Microglial Cell Activation Increases Saturated and Decreases Monounsaturated Fatty Acid Content, but Both Lipid Species are Proinflammatory
Bibliographic record
Abstract
Neuroinflammation is a component of age-related neurodegenerative diseases and cognitive decline. Saturated (SFA) and monounsaturated (MUFA) fatty acids are bioactive molecules that may play different extrinsic and intrinsic roles in neuroinflammation, serving as exogenous ligands for cellular receptors, or endogenous components of cell structural, energetic and signaling pathways. We determined the fatty acyl profile of BV2 microglial cells before and after acute activation with lipopolysaccharide (LPS). We also investigated the effect of SFA and MUFA pretreatment on the production of an invasive, neurotoxic phenotype in BV2 cells. Acute activation of BV2 microglia resulted in an increase in the relative content of SFA (12:0, 16:0, 18:0, 20:0, 22:0, and 24:0 increased significantly), and a relative decrease in the content of MUFA (16:1n7, 18:1n7, 18:1n9, 20:1n9, 24:1n9 decreased significantly). In agreement, the major stearoyl-CoA desaturase (SCD) isoform in BV2 cells, SCD2, was significantly down-regulated by LPS. We next treated cells with SFA (16:0 or 18:0) or MUFA (16:1n7 or 18:1n9), and found that levels of secreted IL6 were increased, as was secreted MMP9-mediated proteolytic activity. To test the functional significance, we treated SH-SY5Y neuronal cells with conditioned medium from BV2 cells pretreated with fatty acids, and found a small but significant induction of cell death. Our findings suggest differential intrinsic roles for SFA and MUFA in activated microglial cells, but similar extrinsic roles for these fatty acid species in inducing activation. Expansion of SFA is important during microglial cell activation, but either supplemental SFA or MUFA may contribute to chronic low-grade neuroinflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.003 | 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".