A CRISPR base editing approach for the functional assessment of telomere biology disorder-related genes in human health and aging
Bibliographic record
Abstract
Telomere Biology Disorders (TBDs) are a group of rare diseases characterized by the presence of short and/or dysfunctional telomeres. They comprise a group of bone marrow failure syndromes, idiopathic pulmonary fibrosis, and liver disease, among other diseases. Genetic alterations (variants) in the genes responsible for telomere homeostasis have been linked to TBDs. Despite the number of variants already identified as pathogenic, an even more significant number must be better understood. The study of TBDs is challenging since identifying these variants is difficult due to their rareness, it is hard to predict their impact on the disease onset, and there are not enough samples to study. Most of our knowledge about pathogenic variants comes from assessing telomerase activity from patients and their relatives affected by a TBD. However, we still lack a cell-based model to identify new variants and to study the long-term impact of such variants on the genes involved in TBDs. Herein, we present a cell-based model using CRISPR base editing to mutagenize the endogenous alleles of 21 genes involved in telomere biology. We identified key residues in the genes encoding 17 different proteins impacting cell growth. We provide functional evidence for variants of uncertain significance in patients with TBDs. We also identified variants resistant to telomerase inhibition that, similar to cells expressing wild-type telomerase, exhibited increased tumorigenic potential using an in vitro tumour growth assay. We believe that such cell-based approaches will significantly advance our understanding of the biology of TBDs and may contribute to the development of new therapies for this group of diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".