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Microenvironmental-derived Regulation of HIF-Signaling Drives

Transcriptional Heterogeneity in Glioblastoma Multiforme

Dieter Henrik Heiland1,10,12, Annette Gaebelein1,10,12, Melanie Börries2,3, Jakob Wörner4, Nils

Pompe4, Pamela Franco1,10, Sabrina Heynckes1,10, Mark Bartholomae5,10, Darren Ó

hAilín1,10,11, Maria Stella Carro1,10, Marco Prinz6,7,10, Stefan Weber4, Irina Mader7,8,10, Daniel

Delev1,10, Oliver Schnell1,10

1Department of Neurosurgery, Medical Center - University of Freiburg, Germany.

2Institute of Molecular Medicine and Cell Research, Albert-Ludwigs-University, Freiburg im Breisgau,

Germany.

3German Cancer Consortium (DKTK), Freiburg and German Cancer Research Center (DKFZ),

Heidelberg, Germany.

4Institute of Physical Chemistry, Faculty of Chemistry and Pharmacy, University of Freiburg, Germany.

5Department of Nuclear Medicine, Medical Center - University of Freiburg

6Institute of Neuropathology, Medical Center - University of Freiburg, Germany

7BIOSS Centre for Biological Signalling Studies, University of Freiburg, Germany

8Department of Neuroradiology, Medical Center - University of Freiburg, Germany

9Clinic for Neuropediatrics and Neurorehabilitation, Epilepsy Center for Children and Adolescents,

Schön Klinik, Vogtareuth, Germany

10Faculty of Medicine, University of Freiburg, Germany

11Faculty of Biology, University of Freiburg, Germany

12These authors contributed equally to this work

RUNNING TITLE: Metabolic Reprogramming Drive Tumor Heterogeneity

KEYWORDS: Tumormetabolism, Glioblastoma multiforme, Heterogeneity, HIF-Signaling,

Creatine Metabolism

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ADDITIONAL INFORMALTION:

FINANCIAL SUPPORT: DHH is funded by the German Cancer Society (Seeding Grand TII)

and Müller-Fahnenberg Stiftung. MB is funded by the German Federal Ministry of Education

and Research (BMBF) within the framework of the e:Med research and funding concept

(DeCaRe, FKZ 01ZX1409B) and by the Deutsche Forschungsgemeinschaft Germany to

CRC 850.

DISCLOSURE OF CONFLICTS OF INTEREST: No potential conflicts of interest were

disclosed by the authors.

Corresponding author:

Dieter Henrik Heiland

Department of Neurosurgery

Medical Center University of Freiburg

Breisacher Straße 64

79106 Freiburg

-Germany-

Tel: +49 (0) 761 270 50010

Fax: +49 (0) 761 270 51020

E-mail: dieter.henrik.heiland@uniklinik-freiburg.de

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ABSTRACT

The evolving and highly heterogeneous nature of malignant brain tumors underlies their

limited response to therapy and poor prognosis. In addition to genetic alterations, highly

dynamic processes such as transcriptional and metabolic reprogramming play an important

role in the development of tumor heterogeneity. The present study reports an adaptive

mechanism in which the metabolic environment of malignant glioma drives transcriptional

reprogramming. Multi-regional analysis of a glioblastoma patient biopsy revealed a metabolic

landscape marked by varying stages of hypoxia and creatine enrichment. Creatine treatment

and metabolism was further shown to promote a synergistic effect through up-regulation of

the glycine-cleavage system and chemical regulation of Prolyl-Hydroxylase Domain (PHD).

Consequently, creatine maintained a reduction of reactive oxygen species and change of the

a-ketoglutarate/succinate ratio leading to an inhibition of HIF-signaling in primary tumor cell

lines. These effects shifted the transcriptional pattern toward a proneural subtype and

reduced the rate of cell migration and invasion in vitro.

Implications: Transcriptional subclasses of glioblastoma multiforme are heterogeneously

distributed within the same tumor. This study uncovered a regulatory function of the tumor

microenvironment by metabolic driven transcriptional reprogramming in infiltrating glioma

cells.

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INTRODUCTION

Glioblastoma Multiforme (GBM) is the most common and malignant primary brain tumor in

adults, with an annual incidence of 3–4 cases per 100,000 people in Europe (1,2) and the

United states (3). In spite of the best available treatment, the prognosis for patients with GBM

is poor, with a median survival of 14–16 months (4–8). During the last decade, technical

advances in high-throughput analysis led to the classification of GBM based on their

mutational, transcriptional or epigenetic profiles. Several recent profiling studies have found

that the proneural (PN) and mesenchymal (Mes) expression phenotypes were the only

classifications which could be consistently validated (9–12). Although these classifications

provide useful insight into the broad transcriptional identity of a GBM tumor, genetic

classifications based on single biopsies are biased by tumor heterogeneity, which is a

hallmark of the malignant and resistant character of GBM.

Heterogeneity is defined by different cell subpopulations within the same tumor harboring

distinct genetic or gene expression profiles (13,14). At present, few studies have uncovered

the mutational heterogeneity of GBM and explored the clonal architecture of glioma (15).

However, the mutational architecture of a tumor is relatively stable by nature (15). In contrast

to these stable heterogenic clusters, Patel and colleagues investigated the transcriptional

heterogeneity in GBM and described a highly inconsistent expression profile of different cells

within the same tumor. Most notably, the expression subtypes characterized by Verhaak et

al. were found to be heterogeneously distributed within the same tumor specimen (14,16–

19).

Metabolism is recognized as one of the most dynamic factors within GBM tumors and their

surrounding environment and serves as a potential mediator of transcriptional identity(20).

Metabolic reprogramming in normal brain conditions has been shown to play an important

role in neural differentiation and other major brain functions (21), highlighting its potential

impact in pathological conditions. Hu and colleagues (22) linked heterogeneous expression

patterns to distinct changes in the metabolome of various cancers. In the context of GBM, a

transcriptional-metabolic network uncovered the connection between the proneural gene

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expression profile and environmental creatine enrichment, while the mesenchymal subtype

was found to be connected to hypoxia-associated metabolites (23). These findings were also

supported by an integrative analysis of transcriptional pattern and magnetic resonance

spectroscopy (24).

This study reports a regulatory mechanism of transcriptional adaptation driven by different

environmental conditions. This mechanism was linked to cellular heterogeneity in malignant

brain tumors. Particularly, the glycine-cleavage system and HIF1A destabilisation were

altered in creatine-enriched environments and led to transcriptional adaptation.

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MATERIAL AND METHODS

Contact for reagent and resource sharing

Further information and requests for resources, raw data and reagents should be directed

and will be fulfilled by the Contact: D. H. Heiland, dieter.henrik.heiland@uniklinik-freiburg.de.

Full table of all materials is given in the supplementary information

Ethical Approval

For this study we included three patients with primary glioblastoma multiforme WHO grade IV

(without known lower-grade lesion in the patient’s history), who underwent surgery at the

Department of Neurosurgery of the Medical Center, University of Freiburg. The local ethics

committee of the University of Freiburg approved data evaluation, imaging procedures and

experimental design (protocol 100020/09 and 5565/15). The methods were carried out in

accordance with the approved guidelines. Written informed consent was obtained. The

studies were approved by an institutional review board.

