Abstract Alu repeats are the most prevalent retrotransposons, comprising approximately 10% of the human genome. Alu retrotransposons have amplified during primate evolution by an RNA-mediated copy and paste mechanism. Among the Alu subfamilies, AluY elements and its variants exhibit the highest rates of retrotransposition. De novo retrotransposition of active Alu elements have given rise to insertion polymorphisms in human populations. While generating methylation maps of Alu elements in human normal cerebellum and in ependymoma tumors, Xie and colleagues (2009, 2010) also identified putative new Alu element insertions. In this study, we explored these Alu libraries to identify recent Alu element insertions and to understand the (epi)genetic variations that result from such events. This study may also enable identification of DNA structural variation that may occur at the onset of tumorigenesis and/or during tumor progression. A total of 327 putative recent Alu element insertions represented by 1,762 sequence reads were identified, including 316 events that have not yet been documented in either dbSNP or dbRIP. Forty-seven out of a total of forty-nine randomly selected events representing nineteen genomic loci were sequence-verified. Alu element insertions remained heterozygous for sixteen out of nineteen genomic loci in one or more individuals. The polymorphic Alu elements are enriched for young Alu families and they present some characteristic sequence features, such as longer poly(A) tails. In addition, they also present the TT/AAAA consensus sequence at their 3’-flanking regions, and AT-rich target site duplications. Despite being homozygous or heterozygous insertions, these Alu elements are heavily methylated. Further methylation analysis on two genomic loci revealed no methylation difference in two heterozygous alleles and also no methylation difference at CpG dinucleotides flanking the Alu insertion sites. These results indicate that the recently integrated Alu elements have not influenced the methylation status of their neighboring CpG dinucleotides. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3009. doi:10.1158/1538-7445.AM2011-3009

IntroductionWe have examined expression of microRNAs (miRNAs) in ependymomas to identify molecular markers of value for clinical management. miRNAs are non-coding RNAs that can block mRNA translation and affect mRNA stability. Changes in the expression of miRNAs have been correlated with many human cancers.Materials and methodsWe have utilized TaqMan Low Density Arrays to evaluate the expression of 365 miRNAs in ependymomas and normal brain tissue. We first demonstrated the similarity of expression profiles of paired frozen tissue (FT) and paraffin-embedded specimens (FFPE). We compared the miRNA expression profiles of 34 FFPE ependymoma samples with 8 microdissected normal brain tissue specimens enriched for ependymal cells. miRNA expression profiles were then correlated with tumor location, histology and other clinicopathological features.ResultsWe have identified miRNAs that are over-expressed in ependymomas, such as miR-135a and miR-17-5p, and down-regulated, such as miR-383 and miR-485-5p. We have also uncovered associations between expression of specific miRNAs which portend a worse prognosis. For example, we have identified a cluster of miRNAs on human chromosome 14q32 that is associated with time to relapse. We also found that miR-203 is an independent marker for relapse compared to the parameters that are currently used. Additionally, we have identified three miRNAs (let-7d, miR-596 and miR-367) that strongly correlate to overall survival.ConclusionWe have identified miRNAs that are differentially expressed in ependymomas compared with normal ependymal tissue. We have also uncovered significant associations of miRNAs with clinical behavior. This is the first report of clinically relevant miRNAs in ependymomas.

Genomic DNA methylation contributes substantively to transcriptional regulations that underlie mammalian development and cellular differentiation. Much effort has been made to decipher the molecular mechanisms governing the establishment and maintenance of DNA methylation patterns. However, little is known about genome-wide variation of DNA methylation patterns. In this study, we introduced the concept of methylation entropy, a measure of the randomness of DNA methylation patterns in a cell population, and exploited it to assess the variability in DNA methylation patterns of Alu repeats and promoters. A few interesting observations were made: (i) within a cell population, methylation entropy varies among genomic loci; (ii) among cell populations, the methylation entropies of most genomic loci remain constant; (iii) compared to normal tissue controls, some tumors exhibit greater methylation entropies; (iv) Alu elements with high methylation entropy are associated with high GC content but depletion of CpG dinucleotides and (v) Alu elements in the intronic regions or far from CpG islands are associated with low methylation entropy. We further identified 12 putative allelic-specific methylated genomic loci, including four Alu elements and eight promoters. Lastly, using subcloned normal fibroblast cells, we demonstrated the highly variable methylation patterns are resulted from low fidelity of DNA methylation inheritance.

