Preeclampsia: novel insights from global RNA profiling of trophoblast subpopulations
Bibliographic record
Abstract
BACKGROUND: The maternal signs of preeclampsia, which include the new onset of high blood pressure, can occur because of faulty placentation. We theorized that transcriptomic analyses of trophoblast subpopulations in situ would lend new insights into the role of these cells in preeclampsia pathogenesis. OBJECTIVE: Our goal was to enrich syncytiotrophoblasts, invasive cytotrophoblasts, or endovascular cytotrophoblasts from the placentas of severe preeclampsia cases. Total RNA was subjected to global transcriptional profiling to identify RNAs that were misexpressed compared with controls. STUDY DESIGN: This was a cross-sectional analysis of placentas from women who had been diagnosed with severe preeclampsia. Gestational age-matched controls were placentas from women who had a preterm birth with no signs of infection. Laser microdissection enabled enrichment of syncytiotrophoblasts, invasive cytotrophoblasts, or endovascular cytotrophoblasts. After RNA isolation, a microarray approach was used for global transcriptional profiling. Immunolocalization identified changes in messenger RNA expression that carried over to the protein level. Differential expression of non-protein-coding RNAs was confirmed by in situ hybridization. A 2-way analysis of variance of non-coding RNA expression identified particular classes that distinguished trophoblasts in cases vs controls. Cajal body foci were visualized by coilin immunolocalization. RESULTS: Comparison of the trophoblast subtype data within each group (severe preeclampsia or noninfected preterm birth) identified many highly differentially expressed genes. They included molecules that are known to be expressed by each subpopulation, which is evidence that the method worked. Genes that were expressed differentially between the 2 groups, in a cell-type-specific manner, encoded a combination of molecules that previous studies associated with severe preeclampsia and those that were not known to be dysregulated in this pregnancy complication. Gene ontology analysis of the syncytiotrophoblast data highlighted the dysregulation of immune functions, morphogenesis, transport, and responses to vascular endothelial growth factor and progesterone. The invasive cytotrophoblast data provided evidence of alterations in cellular movement, which is consistent with the shallow invasion often associated with severe preeclampsia. Other dysregulated pathways included immune, lipid, oxygen, and transforming growth factor-beta responses. The data for endovascular cytotrophoblasts showed disordered metabolism, signaling, and vascular development. Additionally, the transcriptional data revealed the differential expression in severe preeclampsia of 2 classes of non-coding RNAs: long non-coding RNAs and small nucleolar RNAs. The long non-coding RNA, urothelial cancer associated 1, was the most highly up-regulated in this class. In situ hybridization confirmed severe preeclampsia-associated expression in syncytiotrophoblasts. The small nucleolar RNAs, which chemically modify RNA structure, also correlated with severe preeclampsia. Thus, we enumerated Cajal body foci, sites of small nucleolar RNA activity, in primary cytotrophoblasts that were isolated from control and severe preeclampsia placentas. In severe preeclampsia, cytotrophoblasts had approximately double the number of these foci as the control samples. CONCLUSION: A laser microdissection approach enabled the identification of novel messenger RNAs and non-coding RNAs that were misexpressed by various trophoblast subpopulations in severe preeclampsia. The results suggested new avenues of investigation, in particular, the roles of PRG2, Kell blood group determinants, and urothelial cancer associated 1 in syncytiotrophoblast diseases. Additionally, many of the newly identified dysregulated molecules might have clinical utility as biomarkers of severe 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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".