Imaging, tissue Collection and Histology

Preoperative MRI included isotropic (1mm3) 3D data sets consisting of 3D-FLAIR weighted

and 3D-T1 weighted sequences before and after contrast application. Dynamic susceptibility

contrast perfusion measurement during an intravenous bolus injection of 17 ml Gadoteriol

(Bracco, Konstanz, Germany) followed by a chaser of 50 ml saline 0.9% (injection rate 5

ml/s) was performed. In-vivo magnetic spectroscopy was performed by using a chemical shift

imaging sequence (csi_slaser) with a TR of 1.7 s, TE of 40 ms, and a voxel size of 5 x 5 x 20

mm3. Measurements were performed at a 3 T Mangetom Prisma (Siemens, Erlangen,

Germany) in the Department of Neuroradiology, Medical Center, University of Freiburg.

Spectra were fitted by the manufacturer’s software Syngovia®. Presurgical planning

consisted of tumor segmentation (IKT SNAP) and indexing of specific intratumoral biopsies

for further analysis of the tumor heterogeneity. Tumor tissue was sampled from all planned

(n=35) regions identified by intraoperative neuronavigation (Cranial Map Neuronavigation

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Cart 2, Stryker, Freiburg, Germany) during resection. Region segmentation was planned to

maximize the number of sampled regions with a minimum of 2ml total volume per sample.

One half of each sample was used for metabolic profiling (n=35). The other half was divided

into 3 or 4 specimens, which were used for transcriptional analysis (n=115). The tissue was

snap-frozen in liquid nitrogen immediately after resection and processed for further

genetic/metabolic analysis. Representative tissue samples of all main subgroup regions were

fixed using 4% phosphate buffered formaldehyde and paraffin-embedded through standard

procedures. H&E staining was performed on 4 µm paraffin sections using standard protocols.

Immunohistochemistry was applied using an autostainer (Dako) after heat-induced epitope

retrieval in citrate buffer. IDH1 mutation was assessed by immunohistochemistry using an

anti-IDH1-R123H antibody (1:20, Dianova). No tumor contained an IDH mutation.

Cell Culture

Brain tumor tissue was obtained during the neurosurgical tumor resection and further

processed in sterile conditions under a tissue culture hood. First, the tissue was fragmented

to very small pieces and resuspended in cell-dissolving solution. The tissue fragments were

centrifuged at 1000 rpm for 5 minutes and subsequently resuspended with 5ml ACK Lysing

Buffer to remove blood cells. The cells were finally resuspended in medium and transferred

into a tissue culture flask.

Cell Treatment and Environmental Simulation

For metabolic treatment, different metabolites were supplemented into the medium. Creatine-

enriched environment was simulated by 15 mM creatine supplementation. Cells were

cultured under normoxic (21% O2) or hypoxic (3% O2) conditions. Treatment with 200µM

cobalt chloride was used to inhibit VHL-mediated HIF1A degradation.

Metabolite Extraction and H1-NMR Analysis

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Metabolites were extracted with 400μl ice-cold 80% methanol and 400μl ice-cold water,

homogenized by a tissue grinder (VWR, Radnor, USA) and sonicated in 1°C, then

centrifuged at 15,000g for 20 minutes to remove proteins. Extracts were dried by

lyophilization and resuspended in 650μl deuterated water as described in Beckonert et al

(25). 600μl suspension was transferred to NMR-tubes for further NMR procedures. 1H-NMR

was performed at the Institute of Physical Chemistry of the University of Freiburg. 1D-NMR

spectra were performed on a Bruker Avance III HDX 600-MHz FT-NMR spectrometer

(Rheinstetten, Germany), equipped with the probe: PABBO BB/19F-1H/D Z-GRD. Each

individual spectrum was recorded with 2 dummy scans and 32 scans with 64k points in the

time domain. The sweep width was set to 16.02 ppm with an offset of 4.691 ppm. This

resulted in an acquisition time of 3.4 seconds for each scan with a dwell time of 52

microseconds. The relaxation delay was set to 2 seconds, so that the total acquisition time of

each spectrum was 3 minutes and 5 seconds. Water-suppressed 1H NMR spectra were

acquired by using a zgesgp sequence (26). The FID was Fourier transformed and

automatically phase-corrected without any further zero filling or apodization.

Postprocessing of Metabolic Data

The spectra were manually calibrated by setting the peak of L-lactate acid at 1.310 ppm. All

acquisition and processing of the spectra was performed with TopSpin 3.2 patchlevel 6.

Detailed description of the methods was given in a recent published study by Heiland et al.

(23). All spectra data was analyzed by “batman”, an R-software-based tool for metabolite

detection in complex spectra (27). The batman software fits a predefined list of metabolites

by a Bayesian approach. A detailed description of the batman algorithm was given by Hao et

al. (27). Normalization of the spectra was performed by pseudocounted Quantile (pQ)

Normalization algorithm integrated in the KODAMA package. Further processing of metabolic

data is described in the specific method section below.

RNA Extraction and RNA-Sequencing

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RNA was extracted by All Prep Kit (Qiagen, Venlo, Netherlands) according to the

manufacturer’s instructions. RNA integrity was measured using the Agilent RNA Nano Assay

Agilent Bioanalyser 2100 (http://www.home.agilent.com) according to the manufacturer’s

instructions. Sequencing libraries were prepared according to the manufacturer’s instructions

and sequenced on an Illumina HiSeq4000 sequencer. The resulting 74 bp paired-end reads

were quality-checked with FastQC. Sequencing data were mapped to the human genome

(hg19, UCSC, https://genome.ucsc.edu) with STAR (https://github.com/alexdobin/STAR). (--

quantMode GeneCounts –sjdbOverhang n-1). Followed by the alignment, transcripts with

less than 10 reads were removed. The given RNA counts were normalized by DESeq2

based on a negative binomial distribution model (28). Data were Log2 transformed and

median centered across samples (Data available in GEO: GSE108013).

Immunoblotting

Cells were lysed using Radio Immuno Precipitation Buffer (RIPA buffer) and protease

inhibitor on ice. Afterwards, the lysate was centrifuged at 14.000 rpm for 30 minutes at 4°C.

