Cargando…
Timing Is Everything
N. Drayman et al. in their recent article (mBio 8:e01612-17, 2017, https://doi.org/10.1128/mBio.01612-17) have used dynamic proteomics and machine learning to show that the cell cycle state of any individual cell affects the outcome of a productive herpes simplex virus 1 (HSV-1) infection. Cells inf...
Autor principal: | |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750403/ https://www.ncbi.nlm.nih.gov/pubmed/29295914 http://dx.doi.org/10.1128/mBio.02140-17 |
Sumario: | N. Drayman et al. in their recent article (mBio 8:e01612-17, 2017, https://doi.org/10.1128/mBio.01612-17) have used dynamic proteomics and machine learning to show that the cell cycle state of any individual cell affects the outcome of a productive herpes simplex virus 1 (HSV-1) infection. Cells infected from early G(1) through S were most permissive for expression of genes from the HSV-1 genome, whereas cells infected in late G(2) to mitosis were much less so. Most of the infected cells that underwent mitosis became permanently nonpermissive for HSV-1 gene expression afterward. The cell cycle stage accounted for 60% of the success of infection, and cell density and motility accounted for most of the rest. To successfully reactivate, HSV-1 must express its genes in neurons and cells of the spinosum and granulosum epidermis strata. These cells are permanently in the cell cycle stages most permissive for HSV-1 gene expression, and none reenters mitosis, thus maximizing the efficiency of a successful HSV-1 reactivation before the adaptive immunity can control it. |
---|