# An adult Drosophila glioma model to highlight metabolic dysfunctions and evaluate the role of the serotonin 5-HT 7 receptor as a potential therapeutic target

Marylène Bertrand, Frédéric Szeremeta, Nadège Hervouet-Coste, Vincent Sarou-Kanian, Céline Landon, Séverine Morisset-Lopez, Martine Decoville

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Marylène Bertrand, Frédéric Szeremeta, Nadège Hervouet-Coste, Vincent Sarou-Kanian, Céline Landon, et al.. An adult Drosophila glioma model to highlight metabolic dysfunctions and evaluate the role of the serotonin 5-HT 7 receptor as a potential therapeutic target. FASEB Journal, 2023, 37 (11), 10.1096/fj.202300783RR. [DOI]
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An adult *Drosophila* glioma model to highlight metabolic
dysfunctions and evaluate the role of the serotonin 5-HT<sub>7</sub>
receptor as a potential therapeutic target

Marylène Bertrand<fnref n="1" />, Frédéric Szeremeta<fnref n="1" />, Nadège Hervouet-Coste<fnref n="1" />, Vincent Sarou-Kanian<fnref n="2" />, Céline Landon<fnref n="1" />, Séverine Morisset-Lopez<fnref n="1" />* and Martine Decoville<fnref n="1" />,<fnref n="3" />.

<footnote n="1">¹Centre de Biophysique Moléculaire—CBM, UPR 4301, CNRS, Rue Charles Sadron, CEDEX 02,
F-45071 Orléans, France</footnote>

<footnote n="2">²Conditions Extrêmes et Matériaux : Haute Température et Irradiation—CEMHTI-CNRS UPR 3079, CEDEX 02, F-45071 Orléans, France</footnote>

<footnote n="3">³ UFR Sciences et Techniques, Université d'Orléans, 6 Avenue du Parc Floral, F-45100 Orléans, France</footnote>

<footnote>*Correspondence: Séverine Morisset-Lopez, CNRS, CBM UPR4301, Université d'Orléans, F-45071 Orléans, France. Email: severine.morisset-lopez@cnrs-orleans.fr</footnote>
**Abbreviations**: MRI, magnetic resonance imaging; HR-MAS, high resolution magic angle spinning; NMR, nuclear magnetic resonance; HSQC, heteronuclear single quantum coherence; O-PLSDA, orthogonal partial least squares discriminant analysis.

**ABSTRACT:**

Gliomas account for 50% of brain cancers and constitute therefore the most common brain tumors. Molecular alterations involved in adult gliomas have been identified and mainly affect tyrosine kinase receptors with amplification and/or mutation of the epidermal growth factor receptor (EGFR) and its associated signaling pathways. Several targeted therapies have been developed but current treatments remain ineffective for glioblastomas, the most severe forms. Thus, it is a priority to identify new pharmacological targets. *Drosophila* glioma models established in larvae and adults are useful to identify new genes and signaling pathways involved in glioma progression. Here, we used a *Drosophila* glioma model in adult, to characterize metabolic disturbances associated with glioma and assess the consequences of 5-HT<sub>7</sub>R expression on glioma development. First, by using in vivo Magnetic Resonance Imaging we have shown that expression of the constitutively active forms of EGFR and PI3K in adult glial cells induced enlargement of brains. Then, we explored altered cellular metabolism by using High-Resolution Magic Angle Spinning NMR and <sup>1</sup>H-<sup>13</sup>C Heteronuclear Single Quantum Coherence solution state. Discriminant metabolites identified highlight rewiring of metabolic pathways in glioma, and associated cachexia phenotypes. Finally, the expression of 5-HT<sub>7</sub>R in adult model attenuates phenotypes associated with glioma development. Collectively, this whole-animal approach in *Drosophila* allowed us to provide several rapid and robust phenotype readouts such as enlarged brain volume and glioma-associated cachexia, as well as to determine the metabolic pathways involved in glioma genesis and finally to confirm the interest of the 5-HT<sub>7</sub>R for the treatment of glioma.

**KEYWORDS:**

*Drosophila* – cachexia – glioma– metabolism – MRI– NMR – 5-HT<sub>7</sub> receptor – serotonin
# 1 INTRODUCTION

Gliomas constitute 50% of brain cancers and therefore represent the most common brain tumors. The frequency of appearance of gliomas is higher around the age of 50 to 60 (1). However, it also affects children, pediatric glioma accounting for around 35% of central nervous system tumors diagnosed in children (2). The morbidity rate of gliomas is very high regardless of their degree of malignancy.

Gliomas have first been classified according to the predominant cell type that composes them (astrocytes, oligodendrocytes or mixed) and their potential for aggressiveness. The 4 grades range from the most benign tumors, grade 1, to the most aggressive, the glioblastomas (GBMs), grade 4 (3, 4). Considering additional phenotypic and genetic parameters, the World Health Organization (WHO) has refined the classification of gliomas (5).

Different genetic abnormalities are known to cause gliomas. These abnormalities affect genes involved either in cell signaling pathways or in epigenetic processes (1, 6). The most common genetic lesions in gliomas affect tyrosine kinase receptors (RTK). In particular, there is amplification and/or mutation of the epidermal growth factor receptor (EGFR), which plays an essential role in the normal specification, survival, proliferation and migration of neural stem cells as well as precursor cells, astroglial and oligodendroglial (7, 8). Also found are activating mutations and amplifications of other RTKs, such as PDGFRα and FGFR (8). Other mutations in the phosphoinositide 3-kinase (PI3K) signaling pathway, which normally stimulates cell proliferation, growth and migration in various neural cells have been identified (7). The mutations occur in PIK3CA, which encodes the p110α catalytic subunit of PI3K, and PIK3R1, which encodes the p85α adapter subunit of PI3K (9). Key PI3K effectors, such as AKT kinases, are often activated or amplified (10). There is also often a loss of PTEN (Phosphatase and TENsin homolog), which is known as a tumor suppressor gene and negatively control the PI3K signaling pathway. PTEN dysfunction can result from point mutations or epigenetic extinction (11). Mutations in the genes encoding isocitrate dehydrogenases 1 and 2 (IDH1 and IDH2) have also been identified in low grade gliomas (grades II and III) and in secondary GBMs in adult (GBM, IDH-mutant) (5, 12). In the 1990s, the main treatments for gliomas were based on the use of cytotoxic molecules (nimustine (ACNU), carmustine (BCNU), lomustine (CCNU) and temozolomide (TMZ) associated with
radiotherapy. Unfortunately, these treatments showed limited efficacy in the majority of cases (13). Then, targeted therapies against tyrosine kinase receptors such as EGFR as well as the associated signaling cascades (PI3K) have been developed. However, these promising treatments have been abandoned as tumors quickly develop resistance to them, using bypass strategies of EGF receptor blockers (14). Other treatments are now explored that target metabolic pathways and epigenetic processes (15, 16).

Cancer cells significantly modify their metabolism to ensure their proliferation and dissemination. They must also adapt to the evolution of their microenvironment. Very often, cancer cells use the Warburg effect (aerobic glycolysis) to produce energy, although it is less effective than the oxidative metabolism. Glutaminolysis has also been established as characteristic of cancer metabolism (17). Glutamine is essential to provide energy as well as precursors necessary for the synthesis of non-essential amino acids, purines, pyrimidines and fatty acids. It is also involved in the maintenance of mitochondrial metabolism. Glioma cells also have altered metabolism, likely due to activation of signaling pathways such as the RTK/PI3K/AKT/mTOR pathways which regulate glucose and glutamine metabolism. In particular, several studies have shown that glioma cells use the Warburg effect and glutamine to ensure their development (18, 19).

Cancer cells may also affect metabolism in distant organs which can result in cachexia, a pathophysiological condition linked to tumor development. In Humans, more than 80% of patients with advanced cancers displayed cachexia (20, 21). Cancer cachexia results in metabolic disorders that induce in loss of adipose tissues, muscles and fat reserves which cannot be compensated by nutritional support (22, 23). These metabolic disturbances result from alterations in glucose, lipids, and protein metabolism (24). Until now, glioma-induced cachexia symptoms are poorly documented. Only recent works by Cui et al. (25) have demonstrated that murine models of glioma displayed cachexia.

These metabolic disturbances might constitute new targets for drug therapy. For example, targeting the Warburg effect has already shown its effectiveness in limiting tumor growth (15, 16). In addition, recent results have suggested that treatment of cachexia symptoms, in particular muscle wasting, could be of interest (26, 27). Such treatments do not affect the tumor growth, but they do extend the lifespan in multiple murine cancer cachexia models.
The fruit fly *Drosophila melanogaster* has proven to be a very good alternative animal model of many human disease studies, particularly in neurodegenerative diseases and cancers (28-31). *Drosophila* has become an attractive model for investigating tumor's biology and evaluating new molecules of therapeutic interest (32-36). Indeed, *Drosophila* has many advantages: a short generation time, simple rearing conditions, many mutants and sophisticated genetic tools are available. From a genetic point of view, *Drosophila* has 77% orthology with human genes involved in pathologies (28, 37). In particular, most of the signaling pathways controlling cell growth and invasion such as the EGFR and PI3K pathways as well as metabolic pathways have a conserved function in flies. Despite the small size of its brain (almost a million times fewer neurons than the human brain), there are many similarities between the nervous system of *Drosophila* and that of mammals. Both have neurons and glial cells that communicate by using many common neurotransmitters: dopamine, serotonin, acetylcholine, glutamate or GABA. Different types of glial cells (surface glia, cortical glia and neuropil glia) are also found in *Drosophila* brain (38).