The supernatant was used to measure the concentration by NanoDrop. Laemmli buffer was

added to the samples and the concentration was adjusted. Laemmli buffer was prepared

using 4% SDS, 10% 2-mercaptoethanol, 20% glycerol, 0.004% bromophenol blue and

0.125M Tris HCl and adjusted to pH 6.8. For western blotting 4-20% precast gels from

BioRad or self-made polyacrylamide gels were used. 20-100μg of protein was loaded in each

well. 5μl of molecular weight markers were loaded in a separate well. Gels were run for 1.5

hours at 92 V. The proteins were transferred from the gel to nitrocellulose membrane (12

hours at 20 V). To prevent unspecific antibody binding, the membrane was incubated with

blocking buffer (1X PBS, 0.1% TWIN (TBS-0.1%T and 5% Milk Powder) for 1 hour. The

specific antibody was dissolved in 5% BSA TBS-0.1%T buffer, added to the membrane and

incubated under constant agitation at 4°C overnight. The membrane was washed with TBS-

0.1%T three times for 10 minutes and subsequently incubated for 1 hour under constant

agitation with secondary antibody dissolved in TBS- 0.1%T. The membrane was then

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washed three times for 10 minutes with TBS-0.1%T and subsequently incubated with BioRad

Clarity ECL detecting solution. A digital imager ChemiDoc XRS detected the

chemiluminescence emanation from the membrane by transforming the signal into a digital

image.

Quantitative Real-Time PCR

A reverse transcriptase reaction was performed to convert RNA to cDNA. RNA was mixed in

a total concentration of 500ng in 8µl with 1μl NTP and 1μl Random Oligonucleotides and

incubated at 65°C in a PCR machine. After 5 minutes of incubation, the samples were cooled

on ice and all chemicals and enzymes of table 2 and 3 were added. Afterwards, samples

were incubated for 10 minutes at 25°C, 50 minutes at 50°C and 5 minutes at 85°C. Primers

were produced by life technology (www.lifetechnologies.com). The qRT-PCR reaction was

performed using the SYBR Green PCR Master Kit. The obtained cDNA was mixed with the

primers of the selected genes and prepared for qRT-PCR. 1μl Primer was dissolved in 10μl

water, 0.5μl cDNA and 12.5μl SYBR green. The mix was distributed on a 96-well plate, each

sample in triplicates. The PCR reaction was run using the 7900HT Fast Real-Time PCR

System with the standard SYBR green protocol (hold 2min 50°C, hold 10min 95°C, Cycle

40x 15sec 95°C, 1min 60°C). Average cDNA quantities relative to a standard amplified gene

(Housekeeper Gene: 18S) were calculated using R-statistics.

Migration Assay and Invasion Assay

The scratch assay is a straightforward method to evaluate cell motility in vitro. A wound-

healing or scratch assay is based on the premise that cells grown on a monolayer can

occupy and cross an artificially-produced trough until the gap is closed and cell-to-cell

contact is re-established. This assay was performed in 6-well plates. One million cells per

well were seeded on a plate and incubated for 24h at 37°C. After one day of incubation, the

wells were checked and the medium was changed. A 1mm scratch was performed with a 1

ml pipette tip, followed by 3 washes with PBS. Three wells were used as controls and

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another three wells were treated with creatine (15mM). Pictures were taken after 0 h, 2 h, 6

h, 12 h and 24 h. Zeiss Observe D1 Microscope was used to take pictures of the cells at

different time points. Predefined markers on the plate assured measurement of the same

microscopic sections of the scratch. The Matrigel invasion assay was performed using

BioCoat Matrigel Invasion Chambers (BD Bioscience) according to the manufacturer’s

instructions. 8000 cells/well were seeded in the upper compartment and incubated with

Creatine (15mM) at hypoxia (3%). Cells were infected with lentivirus particles

(rLV.EF1.ZsGreen, CloneTech). PDGF-BB (20ng/ml, R&D) was used as a chemoattractant.

After the incubation, cells were fixed with formaldehyde and stained.

Immunofluorescence

For immunostaining, cells were grown on slides and fixed with 3% formaldehyde for 10

minutes at room temperature. Afterwards the slides were incubated in permeabilizing

solution (HEPES, Sucrose, NaCl, MgCl2, 0.5% Triton X-100) for 20 minutes at 4°C and

subsequently blocked in PBS 2% BSA for 30 minutes at 37°C. The slides were then washed

three times for five minutes with PBS. The primary antibody was diluted in blocking buffer

(2% BSA, PBS) and incubated for 90 minutes at 37 °C. After 3 washes (5 minutes) in PBS

the secondary antibodies were diluted in blocking buffer and added to the coverslips for 45

minutes at 37 °C. After another 3 wash cycles a counterstaining with DAPI for 20 minutes

followed. After another wash in water the coverslips were fixed on a glass plate. A Fluoview

FV10i confocal microscope from Olympus was used for fluorescence microscopy. All

measurements and image processing were performed using the company’s software. Optical

magnification settings of 10X and 60X with oil were used. A laser with wavelengths of 647nm

(ALEXA 647), 594nm (ALEXA 594) and 358nm (DAPI) was selected for imaging. Laser

power was manually optimized and used with equal settings for all imaged samples.

Cell viability assay

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Cells were seeded in 96-well plates. Cell viability was measured at 24h after creatine

treatment (normoxia and hypoxia conditions) by (3-(4,5- dimethylthiazol-2-yl)-2,5-

diphenyltetrazolium bromide (MTT)) conversion (Roche, Mannheim, Germany) at 550/700

nm on a plate reader, according to the manufacturer’s instructions. Assays were conducted

in six replicates in two independent experiments.

TransAM Assay of HIF-Signaling

Nuclear and cytoplasmic extracts, and HIF1A DNA binding assay cytoplasmic and nuclear

extracts were prepared using a Nuclear Extract Kit (Active Motif, La Hulpe, Belgium) and

processed using the TransAM assay HIF1A kits (Activ Motif, La Hulpe, Belgium) according to

the manufacturer ́s instructions.

Reactive Oxigene Species (ROS) Assay

Cells were incubated in normoxia and hypoxia conditions with and without Creatine

supplement. After a 24h period, cells were fixed with 3% formaldehyde for 10 minutes at

room temperature. Staining of ROS was performed by CellROX (Thermofisher, C10422)

according to the manufacturer ́s instructions. Counterstaining was performed by DAPI for 20

minutes.

Differential Gene Expression of Multiple Groups

Analysis of differentially expressed genes or differential metabolic intensities was performed

using the DESeq2 package. The algorithm mainly uses a generalized linear model with a

negative binomial distribution. A detailed description is given in the R documentation.

Prediction Model for Condition Specific Gene Expression

Conditions specific genes were identified by their capacity to predict a given environmental

condition based on a random forest algorithm. A model was built based on a random forest

algorithm (R-package: randomForest and VarSelRF) contained expression or metabolic data.

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The given model resulted in condition specific features (gene/metabolites) and ranked them

by their accuracy. The top genes/metabolites were clustered and used for further specific

functional testing.

Weighted Correlation Network Analysis (WCNA)

The WCN-Analysis is a robust tool for integrative network analysis and was used in recent

studies (29–31). It is based on a scaled-topology-free based network approach and uses the

topological overlapping measurement to identify corresponding modules. These modules

were analyzed by their eigengene correlation to each metabolite. The correlation of the

intramodule connectivity (kME) and metabolites was used as input for a “Cluster of Clusters

Analysis”. This analysis integrates expression modules and metabolites, which present equal

correlation values (kME and metabolite intensity values). A detailed description of WCNA is

given in (24).

