Peripheral blood RNA sequencing unravels a differential signature of coding and noncoding genes by types of kidney allograft rejection
Introduction: Peripheral blood (PB) molecular patterns characterizing the different effector immune pathways driving distinct kidney rejection types remain to be fully elucidated. We hypothesized that transcriptome analysis using RNA sequencing (RNAseq) in samples of kidney transplant patients would...
| Autores: | , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/94057 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/94057 |
| Access Level: | acceso abierto |
| Palabra clave: | 616.61 519.2:61 Antibody-mediated rejection Kidney transplantation RNA sequencing Systems biology T cell–mediated rejection Nefrología y urología Biomatemáticas 3205.06 Nefrología 2404.01 Bioestadística |
| Sumario: | Introduction: Peripheral blood (PB) molecular patterns characterizing the different effector immune pathways driving distinct kidney rejection types remain to be fully elucidated. We hypothesized that transcriptome analysis using RNA sequencing (RNAseq) in samples of kidney transplant patients would enable the identification of unique protein-coding and noncoding genes that may be able to segregate different rejection phenotypes. Methods: We evaluated 37 biopsy-paired PB samples from the discovery cohort, with stable (STA), antibody-mediated rejection (AMR), and T cell–mediated rejection (TCMR) by RNAseq. Advanced machine learning tools were used to perform 3-way differential gene expression analysis to identify gene signatures associated with rejection. We then performed functional in silico analysis and validation by Fluidigm (San Francisco, CA) in 62 samples from 2 independent kidney transplant cohorts. Results: We found 102 genes (63 coding genes and 39 noncoding genes) associated with AMR (54 upregulated), TCMR (23 upregulated), and STA (25 upregulated) perfectly clustered with each rejection phenotype and highly correlated with main histologic lesions (r ¼ 0.91). For the genes associated with AMR, we found enrichment in regulation of endoplasmic reticulum stress, adaptive immunity, and Ig class-switching. In the validation, we found that the SIGLEC17P pseudogene and 9 SIGLEC17P-related coding genes were highly expressed among AMR but not in TCMR and STA samples. Conclusions: This analysis identifies a critical gene signature in PB in kidney transplant patients undergoing AMR, sufficient to differentiate them from patients with TCMR and immunologically quiescent kidney allografts. Our findings provide the basis for new studies dissecting the role of noncoding genes in the pathophysiology of kidney allograft rejection and their potential value as noninvasive biomarkers of the rejection process. |
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