Genetic Variations in Telomere Maintenance, with Implications on Tissue Renewal Capacity and Chronic Disease Pathologies
Bibliographic record
Abstract
Premature loss of telomere repeats underlies the pathologies of inherited bone marrow failure syndromes. Over the past decade, researchers have mapped genetic lesions responsible for the accelerated loss of telomere repeats. Haploinsufficiencies in the catalytic core components of the telomere maintenance enzyme telomerase, as well as genetic defects in telomerase holoenzyme components responsible for enzyme stability, have been linked to hematopoietic failure pathologies. Frequencies of these disease-associated alleles in human populations are low. Accordingly, the diseases themselves are rare. On the other hand, single nucleotide polymorphisms of telomerase enzyme components are found with much higher frequencies, with several non-synonymous SNP alleles observed in 2-4% of the general population. Importantly, recent advents of molecular diagnostic techniques have uncovered links between telomere length maintenance deficiencies and an increasing number of pathologies unrelated to the hematopoietic system. In these cases, short telomere length correlates to tissue renewal capacities and predicts clinical progression and disease severity. To the authors of this review, these new discoveries imply that even minor genetic defects in telomere maintenance can culminate in the premature failure of tissue compartments with high renewal rates. In this review, we discuss the biology and molecules of telomere maintenance, and the pathologies associated with an accelerated loss of telomeres, along with their etiologies. We also discuss single nucleotide polymorphisms of key telomerase components and their association with tissue renewal deficiency syndromes and other pathologies. We suggest that inter-individual variability in telomere maintenance capacity could play a significant role in chronic inflammatory diseases, and that this is not yet fully appreciated in the translational research of pharmacogenomics and personalized medicine.
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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) | 0.001 | 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".