Functional Analysis by Enrichment Analysis

A permutation-based pre-ranked Gene Set Enrichment Analysis (GSEA) was applied to each

module to verify its biological functions and pathways (32). The predefined gene sets of the

Molecular Signature Database v5.1 were taken. Enrichment score was calculated by the rank

order of gene/metabolite computed by random forest accuracy (32). For significant

enrichment, p-values were adjusted by FDR. Gene Set Variation Analysis (GSVA) was

performed with the GSVA package implemented in R-software. The analysis based on a

non-parametric unsupervised approach, which transformed a classic gene matrix (gene-by-

sample) into a gene set by sample matrix resulted in an enrichment score for each sample

and pathway (33). Enrichment analysis of metabolic data was performed with DOSE

package and the web-based tool MetaboAnalyst 3.0 (www.metaboanalyst.ca).

Pathway Analysis

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Integrative analysis of transcriptomic and metabolic data was processed by pathviewer, an R

package that includes KEGG pathway maps (34). Expression data (as described above) and

normalized, log2 transformed and median centered metabolic data were integrated in the

pathviewer algorithm.

Analysis of Single-cell Sequencing by tSNE

First, separated single-cell RNA-sequencing data from astrocytes, oligodendrocytes and

neurons of the tumor core and its migrating front (GSE84465)(20) was used to calculat the

over-dispersion of each gene (top 500) and constructed a cell-to-cell distance matrix as

described so far (20). Data was illustrated in a two dimensional matrix by t-Distributed

Stochastic Neighbor Embedding (tSNE) as implemented in package ‘‘tsne’’ for R (perplex- ity

= 50). Enrichment of creatine synthesis was indicated by the expression of glycine-

amidinotransferase (GATM) and guadinoacetate-N-methyltransferase (GAMT).

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RESULTS

Tumor Landscape is Marked by Regional Differences in Transcriptional Status

In a first step, transcriptome data of multiple regions (Ivy GAP Database, 2015) were taken

into account and clustered in an unsupervised manner (Figure1). Glioblastoma expression

subgroups showed a well-defined spatial distribution. The proneural subgroup was

predominantly observed in the leading edge of the tumor or infiltrating regions. In contrast,

the mesenchymal subgroup mainly occurred in hypoxic tumor areas such as

pseudopalisading regions or the cellular tumor core. These findings uncovered a spatial

dominance of expression subgroups within the GBM environmental landscape (35).

Tumor Landscape is Marked by Regional Differences in Metabolic Status

Next, we investigated whether metabolic and transcriptome spatial distribution exhibit close

alignment. For this purpose, we performed metabolic profiling (n=3) in multiple regions

(n=35) within the same tumors based on a presurgical neuronavigated plan (Figure 2A). The

metabolic profiles of in vivo (magnetic resonance spectroscopy, MRS, 3T) and ex vivo

(nuclear resonance spectroscopy, NMR 600Mhz) spectroscopy showed similar spatial

differences of the metabolic environment. Metabolic data of the multiregional metabolic

profiling were clustered in a unsupervised matter and compared to a bulk-tumor dataset (36)

(Figure 2B). For subgroup identification, we tested the expression of key proneural (OLIG2,

SOX2, PDGFRA supplementary figure 1) and mesenchymal genes (CHI3L1, CD44, MET,

supplementary figure 1) of all multiregional biopsies (Figure 2B). A significant association

between proneural gene expression and a distinct metabolic profile was found (Fisher’s

Exact test, p<0.01). Moreover, the newly explored spatial distribution of expression subtypes

could be confirmed (Fisher’s Exact test, p<0.001). We additionally tested the expression of a

differentiation panel (GFAP, NES, RBFOX3, TUBB3, S100B and MAP2) in all samples within

the tumor (Figure 2B). Most regions mainly occupying the infiltration region or tumor edge

exhibited both a proneural gene expression pattern and an increased expression of

neural/oligodendrocytic differentiation genes (GFAP, RBFOX3, TUBB3, S100B and MAP2).

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In contrast, the mesenchymal expression was predominantly observed in the contrast

enriched regions along with a stronger expression of the stemness marker NES. In a next

step, a Weighted Correlation Network Analysis (WCNA) of the multiregional metabolic

profiles was computed and correlated to expression features of each multiregional biopsy

(Figure 2C, supplementary figure 2). The WCNA summarized all metabolites in 11 modules

by pairwise correlation of each metabolic profile followed by hierarchical clustering and

dynamic tree cut. The network of the most significantly proneural- associated module was

shown in Figure 2C. Hub-metabolite of the module was identified by its intramodular

connectivity (kMI), since the kMI reflects how connected a given metabolite is with respect to

the metabolites of its particular module. Creatine was identified as the hub-metabolite of the

module 1.

Environmental Creatine-Enrichment Drives Proneueral Subtype Expression

We next examined whether environmental creatine levels influence the transcriptional pattern

of malignant glioma. Based on our findings, we aimed to simulate creatine-enriched and

hypoxic environments in an in-vitro model. First, brain tumor stem-like cells (BTSC) were

isolated from patient-derived tumor specimens and cultured in Cancer Stem Cell (CSC)

medium (detailed information is given in the methods). Second, cells were cultured under

four different environmental conditions as presented in Figure 3A. The effects of a hypoxic

environment were simulated through moderate hypoxic (4% O2) or normoxic conditions for

48 hours. Hypoxia was recently described as a strong driver of mesenchymal gene

expression(37) which was confirmed as shown in Figure 3 B-D.

Tumor creatine-enrichment was simulated with 15mM creatine (Cr) supplement in the culture

medium in two different time courses. In one course, we investigated whether creatine

protects the mesenchymal-shift. Therefore, we added Cr-supplemented medium initially to

cells in moderate hypoxic conditions. In the other course, cells were initially cultured in

hypoxic conditions for 24 hours (initiation of the mesenchymal-shift) and subsequently

treated with creatine-supplemented medium for another 24 hours to investigate the reversion

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of the mesenchymal-shift (Figure 3A). Creatine concentration of the supplement and duration

was evaluated in a concentration and time dependent manner (supplementary Figure 3). The

environmental impact on tumor transcriptional status was explored by RNA sequencing. A

classification algorithm was used to discover environmental condition specific gene sets.

These gene sets were hierarchically clustered in a supervised (classification algorithm)

manner (Figure 3B) (38). Therefrom, three main expression panels were identified: a

normoxia panel, a creatine panel and a hypoxia panel (Figure 3B). Further, functional

characterization of the transcriptional data was performed by Gene Set Variation Analysis

(GSVA) and subsequent hierarchical clustering (Figure 3C). In the hypoxia panel (marked in

pink), an enrichment of major functional gene sets was discovered: Mesenchymal, stemness,

MAPK pathway activation, hypoxia and glycolytic activation. The distinct pathway activation

in the hypoxia environment was also confirmed by p-ERK, p-AKT and HIF1A through

western blot. The normoxia panel (marked in blue) showed an increased enrichment in the

proneural subtype and immune-related gene sets, which was in line with the results on the

protein level. Hypoxic and normoxic conditions shared an enrichment of the m-TOR target

genes, which could not be confirmed on protein-level (p-AKT). However, in the creatine panel

we found a stable expression of proneural genes in spite of their hypoxic environment. The

additional creatine supplement also reduced the hypoxia, glycolysis and MAPK pathway

enrichment on the mRNA and protein levels. Furthermore, we detected an enrichment of

oligodendrocytic differentiation related genes in the creatine environment (Figure 3C), which

was confirmed in an immunostaining (Figure 3E). We validated the findings by qRT-PCR in

three cell lines, which showed consistent results (Figure 3D).

