Biological relevance of <i>in vitro</i> cellular models to study varicella-zoster virus—host cell interactome: current limitations and future perspectives
Bibliographic record
Abstract
SUMMARY With varicella-zoster virus (VZV) being a strictly human-specific pathogen, in vitro cell culture models to study the VZV-host cell interactome predominantly rely on the use of primary human cells, immortalized cell lines, and—more recently—stem cell-derived models. In this work, based on literature reports published within the past 15 years, we attempted to summarize major lessons learned from in vitro VZV research, with a specific focus on whether and how a variety of host cells respond upon VZV infection at the cellular level. Following this specific approach, we describe the cellular events occurring following VZV infection in a neural cell type context, an immune cell type context, and a skin cell type context. Highly relevant, and for sure subject to the development of future VZV research, cell types within each of the three compartments reviewed display similarities but also significant differences in cellular response to VZV infection. Clearly, these need further clarification on a cell-type and/or VZV strain-specific level. Finally, to increase physiological relevance, we propose an integrated approach for future VZV—host cell interactome studies on a systems level by using advanced human-induced pluripotent stem cell-derived skin, peripheral, and central nervous system compartments that can be complemented with an isogenic immune cell component. Combined with the implementation of state-of-the-art multi-omics analyses, as well as electrophysiological recordings, this next-generation toolbox for advanced virus-host cell interactome studies may help to elucidate important aspects of VZV biology, including the suggested link between VZV pathology and neurodegenerative diseases.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".