FTD-associated behavioural and transcriptomic abnormalities in ‘humanized’ progranulin-deficient mice: A novel model for progranulin-associated FTD
Bibliographic record
Abstract
Frontotemporal dementia (FTD) is an early onset dementia characterized by neuropathology and behavioural changes. A common genetic cause of FTD is haploinsufficiency of the gene progranulin (GRN). Mouse models of progranulin deficiency have provided insight into progranulin neurobiology, but the description of phenotypes with preclinical relevance has been limited in the currently available heterozygous progranulin-null mice. The identification of robust and reproducible FTD-associated behavioural, neuropathological, and biochemical phenotypes in progranulin deficient mice is a critical step in the preclinical development of therapies for FTD. In this work, we report the generation of a novel, 'humanized' mouse model of progranulin deficiency that expresses a single, targeted copy of human GRN in the absence of mouse progranulin. We also report the in-depth, longitudinal characterization of humanized progranulin-deficient mice and heterozygous progranulin-null mice over 18 months. Our analysis yielded several novel progranulin-dependent physiological and behavioural phenotypes, including increased marble burying, open field hyperactivity, and thalamic microgliosis in both models. RNAseq analysis of cortical tissue revealed an overlapping profile of transcriptomic dysfunction. Further transcriptomic analysis offers new insights into progranulin neurobiology. In sum, we have identified several consistent phenotypes in two independent mouse models of progranulin deficiency that are expected to be useful endpoints in the development of therapies for progranulin-deficient FTD. Furthermore, the presence of the human progranulin gene in the humanized progranulin-deficient mice will expedite the development of clinically translatable gene therapy strategies.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".