Prediction of PD-L1 Expression in Neuroblastoma via Computational Modeling

Revisiting Neuroblastoma: Nrf2, NF-κB and Phox2B as a Promising Network in Neuroblastoma

Neuroblastoma is the most common solid extracranial tumor during childhood; it displays extraordinary heterogeneous clinical courses, from spontaneous regression to poor outcome in high-risk patients due to aggressive growth, metastasizing, and treatment resistance. Therefore, the identification and detailed analysis of promising tumorigenic molecular mechanisms are inevitable. This review highlights the abnormal regulation of NF-κB, Nrf2, and Phox2B as well as their interactions among each other in neuroblastoma. NF-κB and Nrf2 play a key role in antioxidant responses, anti-inflammatory regulation and tumor chemoresistance. Recent studies revealed a regulation of NF-κB by means of the Nrf2/antioxidant response element (ARE) system. On the other hand, Phox2B contributes to the differentiation of immature sympathetic nervous system stem cells: this transcription factor regulates the expression of RET, thereby facilitating cell survival and proliferation. As observed in other tumors, we presume striking interactions between NF-κB, Nrf2, and Phox2B, which might constitute an important crosstalk triangle, whose decompensation may trigger a more aggressive phenotype. Consequently, these transcription factors could be a promising target for novel therapeutic approaches and hence, further investigation on their regulation in neuroblastoma shall be reinforced.

Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review

Neuroblastoma, a paediatric malignancy with high rates of cancer-related morbidity and mortality, is of significant interest to the field of paediatric cancers. High-risk NB tumours are usually metastatic and result in survival rates of less than 50%. Machine learning approaches have been applied to various neuroblastoma patient data to retrieve relevant clinical and biological information and develop predictive models. Given this background, this study will catalogue and summarise the literature that has used machine learning and statistical methods to analyse data such as multi-omics, histological sections, and medical images to make clinical predictions. Furthermore, the question will be turned on its head, and the use of machine learning to accurately stratify NB patients by risk groups and to predict outcomes, including survival and treatment response, will be summarised. Overall, this study aims to catalogue and summarise the important work conducted to date on the subject of expression-based predictor models and machine learning in neuroblastoma for risk stratification and patient outcomes including survival, and treatment response which may assist and direct future diagnostic and therapeutic efforts.

A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma

Neuroblastoma is a complex and aggressive type of cancer that affects children. Current treatments involve a combination of surgery, chemotherapy, radiotherapy, and stem cell transplantation. However, treatment outcomes vary due to the heterogeneous nature of the disease. Computational models have been used to analyse data, simulate biological processes, and predict disease progression and treatment outcomes. While continuum cancer models capture the overall behaviour of tumours, and agent-based models represent the complex behaviour of individual cells, multiscale models represent interactions at different organisational levels, providing a more comprehensive understanding of the system. In 2018, the PRIMAGE consortium was formed to build a cloud-based decision support system for neuroblastoma, including a multi-scale model for patient-specific simulations of disease progression. In this work we have developed this multi-scale model that includes data such as patient's tumour geometry, cellularity, vascularization, genetics and type of chemotherapy treatment, and integrated it into an online platform that runs the simulations on a high-performance computation cluster using Onedata and Kubernetes technologies. This infrastructure will allow clinicians to optimise treatment regimens and reduce the number of costly and time-consuming clinical trials. This manuscript outlines the challenging framework's model architecture, data workflow, hypothesis, and resources employed in its development.

Mathematical Model of Clonal Evolution Proposes a Personalised Multi-Modal Therapy for High-Risk Neuroblastoma

Neuroblastoma is the most common extra-cranial solid tumour in children. Despite multi-modal therapy, over half of the high-risk patients will succumb. One contributing factor is the one-size-fits-all nature of multi-modal therapy. For example, during the first step (induction chemotherapy), the standard regimen (rapid COJEC) administers fixed doses of chemotherapeutic agents in eight two-week cycles. Perhaps because of differences in resistance, this standard regimen results in highly heterogeneous outcomes in different tumours. In this study, we formulated a mathematical model comprising ordinary differential equations. The equations describe the clonal evolution within a neuroblastoma tumour being treated with vincristine and cyclophosphamide, which are used in the rapid COJEC regimen, including genetically conferred and phenotypic drug resistance. The equations also describe the agents’ pharmacokinetics. We devised an optimisation algorithm to find the best chemotherapy schedules for tumours with different pre-treatment clonal compositions. The optimised chemotherapy schedules exploit the cytotoxic difference between the two drugs and intra-tumoural clonal competition to shrink the tumours as much as possible during induction chemotherapy and before surgical removal. They indicate that induction chemotherapy can be improved by finding and using personalised schedules. More broadly, we propose that the overall multi-modal therapy can be enhanced by employing targeted therapies against the mutations and oncogenic pathways enriched and activated by the chemotherapeutic agents. To translate the proposed personalised multi-modal therapy into clinical use, patient-specific model calibration and treatment optimisation are necessary. This entails a decision support system informed by emerging medical technologies such as multi-region sequencing and liquid biopsies. The results and tools presented in this paper could be the foundation of this decision support system.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

