Enteric short‐chain fatty acids promote proliferation of human neural progenitor cells
Bibliographic record
Abstract
Short-chain fatty acids (SCFAs) are a group of fatty acids predominantly produced during the fermentation of dietary fibers by the gut anaerobic microbiota. SCFAs affect many host processes in health and disease. SCFAs play an important role in the 'gut-brain axis', regulating central nervous system processes, for example, cell-cell interaction, neurotransmitter synthesis and release, microglia activation, mitochondrial function, and gene expression. SCFAs also promote the growth of neurospheres from human neural stem cells and the differentiation of embryonic stem cells into neural cells. It is plausible that maternally derived SCFAs may pass the placenta and expose the fetus at key developmental periods. However, it is unclear how SCFA exposure at physiological levels influence the early-stage neural cells. In this study, we explored the effect of SCFAs on the growth rate of human neural progenitor cells (hNPCs), generated from human embryonic stem cell line (HS980), with IncuCyte live-cell analysis system and immunofluorescence. We found that physiologically relevant levels (µM) of SCFAs (acetate, propionate, butyrate) increased the growth rate of hNPCs significantly and induced more cells to undergo mitosis, while high levels (mM) of SCFAs had toxic effects on hNPCs. Moreover, no effect on apoptosis was observed in physiological-dose SCFA treatments. In support, data from q-RT PCR showed that SCFA treatments influenced the expression of the neurogenesis, proliferation, and apoptosis-related genes ATR, BCL2, BID, CASP8, CDK2, E2F1, FAS, NDN, and VEGFA. To conclude, our results propose that SCFAs regulates early neural system development. This might be relevant for a putative 'maternal gut-fetal brain-axis'. Cover Image for this issue: doi: 10.1111/jnc.14761.
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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) | 0.002 | 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".