Armadillo-repeat kinesin1 interacts with Arabidopsis atlastin RHD3 to move ER with plus-end of microtubules
Bibliographic record
Abstract
In living cells, dynamics of the endoplasmic reticulum (ER) are driven by the cytoskeleton motor machinery as well as the action of ER-shaping proteins such as atlastin GTPases including RHD3 in Arabidopsis. It is not known if the two systems interplay, and, if so, how they do. Here we report the identification of ARK1 (Armadillo-Repeat Kinesin1) via a genetic screen for enhancers of the rhd3 mutant phenotype. In addition to defects in microtubule dynamics, ER organization is also defective in mutants lacking a functional ARK1. In growing root hair cells, ARK1 comets predominantly localize on the growing-end of microtubules and partially overlap with RHD3 in the cortex of the subapical region. ARK1 co-moves with RHD3 during tip growth of root hair cells. We show that there is a functional interdependence between ARK1 and RHD3. ARK1 physically interacts with RHD3 via its armadillo domain (ARM). In leaf epidermal cells where a polygonal ER network can be resolved, ARK1, but not ARK1ΔARM, moves together with RHD3 to pull an ER tubule toward another and stays with the newly formed 3-way junction of the ER for a while. We conclude that ARK1 acts together with RHD3 to move the ER on microtubules to generate a fine ER network.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".