Immuno-Modulatory Properties of a Quinolin-2-(1H)-on-3-Carboxamide Derivative: Relevance in Multiple Sclerosis

Background: We have recently released the structure of a class of quinolin-2-(1H)-on-3-carboxamide derivatives and among them; the drug A2 has the highest CB2 receptor selectivity. Objective: In this work we assessed the immuno-modulatory properties of A2 in lymphocytes isolated from peripheral blood of multiple sclerosis patients and healthy donors. Methods: Cell proliferative response was measured by 3H-thymidine incorporation, cell viability and apoptosis by trypan blue, annexin V staining and western blot. Cell activation was investigated by flow cytometry and molecular pathways by western blot. Results: A2 exerted anti-proliferative effects with down-regulation of TNF-α , IL-10 and Rantes in both cell types. No relevant changes were observed in cell viability between the two cell types. In cells from healthy subjects, A2 did not induce apoptosis, inhibited the cell cycle and similarly down-regulated in CD4+T cells the markers CD69, CD25, CD49d and CD54. Indeed, A2 also inhibited the phosphorylation of Akt, NF-kB, IKKα/β, ERK and blocked the expression of Cox-2 and CB2 receptor. Published patents also describe CB2 receptor agonists like purine derivatives. Differently, in cells from patients, A2 did not affect CD49d, while potently blocked CD54 expression. A2 inhibitory effects of Akt and Cox-2 expression were confirmed, whereas unchanged level of the CB2 receptor was observed in these cells. Conclusion: We reported similar effects of A2 in both cell types; however, a different mechanism of action might be suggested in cells from patients concerning cell activation and CB2 receptor expression. Overall, these data suggest an anti-inflammatory profile of A2 with potential implication in multiple sclerosis

Lovastatin induces apoptosis of k-ras-transformed thyroid cells via inhibition of ras farnesylation and by modulating redox state.

Transformation of thyroid cells with either K-ras or H-ras viral oncogenes produces cell types with different phenotype and different response to the inhibition of the prenylation pathway by 3-hydroxy-3-methylglutaryl-CoA reductase or farnesyltransferase inhibitors. These inhibitors induce apoptosis in K-ras-transformed FRTL-5 cells (FRTL-5-K-Ras) whereas cell cycle arrest is induced in H-ras-transformed FRTL-5 (FRTL-5-H-Ras). In FRTL-5-K-Ras cells, the product of K-ras gene is implicated in the scavenging of reactive oxygen species (ROS) through the activation of extracellular-signal-regulated kinase (ERK)1/2 kinases. We observed that lovastatin blocked ras activation through inhibition of farnesylation and induced apoptosis, increasing ROS levels through inhibition of ERK1/2 signaling and Mn-SOD expression. Lovastatin-induced apoptosis was due to intracellular ROS increase since both, the antioxidant compound pyrrolidinedithiocarbamate or the SOD-mimetic compound, antagonized apoptosis. Moreover, both p38 mitogen-activated protein kinase and nuclear factor kappaB pathways, activated as a consequence of high ROS levels, are involved in the apoptotic effect, indicating that cell death induced by lovastatin was dependent on oxidative stress. Lovastatin antitumor efficacy in K-ras-dependent thyroid tumors was further confirmed in vivo, proposing a new therapeutic strategy for those tumor diseases that are sustained by an inappropriate K-ras expression

Psychological Stress and Cancer: New Evidence of An Increasingly Strong Link

To date stress, a highly complex process that disrupts homeostasis and involves environmental and psychosocial factors, is considered as one of the most crucial factor that affects our daily life, especially urban dweller's life. Clinical and experimental studies widely support the notion that adrenergic stimulation due to chronic stress affects inflammation and metabolism. In this work, supported by several recent scientific evidences, we show how stress plays a positive role in cancer initiation, progression and cancer metastasis, a negative role for anti-tumor immune function and therapy response. Understanding the intricacies of this interaction could provide an additional help on how act in cancer prevention and therap

Enrichment of CD56dimKIR+CD57+ highly cytotoxic NK cells in tumor infiltrated lymph nodes of melanoma patients

An important checkpoint in the progression of melanoma is the metastasis to lymph nodes. Here, to investigate the role of lymph node NK cells in disease progression, we analyze frequency, phenotype and functions of NK cells from tumor-infiltrated (TILN) and tumor-free ipsilateral lymph nodes (TFLN) of the same patients. We show an expansion of CD56dimCD57dimCD69+CCR7+KIR+ NK cells in TILN. TILN NK cells display robust cytotoxic activity against autologous melanoma cells. In the blood of metastatic melanoma patients the frequency of NK cells expressing the receptors for CXCL8 receptor is increased compared to healthy subjects, and blood NK cells also express the receptors for CCL2 and IL6. These factors are produced in high amount in TILN and in vitro switch the phenotype of blood NK cells from healthy donors to the phenotype associated with TILN. Our data suggest that the microenvironment of TILN generates and/or recruits a particularly effective NK cell subset

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Gene Expression Analysis of Mevalonate Kinase Deficiency Affected Children Identifies Molecular Signatures Related to Hematopoiesis

Mevalonate kinase deficiency (MKD) is a rare autoinflammatory genetic disorder characterized by recurrent fever attacks and systemic inflammation with potentially severe complications. Although it is recognized that the lack of protein prenylation consequent to mevalonate pathway blockade drives IL1β hypersecretion, and hence autoinflammation, MKD pathogenesis and the molecular mechanisms underlaying most of its clinical manifestations are still largely unknown. In this study, we performed a comprehensive bioinformatic analysis of a microarray dataset of MKD patients, using gene ontology and Ingenuity Pathway Analysis (IPA) tools, in order to identify the most significant differentially expressed genes and infer their predicted relationships into biological processes, pathways, and networks. We found that hematopoiesis linked biological functions and pathways are predominant in the gene ontology of differentially expressed genes in MKD, in line with the observed clinical feature of anemia. We also provided novel information about the molecular mechanisms at the basis of the hematological abnormalities observed, that are linked to the chronic inflammation and to defective prenylation. Considering the broad and unspecific spectrum of MKD clinical manifestations and the difficulty in its diagnosis, a better understanding of MKD molecular bases could be translated to the clinical level to facilitate diagnosis, and improve management and therapy