Integrative Gene Network Analysis Identifies Key Signatures, Intrinsic Networks and Host Factors for Influenza Virus A Infections
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE104168 | GEO | 2018/02/14
REPOSITORIES: GEO
Similar Datasets
Project description:Heldt2012 - Influenza Virus Replication
The model describes the life cycle of influenza A virus in a mammalian cell including the following steps: attachment of parental virions to the cell membrane, receptor-mediated endocytosis, fusion of the virus envelope with the endosomal membrane, nuclear import of vRNPs, viral transcription and replication, translation of the structural viral proteins, nuclear export of progeny vRNPs and budding of new virions. It also explicitly accounts for the stabilization of cRNA by viral polymerases and NP and the inhibition of vRNP activity by M1 protein binding. In short, the model focuses on the molecular mechanism that controls viral transcription and replication.
This model is described in the article:
Modeling the intracellular dynamics of influenza virus replication to understand the control of viral RNA synthesis.
Heldt FS, Frensing T, Reichl U.
J Virol.
Abstract:
Influenza viruses transcribe and replicate their negative-sense RNA genome inside the nucleus of host cells via three viral RNA species. In the course of an infection, these RNAs show distinct dynamics, suggesting that differential regulation takes place. To investigate this regulation in a systematic way, we developed a mathematical model of influenza virus infection at the level of a single mammalian cell. It accounts for key steps of the viral life cycle, from virus entry to progeny virion release, while focusing in particular on the molecular mechanisms that control viral transcription and replication. We therefore explicitly consider the nuclear export of viral genome copies (vRNPs) and a recent hypothesis proposing that replicative intermediates (cRNA) are stabilized by the viral polymerase complex and the nucleoprotein (NP). Together, both mechanisms allow the model to capture a variety of published data sets at an unprecedented level of detail. Our findings provide theoretical support for an early regulation of replication by cRNA stabilization. However, they also suggest that the matrix protein 1 (M1) controls viral RNA levels in the late phase of infection as part of its role during the nuclear export of viral genome copies. Moreover, simulations show an accumulation of viral proteins and RNA toward the end of infection, indicating that transport processes or budding limits virion release. Thus, our mathematical model provides an ideal platform for a systematic and quantitative evaluation of influenza virus replication and its complex regulation.
With the current parameter set, the model reproduces an infection at a multiplicity of infection (MOI) of 10. Figure 2A of the paper is reproduced here, with parameters kDegRnp and kSynP changed to zeros.
Initial conditions and parameter changes that were used to obtain specific figures in the article can be found in Table A2.
The model has the correct value for kAttLo as 4.55e-04. The value of this parameter mentioned as 4.55e-02 in Table 1 of the paper is incorrect. This is checked with the author.
This model is hosted on BioModels Database
and identified
by: MODEL1307270000
.
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.
2013-08-07 | BIOMD0000000463 | BioModels
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.
2021-10-05 | BIOMD0000000546 | BioModels
Project description:While a common symptom of influenza and coronavirus disease 2019 (COVID-19) is fever, its physiological role on host resistance to viral infection remains less clear. Here, we demonstrate that exposure of mice to the high ambient temperature of 36 °C increase host resistance to viral pathogens including influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). High heat-exposed mice increase basal body temperature over 38 °C to enable more bile acids production in a gut microbiota-dependent manner. The gut microbiota-derived deoxycholic acid (DCA) and its plasma membrane-bound receptor Takeda G-protein-coupled receptor 5 (TGR5) signaling increase host resistance to influenza virus infection by suppressing virus replication and neutrophil-dependent tissue damage. Furthermore, the DCA and its nuclear farnesoid X receptor (FXR) agonist protect Syrian hamster from lethal SARS-CoV-2 infection. Moreover, we demonstrate that certain bile acids are reduced in the plasma of COVID-19 patients who developed moderate I/II disease compared with minor illness group. These findings uncover an unexpected mechanism by which virus-induced high fever increases host resistance to influenza virus and SARS-CoV-2 in a gut microbiota-dependent manner.
2023-02-14 | ST002479 | MetabolomicsWorkbench
Project description:While a common symptom of influenza and coronavirus disease 2019 (COVID-19) is fever, its physiological role on host resistance to viral infection remains less clear. Here, we demonstrate that exposure of mice to the high ambient temperature of 36 °C increase host resistance to viral pathogens including influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). High heat-exposed mice increase basal body temperature over 38 °C to enable more bile acids production in a gut microbiota-dependent manner. The gut microbiota-derived deoxycholic acid (DCA) and its plasma membrane-bound receptor Takeda G-protein-coupled receptor 5 (TGR5) signaling increase host resistance to influenza virus infection by suppressing virus replication and neutrophil-dependent tissue damage. Furthermore, the DCA and its nuclear farnesoid X receptor (FXR) agonist protect Syrian hamster from lethal SARS-CoV-2 infection. Moreover, we demonstrate that certain bile acids are reduced in the plasma of COVID-19 patients who developed moderate I/II disease compared with minor illness group. These findings uncover an unexpected mechanism by which virus-induced high fever increases host resistance to influenza virus and SARS-CoV-2 in a gut microbiota-dependent manner.