Transcriptomic Data Analysis Reveals a Down-Expression of Galectin-8 in Schizophrenia Hippocampus

Schizophrenia (SCZ) is a severe psychiatric disorder with several clinical manifestations that include cognitive dysfunction, decline in motivation, and psychosis. Current standards of care treatment with antipsychotic agents are often ineffective in controlling the disease, as only one-third of SCZ patients respond to medications. The mechanisms underlying the pathogenesis of SCZ remain elusive. It is believed that inflammatory processes may play a role as contributing factors to the etiology of SCZ. Galectins are a family of β-galactoside-binding lectins that contribute to the regulation of immune and inflammatory responses, and previous reports have shown their role in the maintenance of central nervous system (CNS) homeostasis and neuroinflammation. In the current study, we evaluated the expression levels of the galectin gene family in post-mortem samples of the hippocampus, associative striatum, and dorsolateral prefrontal cortex from SCZ patients. We found a significant downregulation of LGALS8 (Galectin-8) in the hippocampus of SCZ patients as compared to otherwise healthy donors. Interestingly, the reduction of LGALS8 was disease-specific, as no modulation was observed in the hippocampus from bipolar nor major depressive disorder (MDD) patients. Prediction analysis identified TBL1XR1, BRF2, and TAF7 as potential transcription factors controlling LGALS8 expression. In addition, MIR3681HG and MIR4296 were negatively correlated with LGALS8 expression, suggesting a role for epigenetics in the regulation of LGALS8 levels. On the other hand, no differences in the methylation levels of LGALS8 were observed between SCZ and matched control hippocampus. Finally, ontology analysis of the genes negatively correlated with LGALS8 expression identified an enrichment of the NGF-stimulated transcription pathway and of the oligodendrocyte differentiation pathway. Our study identified LGALS8 as a disease-specific gene, characterizing SCZ patients, that may in the future be exploited as a potential therapeutic target.

Personalized Medicine for Neuroblastoma: Moving from Static Genotypes to Dynamic Simulations of Drug Response

High-risk neuroblastoma is an aggressive childhood cancer that is characterized by high rates of chemoresistance and frequent metastatic relapse. A number of studies have characterized the genetic and epigenetic landscape of neuroblastoma, but due to a generally low mutational burden and paucity of actionable mutations, there are few options for applying a comprehensive personalized medicine approach through the use of targeted therapies. Therefore, the use of multi-agent chemotherapy remains the current standard of care for neuroblastoma, which also conceptually limits the opportunities for developing an effective and widely applicable personalized medicine approach for this disease. However, in this review we outline potential approaches for tailoring the use of chemotherapy agents to the specific molecular characteristics of individual tumours by performing patient-specific simulations of drug-induced apoptotic signalling. By incorporating multiple layers of information about tumour-specific aberrations, including expression as well as mutation data, these models have the potential to rationalize the selection of chemotherapeutics contained within multi-agent treatment regimens and ensure the optimum response is achieved for each individual patient.

Transcriptomic analysis of COVID‑19 lungs and bronchoalveolar lavage fluid samples reveals predominant B cell activation responses to infection

The outbreak of the 2019 coronavirus disease (named, COVID‑19), caused by the novel SARS‑CoV‑2 virus, represents a worldwide severe threat to public health. It is of the utmost importance to characterize the immune responses against the SARS‑CoV‑2 and the mechanisms of hyperinflammation, in order to design better therapeutic strategies for COVID‑19. In the present study, a transcriptomic analysis was performed to profile the immune signatures in lung and the bronchoalveolar lavage fluid samples from COVID‑19 patients and controls. Our data concordantly revealed increased humoral responses to infection. The elucidation of the host responses to SARS‑CoV‑2 infection may further improve our understanding of COVID‑19 pathogenesis and suggest better therapeutic strategies.

