Spatially Resolved Bioenergetic and Genetic Reprogramming

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Spatially Resolved Bioenergetic and Genetic Reprogramming Through the Brain of Rats Bearing Implanted C6 Gliomas as Detected by Multinuclear HRMAS and Genomic Analysis Valeria Righi, María-Luisa García-Martín, Adele Mucci, Luisa Schenetti, Vitaliano Tugnoli, Pilar Lopez-Larrubia, and Sebastian Cerdan J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00130 • Publication Date (Web): 21 Aug 2018 Downloaded from http://pubs.acs.org on August 22, 2018

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Journal of Proteome Research

Spatially Resolved Bioenergetic and Genetic Reprogramming Through the Brain of Rats Bearing Implanted C6 Gliomas as Detected by Multinuclear HRMAS and Genomic Analysis Valeria Righi,1,2* María-Luisa García-Martín,1,3 Adele Mucci, 4 Luisa Schenetti,5 Vitaliano Tugnoli,6 Pilar Lopez-Larrubia,1 and Sebastián Cerdán.1 1

Instituto de Investigationes Biomédicas “Alberto Sols” CSIC/UAM, c/ Arturo Duperier 4, E-28029 Madrid ES, 2Present address: Dipartimento di Scienze per la Qualità della Vita, Università di Bologna, via Corso D’Augusto 237, Rimini 47921 IT, 3Present address: BIONAND, Andalusian Centre for Nanomedicine and Biotechnology, C/ Severo Ochoa, 35, Junta de Andalucía, Universidad de Málaga, 29590 Campanillas Málaga, Spain, 4 Dipartimento di Scienze Chimiche e Geologiche, Universitá di Modena, via G. Campi 183, Modena 41125 IT, 5Dipartimento di Scienze della Vita, Universitá di Modena, via G. Campi 183, Modena 41125 IT, 6Dipartimento di Scienze Biomediche e Neuromotorie, Università di Bologna, via Belmeloro 8/2, 40126 Bologna IT. Corresponding author: Prof. Valeria Righi Dipartimento di Scienze per la Qualità della Vita Corso D'Augusto 237, Rimini Phone:+390592058636 Email: [email protected]

Abbreviations: HRMAS; High Resolution Magic Angle Spinning, MRI; Magnetic Resonance Imaging, TR: Repetition time, TE: echo time, CPMG: Carr-Purcell-MeiboomGill sequence, WALTZ-16: Composite pulse water suppression with 16 cycles, Ala: alanine, Glc: glucose, Glu: glutamate, Gln: glutamine, Lac: lactate, NAA: Nacetylaspartate, Cr: total creatine, ChoCC: total choline containing compounds, Myo: myo-inositol, MM: macromolecules, Lip: lipids, PC: phosphocholine.

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Abstract We used 1H,

13

C HRMAS and genomic analysis to investigate regionally the transition

from oxidative to glycolytic phenotype and its relationship with altered gene expression in adjacent biopsies through the brain of rats bearing C6 gliomas. Tumor bearing animals were anesthetized, infused with a solution of [1-13C]-glucose and small adjacent biopsies were obtained spanning transversally from the contralateral hemisphere (regions I and II), the right and left peritumoral areas (regions III and V, respectively) and the tumor core (region IV). These biopsies were analyzed by 1H,

13

C HRMAS and

by quantitative gene expression techniques. Glycolytic metabolism as reflected by the [3-13C]-lactate content, increased clearly from regions I to IV, recovering partially to physiological levels in region V. In contrast, oxidative metabolism as reflected by the [413

C]-glutamate labelling, decreased in regions I-IV, recovering partially in region V. This

metabolic shift from normal to malignant metabolic phenotype paralleled changes in the expression of HIF1α, HIF2α, HIF3α genes, downstream transporters and regulatory glycolytic, oxidative and anaplerotic genes in the same regions. Together, our results indicate that genetic and metabolic alterations occurring in the brain of rats bearing C6 gliomas co-localize in situ and that the profile of genetic alterations in every region can be inferred from the metabolomic profiles observed in situ by multinuclear HRMAS.

Key words. Metabolic reprogramming, Genetic reprogramming, C6 Glioma, HRMAS, metabolomics, genomics.



