ERG Induces Epigenetic Activation of Tudor Domain-Containing Protein 1 (TDRD1) in ERG Rearrangement-Positive Prostate Cancer

Background Overexpression of ERG transcription factor due to genomic ERG- rearrangements defines a separate molecular subtype of prostate tumors. One of the consequences of ERG accumulation is modulation of the cell’s gene expression profile. Tudor domain-containing protein 1 gene (TDRD1) was reported to be differentially expressed between TMPRSS2:ERG-negative and TMPRSS2:ERG-positive prostate cancer. The aim of our study was to provide a mechanistic explanation for the transcriptional activation of TDRD1 in ERG rearrangement-positive prostate tumors. Methodology/Principal Findings Gene expression measurements by real-time quantitative PCR revealed a remarkable co-expression of TDRD1 and ERG (r2 = 0.77) but not ETV1 (r2<0.01) in human prostate cancer in vivo. DNA methylation analysis by MeDIP-Seq and bisulfite sequencing showed that TDRD1 expression is inversely correlated with DNA methylation at the TDRD1 promoter in vitro and in vivo (ρ = −0.57). Accordingly, demethylation of the TDRD1 promoter in TMPRSS2:ERG-negative prostate cancer cells by DNA methyltransferase inhibitors resulted in TDRD1 induction. By manipulation of ERG dosage through gene silencing and forced expression we show that ERG governs loss of DNA methylation at the TDRD1 promoter-associated CpG island, leading to TDRD1 overexpression. Conclusions/Significance We demonstrate that ERG is capable of disrupting a tissue-specific DNA methylation pattern at the TDRD1 promoter. As a result, TDRD1 becomes transcriptionally activated in TMPRSS2:ERG-positive prostate cancer. Given the prevalence of ERG fusions, TDRD1 overexpression is a common alteration in human prostate cancer which may be exploited for diagnostic or therapeutic procedures

High-Throughput miRNA and mRNA Sequencing of Paired Colorectal Normal, Tumor and Metastasis Tissues and Bioinformatic Modeling of miRNA-1 Therapeutic Applications

MiRNAs are discussed as diagnostic and therapeutic molecules. However, effective miRNA drug treatments with miRNAs are, so far, hampered by the complexity of the miRNA networks. To identify potential miRNA drugs in colorectal cancer, we profiled miRNA and mRNA expression in matching normal, tumor and metastasis tissues of eight patients by Illumina sequencing. We validated six miRNAs in a large tissue screen containing 16 additional tumor entities and identified miRNA-1, miRNA-129, miRNA-497 and miRNA-215 as constantly de-regulated within the majority of cancers. Of these, we investigated miRNA-1 as representative in a systems-biology simulation of cellular cancer models implemented in PyBioS and assessed the effects of depletion as well as overexpression in terms of miRNA-1 as a potential treatment option. In this system, miRNA-1 treatment reverted the disease phenotype with different effectiveness among the patients. Scoring the gene expression changes obtained through mRNA-Seq from the same patients we show that the combination of deep sequencing and systems biological modeling can help to identify patient-specific responses to miRNA treatments. We present this data as guideline for future pre-clinical assessments of new and personalized therapeutic options

The metabolic background is a global player in Saccharomyces gene expression epistasis

The regulation of gene expression in response to nutrient availability is fundamental to the genotype–phenotype relationship. The metabolic–genetic make-up of the cell, as reflected in auxotrophy, is hence likely to be a determinant of gene expression. Here, we address the importance of the metabolic–genetic background by monitoring transcriptome, proteome and metabolome in a repertoire of 16 Saccharomyces cerevisiae laboratory backgrounds, combinatorially perturbed in histidine, leucine, methionine and uracil biosynthesis. The metabolic background affected up to 85% of the coding genome. Suggesting widespread confounding, these transcriptional changes show, on average, 83% overlap between unrelated auxotrophs and 35% with previously published transcriptomes generated for non-metabolic gene knockouts. Background-dependent gene expression correlated with metabolic flux and acted, predominantly through masking or suppression, on 88% of transcriptional interactions epistatically. As a consequence, the deletion of the same metabolic gene in a different background could provoke an entirely different transcriptional response. Propagating to the proteome and scaling up at the metabolome, metabolic background dependencies reveal the prevalence of metabolism-dependent epistasis at all regulatory levels. Urging a fundamental change of the prevailing laboratory practice of using auxotrophs and nutrient supplemented media, these results reveal epistatic intertwining of metabolism with gene expression on the genomic scale

ERG transcription factor is required to maintain high <i>TDRD1</i> expression.

<p>(A) <i>ERG</i> and <i>TDRD1</i> mRNA expression levels in VCaP cells measured 72 h after gene silencing with siRNAs. Three independent experiments were performed in triplicate. (B) ERG and TDRD1 protein expression in VCaP cells 72 h after gene silencing with siRNAs.</p

<i>TDRD1</i> promoter associated CpG island is hypomethylated in <i>TMPRSS2:ERG</i>-positive prostate cancer.