Two glioma models have been developed in *Drosophila* by expressing two transgenes encoding a constitutively active form of EGFR (dEGFR$^{λ}$) and of PI3K (dp110$^{CAAX}$) specifically in glial cells. The first one, established by Read et al. (39), is a larval model in which dEGFR$^{λ}$ and dp110$^{CAAX}$ are expressed throughout development and lead to lethality of the third stage larvae. This larval model recapitulates main characteristics of human gliomas. The second model, established by Chi et al. (40) allowed to induce glioma development specifically at adult stage. These two models have been widely used to identify new genes and signaling pathways involved in glioma progression (41-45).

Among the genes possibly involved in the progression of glioma, our recent data demonstrated the role of the serotonin 5-HT<sub>7</sub> receptor (5-HT<sub>7</sub>R) in a *Drosophila* model of glioma (46). The human 5-HT<sub>7</sub>R expression in this animal model reduced larval lethality and partially restored dysregulated biomarkers. In addition, some transcriptomic analysis from GBM patients showed a significant decrease of 5-HT<sub>7</sub>R expression in GBM compared to normal tissue (46). Analysis of its expression and signaling in various glioma cell lines demonstrated that's glioma cells express functional 5-HT<sub>7</sub> receptor (47). Therefore, a pivotal role of 5-HT<sub>7</sub>R in the progression of glioma makes this receptor an attractive target for glioma diagnosis and treatment in neuro-oncology. Among serotonin (5-HT) receptors, the 5-
HT<sub>7</sub>R is the most recently identified. It is highly expressed in different types of human tissues, particularly in the CNS (such as the hippocampus, hypothalamus, thalamus and cortex), the gastrointestinal tract, and in different blood cells (48). 5-HT<sub>7</sub>R plays a key role in a variety of physiological processes including cognition, memory processes, mood, pain processing, and regulation of circadian rhythms (49). Moreover, it is involved in the pathophysiology of depression, schizophrenia, and sleep disorders, as well as in inflammatory and immune-mediated disorders (50). Therefore, based on its therapeutic potential, modulation of 5-HT<sub>7</sub>R activity has been the focus of numerous drug discovery and development programs (51-53). Interestingly, some marketed neuroleptic agents such as pimozide have a high affinity to 5-HT<sub>7</sub>R and demonstrated therapeutic interest with anti-proliferative activity *in vitro* and antitumor actions *in vivo* (54). Whereas some data demonstrated a positive effect of 5-HT<sub>7</sub>R expression in glioblastoma growth, other reported that its blocking could have a therapeutic interest. The discrepancy of these results could be related to the difference in the model used (*in vitro* or *in vivo*) and/or in the intrinsic activity of the tested drugs on 5-HT<sub>7</sub>R signaling pathways (G protein versus arrestin pathway). Alternatively, it can also be due to nonspecific effects on other monoamine receptors.

In a recent study, we demonstrated a beneficial effect of 5-HT<sub>7</sub>R on glioma in a larval model (46), therefore leading us to explore its therapeutic potential in a glioma model in adults. This latter model could be considered closer to human gliomas that develop rather in older people. In addition, the larval context constitutes a very particular environment, which could differ from the adult one, both from a metabolic and signaling point of view. In this study, we used a model of glioma in adult obtained by using the Geneswitch system (55-57). It allows temporal control of expression of the constitutively active forms of PI3K and EGFR in glial cells. The effect on brain morphology was investigated by *in vivo* Magnetic Resonance Imaging (MRI). Consequences on metabolism were characterized by High Resolution Magic Angle Spinning (HR-MAS) NMR and solution state Heteronuclear Single Quantum Coherence (HSQC) NMR spectra. Then, we evaluated the effect of 5-HT<sub>7</sub>R expression on some of the phenotypic changes observed in this glioma model.

# 2 MATERIALS AND METHODS

## 2.1 *Drosophila* Culture
The fly stocks were maintained at 22 °C on a standard medium (per liter: 90.25 g of cornmeal, 82.5 g of dry yeast, 10.75 g of agar, and 37.5 mL of a 10% solution of methyl-4-hydroxybenzoate in ethanol). UAS-dp110<sup>CAAX</sup>; UAS-dEGFR<sup>λ</sup> *Drosophila* were those used in Maravat et al. (58), the *repo-Gal4-GS* strain was kindly provided by H. Tricoire. UAS-dp110<sup>CAAX</sup>; UAS-h5-HT<sub>7</sub>R; UAS-dEGFR<sup>λ</sup> *Drosophila* are described in Courant et al. (46). Other *Drosophila* lines were obtained from the Bloomington Stock Center (Indiana University, USA).

To induce glioma, UAS-dp110<sup>CAAX</sup>; UAS-dEGFR<sup>λ</sup> virgins were crossed with repo-Gal4-GS males at 22°C. Then, 3 days old F1 progeny was transferred in new tubes containing medium with 100mM mifepristone (Sigma-Aldrich, St. Louis, MO, USA) dissolved in ethanol (glioma flies) or medium with ethanol alone (control flies) and maintained at 26°C until observation. The effect of human 5-HT<sub>7</sub>R expression on glioma was investigated by crossing UAS-dp110<sup>CAAX</sup>; UAS-h5-HT<sub>7</sub>R; UAS-dEGFR<sup>λ</sup> virgin females with repo-Gal4-GS males. Three days old F1 progeny was treated with 100mM mifepristone as glioma flies.

## 2.2 MRI experiments

Magnetic resonance imaging (MRI) experiments were conducted at two different magnetic fields. For the first ones, a Bruker BioSpec 94/20 imaging-spectrometer (Bruker BioSpin, Wissembourg, France) operating at 9.4 T (¹H Larmor frequency of 400.13 MHz) equipped with a Bruker BG06S gradient set (950 mT/m) was used. For a better sensitivity, the experiments were performed on a Bruker Avance IIIHD spectrometer operating at 17.6 T (¹H Larmor frequency of 750.13 MHz) equipped with a Bruker micro2.5 gradient system (1500 mT/m). MR images were acquired using homemade scroll-coils built for whole body *Drosophila* imaging, as described in Même et al. (59). The coils are 3.6 mm long and have 2.1 mm inner diameter. For these MRI experiments, two *Drosophila* were put in a glass capillary, one control and one glioma model with heads face to face, allowing imaging both heads in the same time with same conditions. The capillary was filled with fluorinated oil to decrease magnetic susceptibility differences between *Drosophila* body and its environment, and to keep *Drosophila* living like.

MR images at 9.4 T were acquired using a 3D-Flash sequence (TE/TR = 6.9/50 ms, FA = 60°, 49 accumulations, acquisition time of 15 h, field of view 28x21x21 mm³ with an isotropic resolution of 14 µm). Images were resliced with JIVE, an image analysis utility provided by
the Bruker ParaVision software to obtain head images in front view position of each
*Drosophila*.

A 3D-diffusion weighted imaging (DWI) sequence (TE/TR = 15/400 ms, 8-14 accumulations, acquisition time of 3-6 h, field of view 6x1.5x1.5 mm³ with an isotropic resolution of 47 µm) was used at 17.6 T. The DWI images were obtained with *b* = 2000 s/mm² allowing to clearly differentiate the brain in the *Drosophila* head. After reslicing the 3D images with JIVE, the volume of the brains was calculated after an image analysis with the *Image Processing Toolbox™* from Matlab (MathWorks, R2017b).

**2.3 High Resolution-Magic Angle Spinning (HR-MAS) NMR Experiments**

**2.3.1 Sample preparation and High Resolution-Magic Angle Spinning (HR-MAS) acquisition**

Three heads of adults were transferred into a 4 mm HR-MAS rotor filled with PBS in D₂O.
Each sample was analyzed on a Bruker 750 MHz Avance IIIHD spectrometer (Bruker BioSpin,
Germany) equipped with a Bruker 4 mm MAS probe. The sample was kept at 3 °C with a
rotor spinning frequency of 4 kHz. HR-MAS NMR data were acquired as described in Maravat
et al. (58) using a spin echo (echo time of two rotor periods) with a 1 s low-power water
suppression. Each FID was acquired in 40k points with a spectral width of 20 kHz and 1024
scans corresponding to an acquisition time of 53 min. Two-dimensional ¹H–¹H total through
bond correlation spectroscopy experiments (60) were also performed on several samples for
assigning the observed peaks.

