Global Interactomics Uncovers Extensive Organellar Targeting by Zika Virus
Bibliographic record
Abstract
mosquitoes. The geographical range of ZIKV has dramatically expanded in recent decades resulting in increasing numbers of infected individuals, and the spike in ZIKV infections has been linked to significant increases in both Guillain-Barré syndrome and microcephaly. Although a large number of host proteins have been physically and/or functionally linked to other Flaviviruses, very little is known about the virus-host protein interactions established by ZIKV. Here we map host cell protein interaction profiles for each of the ten polypeptides encoded in the ZIKV genome, generating a protein topology network comprising 3033 interactions among 1224 unique human polypeptides. The interactome is enriched in proteins with roles in polypeptide processing and quality control, vesicle trafficking, RNA processing and lipid metabolism. >60% of the network components have been previously implicated in other types of viral infections; the remaining interactors comprise hundreds of new putative ZIKV functional partners. Mining this rich data set, we highlight several examples of how ZIKV may usurp or disrupt the function of host cell organelles, and uncover an important role for peroxisomes in ZIKV infection.
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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 | 0.000 |
| 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".