Through Predictive Personalized Medicine

Neuroblastoma (NBM) is a deadly form of solid tumor mostly observed in the pediatric age. Although survival rates largely differ depending on host factors and tumor-related features, treatment for clinically aggressive forms of NBM remains challenging. Scientific advances are paving the way to improved and safer therapeutic protocols, and immunotherapy is quickly rising as a promising treatment that is potentially safer and complementary to traditionally adopted surgical procedures, chemotherapy and radiotherapy. Improving therapeutic outcomes requires new approaches to be explored and validated. In-silico predictive models based on analysis of a plethora of data have been proposed by Lombardo et al. as an innovative tool for more efficacious immunotherapy against NBM. In particular, knowledge gained on intracellular signaling pathways linked to the development of NBM was used to predict how the different phenotypes could be modulated to respond to anti-programmed cell death-ligand-1 (PD-L1)/programmed cell death-1 (PD-1) immunotherapy. Prediction or forecasting are important targets of artificial intelligence and machine learning. Hopefully, similar systems could provide a reliable opportunity for a more targeted approach in the near future.

Transcriptional landscape of SARS-CoV-2 infection dismantles pathogenic pathways activated by the virus, proposes unique sex-specific differences and predicts tailored therapeutic strategies

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) has posed a serious threat to global health. As no specific therapeutics are yet available to control disease evolution, more in-depth understanding of the pathogenic mechanisms induced by SARS-CoV-2 will help to characterize new targets for the management of COVID-19. The present study identified a specific set of biological pathways altered in primary human lung epithelium upon SARS-CoV-2 infection, and a comparison with SARS-CoV from the 2003 pandemic was studied. The transcriptomic profiles were also exploited as possible novel therapeutic targets, and anti-signature perturbation analysis predicted potential drugs to control disease progression. Among them, Mitogen-activated protein kinase kinase (MEK), serine-threonine kinase (AKT), mammalian target of rapamycin (mTOR) and I kappa B Kinase (IKK) inhibitors emerged as candidate drugs. Finally, sex-specific differences that may underlie the higher COVID-19 mortality in men are proposed.

Meta-Analysis of Transcriptomic Data of Dorsolateral Prefrontal Cortex and of Peripheral Blood Mononuclear Cells Identifies Altered Pathways in Schizophrenia

Schizophrenia (SCZ) is a psychiatric disorder characterized by both positive and negative symptoms, including cognitive dysfunction, decline in motivation, delusion and hallucinations. Antipsychotic agents are currently the standard of care treatment for SCZ. However, only about one-third of SCZ patients respond to antipsychotic medications. In the current study, we have performed a meta-analysis of publicly available whole-genome expression datasets on Brodmann area 46 of the brain dorsolateral prefrontal cortex in order to prioritize potential pathways underlying SCZ pathology. Moreover, we have evaluated whether the differentially expressed genes in SCZ belong to specific subsets of cell types. Finally, a cross-tissue comparison at both the gene and functional level was performed by analyzing the transcriptomic pattern of peripheral blood mononuclear cells of SCZ patients. Our study identified a robust disease-specific set of dysfunctional biological pathways characterizing SCZ patients that could in the future be exploited as potential therapeutic targets.

Transcriptomic Analysis Reveals Abnormal Expression of Prion Disease Gene Pathway in Brains from Patients with Autism Spectrum Disorders

The role of infections in the pathogenesis of autism spectrum disorder (ASD) is still controversial. In this study, we aimed to evaluate markers of infections and immune activation in ASD by performing a meta-analysis of publicly available whole-genome transcriptomic datasets of brain samples from autistic patients and otherwise normal people. Among the differentially expressed genes, no significant enrichment was observed for infectious diseases previously associated with ASD, including herpes simplex virus-1 (HSV-1), cytomegalovirus and Epstein–Barr virus in brain samples, nor was it found in peripheral blood from ASD patients. Interestingly, a significant number of genes belonging to the “prion diseases” pathway were found to be modulated in our ASD brain meta-analysis. Overall, our data do not support an association between infection and ASD. However, the data do provide support for the involvement of pathways related to other neurodegenerative diseases and give input to uncover novel pathogenetic mechanisms underlying ASD.

