Targeted metabolic profiling in determining the metabolic heterogeneity in human biopsies of different grades of glioma

doi:10.21203/rs.3.rs-3436961/v1

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Targeted metabolic profiling in determining the metabolic heterogeneity in human biopsies of different grades of glioma

https://doi.org/10.21203/rs.3.rs-3436961/v1

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published 24 Oct, 2024

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Gliomas are intricate tumors with numerous metabolic and genetic abnormalities contributing to their aggressive phenotypes and poor prognoses. The study aims at identifying the key molecular metabolic as well as gene expressional variations that could be used not only to differentiate between different grades of glioma, with potential for improved early diagnostics but also to obtain a deeper insight about metabolic observation of glioma. In the present study, the metabolomic profiling along with clinical, and expressional analyses of glioma biopsies (n = 56; patients comprising both of benign and malignant lesions) were analyzed. The biopsies were subjected to gene/protein expressional analysis using RT-PCR, western blotting and also were subjected to metabolite analyses. The results of the gene/protein expressional analysis exhibited elevated levels of carnitine palmitoyltransferase, monoglyceride lipase, human phosphofructokinase, and isocitrate dehydrogenase in higher grades of glioma when compared to those of control. Our study suggested that the metabolites and gene/protein expressional levels were found to be discriminative among the grades of glioma. The study is deemed as a provider of deeper insights that are essential for differential therapeutic approaches that specifically target the dysregulated metabolome to the benefit of patients.

Glioma

Metabolites

Carnitine palmitoyltransferase

Monoglyceride lipase

Phosphofructokinase

Globally, gliomas, the intracranial tumour are thought to be the deadliest malignancies (Hanif et al., 2017). The gliomas are most difficult to diagnose and treat. The treatment for gliomas remains to be a challenge due to its diversified molecular heterogeneity (Shergalis et al., 2018). The intra and inter tumor heterogeneity further leads to resistance and subsequently ending in tumor recurrence. The genetic make-up of any cell will be translated into its metabolic phenotype, especially in malignant cell, in which the metabolomic reprogramming occur in response to oncogenic mutations. The metabolites and their reactions may be considered to be the functional fingerprint of protein function, genetic variation and environmental effects. Therefore, untangling the metabolome is a sensitive and robust approach for monitoring changes in a biological system and identifying pathways that are perturbed in a given pathology through observed changes in the metabolic network. Hence, the study had ventured into exploring the metabolomic phenotype in addition to the gene expressional variations of key metabolic enzymes in dreadful glioma that highly deserves high throughput screening owing to its bewildering heterogenecity. This idea originated from earlier observation of our study that low grade (LG) and high grades (HGs) certain metabolites triggering, the present study was designed to understand a detailed metabolomic profile of different grades of glioma along with the molecular signatures of the glioma biopsies.

Study subjects and sample collection:

The human glioma biopsies were collected from Rajiv Gandhi Government General hospital Chennai, Tamil Nadu, India, Chennai, Tamil Nadu, India following proper approval from Institutional Human Ethics (Approval No. 02122020/P&D/Ethics/Dean/GGH/ Chennai, 15-12-2020). The written informed consent was obtained from all of the study subjects. Forty-four samples were collected from RGGH, Chennai. The inclusion criteria include the patients diagnosed with glioma and gradings were identified according to the classification of WHO guidelines. The exclusion criteria of the study were alcoholics, smokers, pregnant and lactating women, patients with the previous history of coronary artery disease, hypertension, diabetes, pulmonary disease, kidney disease, other cancer and immunosuppressive diseases.

The study was grouped as follows:

Group I: Control (Non-Malignant) (n = 13)

Group II: Pilocytic astrocytoma (Grade I) (n = 11)

Group III: Diffuse astrocytoma (Grade II) (n = 8)

Group IV: Anaplastic astrocytomas (AA) (Grade III) (n = 10)

Group V: Glioblastoma multiforme (GBMs) (Grade IV) (n = 10)

The pathological classification and grade of tumour tissues was assessed and confirmed by the experienced neuropathologist. The samples along with the history of the patients were procured from the recruited staff with the guidance of the neurosurgeon and neuropathologist at the hospital immediately after the surgery and further experiments were carried out.

