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The impact of sequence divergence and DNA mismatch repair on homeologous recombination in Arabidopsis

2006· article· en· W2048602196 on OpenAlexafffund
Liangliang Li, Martine Jean, François Belzile

Bibliographic record

VenueThe Plant Journal · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyRecombinationHomologous recombinationGeneticsMutantDNA mismatch repairDNAMolecular biologyDNA repairGene

Abstract

fetched live from OpenAlex

We examined the effects of substrate divergence and DNA mismatch repair (MMR) on recombination in Arabidopsis thaliana. Relative to the frequency observed in plants with a homologous construct (0% divergence), recombination was decreased 4.1-, 9.6-, 11.7- or 20.3-fold, respectively, in lines with constructs containing 0.5%, 2%, 4% or 9% divergence between the recombination substrates. To evaluate the contribution of the MMR system in this decrease, 12 independent reporter lines (two or three lines per reporter construct) were crossed to an AtMSH2 T-DNA insertional mutant. We examined the recombination frequency in progeny homozygous for a reporter T-DNA and homozygous either for the wild type or the mutant allele of AtMSH2. The loss of MMR activity led to a two- to ninefold increase in homeologous recombination and the size of the increase did not seem to correlate with the amount of divergence. Inversely, complementation of the insertional mutant with a wild-type cDNA of AtMSH2 reduced recombination. Our results demonstrate clearly that sequence divergence can dramatically reduce the recombination frequency in plants and that the MMR system plays a part in this decrease.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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

Citations94
Published2006
Admission routes2
Has abstractyes

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