Abstract
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.
Authors
Pietro E. Cippà, Bo Sun, Jing Liu, Liang Chen, Maarten Naesens, Andrew P. McMahon
Figure 1
The kidney allograft transcriptome across individuals and time.
