;16(5):535–543. doi: 10.1038/nm.2144. PubMed DOI PMC

Cippa PE, et al. Transcriptional trajectories of human kidney injury progression. JCI Insight. 2018;3(22):e123151. doi: 10.1172/jci.insight.123151. PubMed DOI PMC

Shavlakadze T, et al. Age-related gene expression signature in rats demonstrate early, late, and linear transcriptional changes from multiple tissues. Cell Rep. 2019;28(12):3263–3273. doi: 10.1016/j.celrep.2019.08.043. PubMed DOI

Reeve J, et al. Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes. JCI Insight. 2017;2(12):e94197. doi: 10.1172/jci.insight.94197. PubMed DOI PMC

Halloran PF, et al. Subthreshold rejection activity in many kidney transplants currently classified as having no rejection. Am J Transplant. 2025;25(1):72–87. doi: 10.1016/j.ajt.2024.07.034. PubMed DOI

Venner JM, et al. Relationships among injury, fibrosis, and time in human kidney transplants. JCI Insight. 2016;1(1):e85323. doi: 10.1172/jci.insight.85323. PubMed DOI PMC

Madill-Thomsen KS, et al. Donor-specific antibody is associated with increased expression of rejection transcripts in renal transplant biopsies classified as no rejection. J Am Soc Nephrol. 2021;32(11):2743–2758. doi: 10.1681/ASN.2021040433. PubMed DOI PMC

Rosales IA, et al. Banff human organ transplant transcripts correlate with renal allograft pathology and outcome: importance of capillaritis and subpathologic rejection. J Am Soc Nephrol. 2022;33(12):2306–2319. doi: 10.1681/ASN.2022040444. PubMed DOI PMC

Mylonas KJ, et al. Cellular senescence inhibits renal regeneration after injury in mice, with senolytic treatment promoting repair. Sci Transl Med. 2021;13(594):eabb0203. doi: 10.1126/scitranslmed.abb0203. PubMed DOI

Dai CL, et al. CXCL6: a potential therapeutic target for inflammation and cancer. Clin Exp Med. 2023;23(8):4413–4427. doi: 10.1007/s10238-023-01152-8. PubMed DOI

Katz-Greenberg G, et al. Sex and gender differences in kidney transplantation. Semin Nephrol. 2022;42(2):219–229. doi: 10.1016/j.semnephrol.2022.04.011. PubMed DOI PMC

Halloran PF, et al. Potential impact of microarray diagnosis of T cell-mediated rejection in kidney transplants: the INTERCOM study. Am J Transplant. 2013;13(9):2352–2363. doi: 10.1111/ajt.12387. PubMed DOI

Halloran PF, et al. Microarray diagnosis of antibody-mediated rejection in kidney transplant biopsies: an international prospective study (INTERCOM) Am J Transplant. 2013;13(11):2865–2874. doi: 10.1111/ajt.12465. PubMed DOI

Reeve J, et al. Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers. Am J Transplant. 2019;19(10):2719–2731. doi: 10.1111/ajt.15351. PubMed DOI

Halloran PF, et al. Real time central assessment of kidney transplant indication biopsies by microarrays: the INTERCOMEX study. Am J Transplant. 2017;17(11):2851–2862. doi: 10.1111/ajt.14329. PubMed DOI

Halloran PF, et al. The trifecta study: comparing plasma levels of donor-derived cell-free DNA with the molecular phenotype of kidney transplant biopsies. J Am Soc Nephrol. 2022;33(2):387–400. doi: 10.1681/ASN.2021091191. PubMed DOI PMC

Halloran PF, et al. Combining donor-derived cell-free DNA fraction and quantity to detect kidney transplant rejection using molecular diagnoses and histology as confirmation. Transplantation. 2022;106(12):2435–2442. doi: 10.1097/TP.0000000000004212. PubMed DOI PMC

Halloran PF, et al. Antibody-mediated rejection without detectable donor-specific antibody releases donor-derived cell-free DNA: results from the Trifecta study. Transplantation. 2023;107(3):709–719. doi: 10.1097/TP.0000000000004324. PubMed DOI PMC

Madill-Thomsen KS, et al. The effect of cortex/medulla proportions on molecular diagnoses in kidney transplant biopsies: rejection and injury can be assessed in medulla. Am J Transplant. 2017;17(8):2117–2128. doi: 10.1111/ajt.14233. PubMed DOI PMC

Konopka T. umap: Uniform Manifold Approximation and Projection. https://cran.r-project.org/web/packages/umap/index.html Accessed April 10, 2025.

Ishwaran H, et al. Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC). https://cran.r-project.org/web/packages/randomForestSRC/randomForestSRC.pdf Accessed April 10, 2025.

Therneau T. survival: Survival Analysis. https://CRAN.R-project.org/package=survival Accessed April 10, 2025.

Kassambara A, et al. survminer: Drawing Survival Curves using ‘ggplot2’. https://cran.r-project.org/web/packages/survminer/survminer.pdf Accessed April 10, 2025.

R: A language and environment for statistical computing. R Foundation for Statistical Computing; 2019. https://www.R-project.org/ Accessed April 10, 2025.

Harrell FE Jr. rms: Regression Modeling Strategies. R package version 6.8-2. https://CRAN.R-project.org/package=rms Accessed April 10, 2025.

Hidalgo LG, et al. The transcriptome of human cytotoxic T cells: similarities and disparities among allostimulated CD4(+) CTL, CD8(+) CTL and NK cells. Am J Transplant. 2008;8(3):627–636. doi: 10.1111/j.1600-6143.2007.02128.x. PubMed DOI

Karlsson M, et al. A single-cell type transcriptomics map of human tissues. Sci Adv. 2021;7(31):eabh2169. doi: 10.1126/sciadv.abh2169. PubMed DOI PMC

Zobrazit více v PubMed

ClinicalTrials.gov
NCT04239703, NCT01299168

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...