Transcriptional trajectories of human kidney injury progression
Transcriptional trajectories of human kidney injury progression
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DOI：
10.1172/jci.insight.123151
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发表时间：
2018-11-15
期刊：
影响因子：
8
通讯作者：
McMahon, Andrew P.
中科院分区：
文献类型：
--
作者：
Cippa, Pietro E.;Sun, Bo;McMahon, Andrew P.
背景从急性到慢性器官损伤的进展的分子理解是有限的。肾移植术后缺血/再灌注损伤（IRI）可导致移植肾功能障碍。在移植后的4个时间点从42个同种异体肾移植物中获得方案活检（n = 163）。采用RNA测序介导的（RNA-seq-mediated）转录谱分析和机器学习计算方法来分析对IRI的分子反应，并确定与不同临床结果相关的共享和不同的转录轨迹。这些数据与急性肾损伤向慢性肾损伤转变的小鼠模型中对IRI的反应进行了比较。在再灌注后的第一个小时，所有患者都表现出类似的转录程序的控制下立即早期反应基因。在接下来的几个月里，我们确定了2个主要的转录轨迹，导致肾脏恢复或持续损伤与相关的纤维化和肾功能障碍。通过这种计算方法生成的分子图谱突出了肾脏疾病进展的早期标志物，并描绘了与向慢性损伤过渡相关的转录程序。在小鼠IRI模型中类似过程的表征将我们的发现的相关性扩展到了移植之外。来自连续活检的多个转录组与先进的计算算法的整合克服了与个体之间的变异性相关的分析障碍，并确定了人类肾脏疾病进展的共享转录元件，这可能被证明是肾移植和肾损伤后疾病进展的有用预测因子。这种普遍适用的方法为人类疾病进展的无偏分析开辟了道路。这项研究得到了加州再生医学研究所和瑞士国家科学基金会的支持。
BACKGROUND. The molecular understanding of the progression from acute to chronic organ injury is limited. Ischemia/reperfusion injury (IRI) triggered during kidney transplantation can contribute to progressive allograft dysfunction.METHODS. Protocol biopsies (n = 163) were obtained from 42 kidney allografts at 4 time points after transplantation. RNA sequencing-mediated (RNA-seq-mediated) transcriptional profiling and machine learning computational approaches were employed to analyze the molecular responses to IRI and to identify shared and divergent transcriptional trajectories associated with distinct clinical outcomes. The data were compared with the response to IRI in a mouse model of the acute to chronic kidney injury transition.RESULTS. In the first hours after reperfusion, all patients exhibited a similar transcriptional program under the control of immediate-early response genes. In the following months, we identified 2 main transcriptional trajectories leading to kidney recovery or to sustained injury with associated fibrosis and renal dysfunction. The molecular map generated by this computational approach highlighted early markers of kidney disease progression and delineated transcriptional programs associated with the transition to chronic injury. The characterization of a similar process in a mouse IRI model extended the relevance of our findings beyond transplantation.CONCLUSIONS. The integration of multiple transcriptomes from serial biopsies with advanced computational algorithms overcame the analytical hurdles related to variability between individuals and identified shared transcriptional elements of kidney disease progression in humans, which may prove as useful predictors of disease progression following kidney transplantation and kidney injury. This generally applicable approach opens the way for an unbiased analysis of human disease progression.FUNDING. The study was supported by the California Institute for Regenerative Medicine and by the Swiss National Science Foundation.