Hepatitis B e Antigen-Negative Single Hepatocyte Analysis Shows Transcriptional Silencing and Slow Decay of Infected Cells With Treatment.

BACKGROUND: Nucleos(t)ide analogues (NUCs) rarely cure chronic hepatitis B (CHB) because they do not eliminate covalently closed circular deoxyribonucleic acid, the stable replication template. In hepatitis B e antigen (HBeAg)-positive CHB during NUCs, HBV-infected cells decline slowly and are transcriptionally silenced. Whether these occur in HBeAg-negative CHB is unknown. METHODS: Using paired liver biopsies separated by 2.7-3.7 years in 4 males with HIV and HBeAg-negative CHB at both biopsies and 1 male with HIV who underwent HBeAg seroconversion between biopsies, we quantified amounts of viral nucleic acids in hundreds of individual hepatocytes. RESULTS: In the 4 persistently HBeAg-negative participants, HBV-infected hepatocytes ranged from 6.2% to 17.7% (biopsy 1) and significantly declined in 3 of 4 by biopsy 2. In the HBeAg seroconverter, the proportion was 97.4% (biopsy 1) and declined to 81.9% at biopsy 2 (P < .05). We extrapolated that HBV eradication with NUCs would take >100 years. At biopsy 1 in the persistently HBeAg-negative participants, 23%-56.8% of infected hepatocytes were transcriptionally inactive-higher than we observed in HBeAg-positive CHB-and significantly declined in 1 of 4 at biopsy 2. CONCLUSIONS: In HBeAg-negative CHB on NUCs, the negligible decline in infected hepatocytes is similar to HBeAg-positive CHB, supporting the need for more potent therapeutics to achieve functional cure.

Chromatin structure regulates cancer-specific alternative splicing events in primary HPV-related oropharyngeal squamous cell carcinoma.

Human papillomavirus-related oropharyngeal squamous cell carcinoma (HPV+ OPSCC) represents a unique disease entity within head and neck cancer with rising incidence. Previous work has shown that alternative splicing events (ASEs) are prevalent in HPV+ OPSCC, but further validation is needed to understand the regulation of this process and its role in these tumours. In this study, eleven ASEs (GIT2, CTNNB1, MKNK2, MRPL33, SIPA1L3, SNHG6, SYCP2, TPRG1, ZHX2, ZNF331, and ELOVL1) were selected for validation from 109 previously published candidate ASEs to elucidate the post-transcriptional mechanisms of oncogenesis in HPV+ disease. In vitro qRT-PCR confirmed differential expression of 9 of 11 ASE candidates, and in silico analysis within the TCGA cohort confirmed 8 of 11 candidates. Six ASEs (MRPL33, SIPA1L3, SNHG6, TPRG1, ZHX2, and ELOVL1) showed significant differential expression across both methods. Further evaluation of chromatin modification revealed that ASEs strongly correlated with cancer-specific distribution of acetylated lysine 27 of histone 3 (H3K27ac). Subsequent epigenetic treatment of HPV+ HNSCC cell lines (UM-SCC-047 and UPCI-SCC-090) with JQ1 not only induced downregulation of cancer-specific ASE isoforms, but also growth inhibition in both cell lines. The UPCI-SCC-090 cell line, with greater ASE expression, also showed more significant growth inhibition after JQ1 treatment. This study confirms several novel cancer-specific ASEs in HPV+OPSCC and provides evidence for the role of chromatin modifications in regulation of alternative splicing in HPV+OPSCC. This highlights the role of epigenetic changes in the oncogenesis of HPV+OPSCC, which represents a unique, unexplored target for therapeutics that can alter the global post-transcriptional landscape.

Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer.

Motivation: Current bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches. Results: We introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g. tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVA's performance against EBSeq, DiffSplice and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVA in identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery. These results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data. Availability and implementation: SEVA is implemented in the R/Bioconductor package GSReg. Contact: bahman@jhu.edu or favorov@sensi.org or ejfertig@jhmi.edu. Supplementary information: Supplementary data are available at Bioinformatics online.