Histone deacetylase inhibition mitigates fibrosis-driven disease progression in recessive dystrophic epidermolysis bullosa.

BACKGROUND: Recessive dystrophic epidermolysis bullosa (RDEB) is a blistering disease caused by mutations in the gene encoding type VII collagen (C7). RDEB is associated with fibrosis, which is responsible for severe complications. The phenotypic variability observed in RDEB siblings suggests that epigenetic modifications contribute to disease severity. Identifying epigenetic changes may help to uncover molecular mechanisms underlying RDEB pathogenesis and new therapeutic targets. OBJECTIVES: To investigate histone acetylation in RDEB skin and to explore histone deacetylase inhibitors (HDACis) as therapeutic molecules capable of counteracting fibrosis and disease progression in RDEB mice. METHODS: Acetylated histone levels were detected in human skin by immunofluorescence and in RDEB fibroblasts by ELISA. The effects of Givinostat and valproic acid (VPA) on RDEB fibroblast fibrotic behaviour were assessed by collagen-gel contraction assay, Western blot and immunocytofluorescence for α-smooth muscle actin, ELISA for released transforming growth factor-ß1 (TGF-ß1). RNA-seq was performed in HDACi- and vehicle-treated RDEB fibroblasts. VPA was systemically administered to RDEB mice, and effects on overt phenotype were monitored. Fibrosis was investigated in the skin using histological and immunofluorescence analyses. Eye and tongue defects were examined microscopically. Mass spectrometry proteomics was performed on skin protein extracts from VPA-treated RDEB and control mice. RESULTS: Histone acetylation decreases in RDEB skin and primary fibroblasts. RDEB fibroblasts treated with HDACis lowered fibrotic traits including contractility, TGF-ß1 release, and proliferation. VPA administration to RDEB mice mitigated severe manifestations affecting eyes and paws. These effects were associated with fibrosis inhibition. Proteomic analysis of mouse skin revealed that VPA almost normalised protein sets involved in protein synthesis and immune response, processes linked to the increased susceptibility to cancer and bacterial infections observed in RDEB patients. CONCLUSIONS: Dysregulated histone acetylation contributes to RDEB pathogenesis by facilitating the progression of fibrosis. Repurposing of HDACi could be considered for disease-modifying treatments of RDEB.

Spontaneous NLRP3 inflammasome-driven IL-1-ß secretion is induced in severe COVID-19 patients and responds to anakinra treatment.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may result in a severe pneumonia associated with elevation of blood inflammatory parameters, reminiscent of cytokine storm syndrome. Steroidal anti-inflammatory therapies have shown efficacy in reducing mortality in critically ill patients; however, the mechanisms by which SARS-CoV-2 triggers such an extensive inflammation remain unexplained. OBJECTIVES: To dissect the mechanisms underlying SARS-CoV-2-associated inflammation in patients with severe coronavirus disease 2019 (COVID-19), we studied the role of IL-1ß, a pivotal cytokine driving inflammatory phenotypes, whose maturation and secretion are regulated by inflammasomes. METHODS: We analyzed nod-like receptor protein 3 pathway activation by means of confocal microscopy, plasma cytokine measurement, cytokine secretion following in vitro stimulation of blood circulating monocytes, and whole-blood RNA sequencing. The role of open reading frame 3a SARS-CoV-2 protein was assessed by confocal microscopy analysis following nucleofection of a monocytic cell line. RESULTS: We found that circulating monocytes from patients with COVID-19 display ASC (adaptor molecule apoptotic speck like protein-containing a CARD) specks that colocalize with nod-like receptor protein 3 inflammasome and spontaneously secrete IL-1ß in vitro. This spontaneous activation reverts following patient's treatment with the IL-1 receptor antagonist anakinra. Transfection of a monocytic cell line with cDNA coding for the ORF3a SARS-CoV-2 protein resulted in ASC speck formation. CONCLUSIONS: These results provide further evidence that IL-1ß targeting could represent an effective strategy in this disease and suggest a mechanistic explanation for the strong inflammatory manifestations associated with COVID-19.

