High-Risk Human Papillomavirus E7 Alters Host DNA Methylome and Represses HLA-E Expression in Human Keratinocytes.

Human papillomavirus (HPV) infection distinctly alters methylation patterns in HPV-associated cancer. We have recently reported that HPV E7-dependent promoter hypermethylation leads to downregulation of the chemokine CXCL14 and suppression of antitumor immune responses. To investigate the extent of gene expression dysregulated by HPV E7-induced DNA methylation, we analyzed parallel global gene expression and DNA methylation using normal immortalized keratinocyte lines, NIKS, NIKS-16, NIKS-18, and NIKS-16∆E7. We show that expression of the MHC class I genes is downregulated in HPV-positive keratinocytes in an E7-dependent manner. Methylome analysis revealed hypermethylation at a distal CpG island (CGI) near the HLA-E gene in NIKS-16 cells compared to either NIKS cells or NIKS-16∆E7 cells, which lack E7 expression. The HLA-E CGI functions as an active promoter element which is dramatically repressed by DNA methylation. HLA-E protein expression on cell surface is downregulated by high-risk HPV16 and HPV18 E7 expression, but not by low-risk HPV6 and HPV11 E7 expression. Conversely, demethylation at the HLA-E CGI restores HLA-E protein expression in HPV-positive keratinocytes. Because HLA-E plays an important role in antiviral immunity by regulating natural killer and CD8+ T cells, epigenetic downregulation of HLA-E by high-risk HPV E7 may contribute to virus-induced immune evasion during HPV persistence.

Systems biology of facial development: contributions of ectoderm and mesenchyme.

The rapid increase in gene-centric biological knowledge coupled with analytic approaches for genomewide data integration provides an opportunity to develop systems-level understanding of facial development. Experimental analyses have demonstrated the importance of signaling between the surface ectoderm and the underlying mesenchyme are coordinating facial patterning. However, current transcriptome data from the developing vertebrate face is dominated by the mesenchymal component, and the contributions of the ectoderm are not easily identified. We have generated transcriptome datasets from critical periods of mouse face formation that enable gene expression to be analyzed with respect to time, prominence, and tissue layer. Notably, by separating the ectoderm and mesenchyme we considerably improved the sensitivity compared to data obtained from whole prominences, with more genes detected over a wider dynamic range. From these data we generated a detailed description of ectoderm-specific developmental programs, including pan-ectodermal programs, prominence- specific programs and their temporal dynamics. The genes and pathways represented in these programs provide mechanistic insights into several aspects of ectodermal development. We also used these data to identify co-expression modules specific to facial development. We then used 14 co-expression modules enriched for genes involved in orofacial clefts to make specific mechanistic predictions about genes involved in tongue specification, in nasal process patterning and in jaw development. Our multidimensional gene expression dataset is a unique resource for systems analysis of the developing face; our co-expression modules are a resource for predicting functions of poorly annotated genes, or for predicting roles for genes that have yet to be studied in the context of facial development; and our analytic approaches provide a paradigm for analysis of other complex developmental programs.

Differential correlation for sequencing data.

BACKGROUND: Several methods have been developed to identify differential correlation (DC) between pairs of molecular features from -omics studies. Most DC methods have only been tested with microarrays and other platforms producing continuous and Gaussian-like data. Sequencing data is in the form of counts, often modeled with a negative binomial distribution making it difficult to apply standard correlation metrics. We have developed an R package for identifying DC called Discordant which uses mixture models for correlations between features and the Expectation Maximization (EM) algorithm for fitting parameters of the mixture model. Several correlation metrics for sequencing data are provided and tested using simulations. Other extensions in the Discordant package include additional modeling for different types of differential correlation, and faster implementation, using a subsampling routine to reduce run-time and address the assumption of independence between molecular feature pairs. RESULTS: With simulations and breast cancer miRNA-Seq and RNA-Seq data, we find that Spearman's correlation has the best performance among the tested correlation methods for identifying differential correlation. Application of Spearman's correlation in the Discordant method demonstrated the most power in ROC curves and sensitivity/specificity plots, and improved ability to identify experimentally validated breast cancer miRNA. We also considered including additional types of differential correlation, which showed a slight reduction in power due to the additional parameters that need to be estimated, but more versatility in applications. Finally, subsampling within the EM algorithm considerably decreased run-time with negligible effect on performance. CONCLUSIONS: A new method and R package called Discordant is presented for identifying differential correlation with sequencing data. Based on comparisons with different correlation metrics, this study suggests Spearman's correlation is appropriate for sequencing data, but other correlation metrics are available to the user depending on the application and data type. The Discordant method can also be extended to investigate additional DC types and subsampling with the EM algorithm is now available for reduced run-time. These extensions to the R package make Discordant more robust and versatile for multiple -omics studies.

The discordant method: a novel approach for differential correlation.

MOTIVATION: Current differential correlation methods are designed to determine molecular feature pairs that have the largest magnitude of difference between correlation coefficients. These methods do not easily capture molecular feature pairs that experience no correlation in one group but correlation in another, which may reflect certain types of biological interactions. We have developed a tool, the Discordant method, which categorizes the correlation types for each group to make this possible. RESULTS: We compare the Discordant method to existing approaches using simulations and two biological datasets with different types of -omics data. In contrast to other methods, Discordant identifies phenotype-related features at a similar or higher rate while maintaining reasonable computational tractability and usability. AVAILABILITY AND IMPLEMENTATION: R code and sample data are available at https://github.com/siskac/discordant CONTACT: katerina.kechris@ucdenver.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Plasma sphingolipids associated with chronic obstructive pulmonary disease phenotypes.

RATIONALE: Chronic obstructive pulmonary disease (COPD) occurs in a minority of smokers and is characterized by intermittent exacerbations and clinical subphenotypes such as emphysema and chronic bronchitis. Although sphingolipids as a class are implicated in the pathogenesis of COPD, the particular sphingolipid species associated with COPD subphenotypes remain unknown. OBJECTIVES: To use mass spectrometry to determine which plasma sphingolipids are associated with subphenotypes of COPD. METHODS: One hundred twenty-nine current and former smokers from the COPDGene cohort had 69 distinct sphingolipid species detected in plasma by targeted mass spectrometry. Of these, 23 were also measured in 131 plasma samples (117 independent subjects) using an untargeted platform in an independent laboratory. Regression analysis with adjustment for clinical covariates, correction for false discovery rate, and metaanalysis were used to test associations between COPD subphenotypes and sphingolipids. Peripheral blood mononuclear cells were used to test associations between sphingolipid gene expression and plasma sphingolipids. MEASUREMENTS AND MAIN RESULTS: Of the measured plasma sphingolipids, five sphingomyelins were associated with emphysema; four trihexosylceramides and three dihexosylceramides were associated with COPD exacerbations. Three sphingolipids were strongly associated with sphingolipid gene expression, and 15 sphingolipid gene/metabolite pairs were differentially regulated between COPD cases and control subjects. CONCLUSIONS: There is evidence of systemic dysregulation of sphingolipid metabolism in patients with COPD. Subphenotyping suggests that sphingomyelins are strongly associated with emphysema and glycosphingolipids are associated with COPD exacerbations.