Evidence map›Paper›PMID 30429361›Full record

ArticleJCI insight2018

Transcriptional trajectories of human kidney injury progression.

Pietro E Cippà, Bo Sun, Jing Liu, Liang Chen, Maarten Naesens, Andrew P McMahon

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in JCI insight, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01331668 (Biobank Renal Transplantation University Hospitals Leuven), which is not on this map. Cited by 84 papers.

0numbers the graph read from it
0cells of the map it votes in
84citing papers in PubMed
8.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT01331668 recruitingnot on this map

Biobank Renal Transplantation University Hospitals Leuven

TypeobservationalSponsorUniversitaire Ziekenhuizen KU LeuvenRan2004 to 2099Enrolled5,000ConditionsKidney Failure
3 · Its place in the literature

Who cites it

84 citing papers in PubMed, 115 citations in OpenAlex.

  1. Article
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  5. Chronic Kidney Disease and Cellular Senescence.International journal of molecular sciences · 2026
    Review
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24 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 3 countries.

Pietro E CippàDepartment of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California (USC), Los Angeles, USA.
Bo SunMolecular and Computational Biology, USC, Los Angeles, USA.
Jing LiuDepartment of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California (USC), Los Angeles, USA.
Liang ChenMolecular and Computational Biology, USC, Los Angeles, USA.
Maarten NaesensDepartment of Microbiology and Immunology, KU Leuven, and Department of Nephrology and Kidney Transplantation, University Hospitals Leuven, Leuven, Belgium.
Andrew P McMahonDepartment of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California (USC), Los Angeles, USA.
Broad Center · USComputational Physics (United States) · USKU Leuven · BEOspedale regionale di Lugano · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodsProtocol 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.

resultsIn 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.

conclusionsThe 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.

fundingThe study was supported by the California Institute for Regenerative Medicine and by the Swiss National Science Foundation.

Indexed as

Transcription, GeneticAdultAgedAnimalsBiopsyComputational BiologyDisease ProgressionFemaleGene Expression ProfilingHumansKidney DiseasesKidney TransplantationMaleMiceMiddle AgedReperfusion InjuryChronic kidney diseaseNephrologyTranscriptionTransplantation

Identifiers

PMID30429361
PMCPMC6302941
OpenAlexW2806846414

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.