Emerging Role of the Macrophage Migration Inhibitory Factor Family of Cytokines in Neuroblastoma. Pathogenic Effectors and Novel Therapeutic Targets?

Neuroblastoma (NB) is the most frequent extracranial pediatric tumor. Despite the current available multiple therapeutic options, the prognosis for high-risk NB patients remains unsatisfactory and makes the disease a clear unmet medical need. Thus, more tailored therapeutic approaches are warranted to improve both the quality of life and the survival of the patients. Macrophage migration inhibitory factor (MIF) is a pleiotropic cytokine that plays a key role in several diseases, including cancer. Preclinical and clinical studies in NB patients convergently indicate that MIF exerts pro-tumorigenic properties in NB. MIF is upregulated in NB tumor tissues and cell lines and it contributes to NB aggressiveness and immune-escape. To date, there are only a few data about the role of the second member of the MIF family, the MIF homolog d-dopachrome tautomerase (DDT), in NB. Here, we review the preclinical and clinical studies on the role of the MIF family of cytokines in NB and suggest that MIF and possibly DDT inhibitors may be promising novel prognostic and therapeutic targets in NB management.

Impaired Expression of Tetraspanin 32 (TSPAN32) in Memory T Cells of Patients with Multiple Sclerosis

Tetraspanins are a conserved family of proteins involved in a number of biological processes. We have previously shown that Tetraspanin-32 (TSPAN32) is significantly downregulated upon activation of T helper cells via anti-CD3/CD28 stimulation. On the other hand, TSPAN32 is marginally modulated in activated Treg cells. A role for TSPAN32 in controlling the development of autoimmune responses is consistent with our observation that encephalitogenic T cells from myelin oligodendrocyte glycoprotein (MOG)-induced experimental autoimmune encephalomyelitis (EAE) mice exhibit significantly lower levels of TSPAN32 as compared to naïve T cells. In the present study, by making use of ex vivo and in silico analysis, we aimed to better characterize the pathophysiological and diagnostic/prognostic role of TSPAN32 in T cell immunity and in multiple sclerosis (MS). We first show that TSPAN32 is significantly downregulated in memory T cells as compared to naïve T cells, and that it is further diminished upon ex vivo restimulation. Accordingly, following antigenic stimulation, myelin-specific memory T cells from MS patients showed significantly lower expression of TSPAN32 as compared to memory T cells from healthy donors (HD). The expression levels of TSPAN32 was significantly downregulated in peripheral blood mononuclear cells (PBMCs) from drug-naïve MS patients as compared to HD, irrespective of the disease state. Finally, when comparing patients undergoing early relapses in comparison to patients with longer stable disease, moderate but significantly lower levels of TSPAN32 expression were observed in PBMCs from the former group. Our data suggest a role for TSPAN32 in the immune responses underlying the pathophysiology of MS and represent a proof-of-concept for additional studies aiming at dissecting the eventual contribution of TSPAN32 in other autoimmune diseases and its possible use of TSPAN32 as a diagnostic factor and therapeutic target.

Neuroprotective Mechanisms of Three Natural Antioxidants on a Rat Model of Parkinson's Disease: A Comparative Study

We compared the neuroprotective action of three natural bio-antioxidants (AOs): ellagic acid (EA), α-lipoic acid (LA), and myrtenal (Myrt) in an experimental model of Parkinson’s disease (PD) that was induced in male Wistar rats through an intrastriatal injection of 6-hydroxydopamine (6-OHDA). The animals were divided into five groups: the sham-operated (SO) control group; striatal 6-OHDA-lesioned control group; and three groups of 6-OHDA-lesioned rats pre-treated for five days with EA, LA, and Myrt (50 mg/kg; intraperitoneally- i.p.), respectively. On the 2nd and the 3rd week post lesion, the animals were subjected to several behavioral tests: apomorphine-induced rotation; rotarod; and the passive avoidance test. Biochemical evaluation included assessment of main oxidative stress parameters as well as dopamine (DA) levels in brain homogenates. The results showed that all three test compounds improved learning and memory performance as well as neuromuscular coordination. Biochemical assays showed that all three compounds substantially decreased lipid peroxidation (LPO) levels, and restored catalase (CAT) activity and DA levels that were impaired by the challenge with 6-OHDA. Based on these results, we can conclude that the studied AOs demonstrate properties that are consistent with significant antiparkinsonian effects. The most powerful neuroprotective effect was observed with Myrt, and this work represents the first demonstration of its anti-Parkinsonian impact.