The identification of molecular signatures predictive of clinical behavior and outcome in brain tumors has been the focus of many studies in the recent years. Despite the wealth of data that are available in the public domain on alterations in the genome, epigenome and transcriptome of brain tumors, the underlying molecular mechanisms leading to tumor initiation and progression remain largely unknown. Unfortunately, most of these data are scattered in multiple databases and supplementary materials of publications, thus making their retrieval, evaluation, comparison and visualization a rather arduous task. Here we report the development and implementation of an open access database (BTECH), a community resource for the deposition of a wide range of molecular data derived from brain tumor studies. This comprehensive database integrates multiple datasets, including transcript profiles, epigenomic CpG methylation data, DNA copy number alterations and structural chromosomal rearrangements, tumor-associated gene lists, SNPs, genomic features concerning Alu repeats and general genomic annotations. A genome browser has also been developed that allows for the simultaneous visualization of the different datasets and the various annotated features. Besides enabling an integrative view of diverse datasets through the genome browser, we also provide links to the original references for users to have a more accurate understanding of each specific dataset. This integrated platform will facilitate uncovering interactions among genetic and epigenetic factors associated with brain tumor development. BTECH is freely available at http://cmbteg.childrensmemorial.org/ .

Global DNA methylation changes have been documented to occur during normal development, aging and disease progression. Genome-wide levels of methylation are commonly inferred from the average level of methylation of large sets of repetitive elements. Alu repeats, in particular, have been exploited in a variety of approaches to monitor genome-wide losses in CpG methylation. Alu elements encompass over 25% of the CpG dinucleotides in the human genome. However, due to the high frequency of deamination of methylated cytosines in repetitive elements occurring in the germline, the sensitivity to detect methylation changes based upon their methylation levels is greatly compromised. In addition, the number of repeats that were targeted in previous investigations for ascertainment of genome-wide levels of methylation could not be accurately estimated. In this study, we developed and implemented an algorithm to design highly specific primers altogether targeting 617,817 CpG sites in over 45,000 Alu elements. In combination with the pyrosequencing technology, these primers were shown to yield highly sensitive and reproducible results.

Whole-cell immunotherapies and other cellular therapies have shown promising results in clinical trials. Due to the complex nature of the whole cell product and of the sometimes limited correlation of clinical potency with the proposed mechanism of action, these cellular immunotherapy products are generally not considered well characterized. Therefore, one major challenge in the product development of whole cell therapies is the ability to demonstrate comparability of product after changes in the manufacturing process. Such changes are nearly inevitable with increase in manufacturing experience leading to improved and robust processes that may have higher commercial feasibility. In order to comprehensively assess the impact of the process changes on the final product, and thus establish comparability, a matrix of characterization assays (in addition to lot release assays) assessing the various aspects of the cellular product are required. In this study, we assessed the capability of DNA-microarray-based, gene-expression analysis as a characterization tool using GVAX cancer immunotherapy cells manufactured by Cell Genesys, Inc. The GVAX immunotherapy product consists two prostate cancer cell lines (CG1940 and CG8711) engineered to secrete human GM-CSF. To demonstrate the capability of the assay, we assessed the transcriptional changes in the product when produced in the presence or absence of fetal bovine serum, and under normal and hypoxic conditions, both changes intended to stress the cell lines. We then assessed the impact of an approximately 10-fold process scale-up on the final product at the transcriptional level. These data were used to develop comparisons and statistical analyses suitable for characterizing culture reproducibility and cellular product similarity. Use of gene-expression data for process characterization proved to be a reproducible and sensitive method for detecting differences due to small or large changes in culture conditions as might be encountered in process scale-up or unanticipated bioprocess failures. Gene expression analysis demonstrated that cell products of representative lots under the same production process and at the same production scale were statistically identical. Large process changes that resulted from the artificial stress conditions used (absence of FBS and induction of hypoxia) displayed profoundly different gene expression patterns. We propose the use of simple t-test analysis in combination with the herein introduced expression ratio with mean intensity (ERMI) analysis as useful tools for process characterization by global gene expression analysis.

DNA methylation, the only known covalent modification of mammalian DNA, occurs primarily in CpG dinucleotides. 51% of CpGs in the human genome reside within repeats, and 25% within Alu elements. Despite that, no method has been reported for large-scale ascertainment of CpG methylation in repeats. Here we describe a sequencing-based strategy for parallel determination of the CpG-methylation status of thousands of Alu repeats, and a computation algorithm to design primers that enable their specific amplification from bisulfite converted genomic DNA. Using a single primer pair, we generated amplicons of high sequence complexity, and derived CpG-methylation data from 31 178 Alu elements and their 5' flanking sequences, altogether representing over 4 Mb of a human cerebellum epigenome. The analysis of the Alu methylome revealed that the methylation level of Alu elements is high in the intronic and intergenic regions, but low in the regions close to transcription start sites. Several hypomethylated Alu elements were identified and their hypomethylated status verified by pyrosequencing. Interestingly, some Alu elements exhibited a strikingly tissue-specific pattern of methylation. We anticipate the amplicons herein described to prove invaluable as epigenome representations, to monitor epigenomic alterations during normal development, in aging and in diseases such as cancer.