Environmental Creatine Enrichment Drives Glycine-Cleavage System

The observation that environmental conditions are sufficient to reshape the transcriptional

pattern suggests the importance of metabolic regulation in malignant glioma. Therefore, we

subsequently performed a metabolic profiling of the in-vitro model by NMR. A supervised

hierarchical clustering (as described above) of the metabolic profiles uncovered a sharp

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separation between the different environmental conditions (Figure 4A). In line with the

differentiation of all simulated environmental conditions on transcriptional level, the metabolic

profiles also showed condition-specific cluster groups containing a hypoxia, normoxia and

creatine panel (Figure 4A). Metabolic alterations of the creatine panel occupied the main

focus of interest. This panel was analyzed by enrichment analysis comprising the majority of

known metabolic pathways (KEGG). The results showed that the creatine environment

altered numerous metabolic pathways of the TCA cycle and glycine-serine metabolism

(Figure 4B). In line with these findings, a Bland-Altman analysis (Figure 4C) of differential

metabolic intensities between Creatine-enriched and hypoxia conditions highlighted glycine

and the a-ketoglutarate/succinate ratio (Figure 4D). The a-ketoglutarate/succinate ratio was

significantly affected by creatine treatment. This metabolic adaptation strongly drives a

chemical reversion of hypoxic environment and supports HIF1A destabilization by an

activation of PHD

Creatine Environment Reduces Reactive Oxygen Species

The previously published observation that the glycine-cleavage system is important to

increase the antioxidative capacity of tumor cells and their oxygenic consumption (39)

suggests that glycine metabolism drives environmentally adapted transcriptional

reprogramming. In simulated creatine-enriched environment, tumor cells showed an up-

regulation of creatine amidinohydrolase (XPNPEP), which metabolizes creatine into

sarcosine and urea (Figure 5A-B). In a next step, Glycine-N-Methyl-Transferase (GNMT),

resulting in a transfer of one mono-carbon to S-adenosylhomocysteine (SAH), decomposed

sarcosine into glycine. High intracellular concentrations of glycine are toxic by nature (39)

and will be reduced by the glycine-cleavage system, which additionally shifts a mono-carbon

to Tetrahydrofolate (THF). The decomposition of creatine maintained mono-carbon

metabolism and consequently increased the antioxidative capacity. We tested this

hypothesis by measuring reactive oxygen species (ROS) in the tumor cells in different

environmental conditions. In normoxia, no differences between control and creatine-enriched

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environment were detected, while in hypoxia, creatine supplement showed a significant

reduction of ROS (Wilcoxon-ranked test p<0.01) (Figure 5C-D).

Creatine Decomposition Drives Destabilization of HIF Transcription Factor

We next investigated the influence of creatine-environment on HIF-signaling. ROS are

essential inhibitor of Prolyl-Hydroxylase Domain (PHD) enzymes, which hydroxylate hypoxia-

inducible factors (HIF) to inhibit their activity. We addressed this question by measuring the

activity of HIF1A in isolated cell nucleoli in the TransAM assay (oligonucleotide binding

assay). In the case of a creatine-enriched environment, a highly significant decrease of

active HIF1A in the nucleus (p<0.01) was detected (Figure 5E). These findings suggested

that a creatine-driven reduction of ROS was sufficient to destabilize HIF1A subunits and

decrease the HIF signal. We then tested this hypothesis by treating the medium with cobalt

chloride (CoCl2), a hypoxia mimetic known to stabilize HIF alpha subunits through

antagonistic function at the VHL binding domains (40). We observed a reduced

transcriptional shift to the proneural gene expression with HIF stabilization (Figure 5F).

Interestingly, activation of other hypoxia-related pathways such as MAPK was still reduced

under creatine supplementation, which highlights the multifactorial nature of metabolic

adaptation (Figure 5G).

Creatine Enrichment Alters Cell Behavior in vitro

Based on the findings of a lower mesenchymal gene expression and altered cell metabolism,

we were interested in the biological behavior in creatine-enriched environments. We

therefore performed a wound-healing assay to measure the tumor cells’ capacity for

migration. In normoxic conditions, cells were cultured in Cr-enriched or normal medium for

24h before the scratch was administered. We detected a significant difference in the cells’

ability to reoccupy the scratch after 12h (p<0.01) and 24h (p<0.01) (Figure 6A). However,

under hypoxic conditions we observed an increased difference between normal and creatine-

supplemented environments (p<0.01) (Figure 6B). In addition, the invasive capacity of cells

was measured. In comparison between normoxia and hypoxia, a significantly increased

20

invasion was observed (Wilcoxon-ranked test p<0.01) in hypoxia environment. Although, the

cells in a creatine-enriched environment showed an overall reduction of their invasive

capacity, hypoxia environment enabled the cells to increase their invasiveness (Figure 6C).

We next investigated the cell viability in creatine-environment. Therefore, we measured the

cell viability in all above described conditions and showed increased cell viability in Cr-

supplemented medium under hypoxic conditions (Figure 6D).

Creatine Synthesis is Linked to Oligodendrocytes of the Leading Edge

Finally, we investigated the link between cellular microenvironment and increased creatine

synthesis. Single-cell RNA sequencing data of astrocytes, oligodendrocytes and neurons

(20) were taken into account and analyzed by expression of glycine-amidinotransferase

(GATM) and guadinoacetate-N-methyltransferase (GAMT). In a tSNE cluster of the cellular

microenvironment, an increased creatine synthesis in oligodendrocytes of the migrating front

was shown (Figure 7A). Moreover, GAMT and GATM were exclusively up-regulated in

oligodendrocytes of the infiltration regions (Wilcoxon-ranked test p<0.01) (Figure 7B-C).

21

DISCUSSION

A number of studies have reported the transcriptional heterogeneity of malignant brain

tumors. Patel and colleges demonstrated a diversely-distributed subtype expression by

single-cell-sequencing. Interestingly, the study also reported a strong association of

environmental conditions concerning expression of neural differentiation-related genes (14).

