Glutamine Metabolism in Gliomas

Overall and individual associations between per- and polyfluoroalkyl substances and liver function indices and the metabolic mechanism

Per- and polyfluoroalkyl substances (PFAS) can disrupt liver homeostasis. Studies have shown that a single exposure to PFAS may provoke abnormal liver function; however, few studies have investigated the overall effect of PFAS mixtures. We aimed to investigate associations between exposure to PFAS mixtures and liver function indices and explore the relevant mechanisms. This study included 278 adult males from Guangzhou, China. Serum metabolite profiles were analyzed using untargeted metabolomics. We applied weighted quantile sum (WQS) regression as well as Bayesian kernel machine regression (BKMR) to analyze the association of nine PFAS mixtures with 14 liver function indices. PFAS mixtures were positively associated with apolipoprotein B (APOB) and gamma-glutamyltransferase (GGT) and negatively associated with direct bilirubin (DBIL) and total bilirubin (TBIL) in both the WQS and BKMR analyses. In addition, Spearman's correlation test showed individual PFAS correlated with APOB, GGT, TBIL, and DBIL, while there's little correlation between individual PFAS and other liver function indices. In linear regression analysis, PFHxS, PFOS, PFHpS, PFNA, PFDA, and PFUdA were associated with APOB; PFOA, PFDA, PFOS, PFNA, and PFUdA were associated with GGT. Subsequently, a metabolome-wide association study and mediation analysis were combined to explore metabolites that mediate these associations. The mechanisms linking PFAS to APOB and GGT are mainly related with amino acid and glycerophospholipid metabolism. High-dimensional mediation analysis showed that glycerophospholipids are the main markers of the association between PFAS and APOB, and that (R)-dihydromaleimide, Ile Leu, (R)-(+)-2-pyrrolidone-5-carboxylic acid, and L-glutamate are the main markers of the association between PFAS and GGT. In summary, overall associations between PFAS and specific indices of liver function were found using two statistical methods; the metabolic pathways and markers identified here may serve to prompt more detailed study in animal-based systems, as well as a similar detailed analysis in other populations.

Deep learning algorithm reveals two prognostic subtypes in patients with gliomas

Background

Gliomas are highly complex and heterogeneous tumors, rendering prognosis prediction challenging. The advent of deep learning algorithms and the accessibility of multi-omic data represent a new approach for the identification of survival-sensitive subtypes. Herein, an autoencoder-based approach was used to identify two survival-sensitive subtypes using RNA sequencing (RNA-seq) and DNA methylation (DNAm) data from The Cancer Genome Atlas (TCGA) dataset. The subtypes were used as labels to build a support vector machine model with cross-validation. We validated the robustness of the model on Chinese Glioma Genome Atlas (CGGA) dataset. DNAm-driven genes were identified by integrating DNAm and gene expression profiling analyses using the R MethylMix package and carried out for further enrichment analysis.

Results

For TCGA dataset, the model produced a high C-index (0.92 ± 0.02), low brier score (0.16 ± 0.02), and significant log-rank p value (p < 0.0001). The model also had a decent performance for CGGA dataset (CGGA DNAm: C-index of 0.70, brier score of 0.21; CGGA RNA-seq: C-index of 0.79, brier score of 0.18). Moreover, we identified 389 DNAm-driven genes of survival-sensitive subtypes, which were significantly enriched in the glutathione metabolism pathway.

Conclusions

Our study identified two survival-sensitive subtypes of glioma and provided insights into the molecular mechanisms underlying glioma development; thus, potentially providing a new target for the prognostic prediction of gliomas and supporting personalized treatment strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04970-x.

ASCT1 and ASCT2: Brother and Sister?

