Syncytiotrophoblast extracellular vesicles contain functional kynurenine metabolising enzymes: potential implications for preeclampsia
Bibliographic record
Abstract
Abstract Background Placentae of women with preeclampsia (PE) exhibit reduced levels of kynurenine (Kyn), a biological compound derived from tryptophan metabolism with antioxidant, vasorelaxant, and hypotensive properties. Little is known regarding functional levels of the Kyn metabolizing enzymes (KYNME) in women with preeclampsia. Since high circulating levels of syncytiotrophoblast extracellular vesicles (STB-EVs) have been associated with preeclampsia onset, we aimed to study whether Kyn reduction in preeclampsia may be attributed to increased degradation by KYNME present in STB-EVs. Methods We conducted a study that included women with normal (n=9) and early-onset preeclamptic (EOPE) pregnancies (n=9). From them, STB-EVs were isolated by dual-lobe placental perfusions from normal (n=3) and EOPE (n=3). KYNME were identified using placental immunohistochemistry and western blot in placental and STB-EV extractions. Serum Kyn levels were measured using gas chromatography mass spectrometry. Results Cargo of STB-EVs consist of functional KYNME, which break down Kyn in a dose and time-dependent manner. No significant differences in the content of Kyn metabolizing enzymes were found in STB-EVs between normal and EOPE pregnancies. However, decreased serum levels of Kyn were found in women with EOPE relative to normal pregnancies. Conclusion STB-EVs carry functional KYNME and may regulate the levels of circulating Kyn during normal pregnancy and preeclampsia. Due to the dose effect of increased STB-EVs in EOPE, the functional KYNME content of said vesicles may contribute to reduced levels of Kyn. This finding opens a new avenue for investigating the potential benefits of KYNME inhibitors in conjunction with kynurenine replacement for managing preeclampsia.
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 | 0.001 |
| 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".