Transcriptional trajectories of human kidney injury progression.

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.

[1]  Benjamin J. Raphael,et al.  Visible Machine Learning for Biomedicine , 2018, Cell.

[2]  Fuhui Long,et al.  An anatomic transcriptional atlas of human glioblastoma , 2018, Science.

[3]  Zoltan Szallasi,et al.  Deterministic Evolutionary Trajectories Influence Primary Tumor Growth: TRACERx Renal , 2018, Cell.

[4]  Qiang Zhao,et al.  The ileal FGF15/19 to hepatic FGFR4 axis regulates liver regeneration after partial hepatectomy in mice , 2018, Journal of Physiology and Biochemistry.

[5]  R. Rieben,et al.  Effects of C1 inhibitor on endothelial cell activation in a rat hind limb ischemia‐reperfusion injury model , 2018, Journal of vascular surgery.

[6]  David van Dijk,et al.  Manifold learning-based methods for analyzing single-cell RNA-sequencing data , 2018 .

[7]  Sarah Webb.  Deep learning for biology , 2018, Nature.

[8]  Audrey Y. Chu,et al.  Epigenome-wide association studies identify DNA methylation associated with kidney function , 2017, Nature Communications.

[9]  A. McMahon,et al.  Molecular characterization of the transition from acute to chronic kidney injury following ischemia/reperfusion , 2017, JCI insight.

[10]  A. Letai,et al.  Functional precision cancer medicine—moving beyond pure genomics , 2017, Nature Medicine.

[11]  P. Bhargava,et al.  Mitochondrial energetics in the kidney , 2017, Nature Reviews Nephrology.

[12]  B. Pillot,et al.  Dose and timing of injections for effective cyclosporine A pretreatment before renal ischemia reperfusion in mice , 2017, PloS one.

[13]  G. Perkins,et al.  Hypoxia-inducible factor-1α activation improves renal oxygenation and mitochondrial function in early chronic kidney disease. , 2017, American journal of physiology. Renal physiology.

[14]  Richard Hotchkiss,et al.  A guiding map for inflammation , 2017, Nature Immunology.

[15]  Qin Zhu,et al.  PIVOT: platform for interactive analysis and visualization of transcriptomics data , 2016, BMC Bioinformatics.

[16]  Paul Martin,et al.  Inflammation and metabolism in tissue repair and regeneration , 2017, Science.

[17]  Irving L. Weissman,et al.  Unifying mechanism for different fibrotic diseases , 2017, Proceedings of the National Academy of Sciences.

[18]  Ben Ke,et al.  Matrix Metalloproteinases-7 and Kidney Fibrosis , 2017, Front. Physiol..

[19]  B. Naini,et al.  Early cytokine signatures of ischemia/reperfusion injury in human orthotopic liver transplantation. , 2016, JCI insight.

[20]  Ashutosh Kumar Singh,et al.  Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015 , 2016, The Lancet.

[21]  Yi Luan,et al.  Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: a multicentre, prospective study , 2016, The Lancet.

[22]  Cynthia C. Hession,et al.  Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons , 2016, Science.

[23]  Yoshihide Hayashizaki,et al.  A predictive computational framework for direct reprogramming between human cell types , 2016, Nature Genetics.

[24]  A. McMahon,et al.  Sox9 Activation Highlights a Cellular Pathway of Renal Repair in the Acutely Injured Mammalian Kidney. , 2015, Cell reports.

[25]  H. Snieder,et al.  Hypoxia and Complement-and-Coagulation Pathways in the Deceased Organ Donor as the Major Target for Intervention to Improve Renal Allograft Outcome , 2015, Transplantation.

[26]  S. Quake,et al.  A survey of human brain transcriptome diversity at the single cell level , 2015, Proceedings of the National Academy of Sciences.

[27]  Dmitri D. Pervouchine,et al.  The human transcriptome across tissues and individuals , 2015, Science.

[28]  W. Weng,et al.  Genome-wide gene expression profiling of ischemia-reperfusion injury in rat kidney, intestine and skeletal muscle implicate a common involvement of MAPK signaling pathway. , 2015, Molecular medicine reports.

[29]  Thomas J. Ha,et al.  Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells , 2015, Science.

[30]  N. Rosenthal,et al.  Preparing the ground for tissue regeneration: from mechanism to therapy , 2014, Nature Medicine.

[31]  Cole Trapnell,et al.  Pseudo-temporal ordering of individual cells reveals dynamics and regulators of cell fate decisions , 2014, Nature Biotechnology.

[32]  Josef Coresh,et al.  Chronic kidney disease , 2012, The Lancet.

[33]  C. Baines,et al.  Cell biology of ischemia/reperfusion injury. , 2012, International review of cell and molecular biology.

[34]  K. Fujiu,et al.  Renal collecting duct epithelial cells regulate inflammation in tubulointerstitial damage in mice. , 2011, The Journal of clinical investigation.

[35]  Cunshuan Xu,et al.  Comparative analysis of expression profiles of chemokines, chemokine receptors, and components of signaling pathways mediated by chemokines in eight cell types during rat liver regeneration. , 2010, Genome.

[36]  Jing Chen,et al.  ToppGene Suite for gene list enrichment analysis and candidate gene prioritization , 2009, Nucleic Acids Res..

[37]  B. Padanilam,et al.  PARP-1 inhibits glycolysis in ischemic kidneys. , 2009, Journal of the American Society of Nephrology : JASN.

[38]  M. Robinson,et al.  Small-sample estimation of negative binomial dispersion, with applications to SAGE data. , 2007, Biostatistics.