Integrated analysis of mRNA-seq and miRNA-seq for host susceptibilities to influenza A (H7N9) infection in inbred mouse lines

  • Suying Bao
  • Lilong Jia
  • Xueya Zhou
  • Zhi-Gang Zhang
  • Hazel Wai Lan Wu
  • Zhe Yu
  • Gordon Ng
  • Yanhui Fan
  • Dana S. M. Wong
  • Shishu Huang
  • Kelvin Kai Wang To
  • Kwok-Yung Yuen
  • Man Lung Yeung
  • You-Qiang Song
Original Article
  • 97 Downloads

Abstract

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.

Keywords

Influenza (H7N9) virus Host susceptibilities mRNA-seq miRNA-seq Inbred mouse lines Temporal dynamics 

Abbreviations

NMF

non-negative matrix factorization

PCA

principle component analysis

DEGs

differentially expressed genes

DTW-MIC

dynamic time warping maximal information coefficient

MMIW

miRNA-mRNA interaction weight

GGIW matrix

gene–gene interaction weight

Notes

Authors’ contributions

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.

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

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Supplementary material

10142_2018_602_MOESM1_ESM.doc (2.5 mb)
Additional File 1 Supplementary figures. Supplementary Fig. S1 showed error bars of virus load in each strain at each day, with mean values and standard deviations calculated from the virus load of all biological replicates at the same day. Supplementary Fig. S2 showed the differential expression levels (log2-fold change) of 10 interferon-stimulated genes between infected and mock-treated mice at day 3. All transcripts for the same gene were presented. Different strains were represented by different colours as shown in the legend. Supplementary Fig. S3 showed the characteristics of genes differentially expressed between C57BL/6J and DBA/2J over time. (A) Heatmap of genes differentially expressed between strains (strain-specific DEGs) showed distinct expression patterns between C57BL/6J and DBA/2J mice. (B) PCA of all samples based on normalized counts of strain-specific DEGs. PC2 revealed distinct transcriptome profile of these DEGs for the two strains due to their genetic background explaining around 26% of the expression variation. Supplementary Fig. S4 demonstrated that genes responsible for host resistance were enriched in DTW-MIC similarities between 0.2-0.25. (A) Histogram of DTW-MIC similarity scores showed two similarity peaks as: between 0.2-0.25 and between 0.7-0.75. (B) PCA of all samples based on normalized counts of genes within different DTW-MIC similarity ranges. The expression variation across samples based on genes with similarity scores between 0.2-0.25 mainly explained the difference from host genetic background, whereas the expression variation based on genes with similarity scores higher than 0.25 were mainly attributed to host response to influenza infection. Supplementary Fig. S5-S6 showed the temporal dynamics of representative genes enriched in cell cycle and ribosome in C57BL/6J and DBA/2J. Supplementary Fig. S7 demonstrated the differential characteristics of time course strain-specific DEGs with high and low temporal similarities between C57BL/6J and DBA/2J. (A) DTW-MIC distribution of all strain-specific DEGs over time. (B) PCA of all samples based on the transcription profiles of strain-specific DEGs with DTW-MIC scores lower than 0.25 (left) and higher than 0.7 (right) respectively C). The same transcription profiles used in B), but presented as heatmaps (DOC 2565 kb)
10142_2018_602_MOESM2_ESM.xls (3.8 mb)
Additional File 2 Supplementary Tables. Supplementary Table S1: Strain-specific DEGs over time. Supplementary Table S2: The DTW-MIC similarity scores and permutation p-values of all genes. Supplementary Table S3: Summary statistics of DTW-MIC similarities. Supplementary Table S4: Host factors responsible for influenza virus replication show significantly differential DTW-MIC similarities between C57BL/6J and DBA/2J. Supplementary Table S5: Host factors interacting with influenza virus show significantly differential DTW-MIC similarities between C57BL/6J and DBA/2J. Supplementary Table S6: The distribution of DTW-MIC similarity scores of host factors either responsible for influenza virus replication or interact with virus. Supplementary Table S7: The list of candidate genes with both significant differential expression levels and distinct temporal expression patterns between C57BL/6J and DBA/2J. Supplementary Table S8: Functional enrichment analysis of candidate genes with both significant differential expression level and distinct temporal expression pattern between C57BL/6J and DBA/2J. Supplementary Table S9: The list of candidate genes which show distinct temporal expression patterns between C57BL/6J and DBA/2J but have no significant differential expression levels between the two mouse strains. Supplementary Table S10: Four clusters of sig.low.simi genes according to the differential expression levels between infected and mock-treated mice over time in both C57BL/6J and DBA/2J strains (XLS 3855 kb)
10142_2018_602_MOESM3_ESM.xlsx (508 kb)
Additional File 3 The main functional modules in the interaction network of sig.low.simi genes from clusters 1, 2 and 3 (XLSX 508 kb)
10142_2018_602_MOESM4_ESM.xlsx (35 kb)
Additional File 4 The modules in the integrated miRNA regulatory network (XLSX 35 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Suying Bao
    • 1
  • Lilong Jia
    • 2
  • Xueya Zhou
    • 3
  • Zhi-Gang Zhang
    • 1
  • Hazel Wai Lan Wu
    • 2
  • Zhe Yu
    • 1
  • Gordon Ng
    • 1
  • Yanhui Fan
    • 1
  • Dana S. M. Wong
    • 1
  • Shishu Huang
    • 4
  • Kelvin Kai Wang To
    • 2
  • Kwok-Yung Yuen
    • 2
  • Man Lung Yeung
    • 2
  • You-Qiang Song
    • 1
    • 3
    • 5
    • 6
  1. 1.Schoolof Biomedical SciencesThe University of Hong KongHong KongChina
  2. 2.Department of MicrobiologyThe University of Hong KongHong KongChina
  3. 3.Department of PsychiatryThe University of Hong KongHong KongChina
  4. 4.Department of Orthopedic Surgery, West China HospitalSichuan UniversityChengduChina
  5. 5.HKU-SIRI/ZIRIThe University of Hong KongHong KongChina
  6. 6.HKU-SUSTech Joint Laboratories of Matrix Biology and DiseasesThe University of Hong KongHong KongChina

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