Antisense oligonucleotides targeting the miR-29b binding site in the GRN mRNA increase progranulin translation
Bibliographic record
Abstract
Heterozygous GRN (progranulin) mutations cause frontotemporal dementia (FTD) due to haploinsufficiency, and increasing progranulin levels is a major therapeutic goal. Several microRNAs, including miR-29b, negatively regulate progranulin protein levels. Antisense oligonucleotides (ASOs) are emerging as a promising therapeutic modality for neurological diseases, but strategies for increasing target protein levels are limited. Here, we tested the efficacy of ASOs as enhancers of progranulin expression by sterically blocking the miR-29b binding site in the 3' UTR of the human GRN mRNA. We found 16 ASOs that increase progranulin protein in a dose-dependent manner in neuroglioma cells. A subset of these ASOs also increased progranulin protein in iPSC-derived neurons and in a humanized GRN mouse model. In FRET-based assays, the ASOs effectively competed for miR-29b from binding to the GRN 3' UTR RNA. The ASOs increased levels of newly synthesized progranulin protein by increasing its translation, as revealed by polysome profiling. Together, our results demonstrate that ASOs can be used to effectively increase target protein levels by partially blocking miR binding sites. This ASO 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".