Pineda Silvia, Sur Swastika, Sigdel Tara, Nguyen Mark, Crespo Elena, Torija Alba, Meneghini Maria, Gomà Montse, Sirota Marina, Bestard Oriol, Sarwal Minnie M
Division of Transplant Surgery, University of California San Francisco, San Francisco, California, USA.
Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA.
Kidney Int Rep. 2020 Jul 26;5(10):1706-1721. doi: 10.1016/j.ekir.2020.07.023. eCollection 2020 Oct.
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.
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.
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 (ρ = 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 pseudogene and 9 -related coding genes were highly expressed among AMR but not in TCMR and STA samples.
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.
表征驱动不同类型肾移植排斥反应的不同效应免疫途径的外周血（PB）分子模式仍有待充分阐明。我们假设，对肾移植患者样本进行RNA测序（RNAseq）的转录组分析能够识别出可能区分不同排斥表型的独特蛋白质编码基因和非编码基因。
我们通过RNAseq评估了来自发现队列的37对活检外周血样本，这些样本包括稳定型（STA）、抗体介导的排斥反应（AMR）和T细胞介导的排斥反应（TCMR）。使用先进的机器学习工具进行三向差异基因表达分析，以识别与排斥反应相关的基因特征。然后，我们通过Fluidigm（加利福尼亚州旧金山）对来自2个独立肾移植队列的62个样本进行了功能计算机分析和验证。
我们发现102个基因（63个编码基因和39个非编码基因）与AMR（54个上调）、TCMR（23个上调）和STA（25个上调）相关，这些基因与每种排斥表型完美聚类，并且与主要组织学病变高度相关（ρ = 0.91）。对于与AMR相关的基因，我们发现其在内质网应激调节、适应性免疫和Ig类别转换方面富集。在验证中，我们发现假基因和9个相关编码基因在AMR样本中高表达，但在TCMR和STA样本中不高表达。
该分析确定了接受AMR的肾移植患者外周血中的关键基因特征，足以将他们与TCMR患者和免疫静止的肾移植受者区分开来。我们的研究结果为剖析非编码基因在肾移植排斥反应病理生理学中的作用及其作为排斥反应过程无创生物标志物的潜在价值的新研究提供了基础。
