Determining the role of replication stress and proliferating cell nuclear antigen SUMOylation in its telomere localization
Bibliographic record
Abstract
In order to achieve replicative immortality, cancer cells must maintain their telomeres - tandem repeats located at the terminal sequences of chromosomes - to counter the loss of genetic material due to the end replication problem. Ten-fifteen percent of all cancers utilize a telomerase-independent homologous recombination-based mechanism to maintain and elongate their telomeres. It is hypothesized that this mechanism is active in ALT-associated promyelocytic leukemia bodies (APBs) due to the recruitment of telomeres, extrachromosomal telomeric repeats, proteins involved in DNA repair and maintenance and the elevated levels of SUMOylated - a post-translational modification important for replication stress response - proteins in these membraneless organelles. Proliferating cell nuclear antigen (PCNA) - a protein crucial for DNA replication and repair - can be SUMOylated at Lysine residue K164 and K254 and may be part of the ALT replisome. As such, my research aims to reveal the role of replication stress and the role of SUMOylation on PCNA localization with the use of recombinant flagged-tagged PCNA (WT, K164R and K164R/K254R) and SUMO-fusions of flagged-tagged PCNA (WT-SUMO, K164R-SUMO and K164R/K254R-SUMO). Treating cells with 6h-2000μM of hydroxyurea - a drug that promotes nucleotide deprivation by inhibiting ribonucleotide reductase - induces replication stress in ALT+ SW-26 and VA13 cells and in TERT+ SW- 39 and HEK293 cells which can be monitored with phosphorylated RPA levels through western blotting. Furthermore, the localization of recombinant PCNA variants to telomeres can be visualized using fluorescence in situ hybridization coupled with immunofluorescence (FISH-IF) as well as by chromatin immunoprecipitation, future experiments needed to carry the research forward
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".