Figure 2

<p>A: The datasets were analysed for differential expression independently using Rank Product, Gene Ontology over-representation (GO ORA) and GSEA. The methods evaluate different fractions of the datasets as biologically relevant when sorted for differential expression, as illustrated for the transcriptomics data set (TR). RP and GO ORA in our case only identified the top ∼1% of the overall sorted gene list as relevant, both for the transcriptomics and proteomics analysis. GSEA on the other hand identified Leading Edge (LE) subsets spanning ∼20% of the overall gene list. B: GSEA based approach for integrating partially overlapping proteomics and transcriptomics data sets. The top differentially expressed entities from one dataset is mapped into corresponding entities from the other dataset and evaluated as a gene set in GSEA. PR: Proteomics dataset, TR: Transcriptomics dataset.</p

Orthotopic Xenograft Brain Tumor Model.

<p>A schematic representation of the tumor model and the phenotypes obtained after transplantation in nude rats. The first transplantation into nude rats often resulted in an invasive phenotype, while serial transplantation of the tumors resulted in angiogenic phenotype after several generations.</p

The set of proteins supported by corresponding transcripts as differentially expressed and upregulated in the invasive samples.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068288#pone-0068288-t002" target="_blank">Table 2</a> consist of the 47 protein profiles in the leading edge of the gene set found to be clearly enriched in the invasive samples (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068288#pone-0068288-g003" target="_blank">Figure 3</a>). The gene set was defined as the set of proteins matching the transcripts found differentially expressed in the Rank Product analysis of the transcriptomics dataset. Signal log2 ratios are listed in Table S13 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068288#pone.0068288.s001" target="_blank">File S1</a>. Cell Comp = Gene Ontology cellular component annotations. Biol Proc = GO biological process annotations. The same protein (Gene Symbol/Entrez ID) may appear several times since it can be identified from different origins and/or in different independent iTRAQ experiments.</p

Overview of trends and statistical significance from the individual analyses of the proteomics and transcriptomics datasets.

<p>PR: Proteomics, TR: Transcriptomics, GO: Gene Ontology, GSEA: Gene Set Enrichment Analysis, FDR: False Discovery Rate.</p

The set of genes supported by corresponding proteins as differentially expressed and upregulated in the angiogenic samples.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068288#pone-0068288-t003" target="_blank">Table 3</a> consist of the 43 transcript measurements in the leading edge of the gene set found to be clearly enriched in the angiogenic samples (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068288#pone-0068288-g003" target="_blank">Figure 3</a>). The gene set was defined as the set of transcripts matching the the proteins found differentially expressed in the Rank Product analysis of the proteomics dataset. Signal log2 ratios are listed in Table S14 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068288#pone.0068288.s001" target="_blank">File S1</a>.</p

Comprehensive Analysis of Glycolytic Enzymes as Therapeutic Targets in the Treatment of Glioblastoma

<div><p>Major efforts have been put in anti-angiogenic treatment for glioblastoma (GBM), an aggressive and highly vascularized brain tumor with dismal prognosis. However clinical outcome with anti-angiogenic agents has been disappointing and tumors quickly develop escape mechanisms. In preclinical GBM models we have recently shown that bevacizumab, a blocking antibody against vascular endothelial growth factor, induces hypoxia in treated tumors, which is accompanied by increased glycolytic activity and tumor invasiveness. Genome-wide transcriptomic analysis of patient derived GBM cells including stem cell lines revealed a strong up-regulation of glycolysis-related genes in response to severe hypoxia. We therefore investigated the importance of glycolytic enzymes in GBM adaptation and survival under hypoxia, both in vitro and in vivo. We found that shRNA-mediated attenuation of glycolytic enzyme expression interfered with GBM growth under normoxic and hypoxic conditions in all cellular models. Using intracranial GBM xenografts we identified seven glycolytic genes whose knockdown led to a dramatic survival benefit in mice. The most drastic effect was observed for <i>PFKP</i> (PFK1, +21.8%) and <i>PDK1</i> (+20.9%), followed by <i>PGAM1</i> and <i>ENO1</i> (+14.5% each), <i>HK2</i> (+11.8%), <i>ALDOA</i> (+10.9%) and <i>ENO2</i> (+7.2%). The increase in mouse survival after genetic interference was confirmed using chemical inhibition of PFK1 with clotrimazole. We thus provide a comprehensive analysis on the importance of the glycolytic pathway for GBM growth in vivo and propose PFK1 and PDK1 as the most promising therapeutic targets to address the metabolic escape mechanisms of GBM.</p></div

Glycolysis-related genes are up-regulated in glioblastoma cells under hypoxia.

