The Leloir Cycle in Glioblastoma: Galactose Scavenging and Metabolic Remodeling

Challenges of Spatially Resolved Metabolism in Cancer Research

Stable isotope-resolved metabolomics comprises a critical set of technologies that can be applied to a wide variety of systems, from isolated cells to whole organisms, to define metabolic pathway usage and responses to perturbations such as drugs or mutations, as well as providing the basis for flux analysis. As the diversity of stable isotope-enriched compounds is very high, and with newer approaches to multiplexing, the coverage of metabolism is now very extensive. However, as the complexity of the model increases, including more kinds of interacting cell types and interorgan communication, the analytical complexity also increases. Further, as studies move further into spatially resolved biology, new technical problems have to be overcome owing to the small number of analytes present in the confines of a single cell or cell compartment. Here, we review the overall goals and solutions made possible by stable isotope tracing and their applications to models of increasing complexity. Finally, we discuss progress and outstanding difficulties in high-resolution spatially resolved tracer-based metabolic studies.

Proteomic, Metabolomic, and Fatty Acid Profiling of Small Extracellular Vesicles from Glioblastoma Stem-Like Cells and Their Role in Tumor Heterogeneity

Glioblastoma is a deadly brain tumor for which there is no cure. The presence of glioblastoma stem-like cells (GSCs) contributes to the heterogeneous nature of the disease and makes developing effective therapies challenging. Glioblastoma cells have been shown to influence their environment by releasing biological nanostructures known as extracellular vesicles (EVs). Here, we investigated the role of GSC-derived nanosized EVs (<200 nm) in glioblastoma heterogeneity, plasticity, and aggressiveness, with a particular focus on their protein, metabolite, and fatty acid content. We showed that conditioned medium and small extracellular vesicles (sEVs) derived from cells of one glioblastoma subtype induced transcriptomic and proteomic changes in cells of another subtype. We found that GSC-derived sEVs are enriched in proteins playing a role in the transmembrane transport of amino acids, carboxylic acids, and organic acids, growth factor binding, and metabolites associated with amino acid, carboxylic acid, and sugar metabolism. This suggests a dual role of GSC-derived sEVs in supplying neighboring GSCs with valuable metabolites and proteins responsible for their transport. Moreover, GSC-derived sEVs were enriched in saturated fatty acids, while their respective cells were high in unsaturated fatty acids, supporting that the loading of biological cargos into sEVs is a highly regulated process and that GSC-derived sEVs could be sources of saturated fatty acids for the maintenance of glioblastoma cell metabolism. Interestingly, sEVs isolated from GSCs of the proneural and mesenchymal subtypes are enriched in specific sets of proteins, metabolites, and fatty acids, suggesting a molecular collaboration between transcriptionally different glioblastoma cells. In summary, this study revealed the complexity of GSC-derived sEVs and unveiled their potential contribution to tumor heterogeneity and critical cellular processes commonly deregulated in glioblastoma.

Serum Metabolomics of Retinoblastoma: Assessing the Differential Serum Metabolic Signatures of Unilateral and Bilateral Patients

Retinoblastoma (Rb) is the most common pediatric eye cancer. To identify the biomarkers for early diagnosis and monitoring the progression of Rb in patients, mapping of the alterations in their metabolic profiles is essential. The present study aims at exploring the metabolic disparity in serum from Rb patients and controls using NMR-based metabolomics. A total of 72 metabolites, including carbohydrates, amino acids, and organic acids, were quantified in serum samples from 24 Rb patients and 26 controls. Distinct clusters of Rb patients and controls were obtained using the partial least-squares discriminant analysis (PLS-DA) model. Further, univariate and multivariate analyses of unilateral and bilateral Rb patients with respect to their age-matched controls depicted their distinct metabolic fingerprints. Metabolites including 2-phosphoglycerate, 4-aminobutyrate, proline, O-phosphocholine, O-phosphoethanolamine, and Sn-glycero-3-phosphocholine (Sn-GPC) showed significant perturbation in both unilateral and bilateral Rb patients. However, metabolic differences among the bilateral Rb cases were more pronounced than those in unilateral Rb cases with respect to controls. In addition to major discriminatory metabolites for Rb, unilateral and bilateral Rb cases showed specific metabolic changes, which might be the result of their differential genetic/somatic mutational backgrounds. This further suggests that the aberrant metabolic perturbation in bilateral patients signifies the severity of the disease in Rb patients. The present study demonstrated that identified serum metabolites have potential to serve as a noninvasive method for detection of Rb, discriminate bilateral from unilateral Rb patients, and aid in better understanding of the RB tumor biology.

Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model

Glioblastoma multiforme (GBM) patients show a variety of signs and symptoms that affect their quality of life (QOL) and self-dependence. Since most existing studies have examined prognostic factors based only on clinical factors, there is a need to consider the value of integrating multi-omics data including gene expression and proteomics with clinical data in identifying significant biomarkers for GBM prognosis. Our research aimed to isolate significant features that differentiate between short-term (≤ 6 months) and long-term (≥ 2 years) GBM survival, and between high Karnofsky performance scores (KPS ≥ 80) and low (KPS ≤ 60), using the iterative random forest (iRF) algorithm. Using the Cancer Genomic Atlas (TCGA) database, we identified 35 molecular features composed of 19 genes and 16 proteins. Our findings propose molecular signatures for predicting GBM prognosis and will improve clinical decisions, GBM management, and drug development.

Integrated Multi-Omics Profiling of Young Breast Cancer Patients Reveals a Correlation between Galactose Metabolism Pathway and Poor Disease-Free Survival

In recent years, there has been a notable rise in the incidence of breast cancer among young patients, who exhibit worse survival outcomes and distinct characteristics compared to intermediate and elderly patients. Therefore, it is imperative to identify the specific features unique to young patients, which could offer insights into potential therapeutic strategies and improving survival outcomes. In our study, we performed an integrative analysis of bulk transcriptional and genomic data from extensive clinical cohorts to identify the prognostic factotrs. Additionally, we analyzed the single-cell transcriptional data and conducted in vitro experiments. Our work confirmed that young patients exhibited higher grading, worse disease-free survival (DFS), a higher frequency of mutations in TP53 and BRCA1, a lower frequency of mutations in PIK3CA, and upregulation of eight metabolic pathways. Notably, the galactose metabolism pathway showed upregulation in young patients and was associated with worse DFS. Further analysis and experiments indicated that the galactose metabolism pathway may regulate the stemness of cancer cells and ultimately contribute to worse survival outcomes. In summary, our finding identified distinct clinicopathological, transcriptional, and genomics features and revealed a correlation between the galactose metabolism pathway, stemness, and poor disease-free survival of breast cancer in young patients.

Integrating Multi-Omics Analysis for Enhanced Diagnosis and Treatment of Glioblastoma: A Comprehensive Data-Driven Approach

The most aggressive primary malignant brain tumor in adults is glioblastoma (GBM), which has poor overall survival (OS). There is a high relapse rate among patients with GBM despite maximally safe surgery, radiation therapy, temozolomide (TMZ), and aggressive treatment. Hence, there is an urgent and unmet clinical need for new approaches to managing GBM. The current study identified modules (MYC, EGFR, PIK3CA, SUZ12, and SPRK2) involved in GBM disease through the NeDRex plugin. Furthermore, hub genes were identified in a comprehensive interaction network containing 7560 proteins related to GBM disease and 3860 proteins associated with signaling pathways involved in GBM. By integrating the results of the analyses mentioned above and again performing centrality analysis, eleven key genes involved in GBM disease were identified. ProteomicsDB and Gliovis databases were used for determining the gene expression in normal and tumor brain tissue. The NetworkAnalyst and the mGWAS-Explorer tools identified miRNAs, SNPs, and metabolites associated with these 11 genes. Moreover, a literature review of recent studies revealed other lists of metabolites related to GBM disease. The enrichment analysis of identified genes, miRNAs, and metabolites associated with GBM disease was performed using ExpressAnalyst, miEAA, and MetaboAnalyst tools. Further investigation of metabolite roles in GBM was performed using pathway, joint pathway, and network analyses. The results of this study allowed us to identify 11 genes (UBC, HDAC1, CTNNB1, TRIM28, CSNK2A1, RBBP4, TP53, APP, DAB1, PINK1, and RELN), five miRNAs (hsa-mir-221-3p, hsa-mir-30a-5p, hsa-mir-15a-5p, hsa-mir-130a-3p, and hsa-let-7b-5p), six metabolites (HDL, N6-acetyl-L-lysine, cholesterol, formate, N, N-dimethylglycine/xylose, and X2. piperidinone) and 15 distinct signaling pathways that play an indispensable role in GBM disease development. The identified top genes, miRNAs, and metabolite signatures can be targeted to establish early diagnostic methods and plan personalized GBM treatment strategies.

