Regulation of IL-6 Secretion by Astrocytes via TLR4 in the Fragile X Mouse Model
Bibliographic record
Abstract
Fragile X syndrome (FXS) is identified by abnormal dendrite morphology and altered synaptic protein expression. Astrocyte secreted factors such as Tenascin C (TNC), may contribute to the synaptic changes, including maturation of the synapse. TNC is a known endogenous ligand of toll-like receptor 4 (TLR4) that has been shown to induce the expression of pro-inflammatory cytokines such as interleukin-6 (IL-6). At the molecular level, elevated IL-6 promotes excitatory synapse formation and increases dendrite spine length. With these molecular changes linked to the phenotype of FXS, we examined the expression and the mechanism of the endogenous TLR4 activator TNC, and its downstream target IL-6 in astrocytes from the FMR1 KO mouse model. Secreted TNC and IL-6 were significantly increased in FMR1 KO astrocytes. Addition of TNC and lipopolysaccharide (LPS) induced IL-6 secretion, whereas the antagonist of TLR4 (LPS-RS) had an opposing effect. Cortical protein expression of TNC and IL-6 were also significantly elevated in the postnatal FMR1 KO mouse. In addition, there was an increase in the number of VGLUT1/PSD95 positive synaptic puncta of both WT and FMR1 KO neurons when plated with astrocyte conditioned media from FMR1 KO astrocytes, compared to those plated with media from wild type astrocytes. By assessing the cellular mechanisms involved, a novel therapeutic option could be made available to target abnormalities of synaptic function seen in FXS.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".