Opposing role of phagocytic receptors MERTK and AXL in Progranulin deficient FTD
Bibliographic record
Abstract
Genetic mutations in the progranulin gene, GRN, cause frontotemporal dementia and a lysosomal storage disorder. Using single-nuclei RNA sequencing of the post-mortem brain tissue from adult heterozygous pathogenic granulin variant (GRN+/-) carriers we find dysregulation of microglia, phagocytosis and the phagocytic receptors MERTK and AXL. Exogenous progranulin regulates MERTK and AXL RNA expression in human microglia induced from iPSCs irrespective of GRN mutation status, without directly binding to MERTK or AXL proteins. We generated double knock-out mice and find that constitutive homozygous loss of Grn and Mertk (Grn-/-;Mertk-/-) rescued microglial disease signature while constitutive homozygous loss of Grn and Axl (Grn-/-;Axl-/-) worsened the microglial disease signature and increased lipofuscin. Lower CSF MERTK but not AXL is associated with lower progranulin levels. Furthermore, CSF MERTK is lower in symptomatic but not presymptomatic FTD patients with genetic mutations (GRN, C9ORF72, and MAPT) whereas AXL does not change between disease state and control. These data explain in part the inflammation seen in GRN-FTD and are applicable to other inflammatory states in which PGRN, MERTK and AXL play regulatory roles beyond neurodegenerative diseases. The interaction between GRN, MERTK, and AXL opens potential new therapeutic avenues to intervene on this inflammatory axis.
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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".