Emergent Complexity in Myosin V-Based Organelle Inheritance
Bibliographic record
Abstract
How is adaptability generated in a system composed of interacting cellular machineries, each with a separate and functionally critical job to perform? The machinery for organelle inheritance is precisely one such system, requiring coordination between robust and ancient cellular modules, including the cell cycle, cytoskeleton, and organelle biogenesis/identity. Budding yeasts have emerged as powerful models to study these processes, which are critical for cellular survival, propagation, and differentiation, as organelles must compete for access to myosin V motors that travel along polarized actin cables to vectorially deliver bound cargo to the bud. Under the direction of the cell cycle, myosin V motors are recruited to organelles by specific interactions between their carboxyl-terminal globular tail domains and organelle-specific receptors. We used comparative genomics, phylogenetics, and secondary structure modeling to characterize the evolutionary history of these organelle-specific receptors. We find that while some receptors are retained widely across the animals and fungi, others are limited primarily to the Saccharomycetaceae family of budding yeast, with the emergent pattern of a conserved biogenic and inheritance factor often paired with an evolutionarily novel inheritance adaptor. We propose an evolutionary model whereby the emergence of myosin V-based organelle inheritance has utilized mechanisms of paralogy, mutation, and the appearance of pliable evolutionarily novel adaptor proteins. Our findings suggest an overarching evolutionary mechanism for how diverse cargoes compete for a single myosin V motor in organelle transport and detail one system's solution to obtaining evolutionary adaptability amongst constrained cellular modules.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".