A siRNA-Based Screen for Genes Involved in Chromosome End Protection
Bibliographic record
Abstract
Telomeres are nucleoprotein complexes which protect the ends of linear chromosomes from detection as DNA damage and provide a sequence buffer against replication-associated shortening. In mammals, telomeres consist of repetitive DNA sequence (TTAGGG) and associated proteins. The telomeric core complex is called shelterin and is comprised of the proteins TRF1, TRF2, POT1, TIN2, TPP1 and RAP1. Excessive telomere shortening or de-protection of telomeres through the loss of shelterin subunits allows the detection of telomeres as DNA damage, which can be visualized as DNA damage protein foci at chromosome ends called TIF (Telomere Dysfunction-Induced Foci). We sought to exploit the TIF phenotype as marker for telomere dysfunction to identify novel genes involved in telomere protection by siRNA-mediated knock-down of a set of 386 candidates. Here we report the establishment, specificity and feasibility of such a screen and the results of the genes tested. Only one of the candidate genes showed a unique TIF phenotype comparable to the suppression of the main shelterin components TRF2 or TRF1 and that gene was identified as a TRF1-like pseudogene. We also identified a weak TIF phenotype for SKIIP (SNW1), a splicing factor and transcriptional co-activator. However, the knock-down of SKIIP also induced a general, not telomere-specific DNA damage response, which complicates conclusions about a telomeric role. In summary, this report is a technical demonstration of the feasibility of a cell-based screen for telomere deprotection with the potential of scaling it to a high-throughput approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".