P1‐177: Progranulin deficiency leads to altered microglial function and neuroinflammation
Bibliographic record
Abstract
Loss-of-function mutations in the progranulin (GRN) gene are a common cause of autosomal dominant frontotemporal lobar degeneration (FTLD), a fatal and progressive neurodegenerative disorder common in people under 65. Progranulin, a pleiotropic protein with diverse roles in the periphery, is expressed in both neurons and microglia, the resident immune cells of the CNS. Haploinsufficiency of progranulin may alter microglial function leading to chronic neuroinflammation and this microglial dysfunction could play a role in FTLD disease pathogenesis. In support of this hypothesis, progranulin-deficient mice have an exaggerated response to inflammatory stimuli and show increased release of inflammatory cytokines from peripheral macrophages. Symptomatic FTLD patients carrying GRN mutations show elevated serum levels of the pro-inflammatory cytokine IL-6. In addition, in the brains both of FTLD patients and of progranulin-deficient mice, there is increased immunoreactivity for markers of activated microglia and reactive astrocytes. Direct evidence as to whether or not microglia exhibit a pro-inflammatory phenotype similar to peripheral macrophages and/or if increased pro-inflammatory cytokine levels are present in the CNS is currently lacking. Primary microglia were isolated from progranulin-deficient and wild-type mice. The cytokine profile of lipopolysaccharide (LPS) stimulated microglial cultures was evaluated using Mesoscale Discovery and enzyme-linked immunosorbent assays (ELISAs). Quantitative polymerase chain reaction was used to quantify cytokine levels in the brain of progranulin-deficient mice. Plasma levels of circulating cytokines were also evaluated by ELISA in these mice. Progranulin-deficient primary microglia showed an altered cytokine profile in response to LPS stimulation in culture. Progranulin-deficient mice also display abnormal plasma cytokine levels and elevated expression of some cytokines in the aged brain. We conclude that progranulin acts as an anti-inflammatory molecule both in the periphery and importantly also in the CNS, and that altered microglial function and chronic neuroinflammation may be important contributors to FTLD disease pathogenesis. Anti-inflammatory therapies may prove beneficial for FTLD patients carrying GRN mutations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".