Spatially-resolved MAS NMR spectroscopy were performed on intact flies using the chemical shift imaging sequence (CSI-MAS) and the experimental protocol proposed by Sarou-Kanian et al. (61). Briefly, each fly was placed in a hydrogen-free insert inside a 3.2 mm MAS rotor. In this way, the *Drosophila* was centered along the rotor axis of rotation in the anteroposterior direction. The fly was also kept at 3°C for anesthesia and was rotated at a spinning frequency of 2630 Hz to ensure its physical integrity. The field of view was of 0.5 mm with a phase encoding of 32 steps corresponding to a resolution of the slices of 156 µm. The spectral dimension (FID of 16k points, spectral width of 20 kHz), was acquired after a recycling delay of 0.5 s and a 1 s low-power water suppression; 96 accumulations were required for a sufficient signal-to-noise ratio which corresponded to a total experimental time of 100 min.
### 2.3.2 NMR Data Processing and Statistical Analysis

Each HR-MAS spectrum was phased with a Bruker TopSpin 3.5. Baseline correction, chemical shift alignment, and then 0.01 ppm bucketing of the preprocessed HR-MAS data were done using the web-based NMR Processing tool NMRProcFlow (62). Statistical analyses were carried out on Workflow4Metabolomics (63). First, all spectra were normalized using the total intensity method. Then, multivariate analyses were performed on each set of normalized data: (i) principal component analysis (PCA) was performed to get an unsupervised overview of the grouping between samples and spot any potential outlier; (ii) orthogonal partial least square discriminant analysis (OPLS-DA) (64) were performed using discriminant conditions (glioma and control) to certify that a prediction model could be built based on the experimental data. A seven-fold cross-validation method was used to prevent over-fitting and to assess the significance of the model by giving the Q2 value. A permutation testing, using 100 permutations, further validates the model. Only variables with a variable importance in projection (VIP) calculated from the OPLS-DA models above 1 were kept; (iii) t-tests were then performed on the filtered data.

Differences between groups were considered significant if false discovery rate (fdr)-corrected p-values were under the 0.05 threshold. The OPLS-DA multivariate analyses permitted to select the variables differentiating the glioma samples from the controls, and univariate analyses (t-test) were done to confirm the significance of the variables.

### 2.3.3 Metabolite Identification and Pathway Analysis

Buckets involved in the differentiation between glioma and controls were identified. Then, the corresponding signals were assigned to metabolites using online databases such as the human metabolome database (65) and the 2D NMR experiments leading to the list of discriminant metabolites between control and glioma samples.

## 2.4 Solution state NMR experiments

### 2.4.1 ¹³C-glucose feeding and sample preparation

Flies fed for 4 days with 100mM mifepristone or vehicle were transferred to vials containing blotting paper moistened with 10% [U-¹³C6] glucose (Cambridge Isotope Laboratories Inc., Tewksbury, MA, USA) and then placed for 12 hours at 22°C. They were sorted into batches of 10 in order to have a good signal to noise ratio in NMR spectra, and were flash frozen in a 1-
mL microtube by immersion in liquid nitrogen. Three samples were prepared for controls and five for glioma-bearing flies.

### 2.4.2 Metabolite extraction and solution state ¹H-¹³C HSQC NMR analyzes

Metabolite extracts were performed as described in Maravat et al. (58).

For NMR analyzes, 300 µL of ice-cold 50 mM phosphate buffer (pH 7.4) in D₂O containing 7.5 µg/L 3-(trimethylsilyl)-propionic-2,2,3,3-d4 acid sodium salt (TSP-D4) was added to the lyophilized samples. After homogenization with vortex and centrifugation for 10 min at 15,300 g at 5°C, 250 µL of the supernatant were transferred to a 3-mm NMR tube and analyzed.

The NMR measurements were performed at 25°C on a Bruker Avance-III HD 700 spectrometer, equipped with a 5-mm HCN triple resonance cryoprobe. Bruker's TopSpin software 3.2 package was used for the acquisition and processing of NMR spectra. ¹H-¹³C-HSQC spectra were acquired (NS=64, acquisition time: 1h55min). An 1D-¹H NMR spectrum was acquired separately with a cpmgpr1d pulse program (NS=128 scans).

Thirteen peaks corresponding to the following metabolites: acetate, β-alanine, aspartate, citrate, glucose, glutamate, glutamine, glycine, glycogen, lactate, malate, proline, succinate (identified using the database HMDB (65)) were integrated from the ¹H-¹³C HSQC spectra of three control and five glioma samples. A total normalization and t-tests were performed to investigate whether they increased or decreased significantly.

## 2.5 Microscopy

Ovaries and crops were dissected in PBS and fixed in 4% paraformaldehyde in PBS for 20 minutes at room temperature. The fixed tissues were washed three times with PBS before observation under a Zeiss stereo-microscope.

## 2.6 Colorimetric food intake.

After 5 days of mifepristone or vehicle treatment, flies were transferred in vials containing 1% agar, 5% sucrose and 0.05% of blue dye (brilliant blue FCF). Number of flies which have fed were determined after 2 hours at 22°C. Three independent experiments were performed (N>50).
## 3 RESULTS

### 3.1 Expression of EGFR and PI3K in adult glial cells induces brain hyperplasia

Two *Drosophila* glioma models have been previously described which express constitutive forms of EGFR (dEGFR$^{λ}$) and PI3K (dp110$^{CAAX}$) in larvae or adults. The adult model used the temperature sensitive mutant gal80ts to control the temporal expression of EGFR and PI3K. Here, we used the Geneswitch system to allow conditional expression of EGFR and PI3K in glial cells of adult brains. The Geneswitch (Gal4-GS) consists in a fusion of the DNA binding and transcriptional activation domains of the yeast GAL4 with the regulatory activation domain of the human progesterone receptor (55-57). GAL4-GS will not bind to UAS site in absence of progesterone, but in presence of progesterone or its analog mifepristone it will bind to UAS site and activate transcription. We used a *repo-GAL4-GS* fly strain to induce EGFR and PI3K specifically in glial cells.

In a first step, we ensured that the addition of 100 mM of mifepristone in the rearing medium was sufficient to mediate gene expression by the *repo-Gal4-GS* driver. To do this, we crossed *UAS-GFP* females with *repo-Gal4-GS* males and 3-days old offspring was treated with 100 mM mifepristone. The GFP fluorescence detected in heads of flies early as one day of treatment, indicates that the concentration of 100mM mifepristone will activate both *UAS-dp110<sup>CAAX</sup>* and *UAS-EGFR<sup>λ</sup>* transgenes to induce glioma in adult flies.

Chi et al. (40) have shown that induction of both EGFR and PI3K expression in adult brains induced an increase of the brain volume as a result of glial cell proliferation. To verify this in our glioma model, MRI images of the head were acquired for glioma-bearing *Drosophila* (treated with mifepristone) and control flies (treated with vehicle). Figure 1A illustrates the coronal slices of heads for representative glioma-bearing and control flies. First images from the 3D-Flash and the DWI (A0) sequences reveal an overexpression of water signal in the whole head of the glioma-bearing flies which suggest either an hyperproliferation of glial cells or/and the formation of edemas. In addition, DWI images (B=2000 s/cm²) cancel the free water signal while preserving the water signal confined in the brain tissues. As shown in the 3D isosurface rendering (Figure 1B, and video in supplementary material) an enlargement of brain in glioma-bearing flies is observed. Calculations of the brains' volume by using these data indicate that glioma brains were at least 25% larger than control brains
(Figure 1C). It can be concluded that a treatment for 5 days with 100mM of mifepristone leads to hyperproliferation of glial cells in agreement with Chi et al (40).

## 3.2 Metabolism is modified in adult glioma model

### 3.2.1 1D ¹H HR-MAS NMR (fly heads)

Metabolism is drastically modified in cancer cells to allow their rapid proliferation and dissemination. Recently, we described alterations of metabolism in a *Drosophila* larval glioma model (58). Here, we used High-Resolution Magic Angle Spinning nuclear magnetic resonance (HR-MAS-NMR) techniques to obtain metabolic profiles in heads of adult flies developing glioma after mifepristone treatment. A representative HR-MAS spectrum of fly heads is shown in Figure 1S as supplementary data.

First of all, we checked that the treatment with mifepristone did not modify by itself the metabolism in a significant way. For this, we compared the metabolic profiles of heads of progeny of a cross between $w^{118}$ females and *repo-Gal4-GS* males treated with mifepristone or vehicle (ethanol alone) during five days. No significant difference was identified between the two groups of samples indicating that, in our conditions, the administration of mifepristone has no effect on metabolism. To compare metabolomic profiles of glioma-bearing flies (*dp110<sup>CAAX</sup>; EGFR<sup>λ</sup>; *repo-Gal4-GS*) flies treated with mifepristone during five days and the profiles of the controls (*dp110<sup>CAAX</sup>; EGFR<sup>λ</sup>; *repo-Gal4-GS*) treated with ethanol alone during the same time), a supervised Orthogonal Partial Least Square discriminant analysis (OPLS-DA) was performed. A prediction model can be built according to the data (Figure 2SA). To minimize the risk of overfitting and possible false-positive finding, this model was validated using a permutation test (100 times) (Figure 2SB). The cross validation of the OPLS-DA (Figure 2A) and the score values indicate that the established model is robust (R2X=0.857, R2Y=0.976, Q2= 0.913, CV-ANOVA *p* = 8.64e-5) and that the metabolism of glioma-bearing flies clearly differs from that observed in control flies. Buckets for which the variable importance in projection (VIP) from the OPLS-DA was greater than 1 were retained and allowed to unambiguously identify 19 discriminant metabolites (Table 1). These metabolites result from alterations in various metabolic pathways as for example energetic pathways (glycolysis, TCA cycle) and anabolic pathways (amino-acid metabolism, TCA cycle). Almost all metabolites are increased in gliomas except glycerol and fatty acids that are
dramatically decreased in glioma samples. Glycerol and fatty acids are components of triglycerides which are involved in energy storage.

Then, we compared discriminant metabolites in the adult glioma model with those previously identified in the larval glioma model by HR-MAS NMR (58). Among the 19 discriminant metabolites in the adult model, 11 have also been identified in the larval model (Figure 2B and Table 1S in supplementary data). Only eight metabolites appeared discriminant in the adult glioma model. We can note that proline (common to larval and adult models) is increased in the adult model whereas it is decreased in the larval model. Three discriminant metabolites previously identified in the larval model (acetate, GABA and lactate) have not been identified as discriminant in this study. In the case of lactate, its two resonances (1.3 and 4.1) are masked by lipids (1.3) or glycerol (4.1) in the adult glioma model. This led us to perform ¹H-¹³C HSQC experiments.

### 3.2.2 2D 1H-13C HSQC analysis (fly extract)

To refine the metabolomic analysis, ¹H-¹³C HSQC NMR spectra of control and glioma-bearing flies were performed from fly extracts, after feeding *Drosophila* with [U-¹³C6] glucose. This enabled specific monitoring of ¹³C distribution in metabolites downstream of glucose, and identification of metabolic pathways differentially used by glioma-bearing flies or controls. First, 20 metabolites were unambiguously identified on the ¹H-¹³C HSQC NMR spectra (Figure 3). As expected, metabolites involved in glycolysis or the TCA cycle were detected, as well as amino acids and metabolites involved in energy storage (trehalose, glycogen, fatty acids). Metabolites not synthesized from glucose, such as PC and PE synthesized from choline or ethanolamine, were not detected. Some metabolites, such as glycogen, citrate or malate, have not been identified in 1D HR-MAS NMR experiments. Glycine and lactate were masked in 1D HR-MAS NMR spectra (performed *ex vivo*). However, they were clearly identifiable in the solution state ¹H-¹³C HSQC spectra (performed on fly extracts) (Figure 3). Second, the comparison of the spectra obtained from glioma-bearing flies and controls revealed clear differences, such as the decrease in fatty acids content (Figure 3B) in glioma-bearing flies, confirming results obtained in 1D HR-MAS experiments. Finally, a relative metabolite quantification from ¹H-¹³C-HSQC data was performed. We selected a set of 13 non-overlapping peaks: β-alanine, acetate, aspartate, citrate, glucose, glutamate, glutamine,
glycine, glycogen, lactate, malate, proline and succinate, and representing of different
metabolic pathways (Table 2). Of these 13 metabolites, six were discriminant between
control and glioma-bearing flies. The level of lactate is higher in glioma-bearing flies. Glucose
also increases, but is not taken into account as it is the source of energy. On the contrary,
levels of four metabolites were lower in glioma-bearing flies than in controls: aspartate,
glutamine, glycogen and β-alanine. Levels of the remaining 7 metabolites (namely acetate,
citrate, glutamine, glycine, malate, proline and succinate) did not differ significantly between
glioma-bearing flies and controls.

**3.3 Adult glioma induces cachexia phenotypes**

Metabolomic 1D ¹H HR-MAS NMR and 2D ¹H-¹³C HSQC solution state NMR analysis of glioma-bearing flies pointed out a decrease in triglycerides (glycerol and fatty acids), which is considered as metabolic hallmark of cachexia or wasting effect in *Drosophila* (66). If the decrease in triglycerides observed in the isolated heads results from a wasting effect, we expect a similar decrease in other parts of the fly body. Thus, CSI NMR spectra on the thorax and abdomen of control and glioma-bearing flies were acquired. Results are presented on Figure 4A and clearly show that loss of triglycerides is not restricted to head of glioma-bearing flies but is also observed in abdomens of these flies. In the thorax, lipids are poorly detected, thus it is difficult to clearly evaluate their decrease.

Cachexia is also characterized by wasting of distant organs such as ovaries and muscles (66).
Therefore, we compared the size of ovaries from control and glioma-bearing flies. Most of
glioma-bearing flies displayed wasted ovaries (Figure 4B) and rare flies had more normal
ovaries. To evaluate muscle degeneration, we used β-alanine, a known metabolic marker of
muscle integrity previously identified (61). Comparison of HR-MAS NMR spectra of thorax of
control and glioma-bearing flies has revealed a decrease in β-alanine, which is consistent
with muscle degeneration (Figure 4A). This result is reinforced by the HSQC analysis which
shows a significant decrease in the biosynthesis of this amino acid in glioma-bearing flies
(Table 2).

Since these phenotypes, i.e., lipid loss and ovarian and muscle wasting, are also associated
with diet, we investigated whether flies with gliomas fed in the same way as controls. For
this, the flies were placed after 5 days of mifepristone treatment in a vial containing 1% agar, 5% sucrose and 0.05% blue dye for 2 hours. Then, the flies that presented the blue dye in their digestive tract were counted. Control and glioma-bearing flies have ingested blue dye. Interestingly, a greater proportion of glioma-bearing flies fed during these 2 hours (91% versus 58% for controls). Inspection of abdomen of these flies revealed that the crop of glioma-bearing flies was abnormally enlarged as compared to control flies (Figure 4C), suggesting that during this period glioma-bearing flies fed more than controls. This could account for the increase of ¹³C glucose in glioma-bearing flies observed in ¹H-¹³C-HSQC experiments. To eliminate a particular appetite of flies for glucose which would explain this phenotype, we checked the crop size in flies which were not fed with glucose, but with standard medium. All glioma-bearing flies displayed an enlarged crop, none had a normal crop (Figure 4D). This phenotype is usually observed when flies are re-fed after starvation. Crop size is regulated by the nutritional status of the fly (67-69), but also by myosuppressin neurons (70). In the case of cachexia, the nutritional state of the fly resembles of that induced by starvation and could lead the fly to ingest more food, thus inducing distention of its crop. A large part of the abdomen is normally occupied by the enlarged ovaries, reducing the space available for crop distention. This is not the case in glioma-bearing flies whose ovaries are severely wasted, hence allowing sufficient space for enlargement of their crop. These results strongly suggest that glioma-bearing flies are able to feed and that the observed wasting or cachexia phenotypes are not the result of diet but rather the presence of the tumor.

Next, we addressed the question of whether the systemic decrease of fatty acids and glycerol was also induced in the larval glioma model. Thus, we performed *in vivo* HR-MAS experiments on whole larvae. The spectra obtained clearly showed that the glycerol and fatty acids levels were not decreased in glioma-bearing larvae. On the contrary, their levels were slightly higher (Figure 3S).

## 3.4 Expression of 5-HT<sub>7</sub>R attenuates glioma-associated phenotypes

Recently, we showed that the human serotonin 5-HT<sub>7</sub> receptor could act as a tumor suppressor in the larval glioma model (46). However, glial cells in adult and larval brains show clearly differences. Indeed, cells that make up neuropil glia in larval and adult
*Drosophila* do not originate from the same precursors. Larval glial cells do not contribute to adult glial cells as they undergo programmed cell death at the end of the larval stage (71). In addition, Huang et al. (72) showed that larval and adult astrocytes (a class of glial cells) express different set of genes, many of them having functions in development and differentiation. Thus, it seemed interesting to test whether this receptor also behaved as a tumor suppressor in the adult model. To answer this question, offspring of *UAS-dp110<sup>CAAX</sup>*, *UAS-h5-HT<sub>7</sub>R*; *UAS-dEGFR<sup>α</sup>* virgins *and repo-Gal4-GS* males have been treated with mifepristone for 5 days before imaging of their brains by MRI (Figure 5A). Interestingly, brains expressing 5-HT<sub>7</sub>R had a phenotype closer to control than glioma-bearing flies, strongly suggesting that the expression of 5-HT<sub>7</sub>R was able to reduce the growth of tumors. However, the amplitude of the effect was more or less important, depending on the individual as shown on Figure 5A.

Then, we studied two other phenotypes associated with glioma, namely ovaries wasting and
crop enlargement. Very rare females expressing 5-HT<sub>7</sub>R exhibited ovaries as wasted as
glioma-bearing flies, most of them had ovaries similar to controls or slightly wasted (Figure
5B). Inspection of crops revealed that flies expressing 5-HT<sub>7</sub>R can be classified in three
categories: those with a small (almost normal) crop, those with a medium crop and those
with a large crop (Figure 5C). A majority of flies expressing 5-HT<sub>7</sub>R exhibited a medium crop
(63%), only 23% have a large crop and 13% a small crop. In contrast, all glioma-bearing flies
have a large crop. Taken together, these observations showed that glioma-associated
phenotypes were less severe when 5-HT<sub>7</sub>R was expressed in glial cells.

4. DISCUSSION

In this study, we have studied an adult glioma model in *Drosophila melanogaster* obtained
by using the Geneswitch (Gal4-GS) system to express constitutive forms of EGFR and PI3K
specifically in glial cells of adult brains. First, by using *in vivo* MRI imaging, we confirmed
enlargement of brains in these adults. Then, we evaluated metabolic disturbances of cancer
cells and the consequences of 5-HT<sub>7</sub>R expression in this model.

**4-1 Metabolism is modified in glioma-bearing flies**

Our study highlighted metabolic pathways that were altered in glioma-bearing flies. Many of
the discriminant metabolites identified in this study have a function in energy metabolism
(glucose, glutamate, glutamine, glycerol, histidine, isoleucine, leucine, fatty acids, myoinositol, proline, pyruvate).

To assure their rapid proliferation, cancer cells require increased biosynthetic activities. This is ensured by reprogramming metabolic pathways and using alternative energy sources (73), thus allowing an increased production of energy and macromolecules. Our HR-MAS experiments revealed an increase of glucose and pyruvate levels, which probably results from an increase of glycolysis in glioma-bearing flies. This may result from the activation of the EGFR and PI3K/AKT pathways, known as key players in this process (74, 75).

