Gliomas that express the mutated isoforms of isocitrate dehydrogenase 1/2 (IDH1/2) have better prognosis than wild-type (wt) IDH1/2 gliomas. However, how these mutant (mut) proteins affect the tumor microenvironment is still a pending question. Here, we describe that the transcription of microtubule-associated protein TAU (MAPT), a gene that has been classically associated with neurodegenerative diseases, is epigenetically controlled by the balance between wt and mut IDH1/2 in mouse and human gliomas. In IDH1/2 mut tumors, we found high expression of TAU that decreased with tumor progression. Furthermore, MAPT was almost absent from tumors with epidermal growth factor receptor (EGFR) mutations, whereas its trancription negatively correlated with overall survival in gliomas carrying wt or amplified (amp) EGFR. We demonstrated that the overexpression of TAU, through the stabilization of microtubules, impaired the mesenchymal/pericyte-like transformation of glioma cells by blocking EGFR, nuclear factor kappa-light-chain-enhancer of activated B (NF-κB) and the transcriptional coactivator with PDZ-binding motif (TAZ). Our data also showed that mut EGFR induced a constitutive activation of this pathway, which was no longer sensitive to TAU. By inhibiting the transdifferentiation capacity of EGFRamp/wt tumor cells, TAU protein inhibited angiogenesis and favored vascular normalization, decreasing glioma aggressiveness and increasing their sensitivity to chemotherapy.

Drug-withdrawal-associated aversive memories might trigger relapse to drug-seeking behavior. However, changes in structural and synaptic plasticity, as well as epigenetic mechanisms, which may be critical for long-term aversive memory, have yet to be elucidated. We used male Wistar rats and performed conditioned-place aversion (CPA) paradigm to uncover the role of glucocorticoids (GCs) on plasticity-related processes that occur within the dentate gyrus (DG) during opiate-withdrawal conditioning (memory formation-consolidation) and after reactivation by re-exposure to the conditioned environment (memory retrieval). Rats subjected to conditioned morphine-withdrawal robustly expressed CPA, while adrenalectomy impaired naloxone-induced CPA. Importantly, while activity-regulated cytoskeletal-associated protein (Arc) expression was induced in sham- and ADX-dependent animals during the conditioning phase, Arc and early growth response 1 (Egr-1) induction was restricted to sham-dependent rats following memory retrieval. Moreover, we found a correlation between Arc induction and CPA score, and Arc was selectively expressed in the granular zone of the DG in dopaminoceptive, glutamatergic and GABAergic neurons. We further found that brain-derived neurotrophic factor was regulated in the opposite way during the test phase. Our results also suggest a role for epigenetic regulation on the expression of glucocorticoid receptors and Arc following memory retrieval. Our data provide the first evidence that GC homeostasis is important for the expression of long-term morphine-withdrawal memories. Moreover, our results support the idea that targeting Arc and Egr-1 in the DG may provide important insights into the role of these signaling cascades in withdrawal-context memory re-consolidation. Together, disrupting these processes in the DG might lead to effective treatments in drug addiction thereby rapidly and persistently reducing invasive memories and subsequent drug seeking.

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Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (< 6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.

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