Identification of Influenza A/H7N9 virus infection-related human genes based on shortest paths in a virus-human
Ning Zhang1, Min Jiang2, Tao Huang3
1Department of Biomedical Engineering, Tianjin University, Tianjin Key Lab of BME Measurement, Tianjin 300072, China.
Abstract:
The recently emerging Influenza A/H7N9 virus is reported to be able to infect humans and cause mortality. However, viral and host factors associated with the infection are poorly understood. It is suggested by the "guilt by association" rule that interacting proteins share the same or similar functions and hence may be involved in the same pathway. In this study, we developed a computational method to identify Influenza A/H7N9 virus infection-related human genes based on this rule from the shortest paths in a virus-human protein interaction network. Finally, we screened out the most significant 20 human genes, which could be the potential infection related genes, providing guidelines for further experimental validation. Analysis of the 20 genes showed that they were enriched in protein binding, saccharide or polysaccharide metabolism related pathways and oxidative phosphorylation pathways. We also compared the results with those from human rhinovirus (HRV) and respiratory syncytial virus (RSV) by the same method. It was indicated that saccharide or polysaccharide metabolism related pathways might be especially associated with the H7N9 infection. These results could shed some light on the understanding of the virus infection mechanism, providing basis for future experimental biology studies and for the development of effective strategies for H7N9 clinical therapies.
Insights
This study identifies 20 key human genes linked to Influenza A/H7N9 infection using a novel computational approach. These genes offer insights into H7N9
Area of Science:
- Virology
- Computational Biology
- Genetics
Background:
- Influenza A/H7N9 poses a significant human health threat with poorly understood infection mechanisms.
- Identifying host factors is crucial for understanding viral pathogenesis and developing therapies.
Purpose of the Study:
- To computationally identify human genes associated with Influenza A/H7N9 virus infection.
- To provide potential targets for further experimental validation and therapeutic development.
Main Methods:
- Developed a computational method utilizing the "guilt by association" rule on a virus-human protein interaction network.
- Identified significant human genes by analyzing shortest paths within the network.
- Compared H7N9 results with those from human rhinovirus (HRV) and respiratory syncytial virus (RSV).
Main Results:
- Screened 20 significant human genes potentially related to H7N9 infection.
- Identified enrichment in protein binding, saccharide/polysaccharide metabolism, and oxidative phosphorylation pathways.
- Saccharide/polysaccharide metabolism pathways appear particularly associated with H7N9 infection compared to HRV and RSV.
Conclusions:
- The identified genes and pathways offer a foundation for understanding H7N9 infection mechanisms.
- Findings guide future experimental studies and the development of clinical strategies for H7N9.
- Highlights the potential role of host metabolic pathways in H7N9 pathogenesis.
More Related Videos
Related Concept Videos
Influenza
Viral Mutations
Leaky Scanning
Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Human Virome
Single Nucleotide Polymorphisms-SNPs


