Transcriptional trajectories of human kidney injury progression
Pietro E. Cippà, … , Maarten Naesens, Andrew P. McMahon
Pietro E. Cippà, … , Maarten Naesens, Andrew P. McMahon
Published November 15, 2018
Citation Information: JCI Insight. 2018;3(22):e123151. https://doi.org/10.1172/jci.insight.123151.
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Clinical Research and Public Health Nephrology Transplantation

Transcriptional trajectories of human kidney injury progression

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

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Figure 2

Early transcriptional response to ischemia/reperfusion.

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Early transcriptional response to ischemia/reperfusion. (A and B) Pseudo...
(A and B) Pseudotime analysis including samples collected before (PRE, n = 38) and after implantation (POST, n = 39). (A) Sample state ordering in the reduced dimensional space, as determined by the Monocle algorithm. PRE samples were classified in 2 groups and are shown in cyan; POST samples ordered along a pseudotime line from right to left and are shown in red. Among the POST samples, the circles mark samples from living donors (LD), and the black squares mark samples from donors after cardiac death (DCD). (B) Cluster analysis of representative genes differentially expressed along the pseudotime: samples are aligned from left to right according to the order shown in A. Genes are vertically aligned and classified in 2 clusters. The colors indicate the relative expression of the genes (log10 scale). The complete list of genes is presented in Supplemental Figure 2 and Supplemental Table 1. (C) Influence score for the top 14 transcription factors as determined in the network analysis based on a modified Mogrify algorithm. (D) Venn diagram including genes differentially expressed in POST compared with PRE (human) and homologous mouse genes differentially expressed 2 hours after IRI compared with control. Significance of enrichment was determined by hypergeometric test. (E and F) Reads per kilobase per million mapped reads (RPKM) values along the early time-course analysis after IRI in mice (n = 3 for each time point). (G) Venn diagram including genes differentially expressed in POST compared with PRE in the human kidney and in the liver. Significance of enrichment was determined by hypergeometric test. (H) Cluster analysis of representative genes differentially expressed along the pseudotime in the liver.