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Abstract B09: DNA damage inducible phosphorylation of ATR at threonine 1989 and quantitative analysis of the effect of ATR inhibition on DNA damage signaling using PTMScan

2017· article· en· W2605162306 on OpenAlexaboutno aff
Hayley J. Roberts, Matthew P. Stokes, Xiaoying Jia, Kimberly A. Lee, Susan Keezer

Bibliographic record

VenueMolecular Cancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsDNA damageCHEK1PhosphorylationAutophosphorylationDNA repairBiologyDNA-PKcsG2-M DNA damage checkpointCell biologyDNAMolecular biologyCell cycleCell cycle checkpointBiochemistryProtein kinase ACell

Abstract

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Abstract DNA repair pathways and checkpoint control are crucial in the maintenance of genome integrity. Agents that cause DNA damage, and agents that perturb DNA repair pathways, have been used successfully in the treatment of human cancer. The PI3K-like protein kinases ATR (ATM and Rad3-related) and ATM (ataxia telangiectasia mutated) are critical regulators of the DNA damage response (DDR), signaling to downstream effector molecules that in turn regulate cellular responses such as DNA repair, cell cycle arrest, and cell death. ATM is activated in response to DNA damage in part via autophosphorylation at serine 1981. ATR was long thought to exist in a constitutively active state in cells, with DNA damage-induced signaling occurring via recruitment of ATR to single stranded DNA and sites of replication stress. Recent work, however, has shown autophosphorylation of ATR at threonine 1989, a homologous site to ATM Ser1981. Like ATM Ser1981, phosphorylation of ATR Thr1989 occurs in response to DNA damage, indicating that phosphorylation at this site is important in ATR-mediated signaling. We have generated highly specific antibodies to both ATM Ser1981 and ATR Thr1989. Using these antibodies, we have identified conditions under which these sites are phosphorylated in cultured human cell lines. While phosphorylation of ATM was induced in response to a wide variety of DNA damage-inducing agents, phosphorylation of ATR was induced under a small subset of these conditions, most robustly with the alkylating agent mitomycin C, the ribonucleotide reductase inhibitor hydroxyurea and the topoisomerase II inhibitor etoposide. All of these agents inhibit DNA synthesis, albeit through different mechanisms. We then identified changes downstream of ATM and ATR under conditions of ATR Thr1989 phosphorylation. Checkpoint kinases Chk1 and Chk2 are phosphorylated and activated by ATR and ATM, respectively, though there is crosstalk between the two signaling pathways. Phosphorylation of ATR Thr1989, as well as phosphorylation of the ATR substrate Chk1 Ser317, but not phosphorylation of ATM Ser1981 or Chk2 Thr68, was abrogated by treatment with the ATR inhibitor VE-821. Using western blotting, we also detected a DNA damage-induced electrophoretic mobility shift of CtIP (CtBP-interacting protein), a protein involved in the generation of single stranded DNA. The change in CtIP was partially abrogated by the ATR inhibitor, indicating that posttranslational modification of CtIP may be related to ATR Thr1989 phosphorylation. Using cell lines with and without DNA damage, and in the presence or absence of kinase inhibitor, we performed PTMScan proteomic analysis to identify and quantify changes in phosphorylation levels of proteins and sites containing a consensus ATM/ATR substrate motif (S*Q/T*Q). Changes in phosphopeptide abundance between samples monitored by PTMScan provides insight into signaling components regulated by the ATM and ATR DNA damage response. Citation Format: Hayley J. Roberts, Matthew P. Stokes, Xiaoying Jia, Kimberly A. Lee, Susan M. Keezer. DNA damage inducible phosphorylation of ATR at threonine 1989 and quantitative analysis of the effect of ATR inhibition on DNA damage signaling using PTMScan [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr B09.

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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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.048
GPT teacher head0.381
Teacher spread0.334 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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