E2F3 gene expression is a potential negative prognostic marker for localised and MYCN not-amplified neuroblastoma: Results of in silico analysis of 786 samples.

BACKGROUND: Neuroblastoma (NB) is an enigmatic childhood malignancy characterised by a wide range of clinical behaviour. Many potential oncogenes for NB have recently been identified. Among them, E2 transcription factor 3 (E2F3) expression was associated with a poor survival in 134 stage 4S patients, but evidence for other stage groups remains poorly investigated. METHODS: We have analysed the expression of E2F3 gene from a database of 786 NB samples. Overall and event-free survivals (EFS) were assessed by the Kaplan-Meier method, splitting the data on the median and tertile expression values. The Cox model was applied to control for the confounding by stage, age and MYCN amplification. Validation was performed by an in silico analysis of an independent cohort of 283 NB patients. Furthermore, an immunofluorescence analysis on 48 formalin-fixed, paraffin-embedded NB specimens was also performed. RESULTS: E2F3 overexpression was associated with a poor survival (EFS = 84%, 95% CI: 79%-95%, for low expression levels; EFS = 62%, 95% CI: 56%-68% for middle levels; EFS = 30%, 95% CI: 24%-36%, for high levels, p < .001). This association was confirmed in multivariable analysis and was more evident in patients with MYCN not-amplified and localised stages. Immunofluorescence results and the validation on an independent cohort of NB primary samples confirmed these findings. CONCLUSIONS: E2F3 is a new potential prognostic marker in NB with favourable characteristics at diagnosis. Further studies are needed to elucidate the potential role of E2F3 in NB oncogenesis and progression, in order to identify new targets for therapeutic interventions.

Laniakea@ReCaS: exploring the potential of customisable Galaxy on-demand instances as a cloud-based service.

BACKGROUND: Improving the availability and usability of data and analytical tools is a critical precondition for further advancing modern biological and biomedical research. For instance, one of the many ramifications of the COVID-19 global pandemic has been to make even more evident the importance of having bioinformatics tools and data readily actionable by researchers through convenient access points and supported by adequate IT infrastructures. One of the most successful efforts in improving the availability and usability of bioinformatics tools and data is represented by the Galaxy workflow manager and its thriving community. In 2020 we introduced Laniakea, a software platform conceived to streamline the configuration and deployment of "on-demand" Galaxy instances over the cloud. By facilitating the set-up and configuration of Galaxy web servers, Laniakea provides researchers with a powerful and highly customisable platform for executing complex bioinformatics analyses. The system can be accessed through a dedicated and user-friendly web interface that allows the Galaxy web server's initial configuration and deployment. RESULTS: "Laniakea@ReCaS", the first instance of a Laniakea-based service, is managed by ELIXIR-IT and was officially launched in February 2020, after about one year of development and testing that involved several users. Researchers can request access to Laniakea@ReCaS through an open-ended call for use-cases. Ten project proposals have been accepted since then, totalling 18 Galaxy on-demand virtual servers that employ ~ 100 CPUs, ~ 250 GB of RAM and ~ 5 TB of storage and serve several different communities and purposes. Herein, we present eight use cases demonstrating the versatility of the platform. CONCLUSIONS: During this first year of activity, the Laniakea-based service emerged as a flexible platform that facilitated the rapid development of bioinformatics tools, the efficient delivery of training activities, and the provision of public bioinformatics services in different settings, including food safety and clinical research. Laniakea@ReCaS provides a proof of concept of how enabling access to appropriate, reliable IT resources and ready-to-use bioinformatics tools can considerably streamline researchers' work.

High-Throughput Gene and Protein Analysis Revealed the Response of Disc Cells to Vitamin D, Depending on the VDR FokI Variants.

