Abnormalities in Oxygen Sensing Define Early and Late Onset Preeclampsia as Distinct Pathologies
Bibliographic record
Abstract
BACKGROUND: The pathogenesis of preeclampsia, a serious pregnancy disorder, is still elusive and its treatment empirical. Hypoxia Inducible Factor-1 (HIF-1) is crucial for placental development and early detection of aberrant regulatory mechanisms of HIF-1 could impact on the diagnosis and management of preeclampsia. HIF-1α stability is controlled by O(2)-sensing enzymes including prolyl hydroxylases (PHDs), Factor Inhibiting HIF (FIH), and E3 ligases Seven In Absentia Homologues (SIAHs). Here we investigated early- (E-PE) and late-onset (L-PE) human preeclamptic placentae and their ability to sense changes in oxygen tension occurring during normal placental development. METHODS AND FINDINGS: Expression of PHD2, FIH and SIAHs were significantly down-regulated in E-PE compared to control and L-PE placentae, while HIF-1α levels were increased. PHD3 expression was increased due to decreased FIH levels as demonstrated by siRNA FIH knockdown experiments in trophoblastic JEG-3 cells. E-PE tissues had markedly diminished HIF-1α hydroxylation at proline residues 402 and 564 as assessed with monoclonal antibodies raised against hydroxylated HIF-1α P402 or P564, suggesting regulation by PHD2 and not PHD3. Culturing villous explants under varying oxygen tensions revealed that E-PE, but not L-PE, placentae were unable to regulate HIF-1α levels because PHD2, FIH and SIAHs did not sense a hypoxic environment. CONCLUSION: Disruption of oxygen sensing in E-PE vs. L-PE and control placentae is the first molecular evidence of the existence of two distinct preeclamptic diseases and the unique molecular O(2)-sensing signature of E-PE placentae may be of diagnostic value when assessing high risk pregnancies and their severity.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".