sPLA2-IIA modifies progranulin deficiency phenotypes in mouse models
Bibliographic record
Abstract
BACKGROUND: Haploinsufficiency of the progranulin (PGRN) protein is a leading cause of frontotemporal lobar degeneration (FTLD). Mouse models have been developed to study PGRN functions. However, PGRN deficiency in the commonly used C57BL/6 mouse strain background leads to very mild phenotypes, and pathways regulating PGRN deficiency phenotypes remain to be elucidated. METHODS: We generated PGRN-deficient mice in the FVB/N background and compared PGRN deficiency phenotypes between C57BL/6 and FVB/N backgrounds via immunostaining, western blot, RNA-seq, and proteomics approaches. We demonstrated a novel pathway in modifying PGRN deficiency phenotypes using inhibitor treatment and AAV-mediated overexpression in mouse models. RESULTS: We report that PGRN loss in the FVB/N mouse strain results in earlier onset and stronger FTLD-related and lysosome-related phenotypes. We found that PGRN interacts with sPLA2-IIA, a member of the secreted phospholipase A2 (sPLA2) family member and a key regulator of inflammation, that is expressed in FVB/N but not C57BL/6 background. sPLA2-IIA inhibition rescues PGRN deficiency phenotypes, while sPLA2-IIA overexpression drives enhanced gliosis and lipofuscin accumulation in PGRN-deficient mice. Additionally, RNA-seq and proteomics analysis revealed that mitochondrial pathways are upregulated in the PGRN-deficient C57BL/6 mice but not in the FVB/N mice. CONCLUSIONS: Our studies establish a better mouse model for FTLD-GRN and uncover novel pathways modifying PGRN deficiency phenotypes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".