Glutaminolysis dynamics during astrocytoma progression correlates with tumor aggressiveness.

BACKGROUND: Glioblastoma is the most frequent and high-grade adult malignant central nervous system tumor. The prognosis is still poor despite the use of combined therapy involving maximal surgical resection, radiotherapy, and chemotherapy. Metabolic reprogramming currently is recognized as one of the hallmarks of cancer. Glutamine metabolism through glutaminolysis has been associated with tumor cell maintenance and survival, and with antioxidative stress through glutathione (GSH) synthesis. METHODS: In the present study, we analyzed the glutaminolysis-related gene expression levels in our cohort of 153 astrocytomas of different malignant grades and 22 non-neoplastic brain samples through qRT-PCR. Additionally, we investigated the protein expression profile of the key regulator of glutaminolysis (GLS), glutamate dehydrogenase (GLUD1), and glutamate pyruvate transaminase (GPT2) in these samples. We also investigated the glutathione synthase (GS) protein profile and the GSH levels in different grades of astrocytomas. The differential gene expressions were validated in silico on the TCGA database. RESULTS: We found an increase of glutaminase isoform 2 gene (GLSiso2) expression in all grades of astrocytoma compared to non-neoplastic brain tissue, with a gradual expression increment in parallel to malignancy. Genes coding for GLUD1 and GPT2 expression levels varied according to the grade of malignancy, being downregulated in glioblastoma, and upregulated in lower grades of astrocytoma (AGII-AGIII). Significant low GLUD1 and GPT2 protein levels were observed in the mesenchymal subtype of GBM. CONCLUSIONS: In glioblastoma, particularly in the mesenchymal subtype, the downregulation of both genes and proteins (GLUD1 and GPT2) increases the source of glutamate for GSH synthesis and enhances tumor cell fitness due to increased antioxidative capacity. In contrast, in lower-grade astrocytoma, mainly in those harboring the IDH1 mutation, the gene expression profile indicates that tumor cells might be sensitized to oxidative stress due to reduced GSH synthesis. The measurement of GLUD1 and GPT2 metabolic substrates, ammonia, and alanine, by noninvasive MR spectroscopy, may potentially allow the identification of IDH1mut AGII and AGIII progression towards secondary GBM.

Complex Network Measures in Autism Spectrum Disorders.

Recent studies have suggested abnormal brain network organization in subjects with Autism Spectrum Disorders (ASD). Here we applied spectral clustering algorithm, diverse centrality measures (betweenness (BC), clustering (CC), eigenvector (EC), and degree (DC)), and also the network entropy (NE) to identify brain sub-systems associated with ASD. We have found that BC increases in the following ASD clusters: in the somatomotor, default-mode, cerebellar, and fronto-parietal. On the other hand, CC, EC, and DC decrease in the somatomotor, default-mode, and cerebellar clusters. Additionally, NE decreases in ASD in the cerebellar cluster. These findings reinforce the hypothesis of under-connectivity in ASD and suggest that the difference in the network organization is more prominent in the cerebellar system. The cerebellar cluster presents reduced NE in ASD, which relates to a more regular organization of the networks. These results might be important to improve current understanding about the etiological processes and the development of potential tools supporting diagnosis and therapeutic interventions.

A comparative study of statistical methods used to identify dependencies between gene expression signals.

One major task in molecular biology is to understand the dependency among genes to model gene regulatory networks. Pearson's correlation is the most common method used to measure dependence between gene expression signals, but it works well only when data are linearly associated. For other types of association, such as non-linear or non-functional relationships, methods based on the concepts of rank correlation and information theory-based measures are more adequate than the Pearson's correlation, but are less used in applications, most probably because of a lack of clear guidelines for their use. This work seeks to summarize the main methods (Pearson's, Spearman's and Kendall's correlations; distance correlation; Hoeffding's D: measure; Heller-Heller-Gorfine measure; mutual information and maximal information coefficient) used to identify dependency between random variables, especially gene expression data, and also to evaluate the strengths and limitations of each method. Systematic Monte Carlo simulation analyses ranging from sample size, local dependence and linear/non-linear and also non-functional relationships are shown. Moreover, comparisons in actual gene expression data are carried out. Finally, we provide a suggestive list of methods that can be used for each type of data set.