The Identification Distinct Antiviral Factors Regulated Influenza Pandemic H1N1 Infection.
Baoxin WangHao ZhengXia DongWenhua ZhangJunjing WuHongbo ChenJing ZhangAo ZhouPublished in: International journal of microbiology (2024)
Influenza pandemic with H1N1 (H1N1pdms) causes severe lung damage and "cytokine storm," leading to higher mortality and global health emergencies in humans and animals. Explaining host antiviral molecular mechanisms in response to H1N1pdms is important for the development of novel therapies. In this study, we organised and analysed multimicroarray data for mouse lungs infected with different H1N1pdm and nonpandemic H1N1 strains. We found that H1N1pdms infection resulted in a large proportion of differentially expressed genes (DEGs) in the infected lungs compared with normal lungs, and the number of DEGs increased markedly with the time of infection. In addition, we found that different H1N1pdm strains induced similarly innate immune responses and the identified DEGs during H1N1pdms infection were functionally concentrated in defence response to virus, cytokine-mediated signalling pathway, regulation of innate immune response, and response to interferon. Moreover, comparing with nonpandemic H1N1, we identified ten distinct DEGs (AREG, CXCL13, GATM, GPR171, IFI35, IFI47, IFIT3, ORM1, RETNLA, and UBD), which were enriched in immune response and cell surface receptor signalling pathway as well as interacted with immune response-related dysregulated genes during H1N1pdms. Our discoveries will provide comprehensive insights into host responding to pandemic with influenza H1N1 and find broad-spectrum effective treatment.
Keyphrases
- immune response
- sars cov
- dendritic cells
- coronavirus disease
- global health
- toll like receptor
- cell surface
- genome wide
- public health
- bioinformatics analysis
- cardiovascular disease
- type diabetes
- early onset
- coronary artery disease
- cardiovascular events
- machine learning
- risk factors
- electronic health record
- dna methylation
- big data
- diabetic rats
- deep learning
- artificial intelligence
- data analysis
- genome wide identification
- stress induced