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INTRODUCTION Reprogramming of energy metabolism constitutes one of the principal landmarks of the tumoral phenotype, providing the vital support required to meet the increased energy demands of tumor proliferation, growth and survival

1-3

. Metabolic reprogramming is

classically understood as the shift from the aerobic metabolism, prevailing in normal tissues, to the predominantly anaerobic metabolism, dominating in tumors, as disclosed initially by the pioneering work of Otto Warburg 4. This process involves generally an increase in aerobic glycolysis, a decrease in the tricarboxylic acid (TCA) cycle activity and an anaplerotic shift towards increased biosynthesis of lipids, nucleic acids and proteins required for cell growth and proliferation 1. This complex metabolic response has been more recently revealed to be integrated at the cell nucleus, through modifications in growth factor signaling pathways, the expression of oncogenes and tumor suppressor genes and the expression of Hypoxia Inducible Factors (HIF)

5,6

. The

latter is a family of transcription factors responding to reduced oxygen tensions eliciting eventually the increase in anaerobic glycolysis and the decrease in oxidative metabolism, among other important consequences 6. Together with the incompetent neovasculature of cancers, metabolic reprogramming leads additionally to characteristic alterations in the tumor extracellular microenvironment, a crucial determinant favoring the natural selection of the invasive, metastatic phenotype 7. Despite enormous progress in the investigation of the molecular mechanisms of Warburg effect in cells and tissues, it remains currently difficult, however, to establish in situ the relationship between the underlying changes in gene expression and the corresponding metabolic responses observed in vivo, or how these alterations may become spatially resolved 3. This entails considerable relevance since establishing the 4 ACS Paragon Plus Environment

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appropriate relationships could allow inferring the underlying genetic alterations from in vivo or in situ measurements of metabolism such as those obtained by magnetic resonance methods. However, to our knowledge, few studies have addressed these relationships in vivo, probably because of the difficulties in measuring gene expression and metabolic performance in the same tissue region in situ. A variety of sophisticated Nuclear Magnetic Resonance approaches allows metabolic measurements to be performed in vivo, non-invasively. Localized, in vivo 1H and

13

C

NMR approaches have provided vital information on the metabolic profile of normal and diseased tissues and the turnover of

13

C labelled precursors through glycolysis, TCA

cycle and glutamate-glutamine-gaba cycles 13

8,9

. More recently, the use of hyperpolarized

C NMR and Dynamic Nuclear Polarization have allowed the investigation of fast

13

C

incorporation reactions in normal and tumoral tissues, mainly at the pyruvate node and the lactate dehydrogenase reaction

10,11

. However, these in vivo NMR approaches

remain limited to relatively large voxel sizes, making it difficult to resolve in space the metabolic information in sufficiently small voxels or, even more difficult, to obtain the gene expression profile and the metabolic information from the same tissue region in space. Notably, 1H and 13C High Resolution Magic Angle Spinning (HRMAS) techniques are well endowed to overcome these limitations since they can provide 1H and

13

C

metabolic profiles from relatively small tissue biopsies, allowing the investigation of both metabolic profile and gene expression in the same sample 12. In this work, we implemented 1H and

13

C HRMAS technologies to investigate the

transition from normal to malignant metabolism and gene expression, in the same biopsy of spatially adjacent regions across the brain of rats bearing C6 gliomas, spanning from the healthy contralateral parenchyma to the ipsilateral peritumoral and tumoral regions. To characterize alterations in the expression of ancillary transporters and genes from oxidative and glycolytic metabolisms in the same biopsies, we used the 5 ACS Paragon Plus Environment


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quantitative polymerase chain reaction (qPCR). We investigated then the relationships between the expression of ancillary genes of glycolysis and tricarboxylic acid cycle, the principal mono-carboxylate transporters, the hypoxia inducible factors HIF1α, HIF2α and HIF3α and their downstream metabolic repercussions in the activity of glycolysis and the TCA cycle, as reflected in the 1H and 13C HRMAS spectra of the same biopsies.

