Plus-end tracking proteins, CLASPs, and a viral Akt mimic regulate herpesvirus-induced stable microtubule formation and virus spread
Bibliographic record
Abstract
Although microtubules (MTs) frequently form highly dynamic networks, subsets of MTs become stabilized in response to environmental cues and function as specialized tracks for vesicle and macromolecular trafficking. MT stabilization is controlled by specialized plus-end tracking proteins (+TIPs) whose accumulation at the MT ends is facilitated by the end-binding protein, EB1, and regulated by various signaling pathways. As cargoes themselves, viruses are dependent on MTs for their intracellular movement. Although many viruses affect MT organization, the potential contribution of MT stabilization by +TIPs to infection remains unknown. Here we show that early in infection of primary human fibroblasts, herpes simplex virus type 1 (HSV-1) disrupts the centrosome, the primary MT organizing center in many cell types. As infection progresses HSV-1 induces the formation of stable MT subsets through inactivation of glycogen synthase kinase 3beta by the viral Ser/Thr kinase, Us3. Stable MT formation is reduced in cells infected with Us3 mutants and those stable MTs that form cluster around the trans-Golgi network. Downstream of glycogen synthase kinase 3beta, cytoplasmic linker-associated proteins (CLASPs), specialized host +TIPs that control MT formation at the trans-Golgi network and cortical capture, are specifically required for virus-induced MT stabilization and HSV-1 spread. Our findings demonstrate the biological importance of +TIPs to viral infection and suggest that HSV-1 has evolved to exploit the trans-Golgi network as an alternate MT organizing center to facilitate virus spread.
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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".