Isolation of RNA for RT-PCR: 10mg of tissue were used for RNA extraction using TRIZOL reagent (Takara, Japan). The RNA was converted to cDNA using the PrimeScript One Step RT-PCR Kit (TAKARA). The converted cDNA was further used for RT-PCR analysis. RT-PCR was carried out to measure the mRNA levels of Human phosphofructokinase (PFK) (FP:CACCGTGGACTGTGTCTGTT; RP:GGCACTCGCCGATTAGGTAT Product length-93), Carnitine palmitoyltransferase 2 (CPT2) (Catalog Number-4331182, Thermofisher Scientific) (FP:TGTAGCACTGCCGCATTCA; RP:GGCCATGGTACTTGGAGCAC; Product length-157), Monoglyceride lipase (MGLL) (FP:GAGGATCCGCTGCGCTC; RP:AGGTCCTGGTAGGGAATGCT Product length-124) and GAPDH with PCR system (Model no: C1000, Bio-Rad Laboratories) using Master mix (Takara, Japan).

Immunoblotting: 100mg of tissue was homogenized in Tris HCl buffer and the homogenate containing 50 µg of total protein was used for western blotting. The homogenate was boiled in sample loading buffer for 5 min, after which the homogenate was loaded in polyacrylamide gel and separated by electrophoresis at room temperature. After electrophoresis, the gels were electroblotted using semi-dry system and Tris-glycine-methanol buffer at 150 mA on methanol-activated PVDF membrane and blocked in 5% BSA at room temperature for 1 h. They were then washed and incubated in GLUT 3 (Santa Cruz (B-6), 200 µg/ml, sc-74497) (1:1000) primary antibody in TBS-T at 4°C for 12 h or overnight. The membranes were washed thrice with diluted TBS-T and incubated with secondary antibody (Goat anti-mouse IgG-HRP: sc-2005; Santa Cruz) conjugated with horseradish peroxidase at 1:2000 dilutions at room temperature for 1 h. α-tubulin served as internal control and the bands were detected using DAB reagent. The intensity of the bands was calculated using an image J software.

Extraction of metabolites: The tissue samples were homogenized in 6% of ice-cold perchloric acid and then the samples were centrifuged at 10 000g in 4°C for 10 min. The supernatant was collected in a tube and the pellet was reextracted once again using perchloric acid. The supernatant was checked to pH 7.4 with 2M potassium carbonate. The supernatant was kept on ice for 30 min and then centrifuged at 1500 rpm for 10 min. The resulting supernatant was further lyophilized.

NMR Analysis: The lyophilized aqueous samples were dissolved in 500 µL of D₂O for the NMR analysis. 0.1% sodium 3- (trimethylsilyl)propionate-2,2,3,3-d4 (TSP), internal control in D₂O (chemical shift reference (δ 0.00)) was added to the aliquot. The mixture was centrifuged at 12 000g in 4°C for 15 min and the supernatants were taken in 5 mm NMR tubes for the analysis. ¹ H NMR analysis were carried out at 25°C in a Bruker AV III 500 MHz spectrometer and the water signal were reduced by coherent irradiation during relaxation delay (0.05 s). The free induction decay was zero-filled to 64 K, and a 0.3 Hz exponential line-broadening function was employed before Fourier transformation.

Multivariate Analysis: The multivariate statistical analysis was carried out to analyse the NMR spectra for identifying the metabolites in the different grades of glioma. The metabolites were predicted from the NMR spectra based on the literatures and sources including Chenomx NMR software, MNOVA software and NMR database. From the NMR spectra, binning was carried out from 0.004 ppm (bin) in the region of δ 9.5 − 0.5 in the MNOVA software and the water region from δ 5.0 − 4.5 was rejected. The spectral bins were normalized by mean-centered and pareto-scaled. The difference between the top 10 metabolites from different grades of glioma was identified by PLS-DA model from the data sets of NMR spectra using the Metaboanalyst 4.0 software package. The robustness of the PLS-DA model was confirmed by the permutation test (1000 cycles). Further, the study also investigated the joint-pathway analysis of significantly changed metabolites and genes from different grades of glioma. The identified metabolites were translated into KEGG IDs before the analysis. Result tables of the joint-pathway analysis were exported (p < 0.05, false discovery rate (FDR)-corrected two-sided Student’s t-test).

Statistics: One-way ANOVA was carried out using Graphpad Prism software followed by Tukey’s honest significant difference test. For NMR, classification analysis including partial least-squares discriminant analysis (PLS-DA), and unsupervised hierarchical clustering using software’s such as MNOVA, Metaboanalyst were used (Chen, et al., 2022). (Statistical significance defined as p < 0.05 considering confidence intervals higher than 95%).

Figure 1A, B shows that the mRNA levels of key glycolytic enzymes including Human phosphofructokinase (PFK), Carnitine palmitoyltransferase 2 (CPT2) and Monoglyceride lipase (MGLL) were found to be significantly (p < 0.001, p < 0.05) in high grades (grade III and grade IV) of glioma compared to those of control.

