Decreased expression of P-glycoprotein in the placenta of women with autoimmune disease
Bibliographic record
Abstract
Autoimmune diseases (ADs), such as systemic lupus erythematosus (SLE), require multiple medications to ensure maternal-fetal health during pregnancy. These medications are often substrates for placental transporters that could cross over to the fetal compartment. However, the effects of ADs on placental transporters remain poorly understood. This study aimed to investigate the impact of ADs on placental transporters and key inflammatory cytokines. Human preterm and term placentas from AD-affected women (n = 28) and gestational age-matched controls (n = 38) were collected. The placentas were examined for transporter expression via quantitative real-time PCR and immunodetection. Subgroup analysis and untargeted proteomic analysis of samples from patients with SLE were performed. P-glycoprotein (P-gp/ABCB1) and organic anion transporter 4 (OAT4/SLC22A11) mRNA expression were significantly decreased and expression of T helper 17- associated cytokines were increased in preterm and term AD placenta relative to controls. P-gp protein expression was also downregulated in preterm, but not in term AD placenta. Subgroup analysis of SLE also detected downregulation of P-gp and OAT4 at the mRNA level in preterm samples. Proteomic analysis of SLE and control samples indicated global changes in proteins related to processes like inflammation, oxidative stress, angiogenesis, and hemostasis. These findings elucidate that ADs such as SLE are associated with the downregulation of the ABC transporter P-gp in the placenta as well as global changes to the placenta proteome. Dysregulation of cytokines and associated pathways was also observed and postulated to cause changes in placental transporters. Future studies that validate these mechanisms could offer potential strategies to mitigate inflammation-mediated alterations in placental transporters, ultimately improving fetal and neonatal health. SIGNIFICANCE STATEMENT: Autoimmune diseases have significant effects on the placenta, influencing pregnancy outcomes and the effectiveness of prescribed medications. The study revealed that autoimmune diseases induce inflammatory cytokines in the placenta and were associated with a significant downregulation of P-glycoprotein. Additionally, in patients affected by lupus, proteomics uncovered the enrichment of pathways associated with placental damage and dysfunction. This work will help inform care plans for these patients by identifying clinically relevant proteins that are affected by the disease, improving maternal-fetal outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".