Transcriptomic analysis reveals moderate modulation of macrophage migration inhibitory factor superfamily genes in alcohol use disorders

Alcohol use disorder (AUD) is a primary, chronic and relapsing disease of brain reward, motivation and memory, which is associated with several comorbidities, including major depression and post-traumatic stress disorder. It has been revealed that Ibudilast (IBUD), a dual inhibitor of phosphodiesterase-4 and −10 and of macrophage migration inhibitory factor (MIF), exerts beneficial effects on AUD in rodent models and human patients. Therefore, IBUD has attracted increasing interest, with research focusing on the elucidation of the pathogenic role of MIF and its homologue, D-dopachrome tautomerase (DDT), in the pathogenesis and maintenance of AUD. By using DNA microarray analysis, the current study performed a transcriptomic expression analysis of MIF, DDT and their co-receptors, including CD74, C-X-C chemokine receptor (CXCR)2, CXCR4 and CXCR7 in patients with AUD. The results revealed that the transcriptomic levels of MIF, DDT and their receptors were superimposable in the prefrontal cortex of rodents and patients with AUD and human patients. Furthermore, peripheral blood cells from heavy drinkers exhibited a moderate increase in MIF and DDT levels, both at the baseline and following exposure to alcohol-associated cues, based on individual situations that included alcohol-related stimuli resulting in subsequent alcohol use (buying alcohol and being at a bar, watching others drink alcohol). Considering the overlapping effects of MIF and DDT, the inverse Fisher's χ² test was performed on unadjusted P-values to evaluate the combined effect of MIF and DDT. The results revealed a significant increase in these cytokines in heavy drinkers compared with controls (moderate drinkers). To the best of our knowledge, the present study demonstrated for the first time that MIF and DDT expression was upregulated in the blood of patients with AUD. These results therefore warrant further study to evaluate the role of MIF and DDT in the development and maintenance of AUD, to evaluate their use as biomarkers to predict the psychotherapeutic and pharmacological response of patients with AUD and for use as therapeutic targets.

In Silico and In Vivo Analysis of IL37 in Multiple Sclerosis Reveals Its Probable Homeostatic Role on the Clinical Activity, Disability, and Treatment with Fingolimod

We evaluated the in silico expression and circulating levels of interleukin (IL)37 in patients with different forms of multiple sclerosis (MS) and also upon treatment with different disease-modifying drugs. The combined interpretation of the resulting data strengthens and extends the current emerging concept that endogenous IL37 plays an important role in determining onset and progression of MS. The in silico analysis revealed that production of IL37 from cluster of differentiation (CD)4+ T cells from MS patients was reduced in vitro as compared to healthy controls. The analysis of the datasets also demonstrated that “higher” levels of IL37 production from PBMC entailed significant protection from MS relapses. In addition, the in vivo part of the study showed that IL37 was selectively augmented in the sera of MS patients during a relapse and that treatment with the high potency disease-modifying drug fingolimod significantly increased the frequency of patients with circulating blood levels of IL37 (6/9, 66%) as compared to patients receiving no treatment (n = 48) or platform therapy (n = 59) who had levels of IL37 below the limit of the sensitivity of the assay. This finding therefore anticipates that fingolimod may at least partially exert its beneficial effects in MS by upregulating the production of IL37.

Effects of Treatment with the Hypomethylating Agent 5-aza-2'-deoxycytidine in Murine Type II Collagen-Induced Arthritis

The emerging role of epigenetics in the pathogenesis of autoimmune diseases has recently attracted much interest on the possible use of epigenetic modulators for the prevention and treatment of these diseases. In particular, we and others have shown that drugs that inhibit DNA methylation, such as azacitidine (AZA) and decitabine (DAC), already used for the treatment of acute myeloid leukemia, exert powerful beneficial effects in rodent models of type 1 diabetes, multiple sclerosis, and Guillain Barrè syndrome. Along this line of research, we have presently studied the effects of DAC in a murine model of rheumatoid arthritis induced by type II collagen and have demonstrated that DAC administration was associated with a significant amelioration of the clinical condition, along with in vivo and ex vivo modification of the immunological profile of the so-treated mice, that exhibited a diminished production of Th1 and Th17 pro-inflammatory cytokines and reduction of anti-type II collagen autoantibodies.