EXPERIMENTAL SECTION Animals and experimental design. All animal procedures were approved by the highest institutional ethical committee (INCSIC) and were performed in accordance with Spanish (Royal Decree 53/2013-Law 32/2007) and European regulations (Directive 2010/63/EU). Mice were housed in cages containing three or four animals per cage, under controlled temperature (21-23 °C) and humidity (47%) conditions and twelve hours light/dark cycles (8 a.m., lights on). C6 gliomas developed in female Wistar rats (180-250 g) after stereotaxic injection of 106 C6 cells (American Type Culture Collection, CCL-107, LGC Standards, Barcelona, ES) in the left caudate nucleus

13

. Tumor growth after

implantation was evaluated in vivo using T1 weighted and T2 weighted MRI as described below. For this purpose, anesthesia was induced with a mixture of oxygen/isoflurane (96/4%) in a plexiglass

chamber, and maintained inside the magnet with

oxygen/isoflurane (99/1% 1 mL/min). Vital signs, respiratory and cardiac rates were continually monitored through the imaging process (Biotrig, Bruker Daltonics, Ettlingen, DE). Body temperature was maintained at 37°C with a water heated pad underneath the animal. Within 3–4 weeks after C6 cell implantation, six rats were anesthetized as indicated above and infused (0,1 mL/min) with 0.2 M solution of [1-13C]-Glc (enriched 99%) (8 µmol/100g, 45 minutes) through the right jugular vein. The left jugular vein was 6 ACS Paragon Plus Environment

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also cannulated to monitor plasma Glc concentration with a glucose strip analyzer (Analox Instruments, MA, USA) at different time points (0, 15, 30, and 45 minutes). After completing the infusion, cerebral metabolism was arrested using high-power (5 kW) focused microwaves (TMW-6402C, Muromachi Kikai Co. Ltd., Tokyo, Japan), the fixed brain tissue removed from the skull and five small biopsies (~20 mg) taken across contiguous brain regions spanning transversally the entire brain from the contralateral to the ipsilateral hemispheres (Figure 1). Briefly, the different regions were: contralateral normal brain (I), normal brain adjacent to the left side of the tumor (II), peripheral (vascularized) tumor (III), central tumor region (IV) and brain tissue adjacent to the right side of the tumor (V). Each biopsy was divided in two contiguous parts, one subjected to multinuclear HRMAS (~10 mg) analysis and the other (~10 mg) used for qPCR as described below. Magnetic Resonance Imaging The development of brain tumors after implantation was followed by MRI to obtain the biopsies from brains with a similar state of tumor development. Briefly, six animals implanted with C6 glioma cells were anesthetized as described above and placed in the isocenter of the MR scanner (Bruker Pharmascan, Ettlingen, DE). MRI was performed using a 7.0 Tesla horizontal-bore (160 mm) superconducting magnet (PharmaScan, Bruker BioSpin Gmbh, Ettlingen, Germany) equipped with a 1H selective birdcage resonator of 38 mm. Spin-echo multi-slice T1-weighted MR images of sagittal sections across the brain (FOV: 22 mm, 256x256 matrix size, 1 mm slice thickness, 5 slices, three averages, 85 µ2 in plane resolution) were acquired successively using TR= 500 ms, TE= 11.8 ms. T2-weighted MR images were acquired under identical resolution conditions (TR= 3000 ms, TE= 60 ms, three averages). Multinuclear HRMAS of adjacent brain biopsies. 7 ACS Paragon Plus Environment


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Before HRMAS analysis, each biopsy was flushed with D2O to remove the residual blood and water with the aim of improving water suppression and resolution

14

. The

biopsy was then introduced in an HRMAS zirconium rotor (4mm OD) fitted with a 50 µl cylindrical insert to increase sample homogeneity and then transferred into the Magic Angle Spinning (MAS) rotor, cooled to 5 °C. HRMAS spectra were acquired on a 11.7 Tesla MHz Bruker AVANCE WB Spectrometer operating at 500.13 and 125.76 MHz for proton and carbon, respectively, at 5 ºC and 4 kHz spinning rate. Two types of 1D 1H HRMAS spectra were acquired on each sample: i) a water presaturated spectrum (5 s relaxation delay) using a pulse-and-acquire sequence (zgcppr) with π/2 pulses, 10 kHz spectral width, 32k data points and 128 scans; ii) a CPMG spectrum using a spin-echo sequence (cpmgpr) with water presaturation (5 s) during relaxation delay, 1 ms echo time and 144 ms total echo time (2nτ), 32k data points and 128 scans. Then, a 1Hdecoupled

13

C HRMAS spectrum was acquired using WALTZ-16

1

H decoupling

sequence during the acquisition and relaxation delay periods. Acquisition conditions were: π/4 pulse, 64k data points, 8k scans with 5 s recycle delay. Total acquisition time was approximately 14 h. Spectral fitting and quantification 1

H HRMAS detectable metabolites were quantified on CPMG spectra using the

LCModel

package

(Linear

Combination

provencher.com/pages/lcmodel.shtml)