Western blot data displayed in Fig. 2A and 2B suggested a significant increase in the level of GLUT-3 (p < 0.001) in high grades (grade III and grade IV) of glioma when compared to that of control.

Figure 3 shows the stacked ¹H NMR spectrum of control and different grades of glioma and Fig. 4A represents the multivariate analysis of different grades of glioma. The partial least squares discriminant analysis (PLS-DA) analysis established the group separation, where different grades of glioma were segregated by four principal components (Fig. 4A). The statistical strength of the model was cross validated by permutation test analysis. The parameters for cross-validation were R2(cum) = 0.45, Q2 (cum) = 0.37, and the statistics for permutation test of 1000 cycles were at p = 0.001 (Fig. 4B, C).

The Fig. 5 represents the variable importance on projection (VIP) scores of PLS-DA data, obtained from the spectral bins that are significantly varied. The top 10 metabolites that were distinguishing the characteristic metabolic profiles of control and different grades of glioma were ranked according to the VIP scores (Fig. 5A). Figure 5B represents the box plot of the relative integrals of significantly varied metabolites of the control and different grades of glioma (Fig. 5B). Glutamate, choline, Tyrosine (p = 0.0001), GABA and myoinositol (p < 0.005) were found to be significantly increased in the higher grades of glioma when compared to the lower grades of glioma as denoted in the table 1.

From the table 1, the average change of spectral integral (ppm) and the corresponding p-value as determined by the mean comparison changes in the low and high grades of glioma, it could be inferred that the metabolites mainly glutamate, choline and tyrosine (p < 0.0003) could purportedly be distinctive in grades III & IV rather than grades I & II.

The mRNA levels of Human phosphofructokinase (PFK), Carnitine palmitoyltransferase 2 (CPT2), Monoglyceride lipase (MGLL) were found to be significantly increased in the higher grades (Grade III and Grade IV) (p < 0.001, p < 0.05) of glioma, while lower grades (Grade I and II) exhibited significant reduction in the mRNA levels when compared to those of control. Protein level of GLUT3 was found to be significantly increased in higher grades of glioma (Grade III and IV) (p < 0.005, p < 0.01) when compared to control and no significant changes in the levels of GLUT3 was found in grade I and II when compared to those of control.

The metabolic pathways with an FDR lower than 0.05 and a pathway impact above 0.5 were selected for further consideration. The overall pathway impact of both the metabolites and genes showed significant changes in the following metabolic pathways such as central carbon metabolism in cancer (p = 0.0019), alanine, aspartate and glutamate metabolism (p = 0.002), ABC Transporter (p = 0.0067), aminoacyl-tRNA biosynthesis (p = 0.0094) and protein digestion and adsorption (p = 0.0104).

The metabolomics deals with the analysis of small molecules known as metabolites (less than 1.5kDa) that are the end products of the genomic, transcriptomic, proteomic or environmental changes affecting the cell or biological system (Wishart et al., 2007). It is possible to obtain a relatively full picture of the state of a given cell or tissue by analysing its endogenous and exogenous metabolites (Johnson et al., 2016). The metabolomics reflects the influence of external factors and phenotype of the cell that cannot be effectively assessed in genomics or proteomics investigations (Schmidt et al., 2004).

The metabolomic reprogramming of cancer cells is a well-known phenomenon (Jaroch et al., 2021). It is well known that the cancer cells maintain the cellular proliferation and survival by exhibiting changes in the metabolism pertaining to the enhanced levels of glucose and energy. Numerous reports have suggested that amino acids, nucleotides and lipids significantly assists in the cancer cell metabolism. Therefore, the present study attempts to sketch the major dysregulated metabolic pathways that supports cancer initiation and progression, including glycolysis and Warburg effect, metabolic symbiosis, metabolism of glutamine, serine, methionine and proline, involvement of arginine and ornithine in linking tricarboxylic acid and urea cycles, and lipid and nucleotide synthesis pathways. Recent findings suggested that altered fatty acid oxidation (FAO) is also an important marker for initiation of glioma and development (Kant et al., 2020; Juraszek et al., 2021). Melone et al., (2018) suggested that carnitine shuttle system regulates the metabolic pathway of fatty acid oxidation, it consists of enzymes and protein transporters that are responsible for transporting of fatty acids through the mitochondrial membrane. Previous investigations have reported the levels of carnitine in other malignant neoplasms such as glioma, hepatocellular carcinoma, breast cancer, and prostate cancer, exhibit increased concentrations in tumour tissues when compared to the control samples

