Effect of <i>TERT</i> and <i>ATM</i> on gene expression profiles in human fibroblasts
Bibliographic record
Abstract
Telomeres protect chromosomes from degradation, end-to-end fusion, and illegitimate recombination. Loss of telomeres may lead to cell death or senescence or may cause genomic instability, leading to tumor formation. Expression of human telomerase reverse transcriptase (TERT) in human fibroblast cells elongates their telomeres and extends their lifespan. Ataxia telangiectasia mutated (ATM) deficiency in A-T human fibroblasts results in accelerated telomere shortening, abnormal cell-cycle response to DNA damage, and early senescence. Gene expression profiling was performed by serial analysis of gene expression (SAGE) on BJ normal human skin fibroblasts, A-T cells, and BJ and A-T cells transduced with TERT cDNA and expressing telomerase activity. In the four SAGE libraries, 36,921 unique SAGE tags were detected. Pairwise comparisons between the libraries showed differential expression levels of 1%-8% of the tags. Transcripts affected by both TERT and ATM were identified according to expression patterns, making them good candidates for further studies of pathways affected by both TERT and ATM. These include MT2A, P4HB, LGALS1, CFL1, LDHA, S100A10, EIF3S8, RANBP9, and SEC63. These genes are involved in apoptosis or processes related to cell growth, and most have been found to be deregulated in cancer. Our results have provided further insight into the roles of TERT and ATM by identifying genes likely to be involved in their function. Supplementary material for this article can be found on the Genes, Chromosomes and Cancer website at http://www.interscience.wiley.com/jpages/1045-2257/suppmat/index.html.
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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) | 0.000 | 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) | 0.001 | 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".