15

of

Model

Spectra,

http://s-

. Briefly, LCModel program fits the biopsy

spectra as a linear combination of model spectra contained in a data base of cerebral metabolites and optional contributions for lipids and macromolecules, yielding values of metabolite concentration and estimated standard deviation (expressed in percent of the estimated concentrations). Only metabolites with SD smaller than 20% were included in the final analysis. The following metabolites were contained in the model basis-set

14

:

alanine (Ala), lactate (Lac), taurine (Tau), phosphocreatine (PCr), creatine (Cr), choline 8 ACS Paragon Plus Environment

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(Cho), glycerophosphocholine (GPC), phosphoethanolamine (PE), phosphocholine (PC), glycine (Gly), aspartate (Asp), glutamine (Gln), glutamate (Glu), myo-inositol (Myo), N-acetylaspartate (NAA), acetate (Ac), threonine (Thr), glutathione (GSH), valine (Val), isoleucine (Ile), leucine (Leu), Glc and γ-aminobutyric acid (GABA). In principle, LCModel can provide absolute metabolite concentrations. However, to accurately estimate these concentrations the effects of relaxation (T1 and T2) must be taking into account, especially when using long echo times to reduce the contribution of macromolecules and lipids, as it is the case for the CPMG sequence described above. These relaxation effects can be greatly reduced by using model spectra that have been acquired under the same conditions as the experimental spectra. However, because the relaxation times can vary between metabolites in solution and metabolites in intact tissue, the estimated absolute concentrations will still be off by an unknown amount (concentration units are then referred to as “institutional units”). Nonetheless, assuming that relaxation times and acquisition conditions do not change significantly during the study, all measurements will deviate from true concentrations by the same factor, and hence comparisons between different subjects are still valid. The LCModel data base was modified to include the doublet of

13

C satellites of the Lac methyl resonance (1JHC=

125 Hz). For this purpose, we generated the new heteronuclear doublet signal by shifting appropriately the doublet of Lac signal at 1.33 ppm, calculating the fractional 13C enrichment in the methyl carbon of Lac as the integral ratio of satellite doublets/(satellite

13

C doublets + central doublet). Once the

13

C

13

C enrichment was

determined from 1H HRMAS spectra, the amount of [3-13C]-Lac could be calculated as the product from total Lac content times the fractional

13

C enrichment. This approach

provided the amount of [3-13C] Lac in every biopsy, as detected by amount of [4-13C] enrichment in the observable

13

13

C HRMAS. The

C resonances of Glu and Gln was

estimated as the ratio between their intensities and the intensity of the [3-13C]-Lac 9 ACS Paragon Plus Environment


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resonance, corrected for the effects of Nuclear Overhauser Enhancement and partial saturation. Gene expression profile in adjacent brain biopsies Total RNA was prepared from biopsies of the normal brain and tumors regions I-V using the RNAspin Mini RNA Isolation Kit (Amersham Biosciences,GE Healthcare, Piscataway, NJ, USA). Approximately 10 mg of tissue were used. The purity and integrity of the labeled cRNA was evaluated from the A260/A280 ratio and on an Agilent 2100 bio-analyzer (Agilent Technologies Inc., Santa Clara, CA, USA), being always between 1.9-2.1. RNA integrity number was evaluated using the Agilent 2100 bioanalyzer, assuming appropriate intensities as those depicting 28S:18S ratio higher than 2. RNA Integrity Number (RIN) values were normally ≥7. Approximately 250 ng of RNA were retro-transcribed to cDNA using the high capacity cDNA reverse transcription kit (Applied Biosystems, Foster City, CA, USA) The expression of the genes involved in the glycolytic and TCA cycle pathways was assayed using the individual probes for each gene commercialized as TaqMan® (Applied Biosystems, Foster City, CA, USA). TaqMan® probes for Gene Expression provide one of the most comprehensive set of pre-designed Real-Time PCR assays available including specifically most genes of carbohydrate and oxidative energy metabolism and related transcription factors. All TaqMan® Gene Expression Assays were run with the same real time PCR protocol, eliminating the need for primer design or PCR optimization. Table 1 summarizes the transporters or enzymes of glycolytic and TCA cycle pathways and the corresponding genes investigated in this study. Statistical Analysis


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The statistical analysis was performed using the SPSS package as implemented on a Windows XP Platform (SPSS Inc., Chicago, Illinois). Results are shown as mean±SE and unpaired Student’s t-test or two-way analysis of variance was used to compare genes in rat brain regions. The level of significance was set at p