Differentiation of hematopoietic stem and progenitor cells is an intricate process controlled in large part at the level of transcription. While some key megakaryocytic transcription factors have been identified, the complete network of megakaryocytic transcriptional control is poorly understood. Using global gene expression microarray analysis, Gene Ontology-based functional annotations, and a novel interlineage comparison with parallel, isogenic granulocytic cultures as a negative control, we closely examined the mRNA level of transcriptional regulators in megakaryocytes derived from human mobilized peripheral blood CD34 + hematopoietic cells. This approach identified 199 differentially expressed transcription factors or transcriptional regulators. We identified and detailed the transcriptional kinetics of most known megakaryocytic transcription factors including GATA1, FLI1, and MAFG. Furthermore, many genes with transcription factor activity or transcription factor binding activity were identified in megakaryocytes that had not previously been associated with that lineage, including BTEB1, NR4A2, FOXO1A, MEF2C, HDAC5, VDR, and several genes associated with the tumor suppressor p53 (HIPK2, FHL2, and TADA3L). Protein expression and nuclear localization were confirmed in megakaryocytic cells for four of the novel candidate megakaryocytic transcription factors: FHL2, MXD1, E2F3, and RFX5. In light of the hypothesis that transcription factors expressed in a particular differentiation program are important contributors to such a program, these data substantially expand our understanding of transcriptional regulation in megakaryocytic differentiation of stem and progenitor cells.

Background T-cell activation is an essential step of immune response. The process of proper T-cell activation is strictly monitored and regulated by apoptosis signaling. Yet, regulation of apoptosis, an integral and crucial facet during the process of T-cell activation, is not well understood. Methods In this study, a Gene-Ontology driven global gene expression analysis coupled with protein abundance and activity assays identified genes and pathways associated with regulation of apoptosis in primary human CD3+ T cells and separately CD4+ and CD8+ T cells. Results We identified significantly regulated apoptotic genes in several protein families, such as BCL2 proteins, CASPASE proteins, and TNF receptors, and detailed their transcriptional kinetics during the T-cell activation process. Transcriptional patterns of a few select genes (BCL2A1, BBC3 and CASP3) were validated at the protein level. Many of these apoptotic genes are involved in NF-κB signaling pathway, including TNFRSF10A, TNFRSF10B, TRAF4, TRAF1, TRAF3, and TRAF6. Upregulation of NF-κB and IκB family genes (REL, RELA, and RELB, NFKBIA, NFKBIE and NFKB1) at 48 to 96 hours, supported by the increase of phosphorylated RELA (p65), suggests that the involvement of the NF-κB complex in the process of T-cell proliferation is not only regulated at the protein level but also at the transcriptional level. Examination of genes involved in MAP kinase signalling pathway, important in apoptosis, suggests an induction of p38 and ERK1 cascades in T-cell proliferation (at 48 to 96 hours), which was explored using phosphorylation assays for p38 (MAPK14) and ERK1 (MAPK3). An immediate and short-lived increase of AP-1 activity measured by DNA-binding activity suggests a rapid and transient activation of p38 and/or JNK cascades upon T-cell activation. Conclusion This comparative genome-scale, transcriptional analysis of T-cell activation in the CD4+ and CD8+ subsets and the mixed CD3+ population identified many apoptosis genes not previously identified in the context of T-cell activation. Furthermore, it provided a comprehensive temporal analysis of the transcriptional program of apoptosis associated with T-cell activation.

