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Record W2330875406 · doi:10.1126/science.aac9786

Stochastic activation of a DNA damage response causes cell-to-cell mutation rate variation

2016· article· en· W2330875406 on OpenAlexfundno aff
Stephan Uphoff, Nathan D. Lord, Burak Okumuş, Laurent Potvin-Trottier, David J. Sherratt, Johan Paulsson

Bibliographic record

VenueScience · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene Regulatory Network Analysis
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologiesBiotechnology and Biological Sciences Research CouncilNational Institute of General Medical SciencesWellcomeMedical Research CouncilNational Institutes of HealthNatural Sciences and Engineering Research Council of CanadaWellcome Trust
KeywordsMutagenesisDNA repairDNA damageDNAMutationCellBiologyMolecular biologyCell biologyGeneticsChemistryGene

Abstract

fetched live from OpenAlex

Cells rely on the precise action of proteins that detect and repair DNA damage. However, gene expression noise causes fluctuations in protein abundances that may compromise repair. For the Ada protein in Escherichia coli, which induces its own expression upon repairing DNA alkylation damage, we found that undamaged cells on average produce one Ada molecule per generation. Because production is stochastic, many cells have no Ada molecules and cannot induce the damage response until the first expression event occurs, which sometimes delays the response for generations. This creates a subpopulation of cells with increased mutation rates. Nongenetic variation in protein abundances thus leads to genetic heterogeneity in the population. Our results further suggest that cells balance reliable repair against toxic side effects of abundant DNA repair proteins.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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

Citations172
Published2016
Admission routes1
Has abstractyes

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