Transcriptomic Analysis Reveals Involvement of the Macrophage Migration Inhibitory Factor Gene Network in Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a progressive hereditary muscular disease with X-linked recessive inheritance, that leads patients to premature death. The loss of dystrophin determines membrane instability, causing cell damage and inflammatory response. Macrophage migration inhibitory factor (MIF) is a cytokine that exerts pleiotropic properties and is implicated in the pathogenesis of a variety of diseases. Recently, converging data from independent studies have pointed to a possible role of MIF in dystrophic muscle disorders, including DMD. In the present study, we have investigated the modulation of MIF and MIF-related genes in degenerative muscle disorders, by making use of publicly available whole-genome expression datasets. We show here a significant enrichment of MIF and related genes in muscle samples from DMD patients, as well as from patients suffering from Becker’s disease and limb-girdle muscular dystrophy type 2B. On the other hand, transcriptomic analysis of in vitro differentiated myotubes from healthy controls and DMD patients revealed no significant alteration in the expression levels of MIF-related genes. Finally, by analyzing DMD samples as a time series, we show that the modulation of the genes belonging to the MIF network is an early event in the DMD muscle and does not change with the increasing age of the patients, Overall, our analysis suggests that MIF may play a role in vivo during muscle degeneration, likely promoting inflammation and local microenvironment reaction.

Overexpression of Macrophage Migration Inhibitory Factor and Its Homologue D-Dopachrome Tautomerase as Negative Prognostic Factor in Neuroblastoma

Neuroblastoma (NB) represents one of the most frequent pediatric solid tumors. Macrophage migration inhibitory factor (MIF) is a cytokine exerting multiple biological functions. More recently, a second member of the MIF family of cytokine has been identified, the D-dopachrome tautomerase (DDT), that exerts several overlapping functions with MIF. Growing evidence suggests a key role for MIF and DDT in the development of cancer. The aim of this study is to characterize the prognostic value of MIF and DDT in NB. We show that higher expression levels of MIF and DDT in Stage 4 NB samples are associated with a poorer prognosis, independently of the presence of MYCN amplification. Moreover, higher levels of MIF are mostly enriched by Th1 cells, while lower levels of MIF are associated with an increased proportion of B cells, Cytotoxic T cells, Dendritic cells and Natural Killer T cells. We also show that treatment with the histone deacetylase (HDAC) inhibitor, vorinostat, of the NB cell line, SH-SY5Y, determines a significant reduction in the expression of both MIF and DDT. Finally, MIF and DDT inhibition by short interfering RNA is able to revert vincristine sensitivity in vitro. Overall, our data suggest that MIF exert pro-tumorigenic properties in NB, likely by dampening antigen presentation and cytotoxic immune responses, and we propose the HDAC inhibitors as a potential therapeutic strategy for NB patients.

Modulation of Tetraspanin 32 (TSPAN32) Expression in T Cell-Mediated Immune Responses and in Multiple Sclerosis

Tetraspanins are a conserved family of proteins involved in a number of biological processes including, cell–cell interactions, fertility, cancer metastasis and immune responses. It has previously been shown that TSPAN32 knockout mice have normal hemopoiesis and B-cell responses, but hyperproliferative T cells. Here, we show that TSPAN32 is expressed at higher levels in the lymphoid lineage as compared to myeloid cells. In vitro activation of T helper cells via anti-CD3/CD28 is associated with a significant downregulation of TSPAN32. Interestingly, engagement of CD3 is sufficient to modulate TSPAN32 expression, and its effect is potentiated by costimulation with anti-CD28, but not anti-CTLA4, -ICOS nor -PD1. Accordingly, we measured the transcriptomic levels of TSPAN32 in polarized T cells under Th1 and Th2 conditions and TSPAN32 resulted significantly reduced as compared with unstimulated cells. On the other hand, in Treg cells, TSPAN32 underwent minor changes upon activation. The in vitro data were finally translated into the context of multiple sclerosis (MS). Encephalitogenic T cells from Myelin Oligodendrocyte Glycoprotein (MOG)-Induced Experimental Autoimmune Encephalomyelitis (EAE) mice showed significantly lower levels of TSPAN32 and increased levels of CD9, CD53, CD82 and CD151. Similarly, in vitro-activated circulating CD4 T cells from MS patients showed lower levels of TSPAN32 as compared with cells from healthy donors. Overall, these data suggest an immunoregulatory role for TSPAN32 in T helper immune response and may represent a target of future immunoregulatory therapies for T cell-mediated autoimmune diseases.