This study also revealed an increase in the level of several amino acids (alanine, arginine, glutamate, glutamine, histidine, isoleucine, leucine, proline, tyrosine and valine) in glioma-bearing flies as compared to controls. Several studies have already reported such modifications in both brain tumors and extracellular fluids of brain tumors (76, 77). More recently, ¹H HR-MAS NMR spectra obtained from human *ex vivo* glioma tissue has also showed an increase of alanine in samples of grade IV compared to grade II or III (78). A high level of amino acids would be beneficial for protein and nucleic synthesis. In addition, they can serve as alternative energy sources to fuel the TCA cycle (17, 19, 79-82).

We also observed an increase of myoinositol (MI) in glioma-bearing flies. MI, produced by astrocytes, is a marker of astrogliosis (83). It is an important metabolite which exerts multiple functions. It acts as an osmolyte in the brain allowing the regulation of the intracellular osmolality to adapt changes in the extracellular compartment. It also participates in the activation of protein kinase C which is involved in the regulation of proteolytic enzymes responsible for cell invasion in glioma, as, for example, matrix metalloproteases (84, 85) and phosphatidylinositol lipids synthesis (86).

The major discriminant biomarkers of glioma-bearing flies are fatty acids and glycerol (constituents of triacyl glycerides or TAG) whose levels decrease dramatically. TAG constitute an energy storage. Several studies show that fatty acids are mainly used as an energy source in glioma cells (87-89). Fatty acid oxidation also plays an important role in limiting ROS production and therefore contribute to glioma cells survival (89, 90). In *Drosophila*, TAG are mainly stored in fat body. In case of starvation, they are hydrolyzed and then lipolysis products can be delivered to other tissues, in particular in the brain, via hemolymph. It has been demonstrated that, under normal conditions, glial cells in *Drosophila* use lipolysis for energy production (91).
Interestingly, solution state NMR ¹H-¹³C HSQC experiments, performed on extracts, allowed us to analyze more metabolites, some of which had not been identified on *in vivo/ex vivo* 1D HR-MAS NMR spectra. The metabolites identified by ¹H-¹³C HSQC experiments are only those that have incorporated ¹³C from ¹³C glucose feeding. Thus, we have identified 6 discriminant metabolites between glioma-bearing flies and controls: β-alanine, aspartate, glucose, glutamine, glycogen and lactate. A higher level of ¹³C lactate was detected in glioma-bearing flies as compared to controls. This is a characteristic of cancer cells and results from the Warburg effect or aerobic glycolysis (92), which hijacks pyruvate from the TCA cycle and uses it to produce lactate through fermentation. This observation strongly supports the use of Warburg effect in adult glioma-bearing flies. This has already been documented in human tumors (88). Lactate is not only a by-product of anaerobic glycolysis, but it is also a multifunctional metabolite (93, 94). It may act as a signaling molecule and to regulate gene expression by acting at the epigenetic level as a precursor of lactylation of histones (95, 96). Glycogen, a form of glucose storage, was also identified as a discriminant metabolite. In *Drosophila*, it is synthetized and stored mainly in muscles and fat body. Several studies have shown that fat body glycogen was essential for metabolic safeguard of energy production in *Drosophila* and that glycogen metabolism played a crucial role in larval development and adult fitness, in particular under fasting conditions (97, 98). In mammal, glycogen is mainly stored in liver (equivalent of fat body) and muscles, but it is also an important metabolite in brain and heart (99-102). Glycogen constitutes an important energy source as its degradation leads to glucose 1-P and glucose that can enter in glycolysis, TCA cycle or PPP (Pentose Phosphate Pathway).

Several studies have revealed that glycogen metabolism was important to assure cancer cell proliferation, survival and protection against ROS production (103). For example, inhibition of PYG in U87 cancer cell line (glioblastoma) results, in particular, in the decrease of its growth rate (103, 104).

We found that there was less glucose derived ¹³C in aspartate and glutamine pools in glioma-bearing flies. This could suggest that its biosynthesis was decreased. This is rather surprising as these two amino acids are essential to assure tumor development. Indeed, both contribute to TCA cycle anaplerosis and are also involved in protein and nucleotide synthesis, as well as in redox homeostasis. In addition, glutamine is a precursor for the synthesis of number of non-essential amino acids. Our results can be explained by an
increase of their consumption (rather than an increase of their biosynthesis). Alternatively, decrease of aspartate synthesis may be a consequence of hypoxia conditions. Indeed, Garcia-Bermudez et al. (105) have shown that hypoxic cells are deficient in aspartate synthesis, and particularly in human glioblastoma.

Interestingly we have identified sarcosine as a discriminant metabolite in HR-MAS experiments, its level being higher in glioma-bearing flies. However, it was not detected in ¹H-¹³C HSQC experiments. Sarcosine is a natural amino acid found in various body tissues, in particular in muscle. It is an N-methyl derivative of glycine and an intermediate of the choline oxidation pathway leading to glycine synthesis (106). Absence of sarcosine ¹³C labeled, with the presence of glycine ¹³C labeled suggests that sarcosine detected in gliomas rather comes from the choline oxidation pathway. Sarcosine was identified as a highly increased metabolite during prostate cancer progression to metastasis (107, 108). More recently, sarcosine was proposed as a potential glioma biomarker in plasma and tumor tissues in a rat glioma model (109). However, function of sarcosine in tumors and plasma remains unclear (110).

Altogether, our results show that in glioma-bearing *Drosophila*, the metabolism is rewired to provide maximum energy to ensure tumor growth. These metabolic alterations could be used as diagnostic and prognostic biomarkers as well as therapeutic targets. Indeed, Chi et al. (40) showed in an adult *Drosophila* glioma model that knockdown of genes involved in glycolysis (aldolase) or lipid metabolism (ACAT1 and ELOVL6) suppressed gliomagenesis.

## 4-2 Glioma induces cachexia in *Drosophila*

Cancer cells modify their own metabolism to assure their proliferation and dissemination. This requires high energy expenditure. To fulfil their energy needs, cancer cells can remotely alter the metabolism of other organs leading to their wasting. This is known as cachexia or wasting effect. In Human, main symptoms of cancer cachexia include loss of body mass, anorexia, general inflammation, skeletal muscle wasting and reduction of fat mass (23). Cachexia may affect up to 74% of cancer patients, depending on the type of cancer, and is responsible of impaired physical functions, lower response to therapies and higher mortality (111). During cancer cachexia, muscles are degraded due to significant proteolysis, thus generating amino acids which can fuel the Krebs cycle to provide energy. At the same time,
TGA are degraded in fatty acids, which are then oxidized, and in glycerol which is used as an energy source, leading to loss of adipose tissues (24). To influence metabolism in different remote organs, cancer cells must secrete signaling molecules (112, 113). To date, several proteins including growth factors, cytokines, interleukines and chemokines are known to be secreted by tumors and implicated in cancer cachexia (114, 115).

As Humans, *Drosophila* can develop cancer cachexia. Several models of cancer in larval or adult *Drosophila* have exhibited organ wasting (66). Several phenotypes are associated with cancer cachexia in *Drosophila*: organ wasting, which induces ovary and adipose tissue atrophy or/and muscle degeneration and metabolic alterations such as increase of trehalose, decrease of glycogen and TAG, and increase in circulating amino acids (in particular proline). All these phenotypes are not always observed in *Drosophila* cancer models, except muscle degeneration which is common to adult and larval tumor models.

In this study, *Drosophila* adult glioma model exhibited several cachexia phenotypes, in particular loss of TAG and wasting of ovaries. Several metabolic markers strongly suggest depletion of muscles: decrease of β-alanine and increase of amino acids, in particular proline. β-alanine is a non-essential amino acid that does not participate to protein biosynthesis. However, β-alanine is a component of dipeptides carnosine and anserine. Carnosine is mainly found in skeletal muscles and brain tissue in mammals (116). β-alanine has an intracellular buffer function which allows to delay lactate accumulation (117). In a previous study, we have shown by ¹H HR-MAS Slice Localized Spectroscopy measurements that a large amount of β-alanine was observed in soft tissues, in the thorax of *Drosophila*, which was greatly reduced in Troponin T mutants (*up*¹⁰¹) showing muscle degeneration (61) indicating that β-alanine was also important in *Drosophila* muscles. Thus, a low level of β-alanine observed in thorax of glioma-bearing flies and a decrease of its synthesis are good indicators of muscle depletion. Muscle wasting is associated with high proteolysis contributing to an increase in free amino acids. In a *Drosophila* model of high sugar diet (HSD)-enhanced tumorigenesis, Newton et al. (118) have shown that muscle wasting was associated with enhanced circulating amino acids, in particular proline. In addition, Khezri et al. (119) have demonstrated, in a *Drosophila* larval malignant tumor model (Ras<sup>V12</sup>, scrib<sup>-/-</sup>), that autophagy is involved in organ wasting and results in an increase of amino acids, including arginine, proline, glutamine, serine, alanine and lysine, those (except serine and lysine) increased in our adult glioma model. In *Drosophila*, autophagy has been associated
with glycogen breakdown in muscles under starvation conditions (120). In their tumor model, Khezri et al. (119) have shown that glycogen store depletion was dependent of autophagy. Interestingly, we have observed, in parallel with the increase in free amino acids, a significant decrease in glycogen, suggesting that autophagy might also be involved in organ wasting in our glioma model. Such a mechanism would provide metabolites (sugar and amino acids) available for tumor growth.

