Identification and Functional Characterization of Three NoLS (Nucleolar Localisation Signals) Mutations of the CDC73 Gene
Bibliographic record
Abstract
Hyperparathyroidism Jaw-Tumour Syndrome (HPT-JT) is characterized by primary hyperparathyroidism (PHPT), maxillary/mandible ossifying fibromas and by parathyroid carcinoma in 15% of cases. Inactivating mutations of the tumour suppressor CDC73/HRPT2 gene have been found in HPT-JT patients and also as genetic determinants of sporadic parathyroid carcinoma/atypical adenomas and, rarely, typical adenomas, in familial PHPT. Here we report the genetic and molecular analysis of the CDC73/HRPT2 gene in three patients affected by PHPT due to atypical and typical parathyroid adenomas, in one case belonging to familial PHPT. Flag-tagged WT and mutant CDC73/HRPT2 proteins were transiently transfected in HEK293 cells and functional assays were performed in order to investigate the effect of the variants on the whole protein expression, nuclear localization and cell overgrowth induction. We identified four CDC73/HRPT2 gene mutations, three germline (c.679_680delAG, p.Val85_Val86del and p.Glu81_Pro84del), one somatic (p.Arg77Pro). In three cases the mutation was located within the Nucleolar Localisation Signals (NoLS). The three NoLS variants led to instability either of the corresponding mutated protein or mRNA or both. When transfected in HEK293 cells, NoLS mutated proteins mislocalized with a predeliction for cytoplasmic or nucleo-cytoplasmic localization and, finally, they resulted in overgrowth, consistent with a dominant negative interfering effect in the presence of the endogenous protein.
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 | 0.000 |
| 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".