EPA stronger than DHA increases the mitochondrial membrane potential and cardiolipin levels but does not change the ATP level in astrocytes

Astrocytes are highly energy-consuming glial cells critical for metabolic support to neurons. A growing body of evidence suggests that mitochondrial dysfunction in astrocytes is involved in age-related neurodegenerative disorders and that fish oil, rich in docosahexaenoic (DHA) and eicosapentaenoic (EPA) fatty acids, may alleviate cognition impairment in Parkinson's and Alzheimer's diseases. The present study examines the effect of DHA and EPA on mitochondrial membrane potential (MMP), apoptosis activation and ATP levels in astrocytes cultured in medium containing glucose or galactose, which limits oxidative phosphorylation (OXPHOS). MMP, expressed as the ratio of red to green JC-10 and MitoTracker fluorescence, increased in EPA-incubated cells in a dose dependent manner and was higher than in DHA-incubated astrocytes, also after uncoupling of OXPHOS by carbonyl cyanide 3-chlorophenylhydrazone (CCCP). In cells cultured in glucose and galactose medium mitochondrial hyperpolarization had no impact on intracellular ATP level. Furthermore, both EPA and DHA elevated mitochondrial cardiolipin content, however only EPA did so in a dose-dependent manner and reduced apoptosis which was analyzed by flow cytometry.

A prognostic pyroptosis-related LncRNA classifier associated with the immune landscape and therapy efficacy in glioma

Background: Glioma has the highest fatality rate among intracranial tumours. Besides, the heterogeneity of gliomas leads to different therapeutic effects even with the same treatment. Developing a new signature for glioma to achieve the concept of “personalised medicine” remains a significant challenge.

Method: The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were searched to acquire information on glioma patients. Initially, correlation and univariate Cox regression analyses were performed to screen for prognostic pyroptosis-related long noncoding RNAs (PRLs). Secondly, 11 PRLs were selected to construct the classifier using certain algorithms. The efficacy of the classifier was then detected by the “timeROC” package for both the training and validation datasets. CIBERSORT and ESTIMATE packages were applied for comparing the differences (variations) in the immune landscape between the high- and low-risk groups. Finally, the therapeutic efficacy of the chemotherapy, radiotherapy, and immunotherapy were assessed using the “oncoPredict” package, survival analysis, and the tumour immune dysfunction and exclusion (TIDE) score, respectively.

Results: A classifier comprising 11 PRLs was constructed. The PRL classifier exhibits a more robust prediction capacity for the survival outcomes in patients with gliomas than the clinical characteristics irrespective of the dataset (training or validation dataset). Moreover, it was found that the tumour landscape between the low- and high-risk groups was significantly different. A high-risk score was linked to a more immunosuppressive tumour microenvironment. According to the outcome prediction and analysis of the chemotherapy, patients with different scores showed different responses to various chemotherapeutic drugs and immunotherapy. Meanwhile, the patient with glioma of WHO grade Ⅳ or aged >50 years in the high risk group had better survival following radiotherapy.

Conclusion: We constructed a PRL classifier to roughly predict the outcome of patients with gliomas. Furthermore, the PRL classifier was linked to the immune landscape of glioma and may guide clinical treatments.

Comparing 2 crystal structures and 12 AlphaFold2-predicted human membrane glucose transporters and their water-soluble glutamine, threonine and tyrosine variants

