Association of<i>MAOA</i>,<i>5-HTT</i>, and<i>NET</i>promoter polymorphisms with gene expression and protein activity in human placentas
Bibliographic record
Abstract
Monoamine oxidase A (MAOA) and the transporters for serotonin (5-HTT) and norepinephrine (NET) may play important roles in regulating maternal monoamine neurotransmitters transferred across the placenta to the fetus. We investigated whether promoter polymorphisms in MAOA (uVNTR), 5-HTT (5-HTTLPR), and NET (NETpPR AAGG(4)) could influence gene expression and protein activity in human placentas. Normal term human placentas (n = 73) were collected, and placental MAOA, 5-HTT, and NET mRNA levels and protein activity were determined. The mRNA levels or protein activities were compared between different genotype groups. Placentas hemizygous (male fetus) or homozygous (female fetus) for MAOA uVNTR 4-repeat allele had significantly higher MAOA mRNA levels than those hemizygous or homozygous for the 3-repeat allele (P = 0.001). However, no significant difference in MAOA enzyme activity was found for these two groups of genotypes (P = 0.161). Placentas with the 5-HTTLPR short (S)-allele (S/S+S/L) had significantly lower 5-HTT mRNA levels and serotonin uptake rate than those homozygous for the long (L)-allele (L/L) (mRNA: P < 0.001; serotonin transporting activity: P < 0.001). Placentas homozygous for the NET AAGG(4) L(4) allele had significantly higher NET mRNA levels, as well as dopamine and norepinephrine uptake rates, than those with the S(4)/L(4) genotype (mRNA: P < 0.001; dopamine transporting activity: P = 0.012; norepinephrine transporting activity: P = 0.011). These findings suggest that the three promoter polymorphisms of MAOA, 5-HTT, and NET influence gene expression levels and protein activity of these genes in human placentas, potentially leading to different fetal levels of maternal monoamine neurotransmitters, which may have an impact on fetal neurodevelopment.
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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 | 0.001 |
| 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".