A Spectrum of Severe Familial Liver Disorders Associate with Telomerase Mutations
Bibliographic record
Abstract
BACKGROUND: Telomerase is an enzyme specialized in maintaining telomere lengths in highly proliferative cells. Loss-of-function mutations cause critical telomere shortening and are associated with the bone marrow failure syndromes dyskeratosis congenita and aplastic anemia and with idiopathic pulmonary fibrosis. Here, we sought to determine the spectrum of clinical manifestations associated with telomerase loss-of-function mutations. METHODOLOGY/PRINCIPAL FINDINGS: Sixty-nine individuals from five unrelated families with a variety of hematologic, hepatic, and autoimmune disorders were screened for telomerase complex gene mutations; leukocyte telomere length was measured by flow fluorescence in situ hybridization in mutation carriers and some non-carriers; the effects of the identified mutations on telomerase activity were determined; and genetic and clinical data were correlated. In six generations of a large family, a loss-of-function mutation in the telomerase enzyme gene TERT associated with severe telomere shortening and a range of hematologic manifestations, from macrocytosis to acute myeloid leukemia, with severe liver diseases marked by fibrosis and inflammation, and one case of idiopathic pulmonary fibrosis but not with autoimmune disorders. Additionally, we identified four unrelated families in which loss-of-function TERC or TERT gene mutations tracked with marrow failure, pulmonary fibrosis, and a spectrum of liver disorders. CONCLUSIONS/SIGNIFICANCE: These results indicate that heterozygous telomerase loss-of-function mutations associate with but are not determinant of a large spectrum of hematologic and liver abnormalities, with the latter sometimes occurring in the absence of marrow failure. Our findings, along with the link between pulmonary fibrosis and telomerase mutations, also suggest a common pathogenic mechanism for fibrotic diseases in which defective telomere repair plays important role.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".