Signaling molecules involved in cachexia have also been identified in *Drosophila*. These include cachexia factors already identified in Humans: Unpaired (IL-6) (121, 122), Eiger (TNF-α) (122), Pvf1 (VEGF and PDGF) (123, 124), Bnl (FGF) (118) and Gbb (BMP) (124), but also the insulin growth factor binding protein (IGFBP) homolog, ImpL2, a secreted insulin/IGF antagonist (124-126). Figueroa-Clarevega and Bilder propose that secretion of ImpL2 by the tumor creates insulin resistance in distant tissues, thus inducing organ wasting. Jarabo et al. (127), in their *Drosophila* adult glioma model, have precisely shown that glioblastoma cells secrete ImpL2. Thus, one can speculate that ImpL2 could promote cachexia in the *Drosophila* adult glioma model.

So far, few studies have investigated the development of cachexia in patients with glioma. In 1988, Griffith and Hochberg (128) have reported cases of anorexia and body weight loss associated with gliomas. The fatigue, another cachexia symptom has also be observed in a great majority of primary brain tumors and in particular in glioma patients (between 39 to 77%) (129). More recently, alteration of temporal muscle was reported in glioblastoma patients and proposed as a prognostic marker (130). Cui et al. (25) have developed a murine model to study glioma cachexia by implanting nude mice with different human glioma cell lines: CHG5 (low grade glioma) and U87 (high grade glioma). They have observed symptoms of cachexia in these mice: weight loss, decrease of muscle weight and strength, and metabolic changes involving glucose and lipids metabolism as well as proteolysis resulting in higher levels of free amino acids. In the present study, we observed some of the cachexia symptoms associated with glioma, suggesting that, in addition to mice models, the adult glioma model in *Drosophila* would be of great interest to better understand the mechanisms involved in cachexia and metabolic disturbances.

### 4-3 Comparison between the larval and adult glioma models.
Two glioma models have been established in *Drosophila*. Both result from expression, specifically in glial cells, of constitutive activated forms of EGFR and PI3K which induce hyperproliferation of glial cells. The first model, the larval one, established by Read et al. (41) led to lethality at the third instar larval stage and recapitulates the main characteristics of glioma. The second model, the adult one, was described simultaneously by Portela et al. (44, 45) and Chi et al. (40). It induces a reduced life time in adults and increase the brain volume as a result of hyperproliferation of glial cells.

In this study, we have compared the metabolic alterations induced by the two glioma models. By using HR-MAS NMR analysis, 19 discriminant metabolites were identified in the adult model and 14 in the larval model. Only 11 metabolites are common to both models. Fatty acids and glycerol are dramatically reduced in the adult glioma model, in a systemic way. This has not been observed in the larval model, where the level of fatty acids is not modified at all. The large decline of fatty acids in the adult model actually reflects glioma-induced cachexia. In contrast, the lack of decrease in fatty acids in the larva suggests that, in this model, glioma would not induce cachexia. From a metabolic point of view, the larval and adult stages are in fact fundamentally different. Indeed, the larval stage is very particular as it has to assume a ~200-fold increase in body mass. This requires a very efficient metabolism to convert nutrients into metabolites necessary for building blocks (amino acids, fatty acids, nucleotides). Several studies have shown that *Drosophila* larvae develop aerobic glycolysis very similar to the Warburg effect used by cancer cells (131-133). This is ensured by an upregulation of genes encoding enzymes involved in particular in glycolysis and lactate production, induced by the *Drosophila* ortholog of mammalian estrogen-related receptor (dERR). *Drosophila* larva is often compared to cancer as it adopts the characteristic metabolic pathways of tumor cells. In such a metabolic context, one can imagine that the larva is quite capable of meeting the nutritional needs of the glioma cells, without disrupting the general metabolism of the larva.

### 4-4 Expression of 5-HT<sub>7</sub>R partially reduces glioma and associated-phenotypes

In a previous study (46), we have shown that expression of the Human serotonin 5-HT<sub>7</sub> receptor in the larval glioma model was able to reduce glial cells proliferation and partly rescue phenotypes associated with glioma. In this study, we show that expression of the serotonin 5-HT<sub>7</sub> receptor in glial cells in the adult glioma model partly suppress phenotypes
associated with expression of the constitutive forms of EGFR and PI3K, as observed in the larval model. MRI brain imaging allowed us to demonstrate that brains expressing 5-HT<sub>7</sub>R in a glioma context are less enlarged compared to gliomas indicating less proliferation of glial cells. The wasting effect, one of the consequences of glioma development in the adult model, is also partly reduced. These results suggest that, although the glial cells are not identical in the larva and adult, the tumor suppressor function of 5-HT<sub>7</sub>R is conserved in the two studied glioma models. Read et al. (41) had suggested that not all the glial cells of the larval brain, but only the precursor cells, participated in the formation of the glioma. Indeed, we recently showed (46) that only a not-fully differentiated glial cells subpopulation participate in glioma in the larva. This suggested that the response of glial cells to EGFR and PI3K activation depends on their status. Thus, it was not obvious that larval and adult glial cells respond in the same way to the expression of 5-HT<sub>7</sub>R. Altogether, the ability of 5-HT<sub>7</sub>R to reduce glioma development in *Drosophila* both in a developmental and an adult model underlies its role as a tumor suppressor gene. The metabolic and signaling pathways involved in this protective effect should have been further investigated. It is well-known that the activation of 5-HT<sub>7</sub>R induces Gs protein coupling and the activation of adenylate cyclase, resulting in an increase of intracellular cAMP (48). Previous works have already demonstrated the role of cAMP and its downstream signaling on glioma growth and glial cell differentiation (134, 135). In particular, dibutyryl cyclic AMP (dbcAMP) reverses the Warburg effect, as evidenced by increased oxygen consumption and reduced lactate production. The activation of the CREB-PGC1α pathway, a downstream cAMP signaling, is known to induce mitochondrial biogenesis which triggers metabolic shift and differentiation. In GBM xenograft models and patient-derived GBM samples, cAMP activators also induce tumor growth inhibition and differentiation (16). Another cAMP dependent kinase, AMPK was also shown to be important in regulating metabolism by reducing the Warburg effect (136). The expression of 5-HT<sub>7</sub>R by reducing tumor growth has also a positive effect on glioma-induced cachexia.

Altogether, these data demonstrated the interest of an adult *Drosophila* glioma model not only for studying the metabolic alteration of cancer cells but also for evaluating cachexia associated with glioma. Furthermore, our results validate the role of 5-HT<sub>7</sub>R as an interesting target for the treatment of glioma. The use of the adult glioma model in *Drosophila* will be
useful to evaluate the effect of specific 5-HT<sub>7</sub>R ligands as well as to decipher the molecular
mechanisms involved in the positive effect of 5-HT<sub>7</sub>R in glioma.

AUTHOR CONTRIBUTIONS

M.Decoville and S. Morisset-Lopez conceived and designed the research; M. Bertrand, F. Szeremeta, V. Sarou-Kanian, N. Hervouet-Coste and M. Decoville performed the research and acquired the data; M. Bertrand, F. Szeremeta, V. Sarou-Kanian and M. Decoville analyzed and interpreted the data; V. Sarou-Kanian, C. Landon, S. Morisset-Lopez and M. Decoville obtained the funds. All authors are involved in writing, revising and editing the manuscript. All authors read and approved the final manuscript.

FUNDING

This work was supported by grants from la Région Centre-Val de Loire (LINGO Project), La Ligue contre le Cancer (Loiret and Sarthe committees), and the IR-RMN-THC Fr3050 CNRS.

ACKNOWLEDGEMENTS

The authors thank the NMR and MRI divisions of the MO2VING facility (Orléans, France). We thank H. Tricoire for the *repo-Gal4-GS* flies and the Bloomington *Drosophila* Stock Center (NIH P40OD018537) for other flies. We are grateful to the https://www.france-bioinformatique.fr/ and the https://www.france-bioinformatique.fr/fr/cluster for computing and storage resources.

DISCLOSURES

The authors declare no conflicts of interest.

DATA AVAILABILITY STATEMENT

The data are currently not available online. They can be sent or shared if required.

