The Spectraplakin Short Stop Is an Actin–Microtubule Cross-Linker That Contributes to Organization of the Microtubule Network
Bibliographic record
Abstract
The dynamics of actin and microtubules are coordinated in a variety of cellular and morphogenetic processes; however, little is known about the molecules mediating this cytoskeletal cross-talk. We are studying Short stop (Shot), the sole Drosophila spectraplakin, as a model actin-microtubule cross-linking protein. Spectraplakins are an ancient family of giant cytoskeletal proteins that are essential for a diverse set of cellular functions; yet, we know little about the dynamics of spectraplakins and how they bridge actin filaments and microtubules. In this study we describe the intracellular dynamics of Shot and a structure-function analysis of its role as a cytoskeletal cross-linker. We find that Shot interacts with microtubules using two different mechanisms. In the cell interior, Shot binds growing plus ends through an interaction with EB1. In the cell periphery, Shot associates with the microtubule lattice via its GAS2 domain, and this pool of Shot is actively engaged as a cross-linker via its NH(2)-terminal actin-binding calponin homology domains. This cross-linking maintains microtubule organization by resisting forces that produce lateral microtubule movements in the cytoplasm. Our results provide the first description of the dynamics of these important proteins and provide key insight about how they function during cytoskeletal cross-talk.
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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 | 0.000 |
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.
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".