The Effects of Short Chain Fatty Acids on Growth Factors and Pro-Inflammatory Cytokine Production in Enteric Glial Cells
Bibliographic record
Abstract
Enteric glial cells (EGCs), the major cell type in the enteric nervous system, are involved in maintaining intestinal homeostasis by secreting growth factors that attenuate intestinal inflammation, support enteric neuronal health, and enhance the intestinal epithelial barrier. However, when exposed to inflammatory stimuli, EGCs become reactive and secrete pro-inflammatory cytokines that may be detrimental to the intestinal barrier. Recently, butyrate, a short chain fatty acid (SCFA) characterized as a histone deacetylase (HDAC) inhibitor, has been shown to elevate expression of growth factors and reduce expression of cytokines in intestinal epithelial cells and glia of the central nervous system. However, it remains unknown if SCFAs affect EGCs in a similar manner. This study investigated the following: (1) if treating EGCs with SCFAs influences production of growth factors; (2) whether SCFAs alter pro-inflammatory cytokine production in EGCs. We hypothesized that SCFAs enhance levels of growth factors and inflammatory cytokine levels in EGCs. An established rat enteroglial cell line was treated with butyrate (0-10 mM), propionate (0-10 mM) or pharmacological HDAC inhibitors suberoylanilide hydroxamic acid (SAHA) or trichostatin A (TSA) (0.1 mM-10 mM) for 8h-72h with or without IFNg (10 mg/mL) to stimulate EGCs into their activated phenotype. Changes in growth factors (GDNF & TGF-b1) and pro-inflammatory cytokines (IL-6, IL-1b, and TNFa) in EGCs were assessed using qPCR and western blotting. Both butyrate and propionate elevated GDNF transcript abundance in EGCs, whereas SAHA and TSA reduced GDNF transcript abundance. At the protein level, butyrate and propionate did not alter GDNF whereas both SAHA and TSA reduced GDNF protein levels. Differences could be explained by cytotoxic effects of prolonged exposure of cells to SAHA and TSA. In contrast butyrate, propionate, and SAHA reduced TGF-b1 transcript abundance and protein levels in EGCs whereas TSA had no effect. Furthermore, treatment with SCFAs significantly lowered TNFa transcript abundance in EGCs. However, at the protein level, only butyrate was effective at lowering TNFa. In contrast, SCFAs elevated IL-6 transcript abundance in EGCs but had no effect on IL-6 protein. Neither butyrate nor propionate altered IL-1b protein levels in EGCs. Collectively, these results suggest that the effects of SCFAs on functional aspects of EGCs varies depending on the type of growth factor and pro-inflammatory cytokine being assessed. Overall, the data presented in thesis provides a new understanding of how SCFAs modify EGCs function, which advances the understanding of EGCs in intestinal physiology and glial cell biology.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".