Vitamin D showed a protective effect on intervertebral disc degeneration (IDD) although conflicting evidence is reported. An explanation could be due to the presence of the FokI functional variant in the vitamin D receptor (VDR), observed as associated with spine pathologies. The present study was aimed at investigating-through high-throughput gene and protein analysis-the response of human disc cells to vitamin D, depending on the VDR FokI variants. The presence of FokI VDR polymorphism was determined in disc cells from patients with discopathy. 1,25(OH)2D3 was administered to the cells with or without interleukin 1 beta (IL-1ß). Microarray, protein arrays, and multiplex protein analysis were performed. In both FokI genotypes (FF and Ff), vitamin D upregulated metabolic genes of collagen. In FF cells, the hormone promoted the matrix proteins synthesis and a downregulation of enzymes involved in matrix catabolism, whereas Ff cells behaved oppositely. In FF cells, inflammation seems to hamper the synthetic activity mediated by vitamin D. Angiogenic markers were upregulated in FF cells, along with hypertrophic markers, some of them upregulated also in Ff cells after vitamin D treatment. Higher inflammatory protein modulation after vitamin D treatment was observed in inflammatory condition. These findings would help to clarify the clinical potential of vitamin D supplementation in patients affected by IDD.

Exosomal MicroRNAs as Potential Biomarkers of Hepatic Injury and Kidney Disease in Glycogen Storage Disease Type Ia Patients.

Glycogen storage disease type Ia (GSDIa) is an inherited metabolic disorder caused by mutations in the enzyme glucose-6-phosphatase-α (G6Pase-α). Affected individuals develop renal and liver complications, including the development of hepatocellular adenoma/carcinoma and kidney failure. The purpose of this study was to identify potential biomarkers of the evolution of the disease in GSDIa patients. To this end, we analyzed the expression of exosomal microRNAs (Exo-miRs) in the plasma exosomes of 45 patients aged 6 to 63 years. Plasma from age-matched normal individuals were used as controls. We found that the altered expression of several Exo-miRs correlates with the pathologic state of the patients and might help to monitor the progression of the disease and the development of late GSDIa-associated complications.

Transcriptome analysis defines myocardium gene signatures in children with ToF and ASD and reveals disease-specific molecular reprogramming in response to surgery with cardiopulmonary bypass.

BACKGROUND: Tetralogy of Fallot (ToF) and Atrial Septal Defects (ASD) are the most common types of congenital heart diseases and a major cause of childhood morbidity and mortality. Cardiopulmonary bypass (CPB) is used during corrective cardiac surgery to support circulation and heart stabilization. However, this procedure triggers systemic inflammatory and stress response and consequent increased risk of postoperative complications. The aim of this study was to define the molecular bases of ToF and ASD pathogenesis and response to CPB and identify new potential biomarkers. METHODS: Comparative transcriptome analysis of right atrium specimens collected from 10 ToF and 10 ASD patients was conducted before (Pre-CPB) and after (Post-CPB) corrective surgery. Total RNA isolated from each sample was individually hybridized on Affymetrix HG-U133 Plus Array Strips containing 38,500 unique human genes. Differences in the gene expression profiles and functional enrichment/network analyses were assessed using bioinformatic tools. qRT-PCR analysis was used to validate gene modulation. RESULTS: Pre-CPB samples showed significant differential expression of a total of 72 genes, 28 of which were overexpressed in ToF and 44 in ASD. According to Gene Ontology annotation, the mostly enriched biological processes were represented by matrix organization and cell adhesion in ToF and by muscle development and contractility in ASD specimens. GSEA highlighted the specific enrichment of hypoxia gene sets in ToF samples, pointing to a role for hypoxia in disease pathogenesis. The post-CPB myocardium exhibited significant alterations in the expression profile of genes related to transcription regulation, growth/apoptosis, inflammation, adhesion/matrix organization, and oxidative stress. Among them, only 70 were common to the two disease groups, whereas 110 and 24 were unique in ToF and ASD, respectively. Multiple functional interactions among differentially expressed gene products were predicted by network analysis. Interestingly, gene expression changes in ASD samples followed a consensus hypoxia profile. CONCLUSION: Our results provide a comprehensive view of gene reprogramming in right atrium tissues of ToF and ASD patients before and after CPB, defining specific molecular pathways underlying disease pathophysiology and myocardium response to CPB. These findings have potential translational value because they identify new candidate prognostic markers and targets for tailored cardioprotective post-surgical therapies.

A Proteomic Analysis of GSD-1a in Mouse Livers: Evidence for Metabolic Reprogramming, Inflammation, and Macrophage Polarization.