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TABLES

<table><caption>**Table 1.** Identified metabolites discriminant gliomas from control samples analyzed by HR-MAS NMR.</caption><thead><tr><th>Metabolite</th><th>Group</th><th><sup>1</sup>H chemical shift (ppm)</th><th>Level</th><th>p-value</th><th>Function</th></tr></thead><tbody><tr><td rowspan="2">Alanine</td><td>CH<sub>3</sub></td><td>1.46-1.47</td><td rowspan="2">↗</td><td rowspan="2">***</td><td rowspan="2">Nucleotide synthesis<br>alanine aspartate and glutamate metabolism</td></tr><tr><td>CH</td><td>3.78</td></tr><tr><td rowspan="3">Arginine</td><td>CH<sub>2</sub></td><td>1.65-1.73</td><td rowspan="3">↗</td><td rowspan="3">***</td><td rowspan="3">Precursor for polyamines, nitric oxide,<br>creatinine, and other amino acids<br>Arginine and proline metabolism</td></tr><tr><td>CH<sub>2</sub></td><td>3.21-3.23</td></tr><tr><td>CH</td><td>3.38-3.42</td></tr><tr><td rowspan="4">Glucose</td><td>CH</td><td>3.48-3.50</td><td rowspan="4">↗</td><td rowspan="4">***</td><td rowspan="4">Glycolysis<br>Energy source</td></tr><tr><td>CH</td><td>3.52-3.54</td></tr><tr><td>CH</td><td>3.69-3.73</td></tr><tr><td>CH</td><td>3.89-3.90</td></tr><tr><td rowspan="2">Glutamate</td><td>CH<sub>2</sub></td><td>2.12-2.15</td><td rowspan="2">↗</td><td rowspan="2">***</td><td rowspan="2">Alternative energy source, precursor of<br>glutathione, neuro transmitter</td></tr><tr><td>CH<sub>2</sub></td><td>2.32-2.35</td></tr></tbody></table>
<table><tbody><tr><td></td><td>CH</td><td>3.73-3.76</td><td></td><td></td></tr><tr><td></td><td>CH<sub>2</sub></td><td>2.10-2.15</td><td></td><td></td></tr><tr><td rowspan="2">Glutamine</td><td>CH<sub>2</sub></td><td>2.40-2.44</td><td>↗</td><td>***</td></tr><tr><td>CH</td><td>3.75-3.77</td><td></td><td></td></tr><tr><td rowspan="2">Glycerol</td><td></td><td>4.09-4.11</td><td>↘</td><td>***</td></tr><tr><td></td><td>4.3-4.32</td><td></td><td></td></tr><tr><td rowspan="3">Histidine</td><td>CH<sub>2</sub></td><td>3.13-3.16</td><td></td><td></td></tr><tr><td>CH<sub>2</sub></td><td>3.22-3.24</td><td>↗</td><td>***</td></tr><tr><td>CH</td><td>3.99-4.00</td><td></td><td></td></tr><tr><td rowspan="2">Isoleucine</td><td>CH<sub>3</sub></td><td>0.93-0.94</td><td>↗</td><td>***</td></tr><tr><td>CH<sub>3</sub></td><td>0.99-1.00</td><td></td><td></td></tr><tr><td rowspan="2">Leucine</td><td>CH<sub>3</sub></td><td>0.94-0.96</td><td>↗</td><td>***</td></tr><tr><td>CH<sub>2</sub></td><td>1.70-1.72</td><td></td><td></td></tr><tr><td rowspan="5">Fatty acids</td><td>CH<sub>3</sub></td><td>0.89-0.91</td><td>↘</td><td>***</td></tr><tr><td>(CH<sub>2</sub>)<sub>n</sub></td><td>1.25-1.29</td><td>↘</td><td>***</td></tr><tr><td>CH<sub>2</sub>-C-CO</td><td>1.55-1.60</td><td>↘</td><td>***</td></tr><tr><td>CH<sub>2</sub>-CO</td><td>2.21-2.27</td><td>↘</td><td>***</td></tr><tr><td>CH=C</td><td>5.32-5.33</td><td>↘</td><td>***</td></tr><tr><td rowspan="3">Myoinositol</td><td>CH</td><td>3.25-3.27</td><td></td><td></td></tr><tr><td>2*CH</td><td>3.52-3.54</td><td>↗</td><td>***</td></tr><tr><td>2*CH</td><td>3.60-3.62</td><td></td><td></td></tr><tr><td rowspan="2">PE</td><td>CH<sub>2</sub></td><td>3.21-3.22</td><td>↗</td><td>***</td></tr><tr><td>CH<sub>2</sub></td><td>3.96-3.98</td><td></td><td></td></tr><tr><td rowspan="3">PC</td><td>CH<sub>3</sub></td><td>3.21-3.22</td><td>↗</td><td>***</td></tr><tr><td>CH<sub>2</sub></td><td>4.15-4.18</td><td></td><td></td></tr><tr><td>CH<sub>2</sub></td><td>2.01-2.06</td><td></td><td></td></tr><tr><td rowspan="4">Proline</td><td>CH<sub>2</sub></td><td>2.31-2.36</td><td></td><td></td></tr><tr><td>CH<sub>2</sub></td><td>3.30-3.34</td><td>↗</td><td>***</td></tr><tr><td>CH<sub>2</sub></td><td>3.39-3.43</td><td></td><td></td></tr><tr><td>CH</td><td>4.12-4.14</td><td></td><td></td></tr><tr><td>Pyruvate</td><td>CH<sub>3</sub></td><td>2.36</td><td>↗</td><td>***</td></tr><tr><td rowspan="2">Sarcosine</td><td>CH<sub>3</sub></td><td>2.73</td><td>↗</td><td>***</td></tr><tr><td>CH<sub>2</sub></td><td>3.6</td><td></td><td></td></tr><tr><td rowspan="2">Succinate</td><td>2*CH<sub>2</sub></td><td>2.40</td><td>↗</td><td>***</td></tr><tr><td>CH<sub>2</sub></td><td>3.03-3.04</td><td></td><td></td></tr><tr><td rowspan="2">Tyrosine</td><td>CH<sub>2</sub></td><td>3.17-3.20</td><td>↗</td><td>***</td></tr><tr><td>CH</td><td>3.93-3.95</td><td></td><td></td></tr><tr><td rowspan="3">Valine</td><td>CH<sub>3</sub></td><td>0.98-0.99</td><td></td><td></td></tr><tr><td>CH<sub>3</sub></td><td>1.03-1.04</td><td>↗</td><td>***</td></tr><tr><td>CH</td><td>3.60-3.61</td><td></td><td></td></tr></tbody></table>

<footnote n="1">¹H HR-MAS signals of the identified metabolites discriminant glioma from control samples and the impacted metabolic pathways. ↗: increased relative levels and ↘: decreased</footnote>
relative levels in the glioma samples compared to the controls. * p value < 0.05, ** p value < 0.01, *** p value < 0.001.
<table><caption>**Table 2.** Quantified metabolites from glioma and control samples analyzed by ¹H-¹³C HSQC NMR.</caption><thead><tr><th>Metabolite</th><th>¹³C (ppm)</th><th>¹H (ppm)</th><th>level</th><th>p-value</th><th>Function</th></tr></thead><tbody><tr><td>acetate</td><td>26.0</td><td>1.92</td><td></td><td></td><td>Alternative energy source</td></tr><tr><td>β-alanine</td><td>36.4</td><td>2.55</td><td>↘</td><td>*</td><td>Muscle function</td></tr><tr><td>aspartate</td><td>39.8</td><td>2.80</td><td>↘</td><td>*</td><td>Alternative energy source</td></tr><tr><td>citrate</td><td>48.8</td><td>2.53</td><td></td><td></td><td>TCA cycle</td></tr><tr><td>glucose</td><td>94.9</td><td>5.22</td><td>↗</td><td>**</td><td>Energy source</td></tr><tr><td>glutamate</td><td>36.4</td><td>2.35</td><td></td><td></td><td>Alternative energy source, precursor of glutathione, neurotransmitter</td></tr><tr><td>glutamine</td><td>34.1</td><td>2.45</td><td>↘</td><td>*</td><td>Alternative energy source</td></tr><tr><td>glycine</td><td>44.6</td><td>3.56</td><td></td><td></td><td>One carbon metabolism</td></tr><tr><td>glycogen</td><td>102.7</td><td>5.41</td><td>↘</td><td>*</td><td>Energy stockage</td></tr><tr><td>lactate</td><td>23.1</td><td>1.33</td><td>↗</td><td>*</td><td>Energy source</td></tr><tr><td>malate</td><td>45.7</td><td>2.38</td><td></td><td></td><td>TCA cycle</td></tr><tr><td>proline</td><td>64.5</td><td>4.13</td><td></td><td></td><td>Alternative energy source</td></tr><tr><td>succinate</td><td>37.2</td><td>2.40</td><td></td><td></td><td>TCA cycle</td></tr></tbody></table>

¹H-¹³C HSQC signals of the identified metabolites and the impacted metabolic pathways. ↗: increased relative levels and ↘: decreased relative levels in the glioma samples compared to the controls. * p value < 0.05, ** p value < 0.01.
FIGURE LEGENDS

**Figure 1.** MRI of *Drosophila melanogaster* brains. (A) Coronal slices of the head of controls and glioma-bearing flies obtained at 17.6 T with the 3D-Flash and the 3D-DWI sequences. (B) 3D isosurface rendering of the *Drosophila* brain for the control and glioma-bearing flies obtained from the DWI 3D images (B = 2000 s/cm²) (C) Calculated brain volume of the flies. Blue: controls (C1-5), red: glioma-bearing flies (G1-5). Brain is larger in glioma-bearing flies. Control: *UAS-dp110<sup>CAAX</sup>*; *UAS-EGFR<sup>λ</sup>*; *repo-Gal4-GS* treated with vehicle during 5 days, glioma-bearing flies: *UAS-dp110<sup>CAAX</sup>*; *UAS-EGFR<sup>λ</sup>*; *repo-Gal4-GS* treated with 100mM mifepristone during 5 days.

