Alkaptonuria (AKU) is a rare metabolic disorder caused by a deficient enzyme in the tyrosine degradation pathway, homogentisate 1,2-dioxygenase (HGD). In 172 AKU patients from 39 countries, we identified 28 novel variants of the HGD gene, which include three larger genomic deletions within this gene discovered via self-designed multiplex ligationdependent probe amplification (MLPA) probes. In addition, using a reporter minigene assay, we provide evidence that three of eight tested variants potentially affecting splicing cause exon skipping or cryptic splice-site activation. Extensive bioinformatics analysis of novel missense variants, and of the entire HGD monomer, confirmed mCSM as an effective computational tool for evaluating possible enzyme inactivation mechanisms. For the first time for AKU, a genotypephenotype correlation study was performed for the three most frequent HGD variants identified in the Suitability Of Nitisinone in Alkaptonuria 2 (SONIA2) study. We found a small but statistically significant difference in urinary homogentisic acid (HGA) excretion, corrected for dietary protein intake, between variants leading to 1% or >30% residual HGD activity. There was, interestingly, no difference in serum levels or absolute urinary excretion of HGA, or clinical symptoms, indicating that protein intake is more important than differences in HGD variants for the amounts of HGA that accumulate in the body of AKU patients.

Background Alkaptonuria (AKU) is an ultra-rare autosomal recessive disease caused by a mutation in the homogentisate 1,2-dioxygenase (HGD) gene. One of the main obstacles in studying AKU, and other ultra-rare diseases, is the lack of a standardized methodology to assess disease severity or response to treatment. Quality of Life scores (QoL) are a reliable way to monitor patients’ clinical condition and health status. QoL scores allow to monitor the evolution of diseases and assess the suitability of treatments by taking into account patients’ symptoms, general health status and care satisfaction. However, more comprehensive tools to study a complex and multi-systemic disease like AKU are needed. In this study, a Machine Learning (ML) approach was implemented with the aim to perform a prediction of QoL scores based on clinical data deposited in the ApreciseKUre, an AKU- dedicated database. Method Data derived from 129 AKU patients have been firstly examined through a preliminary statistical analysis (Pearson correlation coefficient) to measure the linear correlation between 11 QoL scores. The variable importance in QoL scores prediction of 110 ApreciseKUre biomarkers has been then calculated using XGBoost, with K-nearest neighbours algorithm (k-NN) approach. Due to the limited number of data available, this model has been validated using surrogate data analysis. Results We identified a direct correlation of 6 (age, Serum Amyloid A, Chitotriosidase, Advanced Oxidation Protein Products, S-thiolated proteins and Body Mass Index) out of 110 biomarkers with the QoL health status, in particular with the KOOS (Knee injury and Osteoarthritis Outcome Score) symptoms (Relative Absolute Error (RAE) 0.25). The error distribution of surrogate-model (RAE 0.38) was unequivocally higher than the true-model one (RAE of 0.25), confirming the consistency of our dataset. Our data showed that inflammation, oxidative stress, amyloidosis and lifestyle of patients correlates with the QoL scores for physical status, while no correlation between the biomarkers and patients’ mental health was present (RAE 1.1). Conclusions This proof of principle study for rare diseases confirms the importance of database, allowing data management and analysis, which can be used to predict more effective treatments.

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BackgroundAlkaptonuria (AKU; OMIM:203500) is a classic Mendelian genetic disorder described by Garrod already in 1902. It causes urine to turn black upon exposure to air and also leads to ochronosis as well as early osteoarthritis.Main body of the abstractOur objective is the implementation of a Precision Medicine (PM) approach to AKU. We present here a novel ApreciseKUre database facilitating the collection, integration and analysis of patient data in order to create an AKU-dedicated “PM Ecosystem” in which genetic, biochemical and clinical resources can be shared among registered researchers. In order to exploit the ApreciseKUre database, we developed an analytic method based on Pearson’s correlation coefficient and P value that generates as refreshable correlation matrix. A complete statistical analysis is obtained by associating every pair of parameters to examine the dependence between multiple variables at the same time.Short conclusionsEmploying this analytic approach, we showed that some clinically used biomarkers are not suitable as prognostic biomarkers in AKU for a more reliable patients’ clinical monitoring. We believe this database could be a good starting point for the creation of a new clinical management tool in AKU, which will lead to the development of a deeper knowledge network on the disease and will advance its treatment. Moreover, our approach can serve as a personalization model paradigm for other inborn errors of metabolism or rare diseases in general.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0438-0) contains supplementary material, which is available to authorized users.

The chemical profile of the Cannabis sativa L. female inflorescences is rather complex being characterized by a large number of molecules belonging to different chemical classes. Considering the numerous applications in various fields, including the medical and pharmaceutical sectors, that have seen a large use of Cannabis genus in recent years, a precise characterization of the matrices is essential. In this regard, the application of adequate and suitable sampling and analysis techniques becomes important in order to provide an identification of the metabolites characterizing the profile of the sample under examination. The goal of this work is to provide additional information on the chemical composition of the inflorescences of five C. sativa different cultivars grown in Emilia Romagna (Italy) through the application of sophisticated analysis techniques such as Solid-Phase Microextraction-Gas Chromatography-Mass Spectrometry and Ultra-Performance Liquid Chromatography-Mass Spectrometry (SPME-GC-MS and UPLC-MS). The obtained data highlighted the presence of a high number of volatile and non-volatile compounds, thus allowing a comