Combined KEGG Pathway analysis of direct and functional E2A-PBX1 targets.

<p>The analysis was performed using Exploratory Gene Association Networks (EGAN) software tool. E2A-PBX1 direct (108 direct targets identified by ChIP-chip) and functional targets (122 significant differentially expressed genes between E2a-Pbx1 silenced samples and controls) and Pathways that might be regulated by them were visualized. Interfaces of direct and functional E2A-PBX1 targets are depicted. Magenta lines depict the connection of the genes to the direct target and/or functional target group; blue lines show the participation of the genes in KEGG pathways and brown lines show known interaction between genes connected.</p

Expanded combined analysis of direct and functional E2A-PBX1 targets.

<p>The analysis was performed using the 102 direct targets identified by ChIP-chip analysis and the data set of the functional targets including the top 2000 genes up and down regulated after siRNA silencing of E2A-PBX1. Venn diagrams were generated and the interfaces of direct and functional E2A-PBX1 targets are depicted. 10 genes that were both direct targets and functionally down-regulated <b>(</b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087602#pone-0087602-g005" target="_blank"><b>Figure 5A</b></a><b>)</b> and 10 that were both direct targets and functionally up-regulated <b>(</b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087602#pone-0087602-g005" target="_blank"><b>Figure 5B</b></a><b>)</b> dependent on expression of E2A-PBX1 were identified. We are not able to address E2A-PBX1 as a functional repressor (for the 10 down regulated genes) or whether its depletion allowed accessibility to another transcriptional activator. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087602#pone-0087602-g005" target="_blank"><b>Figure 5C</b></a> Selected direct and functional E2A-PBX1 targets are highly expressed in primary E2A-PBX1 leukemias. Pediatric Cancer Genome Project. <a href="http://www.pediatriccancergenomeproject.org/site/accessed" target="_blank">http://www.pediatriccancergenomeproject.org/site/accessed</a> 14 Oct 2013. The expression of four genes UGT2B15, ASNS, WNT16 and HK2, that were among the direct and functional E2A-PBX1 targets in primary leukemia’s are shown. The expression of these genes was analyzed in Hyperdiploid leukemia’s (H), in E2A-PBX1 leukemia’s (E), TEL-AML1 (T) leukemia’s and BCR-ABL1 (B) leukemia’s are shown. The analysis of expression was performed using the gene expression tool and data available at St. Jude Children’s Research Hospital – (Washington University).</p

The 9p21 deletion is not always targeting CDKN2A.

<p>Copy number alterations in a group of 22 t(1;19)⁺<i>E2A-PBX1</i> bone marrow samples are shown <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087602#pone-0087602-g003" target="_blank"><b>Figure 3A</b></a>. Patient samples were subjected to hierarchical clustering analysis; we found the 9p21 deletion to exist in four of the 22 patients we have analyzed, with one patient exhibiting a homozygous deletion, white color refers to no changes, red to gain and blue to loss. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087602#pone-0087602-g003" target="_blank"><b>Figure 3B</b></a>. Fine map of deletions in the 9p21 region showing the deletion endpoints among 8 patients. The commonly deleted region among 10 patients (with 45%, including 7 patients in this figure and the other 3 others with complete arm loss) is the interferon gene cluster telomeric to the <i>CDKN2A/B</i> locus.</p

Identified ChIP-chip targets on expression array in E2A-PBX1 silenced vs control cells.

<p><b>A.</b> ChIP-chip analysis data and expression analysis data upon E2A-PBX1 silencing were combined to get an overview of the direct and functional targets of E2A-PBX1. siRNA to E2A-PBX1 was employed to silence E2A-PBX1. The MA plot depicts the expression array changes upon silencing E2A-PBX1. Genes are ordered on the x-axis on the basis of their expression in untreated 697 cells. The Y-axis displays the change in expression upon E2A-PBX1 silencing. Those genes that are direct hits of E2A-PBX1 by chromatin immunoprecipitation assays (108 genes) were plotted against the expression array changes upon silencing and are shown in red color and marked with a blue dot; grey dots indicate all other genes assessed by the array. <b>B.</b> Here we compare the changes in expression of the 108 E2A-PBX1 direct targets to the expression change of all the other genes (non-direct E2A-PBX1 targets) upon E2A-PBX1 silencing. The set of identified ChIP-chip targets show collective down-regulation trend in the E2A-PBX1 silenced samples compared to controls (p<1.63e-06).</p

E2A-PBX1 ChIP binding.

<p><b>A. Peak promoter sequence analysis</b>. Chromatin immunoprecipitation assays were performed to identify direct targets E2A-PBX1 using the human promoter array; HG18; (NCBI Build 36, Roche Nimblegen, Madison WI). A search was performed using the software WebMOTIFS and de novo motif finding a search for Transcription Factors that are likely to contact and regulate the binding sites identified by ChIP-chip. The 3 most significant transcription factor families (classes of transcription factors most likely to regulate the input sequences) are PBX, POU and STAT. <b>B. Positional binding of E2A-PBX1</b>. Among the 108 top hit ChIP genes, 76 genes have a single annotated transcription start site. The average intensity of ChIP pull-down is graphed along the promoter region in a smoothed plot. This histogram of the positions of maximal peak intensities (mean of 3 replicates) relative to the transcription start site (TSS) in target genes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087602#pone.0087602.s007" target="_blank">Table S3</a>). Most target genes show a maximum probe intensity around −500 bp upstream of the TSS. Probe regions with multiple TSS were excluded from this analysis. The promoter coverage area was complete for all genes between −2200 and +500 by the TSS (solid line) with some additional coverage for some genes outside of that region (dotted line).</p

Over-expression of PKM1 alters metabolism and suppresses the growth of GBM cells.

