Tumor Necrosis Factor-α Induces a Preeclamptic-like Phenotype in Placental Villi via Sphingosine Kinase 1 Activation
Bibliographic record
Abstract
Preeclampsia (PE) involves inadequate placental function. This can occur due to elevated pro-inflammatory tumor necrosis factor-α (TNF-α). In other tissues, TNF-α signals via sphingosine kinase 1 (SphK1). SphK1 hinders syncytial formation. Whether this occurs downstream of TNF-α signaling is unclear. We hypothesized that placental SphK1 levels are higher in PE and elevated TNF-α decreases syncytial function, increases syncytial shedding, and increases cytokine/factor release via SphK1 activity. Term placental biopsies were analyzed for SphK1 using immunofluorescence and qRT-PCR. Term placental explants were treated after 4 days of culture, at the start of syncytial regeneration, with TNF-α and/or SphK1 inhibitors, PF-543. Syncytialization was assessed by measuring fusion and chorionic gonadotropin release. Cell death and shedding were measured by lactate dehydrogenase release and placental alkaline phosphatase-positive shed particles. Forty-two cytokines were measured using multiplex assays. Placental SphK1 was increased in PE. Increased cell death, shedding, interferon-α2, IFN-γ-induced protein 10, fibroblast growth factor 2, and platelet-derived growth factor-AA release induced by TNF-α were reversed upon SphK1 inhibition. TNF-α increased the release of 26 cytokines independently of SphK1. TNF-α decreased IL-10 release and inhibiting SphK1 reversed this effect. Inhibiting SphK1 alone decreased TNF-α release. Hence, SphK1 partially mediates the TNF-α-induced PE placental phenotype, primarily through cell damage, shedding, and specific cytokine release.
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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 | 0.000 | 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 | 0.001 |
| 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".