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Record W4362544444 · doi:10.1158/1538-7445.am2023-4783

Abstract 4783: ADO is essential for redox homeostasis in liver cancer

2023· article· en· W4362544444 on OpenAlexaff
Sandy C-E Lee, Andrea H. Pyo, Helia Mohammadi, Ji Zhang, Anna Dvorkin‐Gheva, Lucie Malbeteau, Stephen W. Chung, Shahbaz Khan, Thomas Kislinger, Julie A. Reisz, Courtney L. Jones, Marianne Koritzinsky

Bibliographic record

VenueCancer Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsCancerCancer researchBiologyCell growthLiver cancerSelenoproteinTranscriptomeMedicineHepatocellular carcinomaImmunologyGlutathioneInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Abstract 2-Aminoethanethiol dioxygenase (ADO) is a thiol dioxygenase that plays a direct role in both metabolism and protein stability. Oxidation of the metabolite cysteamine produces hypotaurine, while oxidation of N-terminal cysteines targets protein substrates for N-degron pathway-mediated degradation. Despite these known functions, the (patho)physiological roles of ADO remain obscure. By analyzing TCGA datasets we discovered that high ADO expression is associated with poor outcome for patients with hepatocellular carcinoma (HCC) (HR 1.2, p<0.001). HCC is linked to viral hepatitis, alcohol, metabolic syndrome, non-alcoholic fatty liver disease and chronic inflammation. It is the third leading cause of cancer-related deaths worldwide and is significantly more common in males than females. With poor outcomes, there is a need to better understand HCC biology and vulnerabilities. To assess the functional roles of ADO, we created ADO knockout mice as well as two liver cancer cell line models where ADO was depleted by siRNA, doxycycline-inducible shRNA, or CRISPR/Cas9. While male ADO knockout mice were viable, fertile and healthy, depletion of ADO in liver cancer models significantly reduced cancer cell proliferation and colony formation. ADO was also essential for supporting xenograft growth when implanted subcutaneously in immune-compromised mice. Taken together, this suggested that ADO depletion represents a cancer-specific vulnerability. Comprehensive metabolomic, transcriptomic and proteomic characterization of HCC cells isogenic for ADO demonstrated that loss of ADO resulted in dysregulation of glutathione, ascorbate, polyamine and proline metabolism. Consistent with this, ADO depleted cells had high levels of reactive oxygen species (ROS) measured by CellROX flowcytometry, and an increased ratio of oxidized to reduced glutathione, indicative of oxidative stress. Since mitochondria represent a substantial source of ROS, we assessed mitochondrial mass and function using MitotrackerTM flow cytometry and Seahorse stress tests, respectively. ADO depleted cancer cells had higher mitochondrial mass and higher maximal respiration rates compared to control cells. Finally, exogenously supplied antioxidants could rescue the survival of HCC cells with ADO depletion, demonstrating that the observed loss of viability is due to oxidative stress. This work shows that ADO is essential for redox homeostasis in liver cancer models and suggests that interfering with ADO function may represent a novel targeting strategy for HCC. Citation Format: Sandy C-E Lee, Andrea H. Pyo, Helia Mohammadi, Ji Zhang, Anna Dvorkin-Gheva, Lucie Malbeteau, Stephen Chung, Shahbaz Khan, Thomas Kislinger, Julie A. Reisz, Courtney Jones, Marianne Koritzinsky. ADO is essential for redox homeostasis in liver cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4783.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.064
GPT teacher head0.410
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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