Δ9-THC alters expression of genes required for placental iron transport and metabolism
Bibliographic record
Abstract
Cannabis use in pregnancy amongst Canadian women is prevalent yet understudied. Placental insufficiency underlies fetal growth restriction; delta9-tetrahydrocannabinol (THC) exposure in-utero causes fetal growth restriction and consequently long-term adverse health outcomes for offspring. Understanding how THC drives placental insufficiency can identify avenues for clinical intervention. While targeted approaches have been taken to understand this pathology, large-scale analyses of THC-exposed placentae have not been conducted. We characterized the transcriptome of THC-exposed placentae to identify differentially expressed genes and/or biological pathways not previously attributed to fetal growth restriction. We used a rat injection model of chronic gestational THC exposure to generate THC-exposed placentae. Differential gene expression analysis was performed on placental bulk RNA-sequencing data, and supervised machine learning was used to identify over-represented biological pathways in THC-exposed samples. Our bioinformatics results were validated by RT-qPCR and immunohistochemistry. Our analysis revealed that hemoglobin subunits B (Hbb) and A-A3 (Hba-a3) were under-expressed with THC exposure. Genes involved in heme biosynthesis and erythropoiesis were also under-expressed with THC exposure. The primary transporter of iron into the placenta, transferrin receptor 1 (Tfrc), was over-expressed in THC-exposed tissue at the transcript and protein level. The erythropoietic findings in concert with the pattern of placental iron transporter expression suggested that gestational THC exposure may influence maternal iron status by promoting anemia in pregnancy. The results further suggested that placental iron is retained at the expense of transfer to the offspring in THC-exposed pregnancies. As maternal iron-deficiency anemia is associated with fetal growth restriction, our findings suggest a novel mechanism for fetal growth restriction in THC-exposed pregnancies.
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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 | 0.001 | 0.001 |
| 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".