Figure 3 depicts the

13

C labeling patterns obtained by

13

C HRMAS NMR from the five

biopsy regions investigated after [1-13C]-Glc infusion. These ACS Paragon Plus Environment

13

C HRMAS spectra are 12

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very similar to the

13

C high resolution spectra, previously obtained from brain extracts

after the metabolism of [1-13C]-Glc

19

. Basically, resonances from the

13

C enriched

carbons of Glu and Gln, Lac and Glc are observed. The oxidative metabolism of Glc in the TCA cycle is revealed by the intensity of [4-13C]-Glu labelling (34.2 ppm) while the activity of anaerobic glycolysis may be estimated by the intensity of the [3-13C]-Lac resonance (20.9 ppm). Thus, analysis of the

13

C HRMAS spectra from biopsies I-V

provides a direct evaluation of the relative contributions of glycolysis or of TCA cycle. Biopsies I and II from the contralateral hemisphere, show the dominant contribution of aerobic metabolism of the normal cerebral phenotype, as revealed by the intensity of the [4-13C]-Glu resonance. In contrast, biopsies III and IV, which include the tumoral regions, depict the larger contribution of glycolytic metabolism characteristic of tumoral tissues, revealed in these cases by the large intensity if the [3-13C]-Lac resonance and the much smaller relative intensities of the [4-13C]-Glu resonances. Interestingly, [4-13C]Gln content decreased progressively from regions I-IV, indicating that C6 tumors not only are not able to produce Gln, but may consume the Gln produced by the surrounding tissues. Summarizing, the transition from normal to malignant phenotype becomes also easily detectable by 13

C HRMAS through the relative increase in the [3-

C]-Lac resonance of adjacent brain biopsies.

Table 3 summarizes the 13

13

13

C fractional enrichment and the concentration (µmol/g) of [3-

C]-Lac, [4-13C]-Glu and [4-13C]-Gln in regions I-V of the six animals investigated.

Genomic analysis of adjacent tissue biopsies To gain insight into the genetic alterations underlying the effects observed in Figures 2 and 3, we investigated the expression of the enzymes involved in glycolytic and TCA cycles as well as those of Hypoxia Inducible Factors. More specifically, we analyzed the expression of genes (Table 1) coding for Hexokinases (HK1, HK2, HK3), Glucokinases 13 ACS Paragon Plus Environment


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(GCK fam, GCKR), Lactate Dehydrogenase (LDH), Monocarboxylic Transporter 1, 2 (MTC1, MTC2), Phosphofructokinases (PFKL, PFKM), Pyruvate Kinases (PKLR, PKM2), Citrate Synthase (CS), Pyruvate Dehydrogenases (PDHA1, PDHA2), Pyruvate Carboxylase (PC) and Hypoxia Inducible factors (HIF1α, HIF2α or EPAS1 and HIF3α) in the five adjacent biopsies of six rats. Figure 4 summarizes the genes that depicted significant changes (p < 0.05) with respect to the normal brain (region I). Briefly, the tumoral region was characterized by increased expression of HIF1α and concomitant increased expression of regulatory genes from glycolysis, HK2-3, as well as LDH and decreased expression of PFK and PKM2 genes. Notably, the tumoral region IV depicted a non-significant, but detectable decrease in the expression of CS, an increase in PDHA2 and a decrease in the expression of the gene coding for the anaplerotic enzyme PC. Relationships between gene expression and energy metabolism in adjacent biopsies. Figure 5 summarizes the relationships between the gene expression profiles shown in Figure 4 and the 13C concentrations of Lac, Glu and Gln measured in Figure 3. We observed that the increase in the [3-13C]-Lac content from regions I to IV was paralleled by increased expressions of HIF1α and decreased expression of EPAS1 and HIF3α, as well as by increases in the expression of HK2 and HK3 and no appreciable changes in the expression of PFKM, PKM2 and LDH. A decrease in [3-13C]-Lac was observed in peritumoral region V, paralleled by decreases in HK2, HK3 and LDH. [413

C]-Gln decreased progressively from region I to V, while [4-13C]-Glu decreased

generally in regions I-V but increase locally in tumoral region IV, in agreement with the increases in PDHA2.

