Integrated analysis of mRNA-seq and miRNA-seq for host susceptibilities to influenza A (H7N9) infection in inbred mouse lines
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Host genetic factors play an important role in diverse host outcomes after influenza A (H7N9) infection. Studying differential responses of inbred mouse lines with distinct genetic backgrounds to influenza virus infection could substantially increase our understanding of the contributory roles of host genetic factors to disease severity. Here, we utilized an integrated approach of mRNA-seq and miRNA-seq to investigate the transcriptome expression and regulation of host genes in C57BL/6J and DBA/2J mouse strains during influenza virus infection. The differential pathogenicity of influenza virus in C57BL/6J and DBA/2J has been fully demonstrated through immunohistochemical staining, histopathological analyses, and viral replication assessment. A transcriptional molecular signature correlates to differential host response to infection has been uncovered. With the introduction of temporal expression pattern analysis, we demonstrated that host factors responsible for influenza virus replication and host–virus interaction were significantly enriched in genes exhibiting distinct temporal dynamics between different inbred mouse lines. A combination of time-series expression analysis and temporal expression pattern analysis has provided a list of promising candidate genes for future studies. An integrated miRNA regulatory network from both mRNA-seq and miRNA-seq revealed several regulatory modules responsible for regulating host susceptibilities and disease severity. Overall, a comprehensive framework for analyzing host susceptibilities to influenza infection was established by integrating mRNA-seq and miRNA-seq data of inbred mouse lines. This work suggests novel putative molecular targets for therapeutic interventions in seasonal and pandemic influenza.
KeywordsInfluenza (H7N9) virus Host susceptibilities mRNA-seq miRNA-seq Inbred mouse lines Temporal dynamics
non-negative matrix factorization
principle component analysis
differentially expressed genes
dynamic time warping maximal information coefficient
miRNA-mRNA interaction weight
- GGIW matrix
gene–gene interaction weight
Y.Q.S, M.L.Y., K.Y.Y., K.K.W.T., S.H., and S.B. designed research; L.J., Z.G.Z., H.W.L.W., Z.Y., NG., and D.SM.W. performed experiments; S.B., X.Z, and Y.F. analyzed results; and Y.Q.S, M.L.Y., S.B., and X.Z wrote the paper. All authors read and approved the final manuscript.
This work was supported by grants from the Health and Medical Research Fund of Hong Kong Government (01121726, RRG-08). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Compliance with ethical standards
Ethics approval and consent to participate
This study was approved by the Committee of the Use of Live Animals in Teaching and Research (CULATR 3275-14).
Consent for publication
The authors declare that they have no competing interests.
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