Evidence map›Paper›PMID 40878031›Full record

ArticleRenal failure2025

Integrated single-cell and clinical transcriptomic analysis identifies blunted glycolytic activation as a hallmark of maladaptive repair in renal ischemia-reperfusion.

Alexandre Torck, Frédéric Sangla, Hervé Quintard, Maarten Naesens, David Legouis

Abstract read
In one paragraph

Article in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Alexandre TorckDepartment of Intensive Care Medicine, Fribourg Hospital, Fribourg, Switzerland.
Frédéric SanglaDepartment of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.
Hervé QuintardDepartment of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.
Maarten NaesensDivision of Intensive Care Medicine, Department of Anesthesiology, Clinical Pharmacology, Critical Care and Emergency Medicine, Geneva University Hospital, Geneva, Switzerland.
David LegouisDepartment of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.ORCID 0000-0002-2924-9737

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute kidney injury (AKI) is a common and increases risk of chronic kidney disease (CKD). While mitochondrial dysfunction drives maladaptive repair, the role of glycolysis in renal recovery remains unclear. Here, we integrated single-cell transcriptomic data from mouse models with clinical transcriptomic and functional data from kidney transplant recipients to investigate how glycolysis influences epithelial repair. Trajectory inference and metabolic flux estimation revealed divergent cellular fates: cell with active glycolysis achieved recovery, while those with blunted glycolysis followed, maladaptive paths. These results were confirmed in an independent single-cell dataset and validated clinically in reperfusion biopsies. A machine-learning model trained on clinical parameters identified patients with underactive glycolysis at reperfusion; this subgroup showed impaired long-term recovery. Although causality is unproven, early glycolytic activation appears linked to regenerative repair. Glycolysis thus emerges as both a biomarker of epithelial fate and a potential therapeutic target to limit CKD progression.

Indexed as

Acute Kidney InjuryGlycolysisKidneyRenal Insufficiency, ChronicReperfusion InjuryAnimalsDisease Models, AnimalFemaleGene Expression ProfilingHumansKidney TransplantationMachine LearningMaleMiceSingle-Cell AnalysisTranscriptomeAKIglycolysismetabolismtransplantation

Identifiers

PMID40878031
PMCPMC12404070

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.