BACKGROUND:T-cell activation is an essential step of the immune response and relies on the tightly controlled orchestration of hundreds of genes/proteins, yet the cellular and molecular events underlying this complex process are not fully understood, especially at the genome-scale. Significantly, a comparative genome-scale transcriptional analysis of two T-cell subsets (CD4+ and CD8+) against each other and against the naturally mixed population (CD3+ cells) remains unexplored.RESULTS:Comparison of the microarray-based gene expression patterns between CD3+ T cells, and the CD4+ and CD8+ subsets revealed largely conserved, but not identical, transcriptional patterns. We employed a Gene-Ontology-driven transcriptional analysis coupled with protein abundance assays in order to identify novel T-cell activation genes and cell-type-specific genes associated with the immune response. We identified potential genes involved in the communication between the two subsets (including IL23A, NR4A2, CD83, PSMB2, -8, MIF, IFI16, TNFAIP1, POU2AF1, and OTUB1) and would-be effector-function-specific genes (XCL2, SLAMF7, TNFSF4, -5, -9, CSF3, CD48 and CD244). Chemokines induced during T-cell activation, but not previously identified in T cells, include CCL20, CXCL9, -10, -11 (in all three populations), and XCL2 (preferentially in CD8+ T cells). Increased expression of other unexpected cytokines (GPI, OSM and MIF) suggests their involvement in T-cell activation with their functions yet to be examined. Differential expression of many receptors, not previously reported in the context of T-cell activation, including CCR5, CCR7, IL1R2, IL1RAP, IL6R, TNFRSF25 and TNFRSF1A, suggests their role in this immune process. Several receptors involved in TCR activation (CD3D, CD3G, TRAT1, ITGAL, ITGB1, ITGB2, CD8A and B (CD8+ T-cell specific) along with LCK, ZAP70 and TYROBP were synchronously downregulated. Members of cell-surface receptors (HLA-Ds and KLRs), none previously identified in the context of T-cell activation, were also downregulated.CONCLUSION:This comparative genome-scale, transcriptional analysis of T-cell activation in the CD4+ and CD8+ subsets and the mixed CD3+ populations made possible the identification of many immune-response genes not previously identified in the context of T-cell activation. Significantly, it made possible to identify the temporal patterns of many previously known T-cell activation genes, and also identify genes implicated in effector functions of and communication between CD4+ and CD8+ T cells.

Objective:To evaluate the biological functions and underlying mechanisms of transcription factor PAX5 in promoting cell proliferation and anti-apoptosis in multiple myeloma cells.Methods:a PAX5 knockdown cell line was established by stable transfection of vector-based PAX5-siRNA into multiple myeloma IM9 cells.The expressing level of PAX5,p53,XBP-1 and c-Myc was examined by either or both Western blot and RT-PCR for the targets.Cell proliferation and apoptosis were assayed by MTT and flow cytometry,respectively.Results:PAX5 was expressed selectively in IM9 cells,but neither in another common used multiple myeloma KAS6 cells nor in the prostate cancer cells DU145 and PC3.Knockdown PAX5 led to a significant up-regulation of p53 and XBP1,but decreased c-Myc expressions in IM9 cells that were correlated with the increased sensitivity of drug-induced apoptosis and decreased proliferation rates when compared to the control cells.Conclusion:PAX5 is specifically expressed in IM9 cells,which promotes cell proliferation and anti drug-induced apoptosis.In addition to inhibiting the expression of p53 and XBP-1,PAX5 is found to induce expression of oncogene c-Myc in IM9 cells,and this finding indicates an undiscovered signal pathway that may contributes to the malignancy of Multiple Myeloma cells.

Background: The differentiation of hematopoietic stem cells into platelet-forming megakaryocytes is of fundamental importance to hemostasis. Constitutive apoptosis is an integral, yet poorly understood, facet of megakaryocytic (Mk) differentiation. Understanding Mk apoptosis could lead to advances in the treatment of Mk and platelet disorders.Results: We used a Gene-ontology-driven microarray-based transcriptional analysis coupled with protein-level and activity assays to identify genes and pathways involved in Mk apoptosis. Peripheral blood CD34(+) hematopoietic progenitor cells were induced to either Mk differentiation or, as a negative control without observable apoptosis, granulocytic differentiation. Temporal gene-expression data were analyzed by a combination of intra- and inter-culture comparisons in order to identify Mk-associated genes. This novel approach was first applied to a curated set of general Mk-related genes in order to assess their dynamic transcriptional regulation. When applied to all apoptosis associated genes, it revealed a decrease in NF-kappa B signaling, which was explored using phosphorylation assays for I kappa B alpha and p65 ( RELA). Up-regulation was noted among several proapoptotic genes not previously associated with Mk apoptosis such as components of the p53 regulon and TNF signaling. Protein-level analyses probed the involvement of the p53-regulated GADD45A, and the apoptosis signal-regulating kinase 1 (ASK1). Down-regulation of anti-apoptotic genes, including several of the Bcl-2 family, was also detected.Conclusion: Our comparative approach to analyzing dynamic large-scale transcriptional data, which was validated using a known set of Mk genes, robustly identified candidate Mk apoptosis genes. This led to novel insights into the molecular mechanisms regulating apoptosis in Mk cells.

Introduction. Ex vivo expanded T cells are increasingly employed in a variety of immunotherapy protocols. The success of such protocols requires large numbers of biologically active T cells. Several activation and culture protocols have been examined, but none in significant or molecular detail to allow more targeted protocol interventions. In this study, our aim was to better understand early T-cell activation using a comprehensive, microarray-based transcriptional analysis, in conjunction with targeted protein-level and functional analyses.