Glycogen storage disease type 1a (GSD-1a) is a rare genetic disease caused by mutations in the catalytic subunit of the enzyme glucose-6-phosphatase-alpha (G6Pase-α). The majority of patients develop long-term complications including renal failure and hepatocellular adenoma/carcinoma. The purpose of this study was to ascertain the proteomic changes in the liver of LS- G6pc-/- mice, a murine model of GSD-1a, in comparison with wild type mice to identify potential biomarkers of the pathophysiology of the affected liver. We used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyze liver lysates from a total of 20 LS- G6pc-/- and 18 wild type (WT) mice. We compared the proteomic expression profile of LS- G6pc-/- and WT mice. We identified 4138 significantly expressed proteins, 1243 of which were differentially represented. Network and pathway analyses indicate that LS- G6pc-/- livers display an age-dependent modulation of the expression of proteins involved in specific biological processes associated with increased progression of liver disease. Moreover, we found upregulation of proteins involved in the process of tissue inflammation and macrophage polarization toward the M2 phenotype in LS- G6pc-/- mice with adenomas. Our results identify a metabolic reprogramming of glucose-6-P and a pathologic environment in the liver compatible with tumor development and progression.

Artificial neural network classifier predicts neuroblastoma patients' outcome.

BACKGROUND: More than fifty percent of neuroblastoma (NB) patients with adverse prognosis do not benefit from treatment making the identification of new potential targets mandatory. Hypoxia is a condition of low oxygen tension, occurring in poorly vascularized tissues, which activates specific genes and contributes to the acquisition of the tumor aggressive phenotype. We defined a gene expression signature (NB-hypo), which measures the hypoxic status of the neuroblastoma tumor. We aimed at developing a classifier predicting neuroblastoma patients' outcome based on the assessment of the adverse effects of tumor hypoxia on the progression of the disease. METHODS: Multi-layer perceptron (MLP) was trained on the expression values of the 62 probe sets constituting NB-hypo signature to develop a predictive model for neuroblastoma patients' outcome. We utilized the expression data of 100 tumors in a leave-one-out analysis to select and construct the classifier and the expression data of the remaining 82 tumors to test the classifier performance in an external dataset. We utilized the Gene set enrichment analysis (GSEA) to evaluate the enrichment of hypoxia related gene sets in patients predicted with "Poor" or "Good" outcome. RESULTS: We utilized the expression of the 62 probe sets of the NB-Hypo signature in 182 neuroblastoma tumors to develop a MLP classifier predicting patients' outcome (NB-hypo classifier). We trained and validated the classifier in a leave-one-out cross-validation analysis on 100 tumor gene expression profiles. We externally tested the resulting NB-hypo classifier on an independent 82 tumors' set. The NB-hypo classifier predicted the patients' outcome with the remarkable accuracy of 87 %. NB-hypo classifier prediction resulted in 2 % classification error when applied to clinically defined low-intermediate risk neuroblastoma patients. The prediction was 100 % accurate in assessing the death of five low/intermediated risk patients. GSEA of tumor gene expression profile demonstrated the hypoxic status of the tumor in patients with poor prognosis. CONCLUSIONS: We developed a robust classifier predicting neuroblastoma patients' outcome with a very low error rate and we provided independent evidence that the poor outcome patients had hypoxic tumors, supporting the potential of using hypoxia as target for neuroblastoma treatment.

Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients.

