Identification of human papillomavirus (HPV) 16 DNA integration and the ensuing patterns of methylation in HPV-associated head and neck squamous cell carcinoma cell lines

Abstract

Recent studies showed that human papillomavirus (HPV) integration contributes to the genomic instability seen in HPV-associated head and neck squamous cell carcinoma (HPV-HNSCC). However, the epigenetic alterations induced after HPV integration remains unclear. To identify the molecular details of HPV16 DNA integration and the ensuing patterns of methylation in HNSCC, we performed next-generation sequencing using a target-enrichment method for the effective identification of HPV16 integration breakpoints as well as the characterization of genomic sequences adjacent to HPV16 integration breakpoints with three HPV16-related HNSCC cell lines. The DNA methylation levels of the integrated HPV16 genome and that of the adjacent human genome were also analyzed by bisulfite pyrosequencing. We found various integration loci, including novel integration sites. Integration loci were located predominantly in the intergenic region, with a significant enrichment of the microhomologous sequences between the human and HPV16 genomes at the integration breakpoints. Furthermore, various levels of methylation within both the human genome and the integrated HPV genome at the integration breakpoints in each integrant were observed. Allele-specific methylation analysis suggested that the HPV16 integrants remained hypomethylated when the flanking host genome was hypomethylated. After integration into highly methylated human genome regions, however, the HPV16 DNA became methylated. In conclusion, we found novel integration sites and methylation patterns in HPV-HNSCC using our unique method. These findings may provide insights into understanding of viral integration mechanism and virus-associated carcinogenesis of HPV-HNSCC.

Keywords: DNA integration; epigenome; head and neck squamous cell carcinoma (HNSCC); human papillomavirus (HPV).

Figure 1

Distribution of integration breakpoints in the human chromosomes and HPV16 genome represented by Circos plots of the UCSC:SCC090 genome, the UCSC:SCC152 genome, and the UCSC:SCC154 genome

Figure 2. Analysis of the HPV16 integration sites

A. Repetitive elements analysis at integration breakpoints in the human genome, compared with expected the results. B. The percentage of breakpoints distribution in the HPV16 genome. C. Comparison between observed and expected integrations harboring MHs of different sizes at the integration sites.

Figure 3. Categories of viral-host breakpoints

(Red) Nucleotides that align to neither reference gene (insertions); (blue) nucleotides that align to both reference genome (microhomology). A. Seamless transition with defined breakpoint. B. Insertion. C. Breakpoint microhomology. D. The number of viral-host junctional sequences.

Figure 4. Allele-specific methylation analysis of 3 cell lines

A. A schema of the allele-specific methylation analysis. B. Methylation levels of the HPV16 and human genomes for the integrated and unintegrated alleles. Detailed results of the HPV16 integrants (LCR, E6, E7, E1, E2, E4, E5, L2, L1) and flanking host genomes (chromosome and location of the genome) are shown. DNA methylation of the integrated HPV16 genomes as well as the flanking human genome was examined by allele-specific DNA methylation analysis using bisulfite pyrosequencing. C. The results of LINE1 methylation analysis.

Figure 5. Correlation analysis between the methylation pattern of the integrated HPV16 DNA and that of the human genome

DNA fragments, including 150bp of the HPV16 DNA and 150 bp of the human genome around the boundary, were analyzed for average methylation. A. Correlation for total integration sites between the methylation levels of the HPV16 DNA and that of the human genome in the 3 cell lines. B. Correlation between 150bp of the HPV16 DNA and 150 bp of the human genome around the boundary analyzed for average methylation. C. Correlation between 150bp of the human genome and 150 bp of the empty allele around the boundary analyzed for average methylation. Correlations were analyzed with Pearson’s correlation coefficients, and P values were computed for the 2-tailed test.