Therefrom, we analyzed the spatial distribution of expression subgroups in public available

data (Ivy Glioblastoma Atlas) and found an enrichment of the proneural subtype in the

infiltration region and boundaries of the tumor. We hypothesized a link between expression

subtype patterns and varying metabolic niches within the same tumor. This assumption was

also investigated by other studies, which showed a strong association between metabolic

environment and cell differentiation (41–43). Our investigation started by measuring multiple

tumor regions of three GBM patients. On one side, anaerobic metabolism was significantly

enriched in mesenchymal tumor regions. Hypoxia is well known to drive mesenchymal gene

expression in malignant glioma (37). Accordingly, an analysis of TCGA samples showed a

significant association with hypoxia-enrichment and the mesenchymal gene expression

phenotype (37). A characteristic feature of glioblastoma is the occurrence of large hypoxic

regions of varying severity (44). Regions marked by hypoxic or anoxic conditions

predominantly contained mesenchymal gene expression and were located in

pseudopalisading cells around necrotic regions (45).

On the other side, global metabolic analysis uncovered a close alignment of creatine

metabolism and the proneural gene expression subtype. Therefrom, we aimed to analyze the

effect of creatine on tumor cells and simulated the co-occurrence of hypoxia and

environmental creatine-enrichment in a cell model. Transcriptional and metabolic profiling

revealed two primarily affected pathways driving transcriptional reprogramming in the

presence of creatine. Both altered pathways are centrally connected by HIF-signaling, which

is often altered in cancer and a well-known driver of tumorigenesis (46–49). In the first

altered pathway, creatine was found to increase the one-carbone metabolism and glycine-

cleavage system. Both are important to increase the antioxidative capacity of cells and

22

reduce reactive oxygen species (ROS). The reduction of ROS inhibits PHD and loss of active

HIF transcriptional factor (50). These findings are in line with recent investigations, which

uncovered the glycine-serine metabolism in glioblastoma as a hallmark of hypoxic resistance

(39).

In the second altered pathway, we reported an accumulation of a-ketogluterate (aKG) in

creatine-enriched conditions. aKG is well known to be involved in the regulation of HIF

transcription factor. As shown in the metabolic profile (Figure 3C, D), succinate showed a

high intensity in hypoxic conditions without creatine supplement. This fits to previous

findings, which reported an accumulation of succinate as inhibitor of HIF hydroxylation and

HIF signaling (51). aKG is a competitive combatant as it activates PHD and reduces the HIF

signaling. Altered succinate is a potent oncometabolite and drives tumorgenesis of several

tumor entities (48,52–54). We showed a strong reduction of HIF signaling at mRNA and

protein levels. Additionally, we measured the HIF activation in the nucleus by TransAM assay

and found a highly significant reduction of active HIF1a. However, we found a combination of

chemical inhibition of PHD and ROS reduction, which reduce the HIF-signaling and

consequently drives transcriptional reprogramming in glioblastoma multiforme.

The link between cellular components of the microenvironment and tumor heterogeneity has

been rarely explored. Single-cell RNA sequencing data from Darmanis and colleges (20)

were used to investigate the impact of oligodendrocytes, astrocytes and neurons in context

of their regulatory function. Creatine synthesis was exclusively up-regulated in

oligodendrocytes of the infiltration region. Their capacity to synthesize creatine and impact

on cell regeneration in demyelinating disorders was reported in recent studies (55,56). These

findings support our results and the important role of the microenvironment on transcriptional

re-programming and heterogeneity in glioblastoma multiforme. In heterogeneous tumors,

different stages of hypoxia and environmental creatine enrichment were observed, which

requires a permanent adaptation to the environmental conditions. In addition to various

genetic alterations, metabolic adaptation plays a major role in transcriptional heterogeneity of

23

malignant brain tumors. Exploiting or targeting metabolic adaption could potentially serve as

a future therapeutic option for malignant brain tumors.

24

Acknowledgement

We thank the MagRes, University of Freiburg, Germany for coordinating the NMR usage.

The authors greatly acknowledge the Genomics and Proteomics Core Facility, German

Cancer Research Center/DKFZ, Heidelberg, Germany for their sequencing service.

25

References

1. Crocetti E, Trama A, Stiller C, Caldarella A, Soffietti R, Jaal J, et al. Epidemiology of glial and non-glial brain tumours in Europe. Eur J Cancer. 2012;48:1532–42.

2. Ostrom QT, Bauchet L, Davis FG, Deltour I, Fisher JL, Langer CE, et al. The epidemiology of glioma in adults: a &quot;state of the science&quot; review. Neuro Oncol. 2014;16:896–913.

3. Ostrom QT, Gittleman H, Liao P, Rouse C, Chen Y, Dowling J, et al. CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2007-2011. Neuro Oncol. 2014;16 Suppl 4:iv1-iv63.

4. Gilbert MR, Dignam JJ, Armstrong TS, Wefel JS, Blumenthal DT, Vogelbaum MA, et al. A randomized trial of bevacizumab for newly diagnosed glioblastoma. N Engl J Med. 2014;370:699–708.

5. Chinot OL, Wick W, Cloughesy T. Bevacizumab for newly diagnosed glioblastoma. N Engl J Med. 2014;370:2049.

6. Taal W, Oosterkamp HM, Walenkamp AME, Dubbink HJ, Beerepoot L V, Hanse MCJ, et al. Single-agent bevacizumab or lomustine versus a combination of bevacizumab plus lomustine in patients with recurrent glioblastoma (BELOB trial): a randomised controlled phase 2 trial. Lancet Oncol. 2014;15:943–53.

7. Vredenburgh JJ, Desjardins A, Herndon JE, Marcello J, Reardon DA, Quinn JA, et al. Bevacizumab plus irinotecan in recurrent glioblastoma multiforme. J Clin Oncol. 2007;25:4722–9.

8. Friedman HS, Prados MD, Wen PY, Mikkelsen T, Schiff D, Abrey LE, et al. Bevacizumab alone and in combination with irinotecan in recurrent glioblastoma. J Clin Oncol. 2009;27:4733–40.

9. Verhaak RGW, Hoadley K a, Purdom E, Wang V, Qi Y, Wilkerson MD, et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. Elsevier Ltd; 2010;17:98–110.

10. Phillips HS, Kharbanda S, Chen R, Forrest WF, Soriano RH, Wu TD, et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell. 2006;9:157–73.

11. Yan W, Zhang W, You G, Zhang J, Han L, Bao Z, et al. Molecular classification of gliomas based on whole genome gene expression: A systematic report of 225 samples from the Chinese Glioma Cooperative Group. Neuro Oncol. 2012;14:1432–40.

12. Li A, Walling J, Ahn S, Kotliarov Y, Su Q, Quezado M, et al. Unsupervised analysis of transcriptomic profiles reveals six glioma subtypes. Cancer Res. 2009;69:2091–9.

13. Snuderl M, Fazlollahi L, Le LP, Nitta M, Zhelyazkova BH, Davidson CJ, et al. Article Mosaic Amplification of Multiple Receptor Tyrosine Kinase Genes in Glioblastoma. Cancer Cell. Elsevier Inc.; 2011;20:810–7.

14. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science (80- ). 2014;344:1396–401.

15. Suzuki H, Aoki K, Chiba K, Sato Y, Shiozawa Y, Shiraishi Y, et al. Mutational

26

landscape and clonal architecture in grade II and III gliomas. Nat Genet. Nature Publishing Group; 2015;47:458–68.

