Glucocorticoids modulate multidrug resistance transporters in the first trimester human placenta
Bibliographic record
Abstract
The placental multidrug transporters, P-glycoprotein (P-gp, encoded by ABCB1) and breast cancer resistance protein (BCRP, ABCG2) protect the foetus from exposure to maternally derived glucocorticoids, toxins and xenobiotics. During pregnancy, maternal glucocorticoid levels can be elevated by stress or exogenous administration. We hypothesized that glucocorticoids modulate the expression of ABCB1/P-gp and ABCG2/BCRP in the first trimester human placenta. Our objective was to examine whether dexamethasone (DEX) or cortisol modulate first trimester placental expression of multidrug transporters and determine whether cytotrophoblasts or the syncytiotrophoblast are/is responsible for mediating these effects. Three models were examined: (i) an ex-vivo model of placental villous explants (7-10 weeks), (ii) a model of isolated first trimester syncytiotrophoblast and cytotrophoblast cells and (iii) the BeWo immortalized trophoblast cell line model. These cells/tissues were treated with DEX or cortisol for 24 hour to 72 hour. In first trimester placental explants, DEX (48 hour) increased ABCB1 (P < .001) and ABCG2 (P < .05) mRNA levels, whereas cortisol (48 hour) only increased ABCB1 mRNA levels (P < .01). Dexamethasone (P < .05) and cortisol (P < .01) increased BCRP but did not affect P-gp protein levels. Breast cancer resistance protein expression was primarily confined to syncytiotrophoblasts. BeWo cells, when syncytialized with forskolin, increased expression of BCRP protein, and this was further augmented by DEX (P < .05). Our data suggest that the protective barrier provided by BCRP increases as cytotrophoblasts fuse to form the syncytiotrophoblast. Increase in glucocorticoid levels during the first trimester may reduce embryo/foetal exposure to clinically relevant BCRP substrates, because of an increase in placental BCRP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".