Fig. 4: In-silico analysis of HEV insertion containing HVRs. | Nature Communications

Fig. 4: In-silico analysis of HEV insertion containing HVRs.

From: Genetic determinants of host- and virus-derived insertions for hepatitis E virus replication

Fig. 4

a The liver cell atlas was used to analyse the expression values of identified insertions in various liver cell types on a single cell level. The frequency of cells expressing the gene is depicted by the size of the dot, while the colour of the dot encodes the average expression level according to Guilliams et al.23 in those cells. b,c Expression of transcripts encoding the insertions was analysed in a published data set of non-infected as well as Kernow-C1-p6 infected primary human hepatocytes (PHH). Depicted is the expression as RPKM values over time for all genes. d Differential expression of indexed transcripts in a data set of PHH treated with PBS (control) or interferon-α (IFNα). The solid black line indicates no regulatory effect, while the dashed lines indicate 4-fold up or down-regulation in either condition. e HVR amino acid sequences were analysed for post-translational modification via musite, for ubiquitination via BDM-PUB, and for acetylation via GPS-Pail. The number of predicted PTM sites is plotted as a heatmap. Kernow-C1-p1 (p1) and Kernow-C1-p6 (p6) were used as reference. f Amino acid composition of insertion containing HVRs (n = 15 sequences examined) was compared to HEV-GLUE deposited HVRs without insertions (n = 289 sequences examined) using the tool composition profiler. Shown are fold changes in amino acid usage of insertion containing HVRs over non-insertion containing HVRs. Positive-charged amino acids are indicated in red, while negatively charged ones are highlighted in blue. The statistical significance associated with a specific enrichment or depletion is estimated using a Bonferroni-corrected two-sample t-test between two sequences of binary indicator variables, one sequence for each of the samples (I p-value = 0.001988 (≤0.0025), K p-value = 0.0 (≤0.0025)). For the calculation of composition differences, 10,000 bootstrap iterations were used for non-parametric estimation of the confidence intervals for the reported amino acid compositions. Source data are provided in the Source Data file.

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