BACKGROUND: Cancer patient's outcome is written, in part, in the gene expression profile of the tumor. We previously identified a 62-probe sets signature (NB-hypo) to identify tissue hypoxia in neuroblastoma tumors and showed that NB-hypo stratified neuroblastoma patients in good and poor outcome 1. It was important to develop a prognostic classifier to cluster patients into risk groups benefiting of defined therapeutic approaches. Novel classification and data discretization approaches can be instrumental for the generation of accurate predictors and robust tools for clinical decision support. We explored the application to gene expression data of Rulex, a novel software suite including the Attribute Driven Incremental Discretization technique for transforming continuous variables into simplified discrete ones and the Logic Learning Machine model for intelligible rule generation. RESULTS: We applied Rulex components to the problem of predicting the outcome of neuroblastoma patients on the bases of 62 probe sets NB-hypo gene expression signature. The resulting classifier consisted in 9 rules utilizing mainly two conditions of the relative expression of 11 probe sets. These rules were very effective predictors, as shown in an independent validation set, demonstrating the validity of the LLM algorithm applied to microarray data and patients' classification. The LLM performed as efficiently as Prediction Analysis of Microarray and Support Vector Machine, and outperformed other learning algorithms such as C4.5. Rulex carried out a feature selection by selecting a new signature (NB-hypo-II) of 11 probe sets that turned out to be the most relevant in predicting outcome among the 62 of the NB-hypo signature. Rules are easily interpretable as they involve only few conditions. CONCLUSIONS: Our findings provided evidence that the application of Rulex to the expression values of NB-hypo signature created a set of accurate, high quality, consistent and interpretable rules for the prediction of neuroblastoma patients' outcome. We identified the Rulex weighted classification as a flexible tool that can support clinical decisions. For these reasons, we consider Rulex to be a useful tool for cancer classification from microarray gene expression data.

Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients.

BACKGROUND: Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting. RESULTS: Shadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging. CONCLUSIONS: The novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four stable rules identifying a new class of poor outcome patients that could benefit from new therapies potentially targeting tumor hypoxia or its consequences.

A Simple, Test-Based Method to Control the Overestimation Bias in the Analysis of Potential Prognostic Tumour Markers.

The early evaluation of prognostic tumour markers is commonly performed by comparing the survival of two groups of patients identified on the basis of a cut-off value. The corresponding hazard ratio (HR) is usually estimated, representing a measure of the relative risk between patients with marker values above and below the cut-off. A posteriori methods identifying an optimal cut-off are appropriate when the functional form of the relation between the marker distribution and patient survival is unknown, but they are prone to an overestimation bias. In the presence of a small sample size, which is typical of rare diseases, the external validation sets are hardly available and internal cross-validation could be unfeasible. We describe a new method to obtain an unbiased estimate of the HR at an optimal cut-off, exploiting the simple relation between the HR and the associated p-value estimated by a random permutation analysis. We validate the method on both simulated data and set of gene expression profiles from two large, publicly available data sets. Furthermore, a reanalysis of a previously published study, which included 134 Stage 4S neuroblastoma patients, allowed for the identification of E2F1 as a new gene with potential oncogenic activity. This finding was confirmed by an immunofluorescence analysis on an independent cohort.

Computational intelligence analysis of high-risk neuroblastoma patient health records reveals time to maximum response as one of the most relevant factors for outcome prediction.

OBJECTIVE: Seek new candidate prognostic markers for neuroblastoma outcome, relapse or progression. MATERIALS AND METHODS: In this multicentre and retrospective study, Random Forests coupled with recursive feature elimination techniques were applied to electronic records (55 clinical features) of 3034 neuroblastoma patients. To assess model performance and feature importance, dataset was split into a training set (80%) and a test set (20%). RESULTS: In the test set, the mean Matthews correlation coefficient for the Random Forests models was greater than 0.46. Feature importance analysis revealed that, together with maximum response to first-line treatment (D_MAX_RESP), time to maximum response to first-line treatment (TIME_MAX_RESP.days) is a relevant predictor of both patients' outcome and relapse\progression. We showed the prognostic value of the max response to first-line treatment in clinically relevant subsets of high-, intermediate-, and low-risk patients for both overall and relapse-free survival (Log-rank p-value<0.0001). In high-risk patients older than 18 months and stage 4 tumour achieving a complete response or very good partial response, patients who exhibited a D_MAX_RESP greater than 9 months showed a better prognosis with respect to patients achieving D_MAX_RESP earlier than 9 months (overall survival): hazard ratio 3.3 95% confidence interval 1.8-5.9, Log-rank p-value p < 0.0001; relapse-free survival: 3.2 95%CI 1.8-5.6, Log-rank p-value p < 0.0001). CONCLUSION: Our findings evidence the emerging role of the TIME_MAX_RESP.days in addition to the D_MAX_RESP as relevant predictors of outcome and relapse\progression in neuroblastoma with potential clinical impact on the management and treatment of patients.

