Transcriptome-Wide Analysis of HuR Function Identifies TXNIP-Mediated Redox Dysregulation in Osteocytes
Bibliographic record
Abstract
Summary Post-transcriptional gene regulation is central to maintaining cellular homeostasis, among its mechanism alternative splicing (AS) fine-tunes cellular adaptation to stress. In this study we employed an approach combining RNA splicing analysis to define RNA binding protein (RBP) motif enrichment around alternatively spliced exons in primary osteocytes cultured in high glucose conditions (HG). We identified the RBP human antigen R (HuR) as a top candidate regulator of AS. Loss of HuR reshaped the transcriptome through widespread changes in gene expression and splicing, converging on two major pathways: stress response and translational control. Functional validation of splicing revealed that HuR depletion heightened oxidative stress sensitivity and compromised cell viability under HG by stabilizing and upregulating TXNIP, a thioredoxin inhibitor. HuR knockdown also impaired mitochondrial mass and function and disrupted key translational signals, despite preserving global protein output. These findings establish HuR as a central post-transcriptional regulator of osteocyte survival and metabolic adaptation under high glucose stress, with potential implications for hyperglycemic bone fragility.
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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 | 0.000 | 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) | 0.001 | 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".