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

Single nucleotide variants in nuclear pore complex disassembly pathway associated with poor survival in osteosarcoma

Introduction:The bone tumor, osteosarcoma, remains challenging to treat in children and young adults, especially when patients present with metastatic disease. Developing new therapies based on genomic data from sequencing projects has proven difficult given the lack of recurrent genetic lesions across tumors. MYC overexpression has been associated with poor outcomes in osteosarcoma. However, other genomic markers of disease severity are lacking.Materials and Methods:We utilized whole genome sequencing of 106 tumors and matched normal controls in order to define genomic characteristics that correlate with overall survival. Single nucleotide variants were overlaid onto annotated molecular pathways in order to define aberrant pathway signatures specific to aggressive osteosarcoma. Additionally, we calculated differential gene expression in a subsample of 71 tumors. Differentially expressed genes were then queried for known MYC-responsive genes.Results:Molecular pathways specific to nuclear pore complex disassembly (NPCD) show significant correlation with poor overall survival in osteosarcoma when mutations were present. Genes involved in immune response and immune regulation are enriched in the differential expression analysis of samples with and without NPCD pathway aberrations. Furthermore, neither MYC nor MYC-responsive genes show differential expression between NPCD-aberrant and non-aberrant groups. The NPCD pathway mutations are dominated by regulatory region variants rather than protein-altering mutations, suggesting that dysregulation of genetic regulatory networks may be the underlying mechanism for their relation to osteosarcoma phenotype.Discussion:Overall survival is significantly worse in patients whose tumors show aberrations in the NPCD pathway. Moreover, this difference in survival is not driven by MYC-overexpression, suggesting a novel mechanism for some aggressive osteosarcomas. These findings add light to the evolving understanding of the drivers of osteosarcoma and may aid in the search for new treatments based on patient-specific genetic data

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