Regulation of MARCKS and MARCKS‐related protein expression in BV‐2 microglial cells in response to lipopolysaccharide
Bibliographic record
Abstract
Myristoylated alanine-rich C kinase substrate (MARCKS) and MARCKS-related protein (MRP) have been implicated in membrane-cytoskeletal events underlying cell adhesion, migration, secretion, and phagocytosis. In BV-2 microglial cells, lipopolysaccharide (LPS) elicited a dose-dependent increase in mRNA of both MRP (sixfold) and MARCKS (threefold) with corresponding increases in [3H]myristoylated and immunoreactive protein levels. LPS also produced significant increases in protein kinase C (PKC)-beta twofold and PKC-epsilon (1.5-fold). Pro-inflammatory cytokines produced by activated microglia (IL-1beta, IL-6, TNF-alpha) did not mimic LPS effects on MARCKS or MRP expression when added individually or in combination. LPS and IFN-gamma produced a synergistic induction of iNOS but not MARCKS or MRP. Induction of MARCKS and MRP by LPS was completely blocked by inhibitors of NF-kappaB (PDTC) and protein tyrosine kinases (herbimycin A), partially blocked by the p38 kinase inhibitor SB203580, and unaffected by the MEK inhibitor PD98059. LPS induction of iNOS was considerably more sensitive to all these inhibitors. The Src kinase inhibitor PP2 had no effect, while the closely related inhibitor PP1 actually increased LPS induction of MARCKS and MRP. Our results suggest that MARCKS and MRP may play an important role in LPS-activated microglia, but are not part of the neuroinflammatory response produced by cytokines.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".