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Record W2915405957 · doi:10.1073/pnas.1815071116

OAS-RNase L innate immune pathway mediates the cytotoxicity of a DNA-demethylating drug

2019· article· en· W2915405957 on OpenAlexafffund
Shuvojit Banerjee, Elona Gusho, Christina Gaughan, Beihua Dong, Xiaorong Gu, Elise Holvey-Bates, Manisha Talukdar, Yize Li, Susan R. Weiss, Frank Sicheri, Yogen Saunthararajah, George R. Stark, Robert H. Silverman

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchCanadian Cancer SocietyDivision of Intramural Research, National Institute of Allergy and Infectious Diseases
KeywordsAzacitidineMyeloid leukemiaInnate immune systemBiologyDrugCytotoxicityCancer researchImmune systemPharmacologyDNA methylationImmunologyBiochemistryGeneIn vitroGene expression

Abstract

fetched live from OpenAlex

-terminal kinases (JNKs) also antagonizes RNase L-dependent cell death in response to AZA, consistent with a role for JNK in RNase L-induced apoptosis. In contrast, the rates of AZA-induced and RNase L-dependent cell death were increased by transfection of 2-5A, by deficiencies in ADAR1 (which edits and destabilizes dsRNA), PDE12 or AKAP7 (which degrade 2-5A), or by ionizing radiation (which induces IFN-dependent signaling). Finally, OAS1 expression correlates with AZA sensitivity in the NCI-60 set of tumor cell lines, suggesting that the level of OAS1 can be a biomarker for predicting AZA sensitivity of tumor cells. These studies may eventually lead to pharmacologic strategies for regulating the antitumor activity and toxicity of AZA and related drugs.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.022
GPT teacher head0.272
Teacher spread0.250 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations84
Published2019
Admission routes2
Has abstractyes

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Same venueProceedings of the National Academy of SciencesSame topicinterferon and immune responsesFrench-language works237,207