Human progranulin-expressing mice as a novel tool for the development of progranulin-modulating therapeutics
Bibliographic record
Abstract
The granulin protein (also known as, and hereafter referred to as, progranulin) is a secreted glycoprotein that contributes to overall brain health. Heterozygous loss-of-function mutations in the gene encoding the progranulin protein (Granulin Precursor, GRN) are a common cause of familial frontotemporal dementia (FTD). Gene therapy approaches that aim to increase progranulin expression from a single wild-type allele, an area of active investigation for the potential treatment of GRN-dependent FTD, will benefit from the availability of a mouse model that expresses a genomic copy of the human GRN gene. Here we report the development and characterization of a novel mouse model that expresses the entire human GRN gene in its native genomic context as a single copy inserted into a defined locus (Hprt) in the mouse genome. We show that human and mouse progranulin are expressed in a similar tissue-specific pattern, suggesting that the two genes are regulated by similar mechanisms. Human progranulin rescues a phenotype characteristic of progranulin-null mice, the exaggerated and early deposition of the aging pigment lipofuscin in the brain, indicating that the two proteins are functionally similar. Longitudinal behavioural and neuropathological analyses revealed no significant differences between wild-type and human progranulin-overexpressing mice up to 18 months of age, providing evidence that long-term increase of progranulin levels is well tolerated in mice. Finally, we demonstrate that human progranulin expression can be increased in the brain using an antisense oligonucleotide that inhibits a known GRN-regulating micro-RNA, demonstrating that the transgene is responsive to potential gene therapy drugs. Human progranulin-expressing mice represent a novel and valuable tool to expedite the development of progranulin-modulating therapeutics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".