Multiple Genes with Potential Tumor Suppressive Activity Are Present on Chromosome 10q Loss in Neuroblastoma and Are Associated with Poor Prognosis.

Neuroblastoma (NB) is a tumor affecting the peripheral sympathetic nervous system that substantially contributes to childhood cancer mortality. Despite recent advances in understanding the complexity of NB, the mechanisms determining its progression are still largely unknown. Some recurrent segmental chromosome aberrations (SCA) have been associated with poor survival. However, the prognostic role of most SCA has not yet been investigated. We examined a cohort of 260 NB primary tumors at disease onset for the loss of chromosome 10q, by array-comparative genomic hybridization (a-CGH) and Single Nucleotide Polymorphism (SNP) array and we found that 26 showed 10q loss, while the others 234 displayed different SCA. We observed a lower event-free survival for NB patients displaying 10q loss compared to patients with tumors carrying other SCA. Furthermore, analyzing the region of 10q loss, we identified a cluster of 75 deleted genes associated with poorer outcome. Low expression of six of these genes, above all CCSER2, was significantly correlated to worse survival using in silico data from 786 NB patients. These potential tumor suppressor genes can be partly responsible for the poor prognosis of NB patients with 10q loss.

Plasma-Derived Exosome Proteins as Novel Diagnostic and Prognostic Biomarkers in Neuroblastoma Patients.

Neuroblastoma (NB) is the most common extracranial solid tumor during infancy, causing up to 10% of mortality in children; thus, identifying novel early and accurate diagnostic and prognostic biomarkers is mandatory. NB-derived exosomes carry proteins (Exo-prots) reflecting the status of the tumor cell of origin. The purpose of this study was to characterize, for the first time, the Exo-prots specifically expressed in NB patients associated with tumor phenotype and disease stage. We isolated exosomes from plasma specimens of 24 HR-NB patients and 24 low-risk (LR-NB) patients at diagnosis and of 24 age-matched healthy controls (CTRL). Exo-prot expression was measured by liquid chromatography-mass spectrometry. The data are available via ProteomeXchange (PXD042422). The NB patients had a different Exo-prot expression profile compared to the CTRL. The deregulated Exo-prots in the NB specimens acted mainly in the tumor-associated pathways. The HR-NB patients showed a different Exo-prot expression profile compared to the LR-NB patients, with the modulation of proteins involved in cell migration, proliferation and metastasis. NCAM, NCL, LUM and VASP demonstrated a diagnostic value in discriminating the NB patients from the CTRL; meanwhile, MYH9, FN1, CALR, AKAP12 and LTBP1 were able to differentiate between the HR-NB and LR-NB patients with high accuracy. Therefore, Exo-prots contribute to NB tumor development and to the aggressive metastatic NB phenotype.

Proteomic profiling of extracellular vesicles in synovial fluid and plasma from Oligoarticular Juvenile Idiopathic Arthritis patients reveals novel immunopathogenic biomarkers.

Introduction: New early low-invasive biomarkers are demanded for the management of Oligoarticular Juvenile Idiopathic Arthritis (OJIA), the most common chronic pediatric rheumatic disease in Western countries and a leading cause of disability. A deeper understanding of the molecular basis of OJIA pathophysiology is essential for identifying new biomarkers for earlier disease diagnosis and patient stratification and to guide targeted therapeutic intervention. Proteomic profiling of extracellular vesicles (EVs) released in biological fluids has recently emerged as a minimally invasive approach to elucidate adult arthritis pathogenic mechanisms and identify new biomarkers. However, EV-prot expression and potential as biomarkers in OJIA have not been explored. This study represents the first detailed longitudinal characterization of the EV-proteome in OJIA patients. Methods: Fourty-five OJIA patients were recruited at disease onset and followed up for 24 months, and protein expression profiling was carried out by liquid chromatography-tandem mass spectrometry in EVs isolated from plasma (PL) and synovial fluid (SF) samples. Results: We first compared the EV-proteome of SF vs paired PL and identified a panel of EV-prots whose expression was significantly deregulated in SF. Interaction network and GO enrichment analyses performed on deregulated EV-prots through STRING database and ShinyGO webserver revealed enrichment in processes related to cartilage/bone metabolism and inflammation, suggesting their role in OJIA pathogenesis and potential value as early molecular indicators of OJIA development. Comparative analysis of the EV-proteome in PL and SF from OJIA patients vs PL from age/gender-matched control children was then carried out. We detected altered expression of a panel of EV-prots able to differentiate new-onset OJIA patients from control children, potentially representing a disease-associated signature measurable at both the systemic and local levels with diagnostic potential. Deregulated EV-prots were significantly associated with biological processes related to innate immunity, antigen processing and presentation, and cytoskeleton organization. Finally, we ran WGCNA on the SF- and PL-derived EV-prot datasets and identified a few EV-prot modules associated with different clinical parameters stratifying OJIA patients in distinct subgroups. Discussion: These data provide novel mechanistic insights into OJIA pathophysiology and an important contribution in the search of new candidate molecular biomarkers for the disease.

NSD1 Mutations in Sotos Syndrome Induce Differential Expression of Long Noncoding RNAs, miR646 and Genes Controlling the G2/M Checkpoint.

An increasing amount of evidence indicates the critical role of the NSD1 gene in Sotos syndrome (SoS), a rare genetic disease, and in tumors. Molecular mechanisms affected by NSD1 mutations are largely uncharacterized. In order to assess the impact of NSD1 haploinsufficiency in the pathogenesis of SoS, we analyzed the gene expression profile of fibroblasts isolated from the skin samples of 15 SoS patients and of 5 healthy parents. We identified seven differentially expressed genes and five differentially expressed noncoding RNAs. The most upregulated mRNA was stratifin (SFN) (fold change, 3.9, Benjamini−Hochberg corrected p < 0.05), and the most downregulated mRNA was goosecoid homeobox (GSC) (fold change, 3.9, Benjamini−Hochberg corrected p < 0.05). The most upregulated lncRNA was lnc-C2orf84-1 (fold change, 4.28, Benjamini−Hochberg corrected p < 0.001), and the most downregulated lncRNA was Inc-C15orf57 (fold change, −0.7, Benjamini−Hochberg corrected p < 0.05). A gene set enrichment analysis reported the enrichment of genes involved in the KRAS and E2F signaling pathways, splicing regulation and cell cycle G2/M checkpoints. Our results suggest that NSD1 is involved in cell cycle regulation and that its mutation can induce the down-expression of genes involved in tumoral and neoplastic differentiation. The results contribute to defining the role of NSD1 in fibroblasts for the prevention, diagnosis and control of SoS.

Inflammatory priming with IL-1ß promotes the immunomodulatory behavior of adipose derived stem cells.

Inflammatory processes contribute to osteoarthritis (OA) severity and progression. Mesenchymal stem cells, particularly those derived from adipose tissue (ASCs), are able to sense and control the inflammatory environment. This immunomodulatory potential can be boosted by different priming strategies based on inflammatory stimulation. The aim of the present study is to investigate the transcriptional modulation of a huge panel of genes and functionally verify the predicted immunomodulatory ability of ASCs after interleukin one beta (IL-1ß) priming. ASCs were isolated from adipose tissue obtained from three donors and expanded. After stimulation with 1 ng/ml of IL-1ß for 48 h, cells were collected for gene array and functional tests. Pooled cells from three donors were used for RNA extraction and gene array analysis. Gene Ontology (GO) enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed to assess the involvement of the modulated genes after priming in specific biological processes and pathways. Functional co-culture tests of ASCs with T cells and macrophages were performed to assess the ability of primed ASCs to modulate immune cell phenotype. Among the overall genes analyzed in the gene array, about the 18% were up- or down-regulated in ASCs after IL-1ß priming. GO enrichment analysis of up- or down-regulated genes in ASCs after IL-1ß priming allowed identifying specific pathways involved in the modulation of inflammation and extracellular matrix remodeling. The main processes enriched according to the GSEA are related to the inflammatory response and cell proliferative processes. Functional tests on immune cells showed that primed and non-primed ASCs induced a decrease in the CD3+ T lymphocytes survival rate and an anti-inflammatory macrophage polarization. In conclusion, IL-1ß priming represents a tailored strategy to enhance the ability of ASCs to direct macrophages towards an anti-inflammatory phenotype and, consequently, improve the efficacy of ASCs in counteracting the OA inflammatory component.

