Evidence map›Paper›PMID 41256619›Full record

ArticlebioRxiv : the preprint server for biology2025

Multi-omics integration at cell type resolution uncovers gene-metabolite mechanisms underlying osteoarthritis heterogeneity.

Abhishek Ojha, Paramita Chatterjee, Linda E Kippner, Hazel Y Stevens, Scott D Boden, Hicham Drissi, Kenneth Mautner, Carolyn Yeago, Krishnendu Roy, Saurabh Sinha

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

10 authors.

Abhishek OjhaThe Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Paramita ChatterjeeMarcus Center for Therapeutic Cell Characterization and Manufacturing, The Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.
Linda E KippnerMarcus Center for Therapeutic Cell Characterization and Manufacturing, The Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.
Hazel Y StevensMarcus Center for Therapeutic Cell Characterization and Manufacturing, The Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.
Scott D BodenDepartment of Orthopedics, Emory University School of Medicine, Atlanta, GA, USA.
Hicham DrissiDepartment of Orthopedics, Emory University School of Medicine, Atlanta, GA, USA.
Kenneth MautnerDepartment of Orthopedics, Emory University School of Medicine, Atlanta, GA, USA.
Carolyn YeagoThe Parker H. Petit Institute for Bioengineering and Biosciences Georgia Institute of Technology, Atlanta, GA, USA.
Krishnendu RoyMarcus Center for Therapeutic Cell Characterization and Manufacturing, The Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.
Saurabh SinhaThe Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

Funding

Quantitative Regulatory Genomics: Networks, CIS-Regulatorycodes, and Phenotypic VariationR35GM131819 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI SINHA, SAURABH · 2019 to 2023
$1.8M
NIGMS NIH HHS R35 GM131819
6 · The paper itself

Abstract

Metabolic dysregulation is an important factor for osteoarthritis pathogenesis, but comprehensive studies of underlying mechanisms and pathways are rare. We analyzed newly generated metabolomics data on bone marrow from 119 osteoarthritis patients, along with single-cell transcriptomics data to reconstruct networks of gene-metabolite associations at cell type resolution. Hubs of these networks - cell type-specific as well as pan-cell type hubs - revealed key molecular factors of osteoarthritis heterogeneity. Systems-level analysis of hubs revealed major roles for glycerophospholipid, glycerolipid and sphingolipid metabolism pathways, along with lipid signaling. We used Machine Learning models of gene-metabolite relationships to discover cell types most relevant to each metabolite. Integrative analysis of disease severity scores along with multi-omics data revealed a shift in specific immune cell subtypes in low versus high grade disease. We conclude that leveraging gene-metabolite covariation in a patient cohort can uncover underlying molecular mechanisms, overcoming the challenges posed by high dimensionality of multi-omics data.

Identifiers

PMID41256619
PMCPMC12622010

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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.