Genetic dissection of MutL complexes in Arabidopsis meiosis
Bibliographic record
Abstract
ABSTRACT During meiosis, homologous chromosomes exchange genetic material through crossing-over. The main crossover pathway relies on ZMM proteins, including ZIP4 and HEI10, and is typically resolved by the MLH1/MLH3 heterodimer, MutLγ. Our analysis of plant fertility and bivalent formation revealed that the MUS81 endonuclease can partially compensate for the MutLγ loss. Comparing genome-wide crossover maps of the mlh1 mutant with ZMM-deficient mutants and lines with varying HEI10 levels reveals that while crossover interference persists in mlh1 , it is weakened. Additionally, mlh1 show reduced crossover assurance, leading to a higher incidence of aneuploidy in offspring. This is likely due to MUS81 resolving intermediates without the crossover bias seen in MutLγ. Comparing mlh1 mlh3 mus81 and zip4 mus81 mutants suggests that additional crossover pathways emerge in the absence of both MutLγ and MUS81. The loss of MutLγ can also be suppressed by eliminating the FANCM helicase. Elevated expression of MLH1 or MLH3 increases crossover frequency, while their overexpression significantly reduces crossover numbers and plant fertility, highlighting the importance for tight control of MLH1/MLH3 levels. By contrast, PMS1, a component of the MutLα endonuclease, appears not to be involved in crossing-over. Together, these findings demonstrate the unique role of MutLγ in ZMM-dependent crossover regulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".