Dissecting Autophagy Gene Expression in Endothelial Cells of Pulmonary Arterial Hypertension at Single-Cell Resolution
Bibliographic record
Abstract
Background: Endothelial cell (EC) dysfunction is one of the pathogeneses of pulmonary arterial hypertension (PAH); autophagy is an important conserved mechanism for maintaining cellular homeostasis. However, to date, transcriptional signatures of autophagy-related genes during PAH are not well characterized. Methods: We analyzed the single-cell RNA sequencing (scRNA-seq) data and revealed the relationship between autophagy and PAH in EC from the perspective of differential expression, transcriptional signature typing, comparative analysis, pseudo-temporal changes of autophagy genes, and high dimensional weighted gene co-expression network analysis (hdWGCNA) networks of autophagy genes. Moreover, we established an in vitro PAH model and verified the expression signatures of candidate autophagy-related genes by western blotting. Student’s t-test was used to detect data differences. Results: Single-cell data showed that ECs had three different subgroups, namely Endo-Nor (normal state), Endo-Mid (transition state), and Endo-PAH (PAH disease group). Functional enrichment analysis of differentially expressed genes between Endo-PAH and Endo-Nor showed that autophagy and phosphatidylinositol 3-kinase (PI3K)-AKT signaling pathways were abnormal. Pseudo-temporal trajectory analysis showed that eight autophagy-related genes were involved in regulating PAH progression. Furthermore, the hdWGCNA network revealed six autophagy-related PAH progression genes. By comprehensively comparing different analysis methods, we identified Ddit4 as an autophagy-related PAH biomarker. Western blot observed that DDIT4 protein was significantly upregulated (P < 0.01) in the PAH model. Conclusion: This study dissected the expression signature of autophagy genes in PAH at single-cell resolution and identified DDIT4 as a potential biomarker.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".