Evidence map›Paper›PMID 40687826›Full record

ArticleiScience2025

Utilizing rat kidney gene co-expression networks to enhance safety assessment biomarker identification and human translation.

Steven J Kunnen, Giulia Callegaro, Jeffrey J Sutherland, Panuwat Trairatphisan, Hugo W van Kessel, Lukas S Wijaya, Git Chung, Keith Pye, Keith M Goldstein, Claire R Teague and 7 more

Abstract read
In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

17 authors.

Steven J KunnenLeiden University, Leiden Academic Centre for Drug Research (LACDR), Division of Cell Systems and Drug Safety, Leiden, the Netherlands.
Giulia CallegaroLeiden University, Leiden Academic Centre for Drug Research (LACDR), Division of Cell Systems and Drug Safety, Leiden, the Netherlands.
Jeffrey J SutherlandVesalius Therapeutics, Cambridge, MA, USA.
Panuwat TrairatphisanSanofi, R&D Preclinical Safety, Industriepark Hoechst, Frankfurt am Main, Germany.
Hugo W van KesselLeiden University, Leiden Academic Centre for Drug Research (LACDR), Division of Cell Systems and Drug Safety, Leiden, the Netherlands.
Lukas S WijayaLeiden University, Leiden Academic Centre for Drug Research (LACDR), Division of Cell Systems and Drug Safety, Leiden, the Netherlands.
Git ChungNewcells Biotech, Newcastle, UK.
Keith PyeNewcells Biotech, Newcastle, UK.
Keith M GoldsteinLilly Research Laboratories, Eli Lilly and Company Ltd, Indianapolis, IN, USA.
Claire R TeagueGSK, Clinical Pharmacology and Experimental Medicine, Stevenage, UK.
Ciaran P FisherGSK, Target and Systems Safety, Nonclinical Safety, Stevenage, UK.
Julio Saez-RodriguezHeidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Heidelberg, Germany.
Colin BrownNewcells Biotech, Newcastle, UK.
Susan A ElmoreElmorePathology, LLC, Chapel Hill, NC, USA.
Kathleen M Heinz-TahenyLilly Research Laboratories, Eli Lilly and Company Ltd, Indianapolis, IN, USA.
James L StevensLeiden University, Leiden Academic Centre for Drug Research (LACDR), Division of Cell Systems and Drug Safety, Leiden, the Netherlands.
Bob van de WaterLeiden University, Leiden Academic Centre for Drug Research (LACDR), Division of Cell Systems and Drug Safety, Leiden, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Toxicogenomic data provide key insights into molecular mechanisms underlying drug-induced organ toxicities. To simplify transcriptomic data interpretation, we applied weighted gene co-expression network analysis (WGCNA) to rat kidney transcriptomics data from TG-GATEs (TG) and DrugMatrix (DM), covering time- and dose-response data for 180 compounds. A total of 347 gene modules were incorporated into the rat kidney TXG-MAPr web-tool, that interactively visualizes and quantifies module activity using eigengene scores (EGSs). Several modules annotated for cellular stress, injury, and inflammation were associated with renal pathologies and included established and candidate biomarker genes. Many rat kidney modules were preserved across transcriptome datasets, suggesting potential applicability to other kidney injury contexts. Cross-species preservation analysis using human kidney data further supported the translational potential of these rat-derived modules. The TXG-MAPr platform facilitates upload and analysis of gene expression data in the context of rat kidney co-expression networks, which could identify mechanisms and safety liabilities of chemical or drug exposures.

Indexed as

biocomputational methoddata processing in systems biologymodel organismtranscriptomics

Identifiers

PMID40687826
PMCPMC12274721

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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 OpenQuestion graph.