E-MEXP-1274
Ontology highlight
ABSTRACT:
PROVIDER: E-MEXP-1274 | ExpressionAtlas | 2025-01-22
REPOSITORIES: ExpressionAtlas
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| E-MEXP-1274-analysis-methods.tsv | Tabular | |||
| E-MEXP-1274-atlasExperimentSummary.Rdata | Rdata | |||
| E-MEXP-1274-configuration.xml | Xml | |||
| E-MEXP-1274-percentile-ranks.tsv | Tabular | |||
| E-MEXP-1274.condensed-sdrf.tsv | Tabular |
Items per page: 5 1 - 5 of 92 |
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Project description:To explore the host responses associated with TLR3 signaling during IAV infection, we used a human bronchial epithelial cell line stably expressing a dominant negative form of TLR3 (pZERO-hTLR3) and examined global impact of TLR3 through an analysis of gene expression changes compared to control cells at 24 h after infection.
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The understanding of how influenza virus infection activates the immune system is crucial to designing prophylactic and therapeutic strategies against the infection. Nevertheless, the immune response to influenza virus infection is complex and remains largely unknown. In this paper we focus in the innate immune response to influenza virus using a mathematical model, based on interferon-induced resistance to infection of respiratory epithelial cells and the clearance of infected cells by natural killers. Simulation results show the importance of IFN-I to prevent new infections in epithelial cells and to stop the viral explosion during the first two days after infection. Nevertheless, natural killers response might be the most relevant for the first depletion in viral load due to the elimination of infected cells. Based on the reproductive number, the innate immune response is important to control the infection, although it would not be enough to clear completely the virus. The effective coordination between innate and adaptive immune response is essential for the virus eradication.
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Project description:Miao2010 - Innate and adaptive immune
responses to primary Influenza A Virus infection
This model is described in the article:
Quantifying the early immune
response and adaptive immune response kinetics in mice infected
with influenza A virus.
Miao H, Hollenbaugh JA, Zand MS,
Holden-Wiltse J, Mosmann TR, Perelson AS, Wu H, Topham DJ.
J. Virol. 2010 Jul; 84(13):
6687-6698
Abstract:
Seasonal and pandemic influenza A virus (IAV) continues to
be a public health threat. However, we lack a detailed and
quantitative understanding of the immune response kinetics to
IAV infection and which biological parameters most strongly
influence infection outcomes. To address these issues, we use
modeling approaches combined with experimental data to
quantitatively investigate the innate and adaptive immune
responses to primary IAV infection. Mathematical models were
developed to describe the dynamic interactions between target
(epithelial) cells, influenza virus, cytotoxic T lymphocytes
(CTLs), and virus-specific IgG and IgM. IAV and immune kinetic
parameters were estimated by fitting models to a large data set
obtained from primary H3N2 IAV infection of 340 mice. Prior to
a detectable virus-specific immune response (before day 5), the
estimated half-life of infected epithelial cells is
approximately 1.2 days, and the half-life of free infectious
IAV is approximately 4 h. During the adaptive immune response
(after day 5), the average half-life of infected epithelial
cells is approximately 0.5 days, and the average half-life of
free infectious virus is approximately 1.8 min. During the
adaptive phase, model fitting confirms that CD8(+) CTLs are
crucial for limiting infected cells, while virus-specific IgM
regulates free IAV levels. This may imply that CD4 T cells and
class-switched IgG antibodies are more relevant for generating
IAV-specific memory and preventing future infection via a more
rapid secondary immune response. Also, simulation studies were
performed to understand the relative contributions of
biological parameters to IAV clearance. This study provides a
basis to better understand and predict influenza virus
immunity.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000546.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
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