Tubulin sequence divergence is associated with the use of distinct microtubule regulators
Bibliographic record
Abstract
Diverse eukaryotic cells assemble microtubule networks that vary in structure and composition. While we understand how cells build microtubule networks with specialized functions, we do not know how microtubule networks diversify across deep evolutionary timescales. This problem has remained unresolved because most organisms use shared pools of tubulins for multiple networks, making it difficult to trace the evolution of any single network. In contrast, the amoeboflagellate Naegleria expresses distinct tubulin genes to build distinct microtubule networks: while Naegleria builds flagella from conserved tubulins during differentiation, it uses divergent tubulins to build its mitotic spindle. This genetic separation makes for an internally controlled system to study independent microtubule networks in a single organismal and genomic context. To explore the evolution of these microtubule networks, we identified conserved microtubule-binding proteins and used transcriptional profiling of mitosis and differentiation to determine which are upregulated during the assembly of each network. Surprisingly, most microtubule-binding proteins are upregulated during only one process, suggesting that Naegleria uses distinct component pools to specialize its microtubule networks. Furthermore, the divergent residues of mitotic tubulins tend to fall within the binding sites of differentiation-specific microtubule regulators, suggesting that interactions between microtubules and their binding proteins constrain tubulin sequence diversification. We therefore propose a model for cytoskeletal evolution in which pools of microtubule network components constrain and guide the diversification of the entire network, so that the evolution of tubulin is inextricably linked to that of its binding partners.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".