Inherited Chromosomally Integrated Human Herpesvirus 6 Demonstrates Tissue-Specific RNA Expression In Vivo That Correlates with an Increased Antibody Immune Response.

Human herpesviruses 6A and 6B (HHV-6A and HHV-6B) are human viruses capable of chromosomal integration. Approximately 1% of the human population carries one copy of HHV-6A/B integrated into every cell in their body, referred to as inherited chromosomally integrated human herpesvirus 6A/B (iciHHV-6A/B). Whether iciHHV-6A/B is transcriptionally active in vivo and how it shapes the immunological response are still unclear. In this study, we screened DNA sequencing (DNA-seq) and transcriptome sequencing (RNA-seq) data for 650 individuals available through the Genotype-Tissue Expression (GTEx) project and identified 2 iciHHV-6A- and 4 iciHHV-6B-positive candidates. When corresponding tissue-specific gene expression signatures were analyzed, low levels HHV-6A/B gene expression was found across multiple tissues, with the highest levels of gene expression in the brain (specifically for HHV-6A), testis, esophagus, and adrenal gland. U90 and U100 were the most highly expressed HHV-6 genes in both iciHHV-6A- and iciHHV-6B-positive individuals. To assess whether tissue-specific gene expression from iciHHV-6A/B influences the immune response, a cohort of 15,498 subjects was screened and 85 iciHHV-6A/B+ subjects were identified. Plasma samples from iciHHV-6A/B+ and age- and sex-matched controls were analyzed for antibodies to control antigens (cytomegalovirus [CMV], Epstein-Barr virus [EBV], and influenza virus [FLU]) or HHV-6A/B antigens. Our results indicate that iciHHV-6A/B+ subjects have significantly more antibodies against the U90 gene product (IE1) than do non-iciHHV-6-positive individuals. Antibody responses against EBV and FLU antigens or HHV-6A/B gene products either not expressed or expressed at low levels, such as U47, U57, and U72, were identical between controls and iciHHV-6A/B+ subjects. CMV-seropositive individuals with iciHHV-6A/B+ have more antibodies against CMV pp150 than do CMV-seropositive controls. These results argue that spontaneous gene expression from integrated HHV-6A/B leads to an increase in antigenic burden that translates into a more robust HHV-6A/B-specific antibody response.IMPORTANCE HHV-6A and -6B are human herpesviruses that have the unique property of being able to integrate into the telomeric regions of human chromosomes. Approximately 1% of the world's population carries integrated HHV-6A/B genome in every cell of their body. Whether viral genes are transcriptionally active in these individuals is unclear. By taking advantage of a unique tissue-specific gene expression data set, we showed that the majority of tissues from iciHHV-6 individuals do not show HHV-6 gene expression. Brain and testes showed the highest tissue-specific expression of HHV-6 genes in two separate data sets. Two HHV-6 genes, U90 (immediate early 1 protein) and U100 (glycoproteins Q1 and Q2), were found to be selectively and consistently expressed across several human tissues. Expression of U90 translates into an increase in antigen-specific antibody response in iciHHV-6A/B+ subjects relative to controls. Future studies will be needed to determine the mechanism of gene expression, the effects of these genes on human gene transcription networks, and the pathophysiological impact of having increased viral protein expression in tissue in conjunction with increased antigen-specific antibody production.

Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling.

BACKGROUND: CUT&RUN is an efficient epigenome profiling method that identifies sites of DNA binding protein enrichment genome-wide with high signal to noise and low sequencing requirements. Currently, the analysis of CUT&RUN data is complicated by its exceptionally low background, which renders programs designed for analysis of ChIP-seq data vulnerable to oversensitivity in identifying sites of protein binding. RESULTS: Here we introduce Sparse Enrichment Analysis for CUT&RUN (SEACR), an analysis strategy that uses the global distribution of background signal to calibrate a simple threshold for peak calling. SEACR discriminates between true and false-positive peaks with near-perfect specificity from "gold standard" CUT&RUN datasets and efficiently identifies enriched regions for several different protein targets. We also introduce a web server ( http://seacr.fredhutch.org ) for plug-and-play analysis with SEACR that facilitates maximum accessibility across users of all skill levels. CONCLUSIONS: SEACR is a highly selective peak caller that definitively validates the accuracy of CUT&RUN for datasets with known true negatives. Its ease of use and performance in comparison with existing peak calling strategies make it an ideal choice for analyzing CUT&RUN data.

