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ABSTRACT: Non-coding (nc) RNAs are increasingly recognized to play important regulatory roles in eukaryotic gene expression. The highly abundant and essential 7SK ncRNA has been shown to negatively regulate RNA Polymerase II transcription by inactivating the positive transcription elongation factor b (P-TEFb) in cellular and Tat-dependent HIV transcription. Here, we identify a more general, P-TEFb-independent role of 7SK RNA in directly affecting the function of the architectural transcription factor and chromatin regulator HMGA1. An important regulatory role of 7SK RNA in HMGA1-dependent cell differentiation and proliferation regulation is uncovered with the identification of over 1500 7SK-responsive HMGA1 target genes. Elevated HMGA1 expression is observed in nearly every type of cancer making the use of a 7SK substructure in the inhibition of HMGA1 activity, as pioneered here, potentially useful in therapy. The 7SK-HMGA1 interaction not only adds an essential facet to the comprehension of transcriptional plasticity at the coupling of initiation and elongation, but also might provide a molecular link between HIV reprogramming of cellular gene expression-associated oncogenesis.
Nucleic Acids Research 11/2010; 39(6):2057-72. · 8.03 Impact Factor
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ABSTRACT: In view of potential application to biomedical diagnosis, tight transcriptome data quality control is compulsory. Usually, quality control is achieved using labeling and hybridization controls added at different stages throughout the processing of the biologic RNA samples. These control measures, however, only reflect the performance of the individual technical manipulations during the entire process and have no bearing as to the continued integrity of the RNA sample itself. Here we demonstrate that intrinsic statistical properties of the resulting transcriptome data signal and signal-variance distributions and their invariance can be identified independently of the animal species studied and the labeling protocol used. From these invariant properties we have developed a data model, the parameters of which can be estimated from individual experiments and used to compute relative quality measures based on similarity with large reference datasets. These quality measures add supplementary, non-redundant information to standard quality control estimates based on spike-in and hybridization controls, and are exploitable in data analysis. A software application for analyzing datasets as well as a reference dataset for AB1700 arrays are provided. They should allow AB1700 users to easily integrate this method into their analysis pipeline, and might instigate similar developments for other transcriptome platforms.
Genomics Proteomics & Bioinformatics 03/2010; 8(1):57-71.
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ABSTRACT: Pea3 and Erm are transcription factors expressed in normal developing branching organs such as the mammary gland. Deregulation of their expression is generally associated with tumorigenesis and particularly breast cancer. By using RNA interference (RNAi) to downregulate the expression of Pea3 and/or Erm in a mammary cancer cell line, we present evidence for a role of these factors in proliferation, migration and invasion capacity of cancer cells. We have used different small interfering RNAs (siRNAs) targeting pea3 and erm transcripts in transiently or stably transfected cells, and assessed the physiological behavior of these cells in in vitro assays. We also identified an in vivo alteration of tumor progression after injection of cells that overexpress pea3 and/or erm short hairpin RNAs (shRNAs) in immunodeficient mice. Using transcriptome profiling in Pea3- or Erm-targeted cells, two largely independent gene expression programs were identified on the basis of their shared phenotypic modifications. A statistically highly significant part of both sets of target genes had previously been already associated with the cellular signaling pathways of the ;proliferation, migration, invasion' class. These data provide the first evidence, by using endogenous knockdown, for pivotal and complementary roles of Pea3 and Erm transcription factors in events crucial to mammary tumorigenesis, and identify sets of downstream target genes whose expression during tumorigenesis is regulated by these transcription factors.
Journal of Cell Science 11/2008; 121(Pt 20):3393-402. · 6.11 Impact Factor
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ABSTRACT: Little is known about the physiological role of the EBER1 and 2 nuclear RNAs during Epstein Barr viral infection. The EBERs are transcribed by cellular RNA Polymerase III and their strong expression results in 106 to 107 copies per EBV infected cell, making them reliable diagnostic markers for the presence of EBV. Although the functions of most of the proteins targeted by EBER RNAs have been studied, the role of EBERs themselves still remains elusive.
The cellular transcription response to EBER2 expression using the wild-type and an internal deletion mutant was determined. Significant changes in gene expression patterns were observed. A functional meta-analysis of the regulated genes points to inhibition of stress and immune responses, as well as activation of cellular growth and cytoskeletal reorganization as potential targets for EBER2 RNA. Different functions can be assigned to different parts of the RNA.
These results provide new avenues to the understanding of EBER2 and EBV biology, and set the grounds for a more in depth functional analysis of EBER2 using transcriptome activity measurements.