Prior studies have examined levels of carnitine (CPT-1, CPT-2, CACT) metabolite in a variety of malignant neoplasms, including glioma, hepatocellular carcinoma, breast cancer, and prostate cancer, with findings showing higher concentrations in malignant tissues compared to histologically healthy samples (Lu et al., 2019; Yu et al., 2020; Zoni et al.,2019). In the present study (Fig. 1), it was found there was significant increase in the mRNA levels of carnitine palmitoyltransferase 2 in high grades of glioma when compared to the lower grades and the results of our study is in agreement with the reports of Bogusiewicz et al., (2021) and Melone et al’s (2018)., suggesting that higher carnitine content in higher-grade tumors was observed when compared to the lower grades of glioma. Thus, suggesting that higher levels of carnitine in high grades could be related to increased metabolism of tumors when compared to those of low grades. The shuttling of the carnitine system exhibits a vital role not only in cancer plasticity but also it enhances the metabolic demands of the proliferating cancerous cells to be attained, even in adverse state. The monoglyceride lipase (MGLL) catabolizes the final step in neutral lipolysis to produce fatty acid and glycerol and it can act as a significant contributor towards cancer aggressiveness (Nomura et al., 2010 & 2011). Thus, it could be inferred that monoacylglycerols-free fatty acid pathway feeding into a diverse lipid network enriched in protumorigenic signaling molecules eventually promoting migration, survival, and in vivo tumor growth. Subsequently, aggressive cancer cells could pair lipogenesis with high lipolytic activity to generate an array of protumorigenic signals could thereby supporting their malignant behavior. Our observation of biopsies of higher grades exhibited increase in the mRNA levels of MGLL provide a striking example of the co-opting of an enzyme by cancer cells to serve a distinct metabolic purpose that supports their pathogenic behavioral progress in higher grades.

The glucose transporter 3 (GLUT 3) in the brain is known as the neuronal glucose transporter and increase in the GLUT3 levels was found to be highly metaboloically plastic in higher grades of glioma called brain tumor initiating cells, which promotes the survival in restricted glucose (Libby et al., 2020; Flavahan et al., 2013). This brain tumor initiating cells were reported to be more invasive than more differentiated cells within high grade tumors (Ruiz-Ontanon et al., 2013; Ortensi et al., 2013). Boral et al., (2017) demonstrated that GLUT3 was increased in circulating tumor cells have higher propensity to target the brain and GLUT 3 was essential for their survival within the brain (Kuo et al., 2019). In the present study, it could be evident that higher grades of glioma exhibited significant increase in the levels of GLUT3 when compared to low grades. The data suggest that glycolytic shift or elevated glucose uptake mediated by GLUT3 could significantly increase the circulating tumor cell survival, which in turn promotes tumor metastasis. The present study coincides with evidences by Puchalski et al (2018) suggesting that high GLUT3 expression positively correlated with an increased incidence of metastasis in breast and head and neck cancers.

Phosphofructokinase-1 (PFK-1) has been widely recognized to catalyze the second rate-limiting procedure of glycolysis, transforming fructose-6-phosphate (F-6-P) to fructose-1, 6-biphosphate (F-1, 6-BP) mediated by Mg2 + and ATP (Rhodes et al., 2004). The glycolytic flux of tumor cells could be modulated by PFK-1, usually activated in a significant number of tumor types, thus affecting cellular growth, invasiveness, and survival (Yalcin et al., 2009; Bartrons et al., 2007). In our present study, increased mRNA levels of phosphofructokinase in higher grades of glioma suggested that enhanced glycolytic flux could be regulated by PFK-1, which could significantly enrich the tumor microenvironment thereby aggravating the aggressive behaviour in higher grades.

The main hallmark of cancer is alteration in the metabolism for the promotion of efficient tumor growth and acquired resistance to therapy (Hanahan et al., 2011). The glioma cells exhibit abnormal energy metabolism, including alterations of glucose, amino acid, and fatty acid metabolism (Vander et al., 2009). Our previous study exhibited that altered metabolic pathways including choline, taurine, hypotaurine, or glutamate/glutamine correlate with different WHO grades in diffuse glioma (Jayalakshmi et al., 2020). In the present study, glutamate, choline and tyrosine metabolites were found to be significantly altered in higher grades of glioma. Our study coincides with the reports of Bianchi, et al. (2004) where increased levels of choline in higher grades of glioma have been reported to be the most reliable indicator for detecting the malignancy in human tumors by proton magnetic resonance spectroscopy (MRS). Bianchi, et al. (2004) suggested that higher levels of choline reflect an increase in the metabolites that are precursors of the membrane phospholipids that are vital for the support of neoplastic proliferation and increase in the tumor cellular turnover. Earlier in the brain, Tedeschi et al. (1997) reported an increase in choline levels was found to parallel clinical deterioration in human samples.

