Epstein - Barr virus latent membrane protein 1 suppresses reporter activity through modulation of promyelocytic leukemia protein-nuclear bodies

The Epstein-Barr virus (EBV) encoded Latent Membrane Protein 1 (LMP1) has been shown to increase the expression of promyelocytic leukemia protein (PML) and the immunofluorescent intensity of promyelocytic leukemia nuclear bodies (PML NBs). PML NBs have been implicated in the modulation of transcription and the association of reporter plasmids with PML NBs has been implicated in repression of reporter activity. Additionally, repression of various reporters in the presence of LMP1 has been noted. This study demonstrates that LMP1 suppresses expression of reporter activity in a dose responsive manner and corresponds with the LMP1 induced increase in PML NB intensity. Disruption of PML NBs with arsenic trioxide or a PML siRNA restores reporter activity. These data offer an explanation for previously conflicting data on LMP1 signaling and calls attention to the possibility of false-positives and false-negatives when using reporter assays as a research tool in cells expressing LMP1

microRNA regulation of mammalian target of rapamycin expression and activity controls estrogen receptor function and RAD001 sensitivity

Background: The AKT/mammalian target of rapamycin (mTOR) signaling pathway is regulated by 17 α -estradiol (E2) signaling and mediates E2-induced proliferation and progesterone receptor (PgR) expression in breast cancer. Methods and results: Here we use deep sequencing analysis of previously published data from The Cancer Genome Atlas to demonstrate that expression of a key component of mTOR signaling, rapamycin-insensitive companion of mTOR (Rictor), positively correlated with an estrogen receptor- α positive (ER α + ) breast tumor signature. Through increased microRNA-155 (miR-155) expression in the ER α + breast cancer cells we demonstrate repression of Rictor enhanced activation of mTOR complex 1 (mTORC1) signaling with both qPCR and western blot. miR-155-mediated mTOR signaling resulted in deregulated ER α signalingbothinculturedcells in vitro and in xenografts in vivo in addition to repressed PgR expression and act ivity.FurthermoreweobservedthatmiR-155 enhanced mTORC1 signaling (observed through western blot for increased phosphorylation on mTOR S2448) and induced inhibition of mTORC2 signaling (evident through repressed Rictor and tuberous sclerosis 1 (TSC1) gene expression). mTORC1 induced deregulation of E2 signaling was confirmed using qPCR and the mTORC1-specific inhibitor RAD001. Co-treatment of MCF7 breast cancer cells stably overexpressing miR-155 with RAD001 and E2 restored E2-induced PgR gene expression. RAD001 treatment of SCID/CB17 mice inhibited E2-induced tumorigenesis of the MCF7 miR-155 overexpressing cell line. Finally we demonstrated a strong positive correlation between Rictor and PgR expression and a negative correlation with Raptor expression in Luminal B breast cancer samples, a breast cancer histological subtype known for having an altered ER α -signaling pathway. Conclusions: miRNA mediated alterations in mTOR and ER α signaling establishes a new mechanism for altered estrogen responses independent of growth factor stimulation

Cyclosporin A and FK506 Block Induction of the Epstein-Barr Virus Lyric Cycle by Anti-Immunoglobulin

AbstractThe Epstein-Barr virus (EBV) BZLF1 gene is expressed early upon induction of the viral lytic cycle and its protein product is unique in its ability to disrupt viral latency in some latently infected cell lines. Anti-immunoglobulin (anti-Ig) treatment of the Burkitt's lymphoma cell line Akata, which bears surface IgG, has previously been shown to synchronously induce transcription of the BZLF1 gene (K. Takada and Y. Ono, 1989, J. Virol. 63, 445-449). We have previously shown that anti-Ig induction of Akata cells activates expression of the tumor necrosis factor alpha (TNF-α) gene via a calcineurin-dependent mechanism (Goldfeld et al., 1992, Proc. Natl. Acad. Sci. USA 89, 12198-12201). Here, we report that anti-Ig induction of the EBV lytic cycle in Akata cells can be blocked by the immunosuppressants cyclosporin A and FK506. Furthermore, we demonstrate that synergistic induction by phorbol ester and calcium ionophore of a BZLF1 promoter-driven reporter construct in an EBV-negative BL cell line can be inhibited by addition of cyclosporin A. Thus, analogous to activation of TNF-α gene in Akata cells, anti-Ig induction of the BZLF1 promoter is most likely mediated by calcineurin and probably involves translocation to the nucleus of a transcription factor sequestered in the cytoplasm. As such, immunosuppressants may be useful probes for dissecting B cell activation pathways involved in regulating EBV gene transcription

Elevated expression of long intergenic non-coding RNA HOTAIR in a basal-like variant of MCF-7 breast cancer cells

Epigenetic regulation of gene expression is critical to phenotypic maintenance and transition of human breast cancer cells. HOX antisense intergenic RNA (HOTAIR) is a long intergenic non-coding RNA that epigenetically represses gene expression via recruitment of enhancer of zeste homolog 2 (EZH2), a histone methyltransferase. Elevated expression of HOTAIR promotes progression of breast cancer. In the current study we examined the expression and function of HOTAIR in MCF-7-TNR cells, a derivative of the luminal-like breast cancer cell line MCF-7 that acquired resistance to TNF-α-induced cell death. The expression of HOTAIR, markers of the luminal-like and basal-like subtypes, and growth were compared between MCF-7 and MCF-7-TNR cells. These variables were further assessed upon inhibition of HOTAIR, EZH2, p38 MAPK, and SRC kinase in MCF-7-TNR cells. When compared with MCF-7 cells, MCF-7-TNR cells exhibited an increase in the expression of HOTAIR, which correlated with characteristics of a luminal-like to basal-like transition as evidenced by dysregulated gene expression and accelerated growth. MCF-7-TNR cells exhibited reduced suppressive histone H3 lysine27 trimethylation on the HOTAIR promoter. Inhibition of HOTAIR and EZH2 attenuated the luminal-like to basal-like transition in terms of gene expression and growth in MCF-7-TNR cells. Inhibition of p38 and SRC diminished HOTAIR expression and the basal-like phenotype in MCF-7-TNR cells. HOTAIR was robustly expressed in the native basal-like breast cancer cells and inhibition of HOTAIR reduced the basal-like gene expression and growth. Our findings suggest HOTAIR-mediated regulation of gene expression and growth associated with the basal-like phenotype of breast cancer cells

miRNA-Mediated Relationships between Cis-SNP Genotypes and Transcript Intensities in Lymphocyte Cell Lines