<p><b>A</b>. Stem-like (NCH644, NCH421k) and classical adherent (U87, U251) glioma cells were cultured in 0.1% O₂ for short term (12 hours = 12h) and long term (7 days = 7d). Differentially expressed genes (DEGs) were established between hypoxic and normoxic cells (n = 3–6). Venn diagrams (top) represent analysis of DEGs after 12h and 7d respectively (FDR<0.001; any fold change (FC)). Red squares highlight the genes commonly modulated in all four glioma cell lines. 120 genes were commonly deregulated upon 12h and 7d hypoxia (Venn diagram, middle) which were strongly associated with glycolysis (9 genes) and glucose metabolism (11 genes) (Revigo representation of significant GO terms, bottom). <b>B</b>. Schematic representation of the glycolytic pathway and associated enzymes. HK2 = hexokinase 2; PFK1 = phosphofructokinase 1 (encoded by <i>PFKP</i> = <i>Phosphofructokinase</i>, <i>platelet</i>); ALDOA = aldolase A; PGAM1 = phosphoglycerate mutase 1; ENO1 = enolase 1; ENO2 = enolase 2; PDH = pyruvate dehydrogenase; PDK1 = pyruvate dehydrogenase kinase 1. <b>C</b>. Quantitative PCR analysis of glycolytic gene expression in adherent glioma cells (U87 and U251) and glioma stem-like cells (NCH421k, NCH644, NCH660h, NCH465 and NCH601), under normoxia and hypoxia (12h and 7d). Data are presented as mean +/- SEM (n = 3). Data were normalized against <i>EZRIN</i> expression. NCH421k cells were used as an internal calibration (value = ‘1’); * p<0.05; p**<0.01; p***<0.001.</p

In vitro effect of glycolytic gene knockdown in glioblastoma cells.

<p><b>A</b>. Cell viability test of 3D spheres carrying gene knockdowns under long-term (7d) hypoxia. Viable cells = ‘green’, dead cells = ‘red’. Representative images are shown (n = 10). <b>B</b>. Quantification of the percentage of dead cells within 3D spheres in hypoxia (n = 10; mean ± SEM) (* p<0.05; ** p<0.01; *** p<0.001).</p

Mouse survival study revealed key glycolysis-related genes for in vivo tumor growth.

<p><b>A</b>. Targeted in vivo shRNA screen in NCH421k cells. From 11 glycolytic target genes, five shRNA containing clones were depleted after in vivo growth compared to in vitro culture (<i>ALDOA</i>, <i>ENO1</i>, <i>ENO2</i>, <i>HK2</i>, <i>PDK1</i>) (* p<0.05; ** p<0.01; *** p<0.001; n = 3 for in vitro, n = 5 for in vivo). The number of shRNAs in each sample was quantified using NGS and is indicated as percentage of control. As <i>PGAM1</i> and <i>PFKP</i> knockdown clones were strongly depleted both in vivo and in vitro, these results were compared to baseline (original cell pool n = 1, p values not available). <b>B</b>. NCH421k cells with the indicated gene specific shRNAs were implanted intracranially into nude mice (n = 21 for control and n = 6–7 for glycolytic genes). Kaplan-Meier graphs show the effect of glycolytic gene knockdown on mouse survival. C. Table summarizing the effect of glycolytic gene knockdown on mouse survival (* p<0.05; ** p<0.01; *** p<0.001).</p

Glycolysis inhibition with clotrimazole affects glioma cell survival in vitro and delays tumor growth in vivo.

<p><b>A</b>. The IC₅₀ of different glycolysis inhibitors was determined for patient derived GBM cells (P3A) and normal human astrocytes (NHA). N: normoxia, H: hypoxia (0.1% O2). Cells were exposed to indicated compounds for 72h and IC₅₀ was determined with the SRB assay (n = 3). <b>B</b>. The cytotoxic effect of clotrimazole (30μM) was assessed on organotypic spheroids of several patient-derived GBM (P3, P8, T16) and NHA, treated for 72h in normoxia and 0,1% O₂ (n = 5). Representative images showing viable cells in ‘green’, dead cells in ‘red’ fluorescence. <b>C</b>. P3 spheroids were implanted intracranially and clotrimazole (CTZ, 150mg/kg) treatment was started 3 weeks after implantation (n = 7). Kaplan-Meier curve shows significantly improved mouse survival (* p<0.05).</p