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CD40L/IL-4–stimulated CLL demonstrates variation in translational regulation of DNA damage response genes including ATM

2018· article· en· W2890395064 on OpenAlexaff
Larissa Lezina, Ruth V. Spriggs, Daniel Beck, Carolyn Jones, Kate Dudek, Aleksandra Bzura, George D.D. Jones, Graham Packham, Anne E. Willis, Simon D. Wagner

Bibliographic record

VenueBlood Advances · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsOccupational Cancer Research Centre
FundersMedical Research CouncilBlood Cancer UKCancer Research UK
KeywordsAtaxia-telangiectasiaDNA damageCD40BiologyGeneDNA repairChronic lymphocytic leukemiaPhosphorylationCancer researchMolecular biologyDNAIn vitroCell biologyImmunologyGeneticsLeukemia

Abstract

fetched live from OpenAlex

CD40L/interleukin-4 (IL-4) stimulation occurs in vivo in the tumor microenvironment and induces global translation to varying degrees in individuals with chronic lymphocytic leukemia (CLL) in vitro. However, the implications of CD40L/IL-4 for the translation of specific genes is not known. To determine the most highly translationally regulated genes in response to CD40L/IL-4, we carried out ribosome profiling, a next-generation sequencing method. Significant differences in the translational efficiency of DNA damage response genes, specifically ataxia-telangiectasia-mutated kinase (ATM) and the MRE11/RAD50/NBN (MRN) complex, were observed between patients, suggesting different patterns of translational regulation. We confirmed associations between CD40L/IL-4 response and baseline ATM levels, induction of ATM, and phosphorylation of the ATM targets, p53 and H2AX. X-irradiation was used to demonstrate that CD40L/IL-4 stimulation tended to improve DNA damage repair. Baseline ATM levels, independent of the presence of 11q deletion, correlated with overall survival (OS). Overall, we suggest that there are individual differences in translation of specific genes, including ATM, in response to CD40L/IL-4 and that these interpatient differences might be clinically important.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.333
Teacher spread0.305 · 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 teacher head, 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

Citations18
Published2018
Admission routes1
Has abstractyes

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