Localization of TFIIB binding regions using serial analysis of chromatin occupancy

<p>Abstract</p> <p>Background:</p> <p>RNA Polymerase II (RNAP II) is recruited to core promoters by the pre-initiation complex (PIC) of general transcription factors. Within the PIC, transcription factor for RNA polymerase IIB (TFIIB) determines the start site of transcription. TFIIB binding has not been localized, genome-wide, in metazoans. Serial analysis of chromatin occupancy (SACO) is an unbiased methodology used to empirically identify transcription factor binding regions. In this report, we use TFIIB and SACO to localize TFIIB binding regions across the rat genome.</p> <p>Results:</p> <p>A sample of the TFIIB SACO library was sequenced and 12,968 TFIIB genomic signature tags (GSTs) were assigned to the rat genome. GSTs are 20–22 base pair fragments that are derived from TFIIB bound chromatin. TFIIB localized to both non-protein coding and protein-coding loci. For 21% of the 1783 protein-coding genes in this sample of the SACO library, TFIIB binding mapped near the characterized 5' promoter that is upstream of the transcription start site (TSS). However, internal TFIIB binding positions were identified in 57% of the 1783 protein-coding genes. Internal positions are defined as those within an inclusive region greater than 2.5 kb downstream from the 5' TSS and 2.5 kb upstream from the transcription stop. We demonstrate that both TFIIB and TFIID (an additional component of PICs) bound to internal regions using chromatin immunoprecipitation (ChIP). The 5' cap of transcripts associated with internal TFIIB binding positions were identified using a cap-trapping assay. The 5' TSSs for internal transcripts were confirmed by primer extension. Additionally, an analysis of the functional annotation of mouse 3 (FANTOM3) databases indicates that internally initiated transcripts identified by TFIIB SACO in rat are conserved in mouse.</p> <p>Conclusion:</p> <p>Our findings that TFIIB binding is not restricted to the 5' upstream region indicates that the propensity for PIC to contribute to transcript diversity is far greater than previously appreciated.</p

A Viral microRNA Down-Regulates Multiple Cell Cycle Genes through mRNA 5 ' UTRs

Global gene expression data combined with bioinformatic analysis provides strong evidence that mammalian miRNAs mediate repression of gene expression primarily through binding sites within the 3′ untranslated region (UTR). Using RNA induced silencing complex immunoprecipitation (RISC-IP) techniques we have identified multiple cellular targets for a human cytomegalovirus (HCMV) miRNA, miR-US25-1. Strikingly, this miRNA binds target sites primarily within 5′UTRs, mediating significant reduction in gene expression. Intriguingly, many of the genes targeted by miR-US25-1 are associated with cell cycle control, including cyclin E2, BRCC3, EID1, MAPRE2, and CD147, suggesting that miR-US25-1 is targeting genes within a related pathway. Deletion of miR-US25-1 from HCMV results in over expression of cyclin E2 in the context of viral infection. Our studies demonstrate that a viral miRNA mediates translational repression of multiple cellular genes by targeting mRNA 5′UTRs

A Simulation Framework to Investigate in vitro Viral Infection Dynamics

AbstractVirus infection is a complex biological phenomenon for which in vitro experiments provide a uniquely concise view where data is often obtained from a single population of cells, under controlled environmental conditions. Nonetheless, data interpretation and real understanding of viral dynamics is still hampered by the sheer complexity of the various intertwined spatio-temporal processes. In this paper we present a tool to address these issues: a cellular automata model describing critical aspects of in vitro viral infections taking into account spatial characteristics of virus spreading within a culture well. The aim of the model is to understand the key mechanisms of SARS-CoV infection dynamics during the first 24hours post infection. We interrogate the model using a Latin Hypercube sensitivity analysis to identify which mechanisms are critical to the observed infection of host cells and the release of measured virus particles

Multi-omic network signatures of disease

To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis. We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures. The data (experiment SM001) are publically available through the NIAID Systems Virology (https://www.systemsvirology.org) and PNNL (http://omics.pnl.gov) web portals. Phenotype data is found in the supplementary information. The ProCoNA package is available as part of Bioconductor 2.13

Immune cell proportions correlate with clinicogenomic features and ex vivo drug responses in acute myeloid leukemia

