Mitochondrial Dysfunction in Systemic Sclerosis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".