How uterine and placental cells communicate to affect placental invasion
Official title: Mapping inter-cellular trophoblast-decidual signaling to its effects on invasion related maternal-fetal
Funded at University of Connecticut Sch of Med/dnt · NIH-11400336 · Data updated 2026-11-12
Using computational genetics, researchers aim to link signals in uterine cells to risks of pregnancy problems like preeclampsia, fetal growth restriction, and placenta previa for pregnant people.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Connecticut Sch of Med/dnt NIH-funded |
| Lab location | 1 site (Farmington, United States) |
| Project start | September 1, 2025 |
| Project end | August 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $249,000 |
| NIH project number | 4R00HD105973-04 |
| Project ID | NIH-11400336 on NIH RePORTER |
Who could benefit from this research
✓Could help
People with a history or high risk of preeclampsia, fetal growth restriction, or placenta previa, or those able to provide endometrial tissue or genetic profiles, would be most relevant to this work.
✕Less likely to help
People who are not pregnant, not planning pregnancy, or unwilling to provide tissue or molecular data are unlikely to directly benefit from participation.
What this research studies
This project looks at how gene regulation in the lining of the uterus (endometrium) controls how permissive the tissue is to placental invasion. The team will use machine learning, comparative genomics, and computational predictions to find DNA regulatory elements that keep the uterine tissue mechanically intact. They will prioritize the most promising elements for follow-up and combine these mechanistic findings into a data-driven model. That model will try to relate an individual's endometrial transcriptional profile to their risk of invasion-related maternal–fetal diseases.
Why it matters
Potential benefit: If successful, this work could help identify people at higher risk for invasion-related pregnancy complications earlier and guide monitoring or preventive care.
How similar studies have gone: Molecular studies have previously linked abnormal placental invasion to conditions like preeclampsia, but applying machine learning to endometrial regulatory elements to predict individual risk is a newer approach.
Where this research is happening
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University of Connecticut Sch of Med/dnt
Farmington, United States Active
Researchers
- Principal investigator: Suhail, Yasir — University of Connecticut Sch of Med/dnt
- Study coordinator: Suhail, Yasir
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.