Orchestrating high-throughput genomic analysis with Bioconductor.

Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.

Parallel evolution of transcriptome architecture during genome reorganization.

Assembly of genes into operons is generally viewed as an important process during the continual adaptation of microbes to changing environmental challenges. However, the genome reorganization events that drive this process are also the roots of instability for existing operons. We have determined that there exists a statistically significant trend that correlates the proportion of genes encoded in operons in archaea to their phylogenetic lineage. We have further characterized how microbes deal with operon instability by mapping and comparing transcriptome architectures of four phylogenetically diverse extremophiles that span the range of operon stabilities observed across archaeal lineages: a photoheterotrophic halophile (Halobacterium salinarum NRC-1), a hydrogenotrophic methanogen (Methanococcus maripaludis S2), an acidophilic and aerobic thermophile (Sulfolobus solfataricus P2), and an anaerobic hyperthermophile (Pyrococcus furiosus DSM 3638). We demonstrate how the evolution of transcriptional elements (promoters and terminators) generates new operons, restores the coordinated regulation of translocated, inverted, and newly acquired genes, and introduces completely novel regulation for even some of the most conserved operonic genes such as those encoding subunits of the ribosome. The inverse correlation (r=-0.92) between the proportion of operons with such internally located transcriptional elements and the fraction of conserved operons in each of the four archaea reveals an unprecedented view into varying stages of operon evolution. Importantly, our integrated analysis has revealed that organisms adapted to higher growth temperatures have lower tolerance for genome reorganization events that disrupt operon structures.

GTC: A web server for integrating systems biology data with web tools and desktop applications.

Gaggle Tool Creator (GTC) is a web application which provides access to public annotation, interaction, orthology, and genomic data for hundreds of organisms, and enables instant analysis of the data using many popular web-based and desktop applications.

Integration and visualization of systems biology data in context of the genome.

BACKGROUND: High-density tiling arrays and new sequencing technologies are generating rapidly increasing volumes of transcriptome and protein-DNA interaction data. Visualization and exploration of this data is critical to understanding the regulatory logic encoded in the genome by which the cell dynamically affects its physiology and interacts with its environment. RESULTS: The Gaggle Genome Browser is a cross-platform desktop program for interactively visualizing high-throughput data in the context of the genome. Important features include dynamic panning and zooming, keyword search and open interoperability through the Gaggle framework. Users may bookmark locations on the genome with descriptive annotations and share these bookmarks with other users. The program handles large sets of user-generated data using an in-process database and leverages the facilities of SQL and the R environment for importing and manipulating data.A key aspect of the Gaggle Genome Browser is interoperability. By connecting to the Gaggle framework, the genome browser joins a suite of interconnected bioinformatics tools for analysis and visualization with connectivity to major public repositories of sequences, interactions and pathways. To this flexible environment for exploring and combining data, the Gaggle Genome Browser adds the ability to visualize diverse types of data in relation to its coordinates on the genome. CONCLUSIONS: Genomic coordinates function as a common key by which disparate biological data types can be related to one another. In the Gaggle Genome Browser, heterogeneous data are joined by their location on the genome to create information-rich visualizations yielding insight into genome organization, transcription and its regulation and, ultimately, a better understanding of the mechanisms that enable the cell to dynamically respond to its environment.

Visualization of omics data for systems biology.

High-throughput studies of biological systems are rapidly accumulating a wealth of 'omics'-scale data. Visualization is a key aspect of both the analysis and understanding of these data, and users now have many visualization methods and tools to choose from. The challenge is to create clear, meaningful and integrated visualizations that give biological insight, without being overwhelmed by the intrinsic complexity of the data. In this review, we discuss how visualization tools are being used to help interpret protein interaction, gene expression and metabolic profile data, and we highlight emerging new directions.