Investigating the Impact of Short-Chain Fatty Acids (SCFAs) from the Maternal Microbiome on Pregnancy and Fetal Health of C57BL/6J Mice: A Research Protocol
Bibliographic record
Abstract
Short-chain fatty acids (SCFAs), produced by commensal gut bacteria through fiber fermentation, are essential for regulating immune responses during pregnancy, benefiting both maternal and fetal health. Propionate, for example, activates G protein-coupled receptors (GPCRs), promoting colonic T regulatory cell (Treg) differentiation that is critical in maintaining inflammatory homeostasis. These molecules are not confined to the gut and can transverse the placental barrier, directly reaching the fetal compartment, where they may influence placental efficiency, impact pregnancy outcomes, and potentially predispose the child to autoimmunity and allergy. Despite the known association between dysbiosis and adverse pregnancy outcomes, few direct mechanistic studies have investigated the interaction between the maternal microbiome and the developing immune system during pregnancy. To develop a robust protocol, we designed a murine model experiment with differential SCFA exposure prior to pregnancy using C57BL/6J mice. Separate groups of female mice will be fed different diets prior to pregnancy: standard diet, high-fiber, or SCFA-supplemented diets. An SCFA-depleted group (via antibiotic treatment) will serve as a comparison. Maternal fecal samples will be collected during the perinatal stage for SCFA analysis using mass spectrometry and for microbiome profiling via 16S ribosomal RNA gene sequencing. Fecal samples from neonatal mice will be collected shortly after birth for the same analyses. Systemic immune alterations will be examined using maternal and fetal blood samples to quantify key cytokines using a multi-plex ELISA. Flow cytometry will be conducted to compare differences in immune cell composition between different maternal diet groups. Other data including birth weight, litter size and gestation duration will also be compared to assess SCFA influence on pregnancy outcomes. We hypothesize that SCFA supplementation fosters an anti-inflammatory microbiome, elevating Tregs and anti-inflammatory cytokines while downregulating proinflammatory responses. This may lead to fewer pregnancy complications and improved fetal development. This research will help elucidate the role of SCFAs in maternal-fetal immune crosstalk and could inform dietary or therapeutic strategies to reduce immune-related diseases in infants and support long-term health.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".