<p><b>A</b>, Parental U87 or T98 GBM cells were transfected with an empty vector (EV) or a construct encoding human PKM1. Two clonal populations (PKM1-1 and PKM1-2) were then established and analyzed by Western blot for levels of PKM1, PKM2, and β-actin. <b>B-F,</b> pyruvate kinase activity, ATP levels, intracellular concentrations of pyruvate and lactate, and proliferation index of cells from panel A. <b>G</b>, photomicrographs (2.5X magnification) of colonies generated by the 24th day growth of EV and PKM1-1 cells in soft agar. <b>H</b>, Colony number and distribution of colony size from experiments in (G). <b>I</b>, FACS-based cell cycle distribution of logarithmically-growing EV and PKM1-1 cells.</p

PKM1 and PKM2 mRNA expression in a series of human normal brain (NB), neural progenitor cells (NSC), and WHO grade I-IV human astrocytoma specimens.

<p>A, RNA was isolated from fixed or frozen normal brain (NB) and grade (Gr)- I, II, III, and IV (primary, P and secondary, S) astrocytoma samples, reverse transcribed, then subjected to triplicate qPCR analysis using primers specific for the PKM1 or PKM2 transcript. All values are the mean normalized to HPRT1 expression. B, mean group PKM1 and PKM2 mRNA expression values from panel A and from NSC and established GBM cell lines. C, cDNAs from representative samples in panel A were subjected to PCR amplification using primers amplifying a 442 bp exon 8–11 region common to PKM1 and PKM2. Following incubation with PstI, the uncleaved (PKM1, 442 bp) and cleaved (PKM2, 246 and 196 bp) amplification products were separated by electrophoresis and quantitated, with total signal (PKM1+PKM2) set at 100 for each lane. P, PCR control (Gr-IV amplification products prior to PstI digestion); R, duplicate restriction enzyme controls (amplification products derived using a PKM2 cDNA template post-PstI digestion).</p

Suppression of PKM2 levels alters metabolism and reduces the growth of GBM cells.

<p><b>A</b>, Parental U87 and T98 GBM cells were infected with lentivirus containing scramble shRNA (Scr) or one of five shRNAs targeting PKM2. Clonal populations (shPKM2-1 and shPKM2-2) were then established and analyzed by Western blot for levels of PKM1, PKM2, and β-actin. <b>B-F</b>, Pyruvate kinase activity, ATP levels, intracellular concentrations of pyruvate and lactate, and proliferation index of cells from panel A. <b>G</b>, photomicrographs (2.5X magnification) of colonies generated by the 24th day growth of Scr and shPKM2-1 cells in soft agar. <b>H</b>, Colony number and distribution of colony size from experiments in (G). <b>I</b>, FACS-based cell cycle distribution of logarithmically-growing Scr and PKM2-1 cells.</p

Suppression of PKM2 levels reduces the <i>in vivo</i> growth of GBM cells.

<p><b>A</b>, cells derived in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057610#pone-0057610-g004" target="_blank">Fig 4A</a> were luciferase modified, implanted intracranially into mice, and monitored for cell growth by bioluminescence imaging. Luminescence was normalized to day-1 post injection values, data presented is the mean of 5 animals at 24 days post implantation. <b>B</b>, Kaplan-Meyer survival curves for animals (N = 5 for each group) intracranially implanted with Scr or shPKM2-1 cells.</p

A comparison of DNA methylation specific droplet digital PCR (ddPCR) and real time qPCR with flow cytometry in characterizing human T cells in peripheral blood

<div><p>Quantitating the copy number of demethylated CpG promoter sites of the CD3Z gene can be used to estimate the numbers and proportions of T cells in human blood and tissue. Quantitative methylation specific PCR (qPCR) is useful for studying T cells but requires extensive calibration and is imprecise at low copy numbers. Here we compared the performance of a new digital PCR platform (droplet digital PCR or ddPCR) to qPCR using bisulfite converted DNA from 157 blood specimens obtained from ambulatory care controls and patients with primary glioma. We compared both ddPCR and qPCR with conventional flow cytometry (FACS) evaluation of CD3 positive T cells. Repeated measures on the same blood sample revealed ddPCR to be less variable than qPCR. Both qPCR and ddPCR correlated significantly with FACS evaluation of peripheral blood CD3 counts and CD3/total leukocyte values. However, statistical measures of agreement showed that linear concordance was stronger for ddPCR than for qPCR and the absolute values were closer to FACS for ddPCR. Both qPCR and ddPCR could distinguish clinically significant differences in T cell proportions and performed similarly to FACS. Given the higher precision, greater accuracy, and technical simplicity of ddPCR, this approach appears to be a superior DNA methylation based method than conventional qPCR for the assessment of T cells.</p></div