A Deterministic Model for Understanding Nonlinear Viral Dynamics in Oysters.
NoV
OsHV-1
depuration
in-host
marine disease
modeling
outbreaks
virus-oyster interaction
Journal
Applied and environmental microbiology
ISSN: 1098-5336
Titre abrégé: Appl Environ Microbiol
Pays: United States
ID NLM: 7605801
Informations de publication
Date de publication:
26 04 2022
26 04 2022
Historique:
pubmed:
30
3
2022
medline:
29
4
2022
entrez:
29
3
2022
Statut:
ppublish
Résumé
Contamination of oysters with a variety of viruses is one key pathway to trigger outbreaks of massive oyster mortality as well as human illnesses, including gastroenteritis and hepatitis. Much effort has gone into examining the fate of viruses in contaminated oysters, yet the current state of knowledge of nonlinear virus-oyster interactions is not comprehensive because most studies have focused on a limited number of processes under a narrow range of experimental conditions. A framework is needed for describing the complex nonlinear virus-oyster interactions. Here, we introduce a mathematical model that includes key processes for viral dynamics in oysters, such as oyster filtration, viral replication, the antiviral immune response, apoptosis, autophagy, and selective accumulation. We evaluate the model performance for two groups of viruses, those that replicate in oysters (e.g., ostreid herpesvirus) and those that do not (e.g., norovirus), and show that this model simulates well the viral dynamics in oysters for both groups. The model analytically explains experimental findings and predicts how changes in different physiological processes and environmental conditions nonlinearly affect in-host viral dynamics, for example, that oysters at higher temperatures may be more resistant to infection by ostreid herpesvirus. It also provides new insight into food treatment for controlling outbreaks, for example, that depuration for reducing norovirus levels is more effective in environments where oyster filtration rates are higher. This study provides the foundation of a modeling framework to guide future experiments and numerical modeling for better prediction and management of outbreaks.
Identifiants
pubmed: 35348387
doi: 10.1128/aem.02360-21
pmc: PMC9046769
doi:
Banques de données
figshare
['10.6084/m9.figshare.19294580.v1']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
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