Metabolic reprogramming is one of the hallmarks of cancer (Faubert et al., 2020; Leone & Powell, 2020; Hoxhaj &Manning, 2020). As an abnormal amino acid metabolism has potential impact on the metabolic control and regulation of cancer microenvironment (Wu et al., 2021). We explored the dysregulation of amino acid metabolism in tumor microenvironment not only facilitate the survival and proliferation of cancer cells but also the non-essential amino acids aid in the production of protein biosynthesis, conversion to glucose, lipids, and precursors of nitrogen-containing metabolites, such as purines and pyrimidines for nucleic acid synthesis by acting as building blocks to the cancer cells. Our NMR analysis had shown the altered levels of pyruvate, citrate, aspartate, N-acetylaspartate (carbohydrate metabolism), choline, myoinositol (lipid metabolism), glutamate, GABA, tyrosine, lysine (amino acid metabolism) that might affect the metabolism represented in the parenthesis.

Glutamatergic signaling is essential for the maintenance, proliferation and invasive behavior of higher grades of glioma. Many investigations have reported that the capability of increased paracrine glutamate signaling between neurons and glioblastoma promotes cell proliferation, migration and tumor establishment (Sontheimer et al., 2008; Corsi et al., 2019). Maier et al., (2021) suggested that there is a strong relationship between glutamate release and stressors to the glioblastoma, validated using a spatial multi-omics approach. They also found that any alterations in the uptake/release of glutamate results in significant changes in the glioblastoma behavior, playing a vital role in proliferation and invasive behavior. Recently, Subramani et al., (2020) investigated the non-invasive detection of glutamate by magnetic resonance spectroscopy, which can serve as a metabolic imaging biomarker in response to temozolomide treatment to IDH1 mutant glioma. In concordance with the reports, we also register the metabolic glutamate in higher grades of glioma. In concordance to the present study, Yamashita et al. (2020) performed comprehensive metabolic analyses using high-grade glioma tissues and glioblastoma patient–derived sphere culture models and reported that tyrosine metabolism could be a novel target in high grades of glioma. Thus, dysregulation in the metabolomic profile could contribute to the aggressiveness of malignancy in higher grades of glioma.

The MetaboAnalyst 4.0 software was used to identify pathways that could be altered in the glioma. From the Fig. 6, the scatterplot of joint metabolic pathway analysis of the metabolites and genes from KEGG database, top six metabolically altered pathways have been identified as follows: central carbon metabolism in cancer, alanine, aspartate and glutamate metabolism, ABC Transporter, aminoacyl-tRNA biosynthesis and also the process of protein digestion and adsorption.

The study had identified deemed-to-be targets metabolites that are exhibiting palpable discrimination among the grades along with the dysregulated metabolic pathways. The combined approach of metabolomic understanding along with the gene expressional variations depicted in the current data leads to a precise view regarding the possibility of exploiting them as better clues for precisional approach for targeting glioma.

Conflict of interest: The authors declare no conflict of interest.

Ethical approval: Rajiv Gandhi Government General hospital Chennai, Tamil Nadu, India, Chennai, Tamil Nadu, India following proper approval from Institutional Human Ethics (Approval No. 02122020/P&D/Ethics/Dean/GGH/ Chennai, 15-12-2020).

Informed Consent: All patients gave their written informed consent for the intervention, and routine follow-up.

Data Availability Statement: The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at University of Madras, Guindy Campus.

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Table.1: Distinctive metabolite variations between different grades of glioma by ¹H NMR analysis

S.No	Metabolite	δ1 H (ppm)	p-value	Significance Grade I & II Vs Grade III & IV
1	Glutamate	3.78 2.44 2.14	0.0001	***
2	Choline	4.05 3.51 3.21	0.0001	***
3	Tyrosine	7.18 6.92 3.05 3.19 3.92	0.0003	***
4	GABA	3.71 1.91 0.98	0.003	**
5	Myoinositol	3.66 4.08	0.004	**
6	Lysine	1.74 3.03	0.006	**
7	Aspartate	3.87 2.81 2.71	0.019	*
8	Citrate	2.7 2.5	0.026	*
9	Pyruvate	2.4	0.039	*
10	N- Acetylaspartate	4.34 2.75 2.56 7.81	0.041	*

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Targeted metabolic profiling in determining the metabolic heterogeneity in human biopsies of different grades of glioma

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Declarations

References

Tables

Status:

Journal Publication

Version 1