BMC Research Notes 11/2008; 1:100.
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Béatrice Jacquelin,
Véronique Mayau, Guillaume Brysbaert,
Béatrice Regnault,
Ousmane M Diop,
Fernando Arenzana-Seisdedos,
Lars Rogge,
Jean-Yves Coppée,
Françoise Barré-Sinoussi,
Arndt Benecke,
Michaela C Müller-Trutwin
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ABSTRACT: Nonhuman primates, including African green monkey (AGM), are important models for biomedical research. The information on monkey genomes is still limited and no versatile gene expression screening tool is available. We tested human whole genome microarrays for cross-species reactivity with AGM transcripts using both long oligonucleotide arrays (60-mer probes) and short oligonucleotide arrays (25-mer). Using the long oligonucleotide arrays, we detected 4-fold more AGM transcripts than with the short oligonucleotide technology. The number of detected transcripts was comparable to that detected using human RNA, with 87% of the detected genes being shared between both species. The specificity of the signals obtained with the long oligonucleotide arrays was determined by analyzing the transcriptome of concanavalin A-activated CD4+ T cells vs. nonactivated T cells of two monkey species AGM and macaque. For both species, the genes showing the most significant changes in expression, such as IL-2R, were those known to be regulated in human CD4+ T cell activation. Finally, tissue specificity of the signals was established by comparing the transcription profiles of AGM brain and tonsil cells. In conclusion, the ABI human microarray platform provides a highly valuable tool for the assessment of AGM gene expression profiles.
The FASEB Journal 11/2007; 21(12):3262-71. · 5.71 Impact Factor
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ABSTRACT: We have previously developed a combined signal/variance distribution model that accounts for the particular statistical properties of datasets generated on the Applied Biosystems AB1700 transcriptome system. Here we show that this model can be efficiently used to generate synthetic datasets with statistical properties virtually identical to those of the actual data by aid of the JAVA application ace.map creator 1.0 that we have developed. The fundamentally different structure of AB1700 transcriptome profiles requires re-evaluation, adaptation, or even redevelopment of many of the standard microarray analysis methods in order to avoid misinterpretation of the data on the one hand, and to draw full benefit from their increased specificity and sensitivity on the other hand. Our composite data model and the ace.map creator 1.0 application thereby not only present proof of the correctness of our parameter estimation, but also provide a tool for the generation of synthetic test data that will be useful for further development and testing of analysis methods.
Genomics Proteomics & Bioinformatics 03/2007; 5(1):45-52.
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ABSTRACT: Novel microarray technologies such as the AB1700 platform from Applied Biosystems promise significant increases in the signal dynamic range and a higher sensitivity for weakly expressed transcripts. We have compared a representative set of AB1700 data with a similarly representative Affymetrix HG-U133A dataset. The AB1700 design extends the signal dynamic detection range at the lower bound by one order of magnitude. The lognormal signal distribution profiles of these high-sensitivity data need to be represented by two independent distributions. The additional second distribution covers those transcripts that would have gone undetected using the Affymetrix technology. The signal-dependent variance distribution in the AB1700 data is a non-trivial function of signal intensity, describable using a composite function. The drastically different structure of these high-sensitivity transcriptome profiles requires adaptation or even redevelopment of the standard microarray analysis methods. Based on the statistical properties, we have derived a signal variance distribution model for AB1700 data that is necessary for such development. Interestingly, the dual lognormal distribution observed in the AB1700 data reflects two fundamentally different biologic mechanisms of transcription initiation.
Genomics Proteomics & Bioinformatics 12/2006; 4(4):212-29.
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ABSTRACT: Studies on high-throughput global gene expression using microarray technology have generated ever larger amounts of systematic transcriptome data. A major challenge in exploiting these heterogeneous datasets is how to normalize the expression profiles by inter-assay methods. Different non-linear and linear normalization methods have been developed, which essentially rely on the hypothesis that the true or perceived logarithmic fold-change distributions between two different assays are symmetric in nature. However, asymmetric gene expression changes are frequently observed, leading to suboptimal normalization results and in consequence potentially to thousands of false calls. Therefore, we have specifically investigated asymmetric comparative transcriptome profiles and developed the normalization using weighted negative second order exponential error functions (NeONORM) for robust and global inter-assay normalization. NeONORM efficiently damps true gene regulatory events in order to minimize their misleading impact on the normalization process. We evaluated NeONORM's applicability using artificial and true experimental datasets, both of which demonstrated that NeONORM could be systematically applied to inter-assay and inter-condition comparisons.
Genomics Proteomics & Bioinformatics 06/2006; 4(2):90-109.
