NLRP7, a Nucleotide Oligomerization Domain-like Receptor Protein, Is Required for Normal Cytokine Secretion and Co-localizes with Golgi and the Microtubule-organizing Center
Bibliographic record
Abstract
A hydatidiform mole (HM) is a human pregnancy with hyperproliferative placenta and abnormal embryonic development. Mutations in NLRP7, a member of the nucleotide oligomerization domain-like receptor family of proteins with roles in inflammation and apoptosis, are responsible for recurrent HMs. However, little is known about the functional role of NLRP7. Here, we demonstrate that peripheral blood mononuclear cells from patients with NLRP7 mutations and rare variants secrete low levels of IL-1β and TNF in response to LPS. We show that the cells from patients, carrying mutations or rare variants, have variable levels of increased intracellular pro-IL-1β indicating that normal NLRP7 down-regulates pro-IL-1β synthesis in response to LPS. Using transient transfections, we confirm the role of normal NLRP7 in inhibiting pro-IL-1β and demonstrate that this inhibitory function is abolished by protein-truncating mutations after the Pyrin domain. Within peripheral blood mononuclear cells, NLRP7 co-localizes with the Golgi and the microtubule-organizing center and is associated with microtubules. This suggests that NLRP7 mutations may affect cytokine secretion by interfering, directly or indirectly, with their trafficking. We propose that the impaired cytokine trafficking and secretion caused by NLRP7 defects makes the patients tolerant to the growth of these earlier arrested conceptions with no fetal vessels and that the retention of these conceptions until the end of the first trimester contribute to the molar phenotype. Our data will impact our understanding of postmolar choriocarcinomas, the only allograft non-self tumors that are able to invade maternal tissues.
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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 | 0.000 |
| 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".