Peripheral Blood RNA Sequencing Unravels a Differential Signature of Coding and Noncoding Genes by Types of Kidney Allograft Rejection

dc.contributor.authorPineda, Silvia
dc.contributor.authorSur, Swastika
dc.contributor.authorSigdel, Tara
dc.contributor.authorNguyen, Mark
dc.contributor.authorCrespo, Elena
dc.contributor.authorTorija Recasens, Alba
dc.contributor.authorMeneghini, Maria
dc.contributor.authorGomà, Montse
dc.contributor.authorSirota, Marina
dc.contributor.authorBestard Matamoros, Oriol
dc.contributor.authorSarwal, Minnie M.
dc.date.accessioned2021-02-17T09:23:40Z
dc.date.available2021-02-17T09:23:40Z
dc.date.issued2020-10-01
dc.date.updated2021-02-08T10:31:20Z
dc.description.abstractIntroduction: 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 = 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.
dc.format.extent16 p.
dc.format.mimetypeapplication/pdf
dc.identifier.pmid33102963
dc.identifier.urihttps://hdl.handle.net/2445/174006
dc.language.isoeng
dc.publisherElsevier Science Inc
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1016/j.ekir.2020.07.023
dc.relation.ispartofKidney International Reports, 2020, Vol. 5, Issue 10, P. 1706-1721
dc.relation.urihttps://doi.org/10.1016/j.ekir.2020.07.023
dc.rightscc by-nc-nd (c) Pineda, Silvia et al., 2020
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.sourceArticles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
dc.subject.classificationTrasplantament renal
dc.subject.classificationRebuig (Biologia)
dc.subject.otherKidney transplantation
dc.subject.otherGraft rejection
dc.titlePeripheral Blood RNA Sequencing Unravels a Differential Signature of Coding and Noncoding Genes by Types of Kidney Allograft Rejection
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

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