16. Meyer M, Reimand J, Lan X, Head R, Zhu X, Kushida M, et al. Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity. Proc Natl Acad Sci U S A. 2015;112:851–6.

17. Parker NR, Hudson AL, Khong P, Parkinson JF, Vafaee F, Chen J, et al. Intratumoral heterogeneity identified at the epigenetic , genetic and transcriptional level in glioblastoma. Nat Publ Gr. Nature Publishing Group; 2016;1–10.

18. Szerlip NJ, Pedraza A, Chakravarty D, Azim M, McGuire J, Fang Y, et al. Intratumoral heterogeneity of receptor tyrosine kinases EGFR and PDGFRA amplification in glioblastoma defines subpopulations with distinct growth factor response. Proc Natl Acad Sci. 2012;109:3041–6.

19. Sottoriva A, Spiteri I, Piccirillo SGM, Touloumis A, Collins VP, Marioni JC, et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci U S A. 2013;110:4009–14.

20. Darmanis S, Sloan SA, Croote D, Mignardi M, Chernikova S, Samghababi P, et al. Single-Cell RNA-Seq Analysis of Infiltrating Neoplastic Cells at the Migrating Front of Human Glioblastoma. Cell Rep. ElsevierCompany.; 2017;21:1399–410.

21. Zheng X, Boyer L, Jin M, Mertens J, Kim Y, Ma L, et al. Metabolic reprogramming during neuronal differentiation from aerobic glycolysis to neuronal oxidative phosphorylation. Elife. 2016;5:1–25.

22. Hu J, Locasale JW, Bielas JH, O’Sullivan J, Sheahan K, Cantley LC, et al. Heterogeneity of tumor-induced gene expression changes in the human metabolic network. Nat Biotechnol. Nature Publishing Group; 2013;31:522–9.

23. Heiland DH, Wörner J, Haaker JG, Delev D, Pompe N, Mercas B, et al. The integrative metabolomic-transcriptomic glioblastome multiforme landscape of. Oncotarget. 2017;

24. Heiland DH, Mader I, Schlosser P, Pfeifer D, Carro MS, Lange T, et al. Integrative Network-based Analysis of Magnetic Resonance Spectroscopy and Genome Wide Expression in Glioblastoma multiforme. Sci Rep. 2016;6:29052.

25. Beckonert O, Keun HC, Ebbels TMD, Bundy J, Holmes E, Lindon JC, et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protoc. 2007;2:2692–703.

26. Hwang TL, Shaka AJ. Water Suppression That Works. Excitation Sculpting Using Arbitrary Wave-Forms and Pulsed-Field Gradients. J. Magn. Reson. Ser. A. 1995. page 275–9.

27. Hao J, Liebeke M, Astle W, De Iorio M, Bundy JG, Ebbels TMD. Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN. Nat Protoc. 2014;9:1416–27.

28. Anders S, Huber W, Nagalakshmi U, Wang Z, Waern K, Shou C, et al. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.

29. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.

30. Holtman IR, Raj DD, Miller JA, Schaafsma W, Yin Z, Brouwer N, et al. Induction of a common microglia gene expression signature by aging and neurodegenerative

27

conditions: a co-expression meta-analysis. Acta Neuropathol Commun. 2015;3:31.

31. Iancu OD, Colville A, Oberbeck D, Darakjian P, McWeeney SK, Hitzemann R. Cosplicing network analysis of mammalian brain RNA-Seq data utilizing WGCNA and Mantel correlations. Front Genet. 2015;6:174.

32. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545–50.

33. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7.

34. Luo W, Brouwer C. Pathview: An R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics. 2013;29:1830–1.

35. Godlewski J, Ferrer-Luna R, Rooj AK, Mineo M, Ricklefs F, Takeda YS, et al. MicroRNA Signatures and Molecular Subtypes of Glioblastoma: The Role of Extracellular Transfer. Stem Cell Reports. 2017;8:1497–505.

36. Heiland DH, Wörner J, Delev D, Pompe N, Mercas B, Franco P, et al. The Integrative Metabolomic-Transcriptomic Landscape of Glioblastome Multiforme. Oncotarget. 2017;accepted.

37. Joseph J V, Conroy S, Pavlov K, Sontakke P, Tomar T, Eggens-Meijer E, et al. Hypoxia enhances migration and invasion in glioblastoma by promoting a mesenchymal shift mediated by the HIF1α-ZEB1 axis. Cancer Lett. 2015;359:107–16.

38. Clark VE, Harmanci A, Bai H, Youngblood M, Lee TI, Baranoski J, et al. Recurrent somatic mutations in RNA polymerase II define a distinct subset of meningiomas. Nat Genet. 2016;48:2–11.

39. Kim D, Fiske BP, Birsoy K, Freinkman E, Kami K, Possemato RL, et al. SHMT2 drives glioma cell survival in ischaemia but imposes a dependence on glycine clearance. Nature. 2015;520:363–7.

40. Yuan Y, Hilliard G, Ferguson T, Millhorn DE. Cobalt inhibits the interaction between hypoxia-inducible factor-?? and von Hippel-Lindau protein by direct binding to hypoxia-inducible factor-?? J Biol Chem. 2003;278:15911–6.

41. Xing F, Luan Y, Cai J, Wu S, Mai J, Gu J, et al. The Anti-Warburg Effect Elicited by the cAMP-PGC1α Pathway Drives Differentiation of Glioblastoma Cells into Astrocytes. Cell Rep. ElsevierCompany.; 2017;18:468–81.

42. Liang C, Qin Y, Zhang B, Ji S, Shi S, Xu W, et al. Metabolic plasticity in heterogeneous pancreatic ductal adenocarcinoma. Biochim Biophys Acta - Rev Cancer. 2016;1866:177–88.

43. Hensley CT, Faubert B, Yuan Q, Lev-Cohain N, Jin E, Kim J, et al. Metabolic Heterogeneity in Human Lung Tumors. Cell. Elsevier Inc.; 2016;164:681–94.

44. Barsoum IB, Koti M, Siemens DR, Graham CH. Mechanisms of hypoxia-mediated immune escape in cancer. Cancer Res. 2014;74:7185–90.

45. Eales KL, Hollinshead KER, Tennant DA. Hypoxia and metabolic adaptation of cancer cells. Oncogenesis. Nature Publishing Group; 2016;5:e190.

46. Turcan S, Rohle D, Goenka A, Walsh L a, Fang F, Yilmaz E, et al. IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature. Nature

28

Publishing Group; 2012;483:479–83.

47. Noushmehr H, Weisenberger DJ, Diefes K, Phillips HS, Pujara K, Berman BP, et al. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell. 2010;17:510–22.

48. Letouzé E, Martinelli C, Loriot C, Burnichon N, Abermil N, Ottolenghi C, et al. SDH Mutations Establish a Hypermethylator Phenotype in Paraganglioma. Cancer Cell. 2013;23:739–52.

