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Record W4411884055 · doi:10.3899/jrheum.2025-0314.95

Mitochondrial Dysfunction in Systemic Sclerosis

2025· article· en· W4411884055 on OpenAlexaffvenue
Junqin Wang, Dylan Hennessey, Aishwarya Iyer, Sandra O’Keefe, Desiree Redmond, Lamia Khan, Mohammed Osman, Robert Gniadecki

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTranscriptomeDermisComputational biologyKEGGBiologyPathologyMedicineGeneGene expressionGenetics

Abstract

fetched live from OpenAlex

Objectives Systemic sclerosis (SSc) is a chronic inflammatory and fibrotic disease with the involvement of skin at its onset. Although numerous pathogenic processes contribute to SSc, its root cause remains poorly understood. We hypothesize that valuable insights into SSc pathogenesis can be gained by characterizing interactions between functional niches within the skin from SSc patients using spatial transcriptomics. We aimed to: 1. To identify and map functional zones in SSc skin based on their cellular composition and patterns of mRNA expression. 2. To objectively designate dominant molecular signatures and pathways to those discrete dermal zones using bioinformatic tools. Methods Skin biopsies from 11 patients with limited and diffuse cutaneous SSc were analyzed using spatial transcriptomic sequencing. Single-cell RNA sequencing (scRNA-seq)[1] were used to deconvolve dermal cell types. Sequencing reads were processed using Space Ranger and Cell Ranger pipelines. Clustering spatial and single-cell data was performed with Seurat. Identified clusters were annotated by differentially expressed genes, with functional enrichment analysis conducted using GO and KEGG pipelines via ClusterProfiler. Results Spatial transcriptomic analysis identified 6 distinct transcriptomic clusters spanning the dermis in an interwoven pattern not aligned with the histological layers of the skin. We focused on 3 major dermal clusters (designated as clusters 0, 1, and 4) comprising >90% of the dermal area. Cluster 0 was heterogeneous with respect to cell types, included inflammatory signature, and was functionally dominated by mitochondrial metabolic pathways. Cluster 1 contained mostly fibroblasts exhibiting fibrotic pathways with minimal inflammation. Cluster 4 represented perivascular areas with prominent smooth muscle cells and myofibroblast signatures. The mitochondrial gene dysregulation in cluster 0 showed downregulation of nuclear DNA-encoded mitochondrial genes and upregulation of mitochondrial DNA-encoded genes, indicating stress-adaptation mechanisms in this functional zone. In contrast, Cluster 1 exhibited downregulation of both nuclear and mitochondrial genes - suggesting complete collapse of mitochondrial function (Figure). Conclusion For the first time, we revealed discrete zones in SSc skin characterized by inflammation accompanied by mitochondrial stress, separated from the areas of fibrosis where the mitochondrial function was disintegrated. Our study supports previous research indicating that dysregulated mitochondria are central, if not fundamental, to the pathogenesis of SSc. [1.] Tabib T. Nat Commun 2021;12:4384.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.250
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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