Diet alters micronutrient pathways in the gut and placenta that regulate fetal growth and development in pregnant mice
Bibliographic record
Abstract
Abstract Maternal malnutrition and micronutrient deficiencies can alter fetal development. However, the mechanisms underlying these relationships are poorly understood. We used a systems-physiology approach to investigate diet-induced effects on maternal gut microbes and folate/inositol transport in the maternal/fetal gut and placenta. Female mice were fed a control diet (CON) diet, undernourished (UN, restricted by 30% of CON intake) or a high fat diet (HF, 60% kcals fat) during pregnancy to model normal pregnancy, fetal growth restriction, or maternal metabolic dysfunction, respectively. At gestational day 18.5 we assessed circulating folate levels by microbiological assay, relative abundance of gut lactobacilli by G3PhyloChip™, and folate/inositol transporters in placenta and maternal/fetal gut by qPCR/immunohistochemistry. UN and HF-fed mothers had lower plasma folate concentrations vs. CON. Relative abundance of three lactobacilli taxa were higher in HF vs. UN and CON. HF-fed mothers had higher gut proton coupled folate transporter ( Pcft ) and reduced folate carrier 1 ( Rfc1) , and lower sodium myo-inositol co-transporter 2 ( Smit2) , mRNA expression vs. UN and CON. HF placentae had increased folate receptor beta ( Fr β) expression vs. UN. mRNA expression of Pcft, folate receptor alpha (Frα) and Smit2 was higher in gut of HF fetuses vs. UN and CON. Transporter protein expression was not different between groups. Maternal malnutrition alters abundance of select gut microbes and folate/inositol transporters, which may influence maternal micronutrient status and delivery to the fetus, impacting pregnancy/fetal outcomes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".