The Impact of Influenza Infection on the Lung Transcriptome During Repair
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE92938 | GEO | 2016/12/28
SECONDARY ACCESSION(S): PRJNA359111
REPOSITORIES: GEO
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Project description:Mechanical forces associated with breathing play fundamental role in lung development and disease but the molecular pathways remain largely unknown. Here, we used a mechanically actuatable Human Lung Alveolus Chip that recapitulates human lung alveolar type I and type II cell differentiation, alveolar-capillary interface formation, and genome-wide gene expression profiles characteristic of the distal lung to investigate the role of physical forces associated with cyclic breathing motions in lung innate immune responses to viral infection. When the mechanically active Alveolus Chips are infected with influenza H3N2 virus, a cascade of host responses is elicited on-chip, including increased production of cytokines and expression of inflammation-associated genes in pulmonary epithelial and endothelial cells, resulting in enhanced recruitment of circulating immune cells as occurs during viral infection in vivo. Surprisingly, studies carried out in parallel with static chips revealed that physiological breathing motions suppress viral replication by activating protective innate immune responses in epithelial and endothelial cells. This is mediated at least in part through upregulation of S100 calcium-binding protein A7 (S100A7), which binds to the Receptor for Advanced Glycation End Products (RAGE), an inflammatory mediator that is most highly expressed in the lung alveolus in vivo. This mechano-immunological control mechanism is further supported by the finding that existing RAGE inhibitor drugs can suppress production of inflammatory cytokines in response to influenza virus infection in this model. S100A7-RAGE interactions and modulation of mechanical ventilation parameters could therefore serve as new targets for therapeutic intervention in patients infected with influenza and other potential pandemic viruses that cause life-threatening lung inflammation.
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Project description:Mechanical forces associated with breathing play fundamental role in lung development and disease but the molecular pathways remain largely unknown. Here, we used a mechanically actuatable Human Lung Alveolus Chip that recapitulates human lung alveolar type I and type II cell differentiation, alveolar-capillary interface formation, and genome-wide gene expression profiles characteristic of the distal lung to investigate the role of physical forces associated with cyclic breathing motions in lung innate immune responses to viral infection. When the mechanically active Alveolus Chips are infected with influenza H3N2 virus, a cascade of host responses is elicited on-chip, including increased production of cytokines and expression of inflammation-associated genes in pulmonary epithelial and endothelial cells, resulting in enhanced recruitment of circulating immune cells as occurs during viral infection in vivo. Surprisingly, studies carried out in parallel with static chips revealed that physiological breathing motions suppress viral replication by activating protective innate immune responses in epithelial and endothelial cells. This is mediated at least in part through upregulation of S100 calcium-binding protein A7 (S100A7), which binds to the Receptor for Advanced Glycation End Products (RAGE), an inflammatory mediator that is most highly expressed in the lung alveolus in vivo. This mechano-immunological control mechanism is further supported by the finding that existing RAGE inhibitor drugs can suppress production of inflammatory cytokines in response to influenza virus infection in this model. S100A7-RAGE interactions and modulation of mechanical ventilation parameters could therefore serve as new targets for therapeutic intervention in patients infected with influenza and other potential pandemic viruses that cause life-threatening lung inflammation.
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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.
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BIOMD0000000546.
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2021-10-05 | BIOMD0000000546 | BioModels
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