miR-374 family is a key regulator of chronic primary pain onset
Bibliographic record
Abstract
Introduction: Chronic primary pain conditions (CPPCs) are linked to catecholamine activation of peripheral adrenergic receptors. Yet, catecholamine-dependent epigenetic mechanisms, such as microRNA (miRNA) regulation of mRNA transcripts, remain largely unknown. Objectives: We sought to identify RNA species correlated with case status in 3 pain cohorts, to validate RNAs found to be dysregulated in a mouse model of CPPC onset, and to directly test the role of adrenergic receptors in miRNA regulation. Furthermore, we tested antinociceptive effects of miR-374 overexpression. Methods: We used RNA-seq and quantitative reverse transcription polymerase chain reaction to measure RNA expression in 3 pain cohorts. Next, we validated identified RNAs with quantitative reverse transcription polymerase chain reaction in a mouse model of CPPC onset, measuring expression in plasma, peripheral (adipose, muscle, dorsal root ganglia [DRG]), and central (spinal cord) tissues. Then, we stimulated adrenergic receptors in primary adipocyte and DRG cultures to directly test regulation of microRNAs by adrenergic signaling. Furthermore, we used in vitro calcium imaging to measure the antinociceptive effects of miR-374 overexpression. Results: We found that one miRNA family, miR-374, was downregulated in the plasma of individuals with temporomandibular disorder, fibromyalgia syndrome, or widespread pain following a motor vehicle collision. miR-374 was also downregulated in plasma, white adipose tissue, and spinal cord from mice with multisite mechanical sensitivity. miR-374 downregulation in plasma and spinal cord was female specific. Norepinephrine stimulation of primary adipocytes, but not DRG, led to decreased miR-374 expression. Furthermore, we identified tissue-specific and sex-specific changes in the expression of predicted miR-374 mRNA targets, including known (HIF1A, NUMB, TGFBR2) and new (ATXN7, CRK-II) pain targets. Finally, we demonstrated that miR-374 overexpression in DRG neurons reduced capsaicin-induced nociceptor activity. Conclusions: Downregulation of miR-374 occurs between adrenergic receptor activation and mechanical hypersensitivity, and its adipocyte source implicates adipose signaling in nociception. Further study of miR-374 may inform therapeutic strategies for the millions worldwide who experience CPPCs.
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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 | 0.000 |
| 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) | 0.003 | 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".