Proteomic Analysis of the Effects of Aged Garlic Extract and Its FruArg Component on Lipopolysaccharide-Induced Neuroinflammatory Response in Microglial Cells
Bibliographic record
Abstract
Aged garlic extract (AGE) is widely used as a dietary supplement, and is claimed to promote human health through anti-oxidant/anti-inflammatory activities with hypolipidemic, antiplatelet and neuroprotective effects. Prior studies of AGE have mainly focused on its organosulfur compounds, with little attention paid to its carbohydrate derivatives, such as N-α-(1-deoxy-D-fructos-1-yl)-L-arginine (FruArg). The goal of this study is to investigate actions of AGE and FruArg on antioxidative and neuroinflammatory responses in lipopolysaccharide (LPS)-activated murine BV-2 microglial cells using a proteomic approach. Our data show that both AGE and FruArg can significantly inhibit LPS-induced nitric oxide (NO) production in BV-2 cells. Quantitative proteomic analysis by combining two dimensional differential in-gel electrophoresis (2D-DIGE) with mass spectrometry revealed that expressions of 26 proteins were significantly altered upon LPS exposure, while levels of 20 and 21 proteins exhibited significant changes in response to AGE and FruArg treatments, respectively, in LPS-stimulated BV-2 cells. Notably, approximate 78% of the proteins responding to AGE and FruArg treatments are in common, suggesting that FruArg is a major active component of AGE. MULTICOM-PDCN and Ingenuity Pathway Analyses indicate that the proteins differentially affected by treatment with AGE and FruArg are involved in inflammatory responses and the Nrf2-mediated oxidative stress response. Collectively, these results suggest that AGE and FruArg attenuate neuroinflammatory responses and promote resilience in LPS-activated BV-2 cells by suppressing NO production and by regulating expression of multiple protein targets associated with oxidative stress.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".