Discussion 14 ACS Paragon Plus Environment

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The purpose of our work was to investigate regionally the transition from oxidative to glycolytic metabolisms and its relationship with altered gene expression of regulatory enzymes of energy metabolism in adjacent biopsies through the brain of rats bearing C6 gliomas, combining 1H and 13C HRMAS and genomic analysis. An important finding relates to the changes in glycolytic metabolism, as reflected by the [3-13C]-Lac content, increasing clearly from regions I to IV, and recovering partially to physiological levels in region V. In contrast, oxidative metabolism, as reflected by the [4-13C]-Glu labelling, decreased generally in regions I-V, recovering partially in region IV. This metabolic shift from normal to malignant metabolic phenotype paralleled changes in the expression of HIF1α, HIF2 α, HIF3 α and genes coding for regulatory glycolytic (HK, PFK, PK and LDH), oxidative (PDH) and anaplerotic genes (PC) suggesting that these genetic alterations may be inferred from the metabolic profiles detected by multinuclear HRMAS in the same biopsy. Hypoxia inducible transcription factors Integral regulation of tumor energy metabolism is an important function of HIF1. Under hypoxic conditions, HIF1 mediates a conversion from oxidative to glycolytic metabolism through its regulation of two important enzymes: PDK1 and LDH 6. In particular, gliomas express the HIF1α, a major transcriptional factor involved in the adaptive response under hypoxic conditions

20

. HIF1α is involved in the resistance against apoptosis,

vascular remodeling, angiogenesis and cell proliferation

21

. In cancer cells, this factor

induces overexpression (region III-V, figure 4) and increases the activity of several glycolytic protein isoforms, including transporters and enzymes. Notably, by performing linear regression analysis to correlate metabolic levels and gene expression profiles, we observed a positive correlation between HIF1α and Lac (both 1H and 13C) (r= 0.528 and 0.498, respectively), supporting that these events are related. Lac production, due to HIF metabolic remodeling, also plays a role in creating a favorable environment for 15 ACS Paragon Plus Environment


glioma

22

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. HIF1α has two closely related homologs, HIF2α and HIF3α that we also

analyzed. HIF2α (also known as endothelial PAS domain protein, or EPAS1) is 48% identical to HIF1α, is induced by hypoxia, and binds to HIF1 to activate transcription of hypoxia-responsive genes

23

. HIF3α seems to be a dominant negative regulator of HIF.

These two homologs EPAS1 and HIF3α do not displayed correlation with the investigated metabolites, but their role in tumor regions seems opposite to HIF1α 24-31. Glycolytic Enzymes The HK family of enzymes catalyzes the first irreversible, rate-limiting step in glycolysis through the phosphorylation of Glc to glucose-6-phosphate (G6P)

32

. HK has four

isoforms I, II, III and IV (this last one also known as glucokinase, GCK) which are identified from different mammalian tissues

33

. In glioblastoma (GBM), HK2 is highly

expressed, whereas HK1 is predominantly expressed in normal brain and low-grade gliomas

34

. Our data agree with those of Wolf and coworkers

34

: HK2 is highly

expressed, whereas HK1 is much lower, in regions III and IV partially in V. Furthermore, we found a significant correlation between the increase of both 1H-Lac and

13

C-Lac and

the expression of HK2 (r= 0.627 and 0.689, respectively), suggesting that this enzyme is an important determinant of the increase in [3-13C]-Lac. Under hypoxic conditions, HIF and stem cell factor c-Myc activate HK2

32

. We also found that HK3 gene

expression paralleled the expression of HK2, increasing in region III and IV and decreasing in region V. Wyatt and coworkers

35

, described for the first time, the

regulation of HK3 expression, characterizing its effects in cytoprotection. HK3 expression is regulated by hypoxia through a HIF-dependent signal transduction pathway, and the activity of HK3 is also regulated at the protein level by the influence of N-terminal substrate binding on C-terminal catalysis. HK3 overexpression promotes cell survival in response to oxidative stress, perhaps by increasing cellular ATP levels, decreasing ROS production, and promoting mitochondrial biogenesis 35. 16 ACS Paragon Plus Environment

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LDH genes encode lactate dehydrogenase activity, which converts pyruvate to Lac

36

. One of the classical hallmarks of cancer cells is their higher glycolysis rate and

Lac production, even in the presence of abundant O2 (Warburg effect). Our results show the increase of 1H and

