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

被引:21
作者
Pineda, Silvia [1 ,2 ]
Sur, Swastika [1 ]
Sigdel, Tara [1 ]
Nguyen, Mark [1 ]
Crespo, Elena [3 ]
Torija, Alba [3 ]
Meneghini, Maria [3 ,4 ]
Goma, Montse [5 ]
Sirota, Marina [2 ,6 ]
Bestard, Oriol [1 ,3 ,7 ]
Sarwal, Minnie M. [1 ]
机构
[1] Univ Calif San Francisco, Div Transplant Surg, S1269,505 Parnassus Ave, San Francisco, CA 94107 USA
[2] Univ Calif San Francisco, Bakar Computat Hlth Sci Inst, San Francisco, CA 94107 USA
[3] Inst Invest Biomed Bellvitge, Lab Expt Nephrol & Transplantat, Barcelona, Spain
[4] Bellvitge Univ Hosp, Inst Invest Biomed Bellvitge, Kidney Transplant Unit, Barcelona, Spain
[5] Barcelona Univ, Bellvitge Univ Hosp, Inst Invest Biomed Bellvitge, Pathol Dept, Barcelona, Spain
[6] Univ Calif San Francisco, Dept Pediat, San Francisco, CA 94107 USA
[7] Bellvitge Univ Hosp, Nephrol Dept, Renal Transplant Unit, Feixa Llarga S-N, Barcelona 08907, Spain
基金
美国国家卫生研究院;
关键词
antibody-mediated rejection; kidney transplantation; RNA sequencing; systems biology; T cell-mediated rejection; CELL-MEDIATED REJECTION; TRANSPLANT REJECTION; CRITERIA; ANNOTATION; EXPRESSION; PREDICTION; BIOMARKERS; BIOPSIES; INJURY; RISK;
D O I
10.1016/j.ekir.2020.07.023
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
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 = 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.
引用
收藏
页码:1706 / 1721
页数:16
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