The Effects of Folate Deficiency and Folic Acid Supplementation on Folate Absorption and Metabolism in a Mouse Model
Bibliographic record
Abstract
The effects of folate deficiency and folic acid (FA) supplementation on folate absorption and metabolism have not been well established in vivo . We investigated the effects of folate deficiency and FA supplementation on gene expression of folate receptors/transporters and metabolic enzymes in a mouse model. Postweaning C57BL/6 mice were randomized to receive diets containing 0 (moderate folate deficiency), 2 (control) or 20 mg FA/kg of diet for 3 months. Gene expression was assessed by real‐time RT‐qPCR. Plasma folate and unmetabolized FA and hepatic and small intestinal folate concentrations accurately reflected the supplemental levels of FA ( p ‐trend<0.0001). Expression of the proton‐coupled folate transporter ( Pcft ), reduced folate carrier ( Rfc ) and folate receptor‐1 ( Folr1 ) in the small intestine and Folr1 in the liver was inversely associated with the supplemental levels of FA ( p‐ trend<0.0001). The supplemental levels of FA were inversely associated with thymidylate synthase ( Tyms ) expression in the liver and serine hydroxymethyltransferase ( Shmt1 ) expression in the small intestine ( p ‐trend<0.05) but were not associated with the expression of dihydrofolate reductase ( Dhfr ) or methylenetetrahydrofolate reductase ( Mthfr ) in either tissue. Our data suggest that folate deficiency and FA supplementation modulate expression of genes involved in folate absorption and metabolism, likely as a homeostatic mechanism. However, FA supplementation may also adversely influence folate absorption and metabolism by dysregulation of these pathways. Supported by CIHR MOP#106446
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".