In metazoans, miRNAs regulate gene expression primarily through binding to target sites in the 3′ UTRs (untranslated regions) of messenger RNAs (mRNAs). Cis-acting variants within, or close to, a gene are crucial in explaining the variability of gene expression measures. Single nucleotide polymorphisms (SNPs) in the 3′ UTRs of genes can affect the base-pairing between miRNAs and mRNAs, and hence disrupt existing target sites (in the reference sequence) or create novel target sites, suggesting a possible mechanism for cis regulation of gene expression. Moreover, because the alleles of different SNPs within a DNA sequence of limited length tend to be in strong linkage disequilibrium (LD), we hypothesize the variants of miRNA target sites caused by SNPs potentially function as bridges linking the documented cis-SNP markers to the expression of the associated genes. A large-scale analysis was herein performed to test this hypothesis. By systematically integrating multiple latest information sources, we found 21 significant gene-level SNP-involved miRNA-mediated post-transcriptional regulation modules (SNP-MPRMs) in the form of SNP-miRNA-mRNA triplets in lymphocyte cell lines for the CEU and YRI populations. Among the cognate genes, six including ALG8, DGKE, GNA12, KLF11, LRPAP1, and MMAB are related to multiple genetic diseases such as depressive disorder and Type-II diabetes. Furthermore, we found that ∼35% of the documented transcript intensity-related cis-SNPs (∼950) in a recent publication are identical to, or in significant linkage disequilibrium (LD) (p<0.01) with, one or multiple SNPs located in miRNA target sites. Based on these associations (or identities), 69 significant exon-level SNP-MPRMs and 12 disease genes were further determined for two populations. These results provide concrete in silico evidence for the proposed hypothesis. The discovered modules warrant additional follow-up in independent laboratory studies

Long noncoding RNA expression in lymphoma

Diffuse large B cell lymphoma (DLBCL), the most common non-Hodgkin’s lymphoma, is an aggressive lymphoma that affects patients worldwide. Although it is curable in some cases, many patients remain refractory to treatment and a better understanding of disease mechanisms could lead to improved therapies. Using expression data from protein-coding genes DLBCL can be classified into the activated B cell (ABC) and germinal centre B cell (GCB) subtypes (1), which are predictive of different outcomes (2). However, many tumours remain unclassifiable with this data. Furthermore, DLBCL can be difficult to distinguish from the more easily treatable Burkitt’s Lymphoma (BL) using histology and even protein-coding gene expression (3). The proliferation of publicly available datasets and lncRNA annotation offers the opportunity to re-examine this clinically important problem. We are using RNA-Seq datasets from DLBCL and BL available from the NCBI’s Sequence Read Archive (4, 5) in conjunction with the transcript assembler Cufflinks and noncoding RNA catalogs from the ENCODE project and John Rinn’s laboratory (6) to detect and quantify novel and known long noncoding transcripts in these tumours. Using hierarchical clustering and differential expression analysis we are characterizing lncRNA expression profiles in both established and lncRNA-defined subtypes. In addition, both DLBCL and BL are known in some cases to be driven by Epstein-Barr Virus (EBV), a factor that is potentially confounding to attempts to classify tumours and characterize pathways. Using the RNA Comprehensive Multi-Processor Analysis System for Sequencing (RNA CoMPASS) developed in our laboratory (7), we are able to sensitively detect and quantify EBV RNA expression in RNA-Seq datasets. LncRNA expression in different tumours can then be analyzed in this context, an

Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis

Abstract Background Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date. Methods In this work, taking M. tuberculosis H37Rv (MTB) that is co-evolving with its human host as an example, we propose a general computational framework that exploits the known bacterial pathogen protein interaction networks in STRING database to predict pathogen-host protein interactions and their signaling cross-talks. In this framework, significant interlogs are derived from the known pathogen protein interaction networks to train a predictive l2-regularized logistic regression model. Results The computational results show that the proposed method achieves excellent performance of cross validation as well as low predicted positive rates on the less significant interlogs and non-interlogs, indicating a low risk of false discovery. We further conduct gene ontology (GO) and pathway enrichment analyses of the predicted pathogen-host protein interaction networks, which potentially provides insights into the machinery that M. tuberculosis H37Rv targets human genes and signaling pathways. In addition, we analyse the pathogen-host protein interactions related to drug resistance, inhibition of which potentially provides an alternative solution to M. tuberculosis H37Rv drug resistance. Conclusions The proposed machine learning framework has been verified effective for predicting bacteria-host protein interactions via known bacterial protein interaction networks. For a vast majority of bacterial pathogens that lacks experimental studies of bacteria-host protein interactions, this framework is supposed to achieve a general-purpose applicability. The predicted protein interaction networks between M. tuberculosis H37Rv and Homo sapiens, provided in the Additional files, promise to gain applications in the two fields: (1) providing an alternative solution to drug resistance; (2) revealing the patterns that M. tuberculosis H37Rv genes target human immune signaling pathways