Evidence map›Paper›PMID 42373672›Full record

ArticleNature communications2026

Transcriptome analysis in osteoarthritis primary tissues identifies high-confidence effector genes.

Georgia Katsoula, Ana Luiza Arruda, Mauro Tutino, Ene Reimann, Norbert Bittner, Peter Kreitmaier, Karan M Shah, Diane Swift, Lorraine Southam, Siim Suutre and 6 more

Abstract read
In one paragraph

Article in Nature communications, 2026. 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

16 authors.

Georgia Katsoula *Technical University of Munich (TUM), TUM University Hospital, TUM School of Medicine and Health, Munich, Germany.
Ana Luiza Arruda *Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
Mauro TutinoInstitute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
Ene ReimannInstitute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
Norbert BittnerInstitute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
Peter KreitmaierTechnical University of Munich (TUM), TUM University Hospital, TUM School of Medicine and Health, Munich, Germany.
Karan M ShahSchool of Medicine and Population Health, University of Sheffield, Sheffield, UK.ORCID http://orcid.org/0000-0001-9909-6409
Diane SwiftSchool of Medicine and Population Health, University of Sheffield, Sheffield, UK.
Lorraine SouthamInstitute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
Siim SuutreDepartment of Anatomy, University of Tartu, Tartu, Estonia.
Galadriel Lucía Velázquez SilvaEstonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.ORCID http://orcid.org/0009-0005-7691-7805
Kaspar TootsiDepartment of Orthopaedics, University of Tartu, Tartu, Estonia.
Aare MärtsonDepartment of Orthopaedics, University of Tartu, Tartu, Estonia.
Reedik MägiEstonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.ORCID http://orcid.org/0000-0002-2964-6011
J Mark WilkinsonSchool of Medicine and Population Health, University of Sheffield, Sheffield, UK. j.m.wilkinson@sheffield.ac.uk.ORCID http://orcid.org/0000-0001-5577-3674
Eleftheria ZegginiTechnical University of Munich (TUM), TUM University Hospital, TUM School of Medicine and Health, Munich, Germany. eleftheria.zeggini@helmholtz-munich.de.ORCID http://orcid.org/0000-0003-4238-659X

Funding

Wellcome TrustWellcome Trust 206194Wellcome Trust (Wellcome) 206194
6 · The paper itself

Abstract

Osteoarthritis, a whole-joint degenerative disorder, is a major public health burden that affects nearly 600 million individuals worldwide, with no disease-modifying treatment. Molecular profiling of relevant tissues is crucial for understanding the biology of disease development. Here, we generate a comprehensive map of cis- and trans- transcriptional regulation in disease-relevant primary tissues from knee osteoarthritis patients: macroscopically intact (low-grade, n = 261) and degenerated (high-grade, n = 212) cartilage, synovium (n = 277), and fat pad (n = 92). We identify 10,166 unique expression quantitative trait loci (eQTL)-associated genes, 61.1% of which have not been reported in previous osteoarthritis eQTL studies, and uncover cartilage grade-specific genetic regulation. Using the largest osteoarthritis genome-wide association study to date, we find colocalization evidence with 136 genes and prioritize 45 high-confidence effector genes. At colocalizing loci, osteoarthritis risk alleles are associated with increased expression of genes involved in chondrogenic and hypertrophic signaling and decreased expression of genes encoding regulatory and modulatory components of these pathways. By integrating the eQTL maps with functional data, we delineate regulatory architectures for osteoarthritis risk variants, including promoter-enhancer loops and transcription factor binding effects. Finally, we provide directional evidence highlighting drugs targeting LGALS3 and SMAD7 as repurposing opportunities for osteoarthritis treatment.

Indexed as

Gene Expression ProfilingOsteoarthritisOsteoarthritis, KneeTranscriptomeAdipose TissueCartilage, ArticularGene Expression RegulationGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansPolymorphism, Single NucleotideQuantitative Trait LociSynovial Membrane

Identifiers

PMID42373672
PMCPMC13458226

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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.