Untargeted LC-HRMS Based-Plasma Metabolomics Reveals 3-O-Methyldopa as a New Biomarker of Poor Prognosis in High-Risk Neuroblastoma.

Neuroblastoma (NB) is the most common extracranial malignant tumor in children. Although the survival rate of NB has improved over the years, the outcome of NB still remains poor for over 30% of cases. A more accurate risk stratification remains a key point in the study of NB and the availability of novel prognostic biomarkers of "high-risk" at diagnosis could help improving patient stratification and predicting outcome. In this paper we show a biomarker discovery approach applied to the plasma of 172 NB patients. Plasma samples from a first cohort of NB patients and age-matched healthy controls were used for untargeted metabolomics analysis based on high-resolution mass spectrometry (HRMS). Differential expression analysis highlighted a number of metabolites annotated with a high degree of identification. Among them, 3-O-methyldopa (3-O-MD) was validated in a second cohort of NB patients using a targeted metabolite profiling approach and its prognostic potential was also analyzed by survival analysis on patients with 3 years follow-up. High expression of 3-O-MD was associated with worse prognosis in the subset of patients with stage M tumor (log-rank p < 0.05) and, among them, it was confirmed as a prognostic factor able to stratify high-risk patients older than 18 months. 3-O-MD might be thus considered as a novel prognostic biomarker of NB eligible to be included at diagnosis among catecholamine metabolite panels in prospective clinical studies. Further studies are warranted to exploit other potential biomarkers highlighted using our approach.

Circulating microRNAs as novel non-invasive biomarkers of paediatric celiac disease and adherence to gluten-free diet.

BACKGROUND: Celiac Disease (CD) is a multifactorial autoimmune enteropathy (with a prevalence of approximately 1% worldwide) that exhibits a wide spectrum of clinical, serological and histological manifestations. For the diagnosis of paediatric CD, the gold standard is the combination of serological tests (with high TGA-IgA values greater than 10 times the upper limit of normal) and duodenal biopsy (with a positive TGA-IgA but low titer). Therefore, a diagnostic test that totally excludes an invasive approach has not been discovered so far and the discovery of novel biological markers would represent an undoubted advantage for the diagnosis of CD and prognostic evaluation. MicroRNAs (miRNAs), small non-coding RNAs (18-22 nucleotides) that regulate gene expression at post-transcriptional level and play important roles in many biological processes, represent a novel class of potential disease biomarkers. Their presence in biological fluids (i.e., serum, plasma, saliva, urine) provides the opportunity to employ circulating miRNAs as novel non-invasive biomarkers. METHODS: In our prospective observational study, we examined the expression of circulating miRNAs in a cohort of CD patients (both at diagnosis and on gluten-free diet, respectively referred as CD and GFD) compared to healthy controls. By small RNA-Seq we discovered a set of circulating miRNAs that were further validated by qPCR with specific assays. FINDINGS: We found that out of the 13 miRNAs able to discriminate the three groups (i.e., CD, GFD and controls), three of them, namely miR-192-5p, miR-215-5p and miR-125b-5p (alone or in combination), were able to discriminate these three groups with high accuracy and specificity. INTERPRETATION: Our conclusions emphasize that these circulating miRNAs can be employed not only for the diagnosis of CD patients with a low TGA-IgA titer but also to monitor the adherence to a gluten-free diet by CD patients. In conclusion, we suggest the use of the circulating miRNAs identified in this work as a novel diagnostic and follow-up tool for paediatric CD. FUNDING: This work was supported by Fondazione Celiachia Onlus (FC) Grant n° 018/FC/2013 and by Italian Ministry of Health (Ricerca Corrente).