MAPK15 controls cellular responses to oxidative stress by regulating NRF2 activity and expression of its downstream target genes.

Oxidation processes in mitochondria and different environmental insults contribute to unwarranted accumulation of reactive oxygen species (ROS). These, in turn, rapidly damage intracellular lipids, proteins, and DNA, ultimately causing aging and several human diseases. Cells have developed different and very effective systems to control ROS levels. Among these, removal of excessive amounts is guaranteed by upregulated expression of various antioxidant enzymes, through activation of the NF-E2-Related Factor 2 (NRF2) protein. Here, we show that Mitogen Activated Protein Kinase 15 (MAPK15) controls the transactivating potential of NRF2 and, in turn, the expression of its downstream target genes. Specifically, upon oxidative stress, MAPK15 is necessary to increase NRF2 expression and nuclear translocation, by inducing its activating phosphorylation, ultimately supporting transactivation of cytoprotective antioxidant genes. Lungs are continuously exposed to oxidative damages induced by environmental insults such as air pollutants and cigarette smoke. Interestingly, we demonstrate that MAPK15 is very effective in supporting NRF2-dependent antioxidant transcriptional response to cigarette smoke of epithelial lung cells. Oxidative damage induced by cigarette smoke indeed represents a leading cause of disability and death worldwide by contributing to the pathogenesis of different chronic respiratory diseases and lung cancer. Therefore, the development of novel therapeutic strategies able to modulate cellular responses to oxidative stress would be highly beneficial. Our data contribute to the necessary understanding of the molecular mechanisms behind such responses and identify new potentially actionable targets.

Signaling Pathways and Therapeutic Strategies in Advanced Basal Cell Carcinoma.

Non-melanoma skin cancers (NMSCs) are the most common human neoplasms world-wide. In detail, basal cell carcinoma (BCC) is the most frequent malignancy in the fair-skinned population. The incidence of BCC remains difficult to assess due to the poor registration practice; however, it has been increasing in the last few years. Approximately, 85% of sporadic BCCs carry mutations in Hedgehog pathway genes, especially in PTCH, SUFU and SMO genes, which lead to the aberrant activation of GLI transcriptional factors, typically silent in cells of adult individuals. The management of advanced BCC (aBCC), both metastatic (mBCC) and locally advanced BCC (laBCC), not candidates for surgical excision or radiotherapy, remains challenging. The discovery of mutations in the Hh signaling pathway has paved the way for the development of Hh pathway inhibiting agents, such as vismodegib and sonidegib, which have represented a breakthrough in the aBCC management. However, the use of these agents is limited by the frequent occurrence of adverse events or the development of drug resistance. In this review, we thoroughly describe the current knowledge regarding the available options for the pharmacological management of aBCCs and provide a forward-looking update on novel therapeutic strategies that could enrich the therapeutic armamentarium of BCC in the near future.

A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling.

Metabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [18F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio (SUVR) vs. [18F]FDG kinetic parameters fully describing the tracer behavior (i.e., Ki, K1, k3); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR, Ki, K1, k3 produced different networks depending on the chosen [18F]FDG parameter (k3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47-0.63) than for ai-MC (0.24-0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures.