The signature of HBV-related liver disease in peripheral blood mononuclear cell DNA methylation

Abstract

Background: Hepatitis B virus (HBV)-related liver disease induces liver damage by hepatic immune and inflammatory response. The association between aberrant peripheral blood mononuclear cell (PBMC) DNA methylation and progression of liver disease and fibrosis remains unclear.

Results: Here we applied Infinium 450 K BeadChip investigating PBMC genome-wide methylation profiling of 48 HBV-related liver disease patients including 24 chronic hepatitis B (CHB), 14 compensated liver cirrhosis (LC), and 10 decompensated liver cirrhosis (DLC). In total, there were 7888 differentially methylated CpG sites (36.06% hypermethylation, 63.94% hypomethylation) correlate with liver disease progression. LC was difficult to be diagnosed, intermediating between CHB and DLC. We used least absolute shrinkage and selection operator (LASSO)-logistic regression method to perform a LC predictive model. The predicted probability (P) of having LC was estimated by the combined model: P = 1/(1 - e^-x), where X = 11.52 - 2.82 × (if AST within the normal range - 0.19 × (percent methylation of cg05650055) - 0.21 × (percent methylation of cg17149911 ). Pyrosequencing validation and confusion matrix analysis was used for internal testing, area under receiver operating characteristic curve (AUROC) of model was 0.917 (95% CI, 0.80-0.977). On the fibrosis progress, there were 1705 genes in LC compared with CHB, whose differentially methylated CpG sites loading within the "promoter" regions (including TSS1500, TSS200, 5'UTR, and the 1st exon of genes) subject into the enrichment analysis using Ingenuity Pathway Analysis (IPA). There were 113 enriched immune-related pathways indicated that HBV-related liver fibrosis progression caused epigenetic reprogramming of the immune and inflammatory response.

Conclusions: These data support idea that development of HBV-related chronic liver disease is linked with robust and broad alteration of methylation in peripheral immune system. CpG methylation sites serve as relevant biomarker candidates to monitor and diagnose LC, providing new insight into the immune mechanisms understanding the progression of HBV-related liver fibrosis and cirrhosis.

Keywords: DNA methylation; Fibrosis progression; HBV-related liver disease; Immune-related pathways; LC predictive model.

Fig. 1

Heatmap showing hierarchical clustering based one minus Pearson correlation of 48 liver disease patients by beta values of 7888 CpG sites

Fig. 2

STEM clustering was used to analyze profiles and cluster of methylation level of CpG sites. The upper numbers indicate type of profiles. The lower contained P value of the profiles. The black lines showed mean pattern of CpG sites methylation level of the profile. The pink lines showed all CpG sites methylation level of profiles. Colorful background showed significantly different profiles. The x-axis represents 5 stages (S1, S2, S3, LC, DLC), and the y-axis represents CpG sites beta values

Fig. 3

Biomarker CpG sites selection using hierarchical clustering and LASSO-regularized regression. a Hierarchical clustering analysis consisting of nine differentially CpG sites in PBMC DNA from 48 liver disease patients. b Representative repetition of five-fold CV LASSO coefficients of nine candidate CpG sites. The first vertical dotted line corresponds to the λ_min that minimized binomial deviance during CV. The second dotted line corresponds to λ_1se (0.051), used for the selection of biomarker CpG sites. c LASSO coefficient profile plot of the coefficient paths. Nine CpG sites had their coefficients significantly different from zero

Fig. 4

Correlations between Illumina 450 K Array data and PyroMark sequencing data of three CpG sites, a cg20332088, b cg05650055 (MYEOV), c cg17149911(AAK1), d ROC curves of the CpG sites (cg17149911 + cg05650055) model and LC-combined model, and their respective AUROC, 95% CIs, sensitivity and specificity were reported in parentheses.

Fig. 5

a The volcano plot for differential DNA methylation CpG sites between LC and CHB. The x-axis showed the mean DNA-methylation difference (delta Beta), whereas the y-axis showed the –log10 of the adjusted P value, hypermethylated CGs were shown in red, hypomethylated CGs were shown in red blue, non-significant methylated change CGs were shown in green (Bonferroni correction P value < 0.05). b Immune functional and canonical pathway changes between LC and CHB. Ten most significant pathways identified by the IPA canonical pathways analysis (“Cellular Immune Response,” “Cytokine Signaling,” and “Humoral Immune Response”) of genes, whose “promoter” regions containing differentially methylated CpG probes. Upper x-axis represented negative –log (P value) of the enrichment score, which calculated by IPA using Fisher’s exact test, right-tailed. Lower x-axis represented the ratio values between selected genes and the total number of genes in each of these curated pathways, and the orange curve pointed out the ratio, orange vertical line represented threshold value was 1.3.