Antisense oligonucleotides targeting miR‐29b binding site increase translation of progranulin protein: potential therapeutic strategy for progranulin‐deficient frontotemporal dementia
Bibliographic record
Abstract
Abstract Background GRN mutations cause frontotemporal dementia (FTD) due to haploinsufficiency of progranulin. Several microRNAs (miRs), including miR‐29b, have been reported to negatively regulate progranulin protein levels. Here, we tested if antisense oligonucleotides (ASOs) – which are versatile modulators of target mRNA/protein levels – can be used to increase progranulin levels by sterically blocking the miR‐29b binding site. Method We designed 48 ASOs targeting the miR‐29b binding site in the 3’ UTR of the human GRN mRNA. We treated H4 neuroglioma cells and iPSC‐derived neurons with these ASOs and subsequently measured progranulin protein levels by western blot and ELISA. We performed further studies to determine the mechanism of action of these ASOs using ribosomal profiling, metabolic labeling, and FRET assays. Results We identified 16 ASOs that increased progranulin protein levels in a dose‐dependent manner. Ribosomal profiling experiments revealed that cells treated with ASOs had marked enrichment in GRN mRNA in heavy polyribosome fractions, compared to cells treated with a scrambled control ASO, suggesting that the ASOs increase the rate of progranulin translation. Consistent with this, ASO treatment resulted in increased levels of newly synthesized progranulin protein. FRET‐based assays showed that ASOs can effectively compete miR‐29b from its binding site in the GRN 3’ UTR RNA under in vitro conditions. Conclusions Together, our results demonstrate that ASOs can be used to effectively increase target protein levels by partially blocking miR binding sites. This strategy may be therapeutically feasible for progranulin‐deficient FTD as well as other conditions of haploinsufficiency.
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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".