2023-01-24 | ST002476 | MetabolomicsWorkbench
Project description:Host cell lipids play a pivotal role in the pathogenesis of respiratory virus infection. However, a direct comparison of the lipidomic profile of influenza virus and rhinovirus infections is lacking. In this study, we first compared the lipid profile of influenza virus and rhinovirus infection in a bronchial epithelial cell line. Most lipid features were downregulated for both influenza virus and rhinovirus, especially for the sphingomyelin features. Pathway analysis showed that sphingolipid metabolism was the most perturbed pathway. Functional study showed that bacterial sphingomyelinase suppressed influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication, but promoted rhinovirus replication. These findings suggest that sphingomyelin pathway can be a potential target for antiviral therapy, but should be carefully evaluated as it has opposite effects on different respiratory viruses. Furthermore, the differential effect of sphingomyelinase on rhinovirus and influenza virus may explain the interference between rhinovirus and influenza virus infection.
2022-11-15 | MTBLS2352 | MetaboLights
Project description:Influenza infection is a worldwide health and financial burden posing a significant risk to the immune-compromised, obese, diabetic, elderly, and pediatric populations. We identified increases in glucose metabolism in the lungs of pediatric patients infected with respiratory pathogens. Using quantitative mass spectrometry we found metabolic changes occurring after influenza infection in primary human respiratory cells, and validated infection associate increases in c-Myc, glycolysis, and glutaminolysis. We confirmed these findings with a metabolic drug screen and high throughput titering that identified the PI3K/mTOR inhibitor BEZ235 as a regulator of infectious virus production. BEZ235 treatment ablated the transient induction of c-Myc, restored PI3K/mTOR pathway homeostasis measured by 4E-BP1 and p85 phosphorylation, and reversed infection-induced changes in glucose and glutamine metabolism. Importantly, BEZ235 reduced infectious progeny but had no effect on viral entry or the early stages of viral replication. In a lethal infection model, BEZ235 significantly increased survival while reducing viral titer and respiratory distress. Here we show metabolic reprogramming of host cells by influenza virus exposes targets for therapeutic intervention.
2017-05-24 | PXD006263 | Pride
Project description:Over the last decade, more than half of humans infected with highly pathogenic avian influenza (HPAI) H5N1 viruses have died, and yet virus-induced host signaling has yet to be clearly elucidated. Airway epithelia are known to produce inflammatory mediators that contribute to HPAI H5N1-mediated pathogenicity, but a comprehensive analysis of the host response in this cell type is lacking. Here, we leveraged a systems biology method called weighted gene correlation network analysis (WGCNA) to identify and statistically validate signaling sub-networks that define the dynamic transcriptional response of human bronchial epithelial cells after infection with influenza A/Vietnam/1203/2004 (H5N1, VN1203). A detailed examination of two sub-networks involved in the immune response and keratin filament formation revealed potential novel mediators of HPAI H5N1 pathogenesis, and additional experiments validated upregulation of these transcripts in response to VN1203 infection in C57BL/6 mice. Using emergent network properties, we provide fresh insight into the host response to HPAI H5N1 virus infection, and identify novel avenues for perturbation studies and potential therapeutic intervention of fatal HPAI H5N1 disease. Calu-3 cells were infected with VN1203 influenza virus and profiled at 0, 3, 7, 12, 18, and 24 hours post infection. There are 3 mock and infected replicates for each time point.
2011-09-01 | E-GEOD-28166 | biostudies-arrayexpress
Project description:We utilize the natural cell line model (LMH and DF1) with different susceptibiltiy to H9N2 avian influenza virus to find out more and new potential key factors of influencing AIV infection and replication via a high-throughput RNA sequencing (RNA-seq).
2019-07-04 | GSE133771 | GEO
Project description:Secondary bacterial infections (SBIs) exacerbate influenza-associated disease and mortality. Antimicrobial agents can reduce the severity of SBIs, but many have limited efficacy or cause adverse effects. Thus, new treatment strategies are needed. Kinetic models describing the infection process can help determine optimal therapeutic targets, the time scale on which a drug will be most effective, and how infection dynamics will change under therapy. To understand how different therapies perturb the dynamics of influenza infection and bacterial coinfection and to quantify the benefit of increasing a drug’s efficacy or targeting a different infection process, I analyzed data from mice treated with an antiviral, an antibiotic, or an immune modulatory agent with kinetic models. The results suggest that antivirals targeting the viral life cycle are most efficacious in the first 2 days of infection, potentially because of an improved immune response, and that increasing the clearance of infected cells is important for treatment later in the infection. For a coinfection, immunotherapy could control low bacterial loads with as little as 20 % efficacy, but more effective drugs would be necessary for high bacterial loads. Antibiotics targeting bacterial replication and administered 10 h after infection would require 100 % efficacy, which could be reduced to 40 % with prophylaxis. Combining immunotherapy with antibiotics could substantially increase treatment success. Taken together, the results suggest when and why some therapies fail, determine the efficacy needed for successful treatment, identify potential immune effects, and show how the regulation of underlying mechanisms can be used to design new therapeutic strategies.
Model is encoded by Ruby and submitted to BioModels by Ahmad Zyoud
2020-04-02 | MODEL1812040005 | BioModels
Project description:In order to identify the swine genes which play roles in the regulation of swine influenza A virus replication, the gene microarray was performed to explore the systematical host response to the swine H1N1/2009 influenza A virus infection in porcine cells.
2017-03-24 | GSE96952 | GEO