The SLC1 family includes seven members divided into two groups, namely, EAATs and ASCTs, that share similar 3D architecture; the first one includes high-affinity glutamate transporters, and the second one includes SLC1A4 and SLC1A5, known as ASCT1 and ASCT2, respectively, responsible for the traffic of neutral amino acids across the cell plasma membrane. The physiological role of ASCT1 and ASCT2 has been investigated over the years, revealing different properties in terms of substrate specificities, affinities, and regulation by physiological effectors and posttranslational modifications. Furthermore, ASCT1 and ASCT2 are involved in pathological conditions, such as neurodegenerative disorders and cancer. This has driven research in the pharmaceutical field aimed to find drugs able to target the two proteins.This review focuses on structural, functional, and regulatory aspects of ASCT1 and ASCT2, highlighting similarities and differences.

Beyond glucose: alternative sources of energy in glioblastoma

Glioblastoma multiforme (GBM) is the most common malignant brain tumor in adults. With a designation of WHO Grade IV, it is also the most lethal primary brain tumor with a median survival of just 15 months. This is often despite aggressive treatment that includes surgical resection, radiation therapy, and chemotherapy. Based on the poor outcomes and prevalence of the tumor, the demand for innovative therapies continues to represent a pressing issue for clinicians and researchers. In terms of therapies targeting metabolism, the prevalence of the Warburg effect has led to a focus on targeting glucose metabolism to halt tumor progression. While glucose is the dominant source of growth substrate in GBM, a number of unique metabolic pathways are exploited in GBM to meet the increased demand for replication and progression. In this review we aim to explore how metabolites from fatty acid oxidation, the urea cycle, the glutamate-glutamine cycle, and one-carbon metabolism are shunted toward energy producing pathways to meet the high energy demand in GBM. We will also explore how the process of autophagy provides a reservoir of nutrients to support viable tumor cells. By so doing, we aim to establish a foundation of implicated metabolic mechanisms supporting growth and tumorigenesis of GBM within the literature. With the sparse number of therapeutic interventions specifically targeting metabolic pathways in GBM, we hope that this review expands further insight into the development of novel treatment modalities.

Phospho-mTOR expression in human glioblastoma microglia-macrophage cells

The glioblastoma (GBM) immune microenvironment is highly heterogeneous, and microglia may represent 30-70% of the entire tumor. However, the role of microglia and other specific immune populations is poorly characterized. Activation of mTOR signaling occurs in numerous human cancers and has roles in microglia-glioma cell interactions. We now show in human tumor specimens (42 patients), that 39% of tumor-associated microglial (TAM) cells express mTOR phosphorylated at Ser-2448; and similar mTOR activation is observed using a human microglia-glioma interaction paradigm. In addition, we confirm previous studies that microglia express urea and ARG1 (taken as M2 marker) in the presence of glioma cells, and this phenotype is down-regulated in the presence of a mTOR inhibitor. These results suggest that mTOR suppression in GBM patients might induce a reduction of the M2 phenotype expression in up to 40% of all TAMs. Since the M2 profile of microglial activation is believed to be associated with tumor progression, reductions in that phenotype may represent an additional anti-tumor mechanism of action of mTOR inhibitors, along with direct anti-proliferative activities.

Plasticity in Glioma Stem Cell Phenotype and Its Therapeutic Implication

The marked heterogeneity in glioblastoma (GBM) may be induced through dynamic differentiation and dedifferentiation process of glioma cells. The hypothesis that environmental stimuli induce these phenotypic changes, including dedifferentiation into the stem cell phenotype which contributes to the high invasiveness and resultant poor outcome in GBM patients, is recently being proven. In the process of cancer invasion and metastasis, the phenotypic change has also been described as epithelial-mesenchymal transition (EMT). This biological process is mainly dependent on hypoxic stimuli and also on transforming growth factor-β (TGF-β) released from glioma stem cells, mesenchymal stem cells, and myeloid cells recruited by hypoxia. The tumor microenvironment, especially hypoxia, inducing such dynamic phenotypic changes can be a good therapeutic target in the treatment of GBM.