Membrane transporters including glucose transporters (GLUTs) are involved in cellular energy supplies, cell metabolism and other vital biological activities. They have also been implicated in cancer proliferation and metastasis, thus they represent an important target in combatting cancer. However, membrane transporters are very difficult to study due to their multispan transmembrane properties. The new computational tool, AlphaFold2, offers highly accurate predictions of three-dimensional protein structures. The glutamine, threonine and tyrosine (QTY) code provides a systematic method of rendering hydrophobic sequences into hydrophilic ones. Here, we present computational studies of native integral membrane GLUTs with 12 transmembrane helical segments determined by X-ray crystallography and CryoEM, comparing the AlphaFold2-predicted native structure to their water-soluble QTY variants predicted by AlphaFold2. In the native structures of the transmembrane helices, there are hydrophobic amino acids leucine (L), isoleucine (I), valine (V) and phenylalanine (F). Applying the QTY code, these hydrophobic amino acids are systematically replaced by hydrophilic amino acids, glutamine (Q), threonine (T) and tyrosine (Y) rendering them water-soluble. We present the superposed structures of native GLUTs and their water-soluble QTY variants. The superposed structures show remarkable similar residue mean square distance values between 0.47 and 3.6 Å (most about 1–2 Å) despite >44% transmembrane amino acid differences. We also show the differences of hydrophobicity patches between the native membrane transporters and their QTY variants. We explain the rationale why the membrane protein QTY variants become water-soluble. Our study provides insight into the differences between the hydrophobic helices and hydrophilic helices, and offers confirmation of the QTY method for studying multispan transmembrane proteins and other aggregated proteins through their water-soluble variants.

Rotating Magnetic Fields Inhibit Mitochondrial Respiration, Promote Oxidative Stress and Produce Loss of Mitochondrial Integrity in Cancer Cells

Electromagnetic fields (EMF) raise intracellular levels of reactive oxygen species (ROS) that can be toxic to cancer cells. Because weak magnetic fields influence spin state pairing in redox-active radical electron pairs, we hypothesize that they disrupt electron flow in the mitochondrial electron transport chain (ETC). We tested this hypothesis by studying the effects of oscillating magnetic fields (sOMF) produced by a new noninvasive device involving permanent magnets spinning with specific frequency and timing patterns. We studied the effects of sOMF on ETC by measuring the consumption of oxygen (O₂) by isolated rat liver mitochondria, normal human astrocytes, and several patient derived brain tumor cells, and O₂ generation/consumption by plant cells with an O₂ electrode. We also investigated glucose metabolism in tumor cells using ¹H and ¹³C nuclear magnetic resonance and assessed mitochondrial alterations leading to cell death by using fluorescence microscopy with MitoTracker™ and a fluorescent probe for Caspase 3 activation. We show that sOMF of appropriate field strength, frequency, and on/off profiles completely arrest electron transport in isolated, respiring, rat liver mitochondria and patient derived glioblastoma (GBM), meningioma and diffuse intrinsic pontine glioma (DIPG) cells and can induce loss of mitochondrial integrity. These changes correlate with a decrease in mitochondrial carbon flux in cancer cells and with cancer cell death even in the non-dividing phase of the cell cycle. Our findings suggest that rotating magnetic fields could be therapeutically efficacious in brain cancers such as GBM and DIPG through selective disruption of the electron flow in immobile ETC complexes.

Hijacking Sexual Immuno-Privilege in GBM-An Immuno-Evasion Strategy

Regulatory T-cells (Tregs) are immunosuppressive T-cells, which arrest immune responses to 'Self' tissues. Some immunosuppressive Tregs that recognize seminal epitopes suppress immune responses to the proteins in semen, in both men and women. We postulated that GBMs express reproductive-associated proteins to manipulate reproductive Tregs and to gain immune privilege. We analyzed four GBM transcriptome databases representing ≈900 tumors for hypoxia-responsive Tregs, steroidogenic pathways, and sperm/testicular and placenta-specific genes, stratifying tumors by expression. In silico analysis suggested that the presence of reproductive-associated Tregs in GBM tumors was associated with worse patient outcomes. These tumors have an androgenic signature, express male-specific antigens, and attract reproductive-associated Related Orphan Receptor C (RORC)-Treg immunosuppressive cells. GBM patient sera were interrogated for the presence of anti-sperm/testicular antibodies, along with age-matched controls, utilizing monkey testicle sections. GBM patient serum contained anti-sperm/testicular antibodies at levels > six-fold that of controls. Myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs) are associated with estrogenic tumors which appear to mimic placental tissue. We demonstrate that RORC-Tregs drive poor patient outcome, and Treg infiltration correlates strongly with androgen levels. Androgens support GBM expression of sperm/testicular proteins allowing Tregs from the patient's reproductive system to infiltrate the tumor. In contrast, estrogen appears responsible for MDSC/TAM immunosuppression.