Discordant Regulation of microRNA Between Multiple Experimental Models and Human Pulmonary Hypertension
Bibliographic record
Abstract
BACKGROUND: The dysregulation of microRNA (miRNA) is known to contribute to the pathobiology of pulmonary arterial hypertension (PAH). However, the relationships between changes in tissue and circulating miRNA levels associated with different animal models and human pulmonary hypertension (PH) have not been defined. METHODS: A set of miRNAs that have been causally implicated in PH, including miR-17, -21, -130b, -145, -204, -424, and -503, were measured by reverse transcription-quantitative polymerase chain reaction in the plasma, lung, and right ventricle of three of the most common rodent models of PH: the rat monocrotaline and SU5416 plus chronic hypoxia (SuHx) models and the mouse chronic hypoxia model. Plasma miRNA levels were also evaluated in a cohort of patients with PAH and healthy subjects. RESULTS: Several miRNA showed PH model-dependent perturbations in plasma and tissue levels; however, none of these were conserved across all three experimental models. Principle component analysis of miR expression changes in plasma revealed distinct clustering between rodent models, and SuHx-triggered PH showed the greatest similarity to human PAH. Changes in the plasma levels of several miRNA also correlated with changes in tissue expression. In particular, miR-424 was concordantly increased (1.3- to 1.5-fold, P < .05) in the plasma, lung, and right ventricle of hypoxic mice and in the plasma of patients with PAH. CONCLUSIONS: miRNAs with established etiologic roles in PH showed context-dependent changes in tissue and circulating levels, which were not consistent across rodent models and human PAH. This suggests different miRNA-dependent mechanisms may contribute to experimental and clinical PH, complicating potential diagnostic and therapeutic applications amenable to these miRNAs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".