Mutations in <i>TERT,</i> the Gene for Telomerase Reverse Transcriptase, in Aplastic Anemia
Bibliographic record
Abstract
BACKGROUND: Mutations in TERC, the gene for the RNA component of telomerase, cause short telomeres in congenital aplastic anemia and in some cases of apparently acquired hematopoietic failure. We investigated whether mutations in genes for other components of telomerase also occur in aplastic anemia. METHODS: We screened blood or marrow cells from 124 patients with apparently acquired aplastic anemia and 282 control subjects for sequence variations in the TERT, DKC1, NHP2, and NOP10 genes; an additional 81 patients and 246 controls were examined for genetic variations in TERT. Telomere lengths and the telomerase activity of peripheral-blood leukocytes were evaluated in patients carrying genetic variants. Identified mutations were transfected into telomerase-deficient cell lines to examine their effects and their mechanism of action on telomerase function. RESULTS: Five heterozygous, nonsynonymous mutations (which cause an amino acid change in the corresponding protein) were identified in TERT, the gene for the telomerase reverse transcriptase catalytic enzyme, among seven unrelated patients. Leukocytes from these patients had short telomeres and low telomerase enzymatic activity. In three of these patients, the mutation was also detected in buccal mucosa cells. Family members carrying the mutations also had short telomeres and reduced telomerase activity but no evident hematologic abnormality. The results of coexpression of wild-type TERT and TERT with aplastic anemia-associated mutations in a telomerase-deficient cell line suggested that haploinsufficiency was the mechanism of telomere shortening due to TERT mutations. CONCLUSIONS: Heterozygous mutations in the TERT gene impair telomerase activity by haploinsufficiency and may be risk factors for marrow failure.
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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 | 0.000 |
| 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".