13

C Lac signals in regions III-IV directly correlate to the

expression of the LDH gene (figure 5), suggesting that increased lactate is derived from increased LDH protein content and enzymatic capacity in glioma. In fact, increased LDH activity has gained attention as a contributor to the aerobic glycolysis phenotype of cancer cells 37,38. A common metabolic feature of cancer cells is a high rate of Gln consumption normally exceeding their biosynthetic and energetic needs, in agreement with our metabolic results (see Figure 3 and Table 1 and 3). The term Gln addiction is now widely used to reflect the strong dependence shown by most cancer cells for this essential nitrogen substrate after metabolic reprogramming. A Gln/Glu cycle occurs between host tissues and the tumor in order to maximize its growth and proliferation rates 22. PFK and PKM2 play a key role in regulating glycolytic flux by converting fructose 6-phosphate to fructose 1,6-bisphosphate and by converting phosphoenolpyruvate to pyruvate, respectively; two committed steps in the glycolytic pathway

39

. The precise

role of PFK and the effects of its reduction in normal and tumoral brain regions remain insufficiently understood. As for the observed decrease of PKM2, recently it has been reported

40

that the increase of serine hydroxyl-methyltransferase (SHMT2) activity in

GBM can limit PKM2 activity, increasing PPP flux and decreasing TCA's cycle intermediates and oxygen consumption 41. In summary, our study demonstrated that combined 1H and

13

C HRMAS and

genomic analysis of adjacent brain biopsies in the C6 glioma model, revealed clearly the transition from oxidative to glycolytic phenotypes through the brain of implanted 17 ACS Paragon Plus Environment


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animals. Combined HRMAS and genomic analysis revealed increases in the expression of HIF1α, HK2, HK3 and LDH underlied mainly the increases in tumoral (4-13C)-Lac resonance, while changes in PDH1, PDH2 and CS paralleled the profile of (4-13C)-Glu changes.

Acknowledgements The present work was supported by grants SAF2014-53739-R, SAF2017-83043-R and B2017/BMD-3688 from the Ministry of Economy and Competitiveness and from the Community of Madrid to PL-L and SC, Acciones Integradas IH-HI2006–0101 from the Ministry of Science and Technology to LS and SC. Authors are indebted to Mr. Javier Pérez CSIC for careful drafting of the illustrations and to Mrs. Teresa Navarro CSIC for granting access to the Biomedical NMR Services of the Institute of Biomedical Research “Alberto Sols” CSIC.

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Figure Legends

Figure 1. T2-weighted spin-echo axial MR images of a representative rat brain bearing an implanted C6 glioma after the injection Gd(III)DTPA. Regions labeled I to V indicate the location of the five adjacent cerebral regions where biopsies were obtained. 22 ACS Paragon Plus Environment

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Figure 2. Average ex vivo HRMAS (A) and CPMG (B) 1H NMR spectra from biopsies of the five adjacent cerebral regions of Figure 1. Acquisition conditions were as described in Methods.

Figure 3. Average ex vivo

13

C HRMAS spectra from biopsies of the five adjacent

cerebral regions indicated of Figure 1. Acquisition conditions were as described in Methods. * Signal at 55 ppm derived from

13

C of ChoCC, Gly and some CHα of some

aminoacids.

Figure 4. Statistically significant changes in the expression of oxidative and glycolytic genes of five adjacent biopsies across the brain of rats bearing implanted C6 gliomas. Gene expression was determined as indicated in Methods.

Figure 5. Time-course of

13

C Lac, Gln, Glu concentrations and expression of glycolytic

and oxidative genes in the five adjacent biopsies of Figure 1.

Table 1. Transporters enzymes of energy metabolism investigated in the present study and the corresponding genes (Rattus Norvegicus) and TaqMan probes. Enzyme Monocarboxylic Trasporter Hexokinase EC 2.7.1.1 Glucokinase EC 2.7.1.2 Phosphofructokinase EC 2.7.1.11 Pyruvate Kinase

Gene Abbreviation MTC1 or Slc16a1 MTC2 or Slc16a7 HK1 HK2 HK3 GCK GCKR PFKM PFKL PKLR

Assay ID Rn00562332_m1 Rn00568872_m1 Rn00562436_m1 Rn00562457_m1 Rn00573299_m1 Rn00561265_m1 Rn00565467_m1 Rn00581848_m1 Rn00566132_m1 Rn00561764_m1



EC 2.7.1.40 Lactate dehydrogenase EC 1.1.1.27 Pyruvate Dehydrogenase EC 1.2.4.1 Pyruvate Carboxylase EC 6.4.1.1 Citrate Synthase EC 2.3.3.1 Hypoxia Inducible factor

Table 2.

