Abstract 1431: ADO contributes to tumour initiating phenotypes
Bibliographic record
Abstract
Abstract 2-aminoethanethiol dioxygenase (ADO) is a thiol dioxygenase that plays a role in both metabolism and protein stability. ADO directly metabolizes cysteamine to produce hypotaurine and taurine in mammals. ADO has also been recently identified to promote oxygen dependent stability of a subset of substrates involved in the N-degron pathway in mammals (IL32, RGS4 and RGS5). The ability of ADO to target protein stability of signaling molecules suggests that it may have the potential to transduce rapid responses to hypoxia and affect tumour initiation and progression phenotypes. Here, we have successfully knocked down and knocked out ADO using two independent siRNAs and clustered regularly interspaced short palindromic repeats associated protein 9 (CRISPR-Cas9) system, respectively. We have assessed proliferation and migration through the Incucyte® ZOOM system by imaging cell confluency over time. Survival was assessed through a clonogenic assay. siRNA mediated knockdown of ADO in cervical (HeLa and SiHa), pancreatic (Panc1 and Capan2) and liver (SNU499 and Huh6) cancer cell lines drastically reduced proliferation, survival, and migration in normoxia. These results were also replicated in hypoxia (0.2% O2) across all 6 cell lines. Out of the 6 cell lines, the liver cancer cell lines were most drastically affected by the knockdown of ADO. This phenotype was replicated in the ADO KO cell lines. Taken together, these data suggest that expression of ADO may contribute to phenotypes that induce aggressive tumour phenotypes by targeting the stability of specific proteins and altering cellular metabolism in mammals. Citation Format: Sandy Che-Eun Serena Lee, Andrea Hye An Pyo, Marianne Koritzinsky. ADO contributes to tumour initiating phenotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1431.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".