<p>(A) Analysis of <i>TDRD1</i> promoter methylation in prostate tumors by MeDIP-Seq. The values represent the average degree of DNA methylation of the 500-bp bins. (B) Correlation analysis of <i>TDRD1</i> promoter methylation and <i>TDRD1</i> mRNA expression in prostate cancer. The Spearman correlation coefficient is shown.</p

<i>TDRD1</i> is co-expressed with <i>ERG</i> but not with <i>ETV1</i> in prostate cancer.

<p>(A) Correlation analysis of mRNA levels measured by qRT-PCR in <i>TMPRSS2:ERG</i>-negative (ERG-, n = 30) and <i>TMPRSS2:ERG</i>-positive (ERG+, n = 17) prostate cancers as well as adjacent benign prostate tissue (n = 46). Pearson correlation coefficient is shown. (B) Analysis of mRNA expression in prostate cell lines by qRT-PCR. Two independent experiments were performed in triplicate. Human testis RNA was used as positive control for <i>TDRD1</i> expression. (C) Analysis of protein expression in prostate cell lines by western blotting. (D) Analysis of mRNA expression in hematopoietic cancer cell lines by qRT-PCR. Two independent experiments were performed in triplicate.</p

<i>TDRD1</i> does not control LINE1 activity in <i>TMPRSS2:ERG</i>-positive VCaP cells.

<p>(A) mRNA expression analysis of <i>PIWIL</i> genes in prostate cell lines by qRT-PCR. Testis RNA was used as a positive control. (B) mRNA expression analysis of LINE1 ORF2 in VCaP cells following 5 days of treatment with 5-aza-2′-deoxycytidine. (C) mRNA expression analysis of LINE1 ORF2 in VCaP cells following prolonged (8 days) <i>ERG</i> or <i>TDRD1</i> silencing. (D) Metabolic viability assay of VCaP cells treated with siRNAs. One (B), two (A) or three (C, D) independent experiments were performed in triplicate.</p

ERG-induced loss of epigenetic repression at the <i>TDRD1</i> promoter is a major mechanism of <i>TDRD1</i> overexpression.

<p>(A) DNA methylation analysis of the <i>TDRD1</i> promoter-associated CpG island in prostate cell lines by bisulfite sequencing. Average methylation level of the whole CpG island calculated from five sequenced colonies is shown (%). (B) Analysis of mRNA expression in LNCaP cells after treatment with the demethylating agent 5-aza-2′-deoxycytidine. Insert: analysis of protein expression in LNCaP cells after treatment with 5-aza-2′-deoxycytidine. 75µg of protein lysate from LNCaP cells was used per lane. (C) Analysis of mRNA expression in stable LNCaP clones overexpressing <i>ERG</i>. Two independent experiments were performed in triplicate. Insert: ERG expression analysis at 48 h in LNCaP clones by western blotting. (D) Bisulfite sequencing of the <i>TDRD1</i> promoter-associated CpG island in LNCaP cells 48 h after induction of ERG expression with doxycycline. (E) Bisulfite sequencing of the <i>TDRD1</i> promoter-associated CpG island 96 h after silencing of <i>ERG</i> in VCaP cells. The data shown in (D) and (E) are mean % of methylation of the entire CpG island calculated from 11–12 sequenced clones.</p

Differential expression of miRNAs in colon tumor and metastasis tissues.

<p>(<b>A</b>) Top 25 up- and down-regulated miRNAs comparing tumor (left) or metastasis (right) tissues versus normal colon samples as analyzed by Illumina sequencing. All depicted miRNAs sufficed a p-value threshold ≤0.05. A star indicates samples with p≤0.01. (<b>B</b>) Venn diagram of microRNAs expressed in colorectal cancer patients, as determined by Illumina sequencing. (Left) Numbers of detected miRNAs, specific for each tissue (normal (N) = 19, tumor (T) = 34, metastases (M) = 29) and in all tissues (559). (Middle) Venn diagram of the significantly up-regulated miRNAs (p-value ≤0.05) for all comparisons (N/T, N/M and T/M). (Right) Venn diagram of the significantly down-regulated miRNAs (p-value ≤0.05) for all comparisons (N/T, N/M and T/M).</p

Functional assays on miRNA-1 as a potential tumor-suppressor gene.

<p>(<b>A</b>) AlamarBlue cell viability assay to test the effect of miRNA-1. SW480 (primary colon cancer cell line) and SW620 (corresponding metastases cell line) cells were transfected with miRNA-1 mimics (+miR-1) or mock transfected (−miR-1) and measured using an spectrophotometer after 24 h, 48 h and 72 h. The miRNA-1 level was determined by TaqMan assays for mature microRNAs. (<b>B</b>) “Wound healing” assay for miRNA-1 in SW480 and SW620 cells. After 24 h of transfection with miRNA-1 mimics a uniform scratch was generated through each confluent cell layer and “wound” closure was documented after 24 h using a phase-contrast microscope (n = 2). (<b>C</b>) AlamarBlue cell viability assay in SW480 and SW620 cells after camptothecin treatment alone or in combination with miRNA-1. Cell viability was measured after 0 h, 24 h and 48 h of drug treatment using a spectrophotometer.</p