IntroductionThe implementation of small-molecule and immunotherapies in acute myeloid leukemia (AML) has been challenging due to genetic and epigenetic variability amongst patients. There are many potential mechanisms by which immune cells could influence small-molecule or immunotherapy responses, yet, this area remains understudied.MethodsHere we performed cell type enrichment analysis from over 560 AML patient bone marrow and peripheral blood samples from the Beat AML dataset to describe the functional immune landscape of AML.ResultsWe identify multiple cell types that significantly correlate with AML clinical and genetic features, and we also observe significant correlations of immune cell proportions with ex vivo small-molecule and immunotherapy responses. Additionally, we generated a signature of terminally exhausted T cells (Tex) and identified AML with high monocytic proportions as strongly correlating with increased proportions of these immunosuppressive T cells.DiscussionOur work, which is accessible through a new “Cell Type” module in our visualization platform (Vizome; http://vizome.org/), can be leveraged to investigate potential contributions of different immune cells on many facets of the biology of AML

High throughput sequencing in mice: a platform comparison identifies a preponderance of cryptic SNPs

<p>Abstract</p> <p>Background</p> <p>Allelic variation is the cornerstone of genetically determined differences in gene expression, gene product structure, physiology, and behavior. However, allelic variation, particularly cryptic (unknown or not annotated) variation, is problematic for follow up analyses. Polymorphisms result in a high incidence of false positive and false negative results in hybridization based analyses and hinder the identification of the true variation underlying genetically determined differences in physiology and behavior. Given the proliferation of mouse genetic models (e.g., knockout models, selectively bred lines, heterogeneous stocks derived from standard inbred strains and wild mice) and the wealth of gene expression microarray and phenotypic studies using genetic models, the impact of naturally-occurring polymorphisms on these data is critical. With the advent of next-generation, high-throughput sequencing, we are now in a position to determine to what extent polymorphisms are currently cryptic in such models and their impact on downstream analyses.</p> <p>Results</p> <p>We sequenced the two most commonly used inbred mouse strains, DBA/2J and C57BL/6J, across a region of chromosome 1 (171.6 – 174.6 megabases) using two next generation high-throughput sequencing platforms: Applied Biosystems (SOLiD) and Illumina (Genome Analyzer). Using the same templates on both platforms, we compared realignments and single nucleotide polymorphism (SNP) detection with an 80 fold average read depth across platforms and samples. While public datasets currently annotate 4,527 SNPs between the two strains in this interval, thorough high-throughput sequencing identified a total of 11,824 SNPs in the interval, including 7,663 new SNPs. Furthermore, we confirmed 40 missense SNPs and discovered 36 new missense SNPs.</p> <p>Conclusion</p> <p>Comparisons utilizing even two of the best characterized mouse genetic models, DBA/2J and C57BL/6J, indicate that more than half of naturally-occurring SNPs remain cryptic. The magnitude of this problem is compounded when using more divergent or poorly annotated genetic models. This warrants full genomic sequencing of the mouse strains used as genetic models.</p

Conserved host response to highly pathogenic avian influenza virus infection in human cell culture, mouse and macaque model systems

<p>Abstract</p> <p>Background</p> <p>Understanding host response to influenza virus infection will facilitate development of better diagnoses and therapeutic interventions. Several different experimental models have been used as a proxy for human infection, including cell cultures derived from human cells, mice, and non-human primates. Each of these systems has been studied extensively in isolation, but little effort has been directed toward systematically characterizing the conservation of host response on a global level beyond known immune signaling cascades.</p> <p>Results</p> <p>In the present study, we employed a multivariate modeling approach to characterize and compare the transcriptional regulatory networks between these three model systems after infection with a highly pathogenic avian influenza virus of the H5N1 subtype. Using this approach we identified functions and pathways that display similar behavior and/or regulation including the well-studied impact on the interferon response and the inflammasome. Our results also suggest a primary response role for airway epithelial cells in initiating hypercytokinemia, which is thought to contribute to the pathogenesis of H5N1 viruses. We further demonstrate that we can use a transcriptional regulatory model from the human cell culture data to make highly accurate predictions about the behavior of important components of the innate immune system in tissues from whole organisms.</p> <p>Conclusions</p> <p>This is the first demonstration of a global regulatory network modeling conserved host response between <it>in vitro </it>and <it>in vivo </it>models.</p