49. Pavlova NN, Thompson CB. The Emerging Hallmarks of Cancer Metabolism. Cell Metab. Elsevier Inc.; 2016;23:27–47.

50. Amelio I, Cutruzzol?? F, Antonov A, Agostini M, Melino G. Serine and glycine metabolism in cancer. Trends Biochem Sci. 2014;39:191–8.

51. Fong G-H, Takeda K. Role and regulation of prolyl hydroxylase domain proteins. Cell Death Differ. 2008;15:635–41.

52. Zhou Y, Zhou Y, Shingu T, Feng L, Chen Z, Ogasawara M, et al. Metabolic alterations in highly tumorigenic glioblastoma cells: Preference for hypoxia and high dependency on glycolysis. J Biol Chem. 2011;286:32843–53.

53. Thompson CB. Metabolic enzymes as oncogenes or tumor suppressors. N Engl J Med. 2009;360:813–5.

54. Lee S, Nakamura E, Yang H, Wei W, Linggi MS, Sajan MP, et al. Neuronal apoptosis linked to EglN3 prolyl hydroxylase and familial pheochromocytoma genes: Developmental culling and cancer. Cancer Cell. 2005;8:155–67.

55. Joncquel-Chevalier Curt M, Voicu PM, Fontaine M, Dessein AF, Porchet N, Mention-Mulliez K, et al. Creatine biosynthesis and transport in health and disease. Biochimie. 2015;119:146–65.

56. Chamberlain KA, Chapey KS, Nanescu SE, Huang JK. Creatine Enhances Mitochondrial-Mediated Oligodendrocyte Survival After Demyelinating Injury. J Neurosci. 2017;37:1479–92.

29

FIGURE LEGENDS

Figure 1.

Transcriptional Landscape is Marked by Regional Differences.

(A) RNA-sequencing data of varying regions are hierarchical clustered based on the

proneural, mesenchymal and classical signature genes in an unsupervised manner.

Expression values are displayed in red for high and green for low expression. In the upper

panel, colored bars indicate the expression subgroup based on a random forest prediction

model, the region and the cluster group given by an unsupervised clustering.

Figure 2.

Tumor Landscape is Marked by Regional Differences in Metabolic Status.

(A) Workflow of in-vivo and ex-vivo analysis of multiple tumor regions. In the left panel,

presurgical planning of a T1- weighted MRI and a 3D reconstruction of the tumor is shown.

Colors indicate multiple regions, which were separately taken for tumor sampling. In the right

panel, two representative in-vivo and ex-vivo spectra are shown. In the right upper panel, a

heatmap of creatine and lactate (normalized ratios of MRS) is presented. In the right bottom

panel, the cerebral blood volume (CBV) shows an increased blood volume in the occipital

tumor region. (B) A heatmap of metabolic intensities and expression values of multiple

biopsies were illustrated. Metabolic values are hierarchical clustered in an unsupervised

manner. Low intensities are colored in blue, high in brown. Colored bars in the upper panel

indicate cluster subgroup, region and patient. Reference data of bulk tumors were given in

the right heatmap. The bottom panel illustrates expression profiles of each biopsy with

including proneural and mesenchymal key genes. (C) A Weighted Correlation Network

Analysis (WCNA) of metabolic profiling presents highly-associated metabolites, which are

clustered into modules. A heatmap in the bottom panel indicates the correlation coefficient of

mesenchymal and proneural genes and each metabolite. In the bottom panel, a computed

network is illustrated.

Figure 3.

Environmental Creatine Enrichment Drives Proneural Gene Expression Shift.

(A) Workflow of the in-vivo/in-vitro Model. (B) A heatmap of transcriptome data shows a clear

separation between hypoxia (left, marked in pink), creatine (middle, marked in yellow) and

normoxia (right, marked in blue) specific gene expression. (C) A functional analysis of gene

set variation analysis (GSVA) is clustered and illustrated in the heatmap. Specific functions

30

are highlighted in a detailed expression heatmap in the right upper panel. Pathway activation

tested by western blot is illustrated in the right bottom panel. (D) Differentiation (β-III-Tubulin,

GFAP and Nestin) and cell morphology is presented on the left side. Immunostaining of

OLIG2 is shown as a representative proneural key gene (right side). (E) Experimental

validation was performed in three different cell lines with consistent results. A heatmap of

selected mesenchymal and proneural key genes is shown.

Figure 4.

Environmental Creatine Enrichment Alters Tumor Metabolism.

(A) A heatmap of metabolomic data of the in-vitro model shows a clear separation between

hypoxia (left, marked in pink), creatine (middle, marked in darkyellow) and normoxia (right,

marked in blue) specific metabolic intensities. (B) The creatine specific panel was tested by

enrichment analysis of metabolic pathways. A barplot shows the top 50 enrichment-scores.

(C) A MA-plot of differential metabolic intensities between creatine-enriched environment and

hypoxia environment was shown. Contours mark the density of differential intensities. (D)

Alpha-ketoglutarate/succinate ratio for each treatment condition is illustrated in a bar plot.

Error bars represent the mean ± standard deviation of at least three independent

experiments. *p < 0.05 **p < 0.01 ***p < 0.001

Figure 5.

Creatine Treatment Alters Hypoxic Signaling.

(A) An illustration of the hypoxia signaling pathway and the function of PHD. The right panel

illustrates the creatine metabolism. (B) Key enzymes of the creatine metabolism are

displayed. Both enzymes are significantly up-regulated after creation supplementation. (C)

Two scatter plots show the intensity of reactive oxygene species in normoxia (left plot) and

hypoxia (right plot). The fluorescence signal is given on the y-axis and the DAPI fluorescence

signal on the x-axis. (D) Additional immunostainings of the ROS signal were displayed. (E)

HIF1α activity in the nucleus was measured with the TransAM assay. (F) A heatmap of

mesenchymal and proneural key gene expression is presented. The proneural gene

expression is reduced after HIF1α stabilization. Other pathways such as MAPK pathway

were not affected by HIF-stabilization as shown in the Western blot (G). Error bars represent

the mean ± standard deviation of at least three independent experiments. *p < 0.05 **p <

0.01 ***p < 0.001

31

Figure 6.

Creatine Enrichment Alters Cell Migration In Vitro

(A) A migration assay (left panel) and quantification (right panel) is shown in normoxic and

hypoxic conditions (B). (C) Invasion assay was displayed by fluorescence imging and

quantification (right panel). (E) Cell viability is measured in normoxia and hypoxia conditions

by an MTT assay. Error bars represent the mean ± standard deviation of at least three

independent experiments. *p < 0.05 **p < 0.01 ***p < 0.001

Figure 7.

2D tSNE Map of Single-Cell RNA Sequencing

(A) Map of astrocytes, oligodendrocytes and neurons based and a tSNE cluster analysis is

displayed. Cells from the tumor core are colored in green, red cells are from the migrating

front of the tumor. The size of each given point indicats the enrichment of creatine synthesis.

(B) Key enzymes of the creatine synthesis are illustrated with respect to their spatial origin.

(C) An Illustration of the creatine synthesis in the brain.