**Figure 2.** 1D HR-MAS NMR metabolomic analysis. (A) Cross Validation (CV) score plots of the supervised multivariate analysis OPLS-DA on the adult glioma model. Red : glioma-bearing fly samples (*UAS-dp110<sup>CAAX</sup>*; *UASEGFR<sup>λ</sup>*; *repo-Gal4-GS* treated with 100mM mifepristone during 5 days). Blue : control fly samples (*UAS-dp110<sup>CAAX</sup>*; *UAS-EGFR<sup>λ</sup>*; *repo-Gal4-GS* treated with vehicle during 5 days) (B) Comparison of discriminant metabolites in the adult glioma model identified in this study and discriminant metabolites previously identified by HR-MAS NMR in the larval glioma model (58). In total, 19 metabolites were found discriminant in the adult glioma model. Eleven of these biomarkers were common to adult and larval glioma models.

**Figure 3:** ¹H-¹³C HSQC spectra from fly extracts. Top, 1D-¹H NMR spectrum (A) Contour plots of ¹H-¹³C HSQC spectrum from control flies. (B) Enlargement of the boxed region of the HSQC spectrum from control (blue) and glioma-bearing (red) flies. Signals from lipids are dramatically decreased in glioma-bearing flies. Lactate is increased in flies bearing glioma compared to controls.

**Figure 4.** Glioma induces wasting in peripheral tissues. (A) Averaged spatially resolved ¹H HR-MAS NMR spectra of the thorax and abdomen regions for glioma-bearing flies (red, n=6) and controls (blue, n=5). L1, L2, L3, L4 and L5: fatty acids. L1: CH₃; L2: (CH₂)ₙ; L3: CH₂-CH₂-CO; L4:
CH₂-C=; L5: CH₂-CO; L6: CH=CH. β-ala: β- alanine. Decrease of fatty acids was also observed in thorax and abdomen. Level of β- alanine in thorax was lower in glioma-bearing flies. (B) Phenotype of ovaries in controls and glioma-bearing flies. Presence of glioma induces wasting of ovaries. (C) Abdomen of flies after two hours of blue dyed medium ingestion. Glioma-bearing flies exhibit an enlarged crop. (D) Phenotype of crops in controls and glioma-bearing flies fed on normal food. Crops from glioma-bearing flies are dramatically enlarged as compared to controls. Controls: UAS-dp110<sup>CAAX</sup>; UAS-EGFR<sup>λ</sup>; repo-Gal4-GS treated with vehicle during 5 days; glioma-bearing flies: UAS-dp110<sup>CAAX</sup>; UAS-EGFR<sup>λ</sup>; repo-Gal4-GS treated with 100mM mifepristone during 5 days.

**Figure 5.** Expression of the serotonin 5-HT<sub>7</sub>R reduced the severity of glioma phenotypes. (A) MRI of adult brains acquired at 9.4T with a 3-D FLASH sequence. Axial views of brain from control, glioma model and two different glioma-bearing flies expressing 5-HT<sub>7</sub>R. Using JIVE utility, provided with ParaVision software, these images have been obtained by reslicing to an isotropic resolution of 20µm and changing the orientation to the same axial view of the head, allowing better comparison between the flies. (B) Phenotypes of ovaries in controls and glioma-bearing flies expressing 5-HT<sub>7</sub>R. (C) Phenotypes of crops in glioma-bearing flies expressing 5-HTR. Controls UAS-dp110<sup>CAAX</sup>; UAS-h5-HT<sub>7</sub>R; UAS-EGFR<sup>λ</sup>; repo-Gal4-GS treated with vehicle during 5 days, glioma-bearing flies expressing 5-HT<sub>7</sub>R: UAS-dp110<sup>CAAX</sup>; UAS-h5-HT<sub>7</sub>R; UAS-EGFR<sup>λ</sup>; repo-Gal4-GS treated with 100mM mifepristone during 5 days.
Figures

<figure><img src="image_1.png" /><figcaption>Figure 1</figcaption></figure>
Figures

<figure><img src="image_2.png" /><figcaption>Figure 2</figcaption></figure>
Figures

<figure><img src="image_3.png" /><figcaption>Figure 3</figcaption></figure>
Figures

<figure><img src="image_4.png" /><figcaption>Figure 4</figcaption></figure>
Figures

<figure><img src="image_5.png" /><figcaption>Figure 5</figcaption></figure>
# Supplementary data

## Sup Table 1S.

Discriminant metabolites identified in larval et adult models of *Drosophila*.

<table><thead><tr><th rowspan="3">Discriminant<br>Metabolites</th><th colspan="4">Larvae</th><th colspan="2">Adult</th></tr><tr><th colspan="2">Solution-state NMR</th><th colspan="2">HR-MAS</th><th colspan="2">HR-MAS</th></tr><tr><th>Levels</th><th>p value</th><th>Levels</th><th>p value</th><th>Levels</th><th>p value</th></tr></thead><tbody><tr><td>Acetate</td><td>↗</td><td>**</td><td>↗</td><td>***</td><td></td><td></td></tr><tr><td>Alanine</td><td>↗</td><td>***</td><td>↗</td><td>*</td><td>↗</td><td>***</td></tr><tr><td>Arginine</td><td>↗</td><td>***</td><td>↗</td><td>**</td><td>↗</td><td>***</td></tr><tr><td>Benzoate</td><td>↘</td><td>***</td><td></td><td></td><td></td><td></td></tr><tr><td>Fatty acids</td><td></td><td></td><td></td><td></td><td>↘</td><td>***</td></tr><tr><td>GABA</td><td>↘</td><td>**</td><td>↘</td><td>**</td><td></td><td></td></tr><tr><td>Glucose</td><td>↗</td><td>***</td><td>↗</td><td>**</td><td>↗</td><td>***</td></tr><tr><td>Glutamate</td><td></td><td></td><td></td><td></td><td>↗</td><td>***</td></tr><tr><td>Glutamine</td><td>↗</td><td>***</td><td>↗</td><td>*</td><td>↗</td><td>***</td></tr><tr><td>Glycerol</td><td></td><td></td><td></td><td></td><td>↘</td><td>***</td></tr><tr><td>Glycine</td><td>↗</td><td>***</td><td></td><td></td><td></td><td></td></tr><tr><td>Histidine</td><td></td><td></td><td></td><td></td><td>↗</td><td>***</td></tr><tr><td>Isoleucine</td><td>↘</td><td>***</td><td></td><td></td><td>↗</td><td>***</td></tr><tr><td>Lactate</td><td>↗</td><td>***</td><td>↗</td><td>*</td><td></td><td></td></tr><tr><td>Leucine</td><td>↗</td><td>**</td><td>↗</td><td>**</td><td>↗</td><td>***</td></tr><tr><td>Myo-inositol</td><td>↗</td><td>***</td><td>↗</td><td>***</td><td>↗</td><td>***</td></tr><tr><td>PC</td><td></td><td></td><td>↗</td><td>**</td><td>↗</td><td>***</td></tr><tr><td>PE</td><td>↗</td><td>**</td><td>↗</td><td>**</td><td>↗</td><td>***</td></tr><tr><td>Proline</td><td>↘</td><td>**</td><td>↘</td><td>**</td><td>↗</td><td>***</td></tr><tr><td>Pyruvate</td><td>↗</td><td>***</td><td></td><td></td><td>↗</td><td>***</td></tr><tr><td>Sarcosine</td><td>↗</td><td>***</td><td></td><td></td><td>↗</td><td>***</td></tr><tr><td>Succinate</td><td>↗</td><td>***</td><td>↗</td><td>*</td><td>↗</td><td>***</td></tr><tr><td>Tyrosine</td><td>↗</td><td>***</td><td></td><td></td><td>↗</td><td>***</td></tr><tr><td>Valine</td><td>↗</td><td>*</td><td>↗</td><td>*</td><td>↗</td><td>***</td></tr></tbody></table>

↗: increased relative levels and ↓: decreased relative levels in the glioma samples compared to the controls. * p value < 0.05, ** p value < 0.01, *** p value < 0.001.
<figure><img src="image_6.png" /><figcaption>**Sup Figure 1S.** ¹H HRMAS NMR spectrum of three heads of glioma-bearing flies. The NMR spectrum was obtained at 17.6T (Larmor frequency of 750.13MHz) with a 4.0 mm MAS probe. The spinning frequency was set to 4000 Hz. A 1s low-power presaturation was performed to eliminate signal from the water before acquisition. Number of accumulations: 288. Experimental time: 15 min.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Sup Figure 2S.** (A) OPLS-DA on the adult glioma model and (B) Permutation testing for OPLS-DA model with 100 permutations.</figcaption></figure>

Scatterplot of R2Y and Q2Y actual and simulated models after random permutation of response values: all black values of Q2 are lower than the original point on the right
<figure><img src="image_8.png" /><figcaption>**Sup Figure 3S.** Averaged ¹H HRMAS NMR spectra for glioma (red, n=7) and controls (Gal4 controls, n=6; UAS controls n=6) drosophila larvae. Each NMR spectrum was obtained at 17.6T (Larmor frequency of 750.13MHz) with a 3.2 mm MAS probe. The spinning frequency was set to 2630 Hz and the larva was cooled at 4°C for anesthesia. A 1s low-power presaturation was performed to eliminate signal from the water before acquisition. Number of accumulations: 16. Experimental time: 20 s. Larvae were obtained as described in Maravat et al. (2021). Glioma-bearing larvae: *UAS-dp110<sup>CAAX</sup>*; *UAS-GFP*; *UAS-EGFR<sup>λ</sup>/repo-Gal4*; Gal4 controls: *UAS-GFP*; *repo-Gal4*; UAS controls: *UAS-dp110<sup>CAAX</sup>*; *UAS-EGFR<sup>λ</sup>*</figcaption></figure>