1

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PKM2 LDH

Rn00583975_m1 Rn00820751_g1

PDHA1 PDHA2 PC

Rn01424350_g1 Rn01523421_s1 Rn00562534_m1

CS

Rn00756225_m1

EPAS1 HIF1α HIF3α

Rn00576515_m1 Rn00577560_m1 Rn00574710_m1

H HRMAS concentration of metabolites (µmol/g) derived from the five

adjacent cerebral regions investigated as calculated with LCModel Analysis. Region I Ac Ala Asp Cho Cr GABA Glc Gln Glu Gly GPC GSH Ile Lac Leu Myo NAA PCh PCr PE Tau Thr Val 1

1

0.3±0.1 0.7±0.2 1.2±0.3 n.d. 5,0±1,1 1.3±0.2 3,4±1,4 5.7±1.2 8.1 ±1.3 0.6±0.2 0.7±0.2 0.1±0.0 n.d 1.4±0.3 0.2±0.1 3.8±0.7 8.4±1.5 0.7±0.2 2.4±0.8 1.7±0.3 4.0±0.9 0.5±0.2 0.1±0.0

Region II

Region III

Region IV

Region V

0.5±0.2 0.8±0.3 0.8±0.3 n.d 4.6±1.3 2.3±0.8 3.1±1.6 5.8±1.6 7.1±1.7 0.7±0.4 1.1±0.3 n.d n.d 1.8±0.4 0.2±0.1 4.3±0.9 7.6±2.3 0.7±0.2 2.9±0.8 0.6±0.3 3.6±1.0 0.5±0.2 0.1±0.0

0.3±0.2 1.5±0.3 0.3±0.1 0.2±0.1 2.5±0.9 0.7±0.4 2.1±0.8 5.1±2.1 4.5±1.6 1.3±0.5 0.8±0.2 0.1±0.1 0.1±0.1 3.8±1.0 0.6±0.2 2.0±0.7 3.5±1.8 0.6±0.2 1.6±0.6 2.1±0.9 3.7±1.1 0.6±0.4 0.3±0.1

0.1±0.0 2.4±0.7 0.6±0.1 0.2±0.1 2.4±0.6 0.7±0.3 3.9±1.3 4.0±0.9 4.8±0.9 2.1±1.5 1.1±0.2 0.3±0.2 0.2±0.1 6.0±2.1 0.6±0.1 3.1±0.7 2.2±1.2 1.5±0.3 1.5±0.6 3.6±0.8 6.0±1.5 1.6±0.7 0.3±0.1

0.2±0.0 1.2±0.6 0.4±0.2 n.d 2.8±0.8 0.7±0.3 1.8±1.1 4.2±1.8 4.0±1.1 1.0±1.0 0.8±0.3 0.1±0.1 0.0±0.0 3.0±1.5 0.3±0.2 2.3±0.7 3.7±1.1 0.6±0.2 1.8±0.8 2.8±1.7 3.8±1.7 0.8±0.3 0.2±0.1

Results are reported as mean ± standard errors (n=6). n.d. not detectable


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Table 3.

13

C concentration of Lac_C3, Glu_C4 and Gln_C4 and corresponding

fractional 13C enrichments (F.E,) in the five adjacent brain regions.

Lac_C3 Region I 0.4±0.11 Region II 0.4±0.1 Region III 0.8±0.1 Region IV 1.1±0.4 Region V 0.4±0.2

[13Cµmol/g] Glu_C4 Gln_C4 1.0±0.3 0.4±0.1 0.4±0.1 0.3±0.1 0.4±0.1 0.3±0.1 0.6±0.2 0.3±0.1 0.3±0.1 0.2±0.1

Lac_C3 21±6 24±6 26±4 31±11 26±9

F. E.% Glu_C4 12±3 5±2 7±2 10±4 7±3

Gln_C4 7±2 4±1 6±1 5±2 4±1

1

Results are reported as mean ± standard errors (n=6).



Table of contents graphic (TOC) 244x168mm (150 x 150 DPI)

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I

II

III

IV

V

Righi et al. Figure 1



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Region

I

 -Glc -Glc

*

Gln C4 Glu C4 Lac C3

II

III

IV

V

Righi et al Figure 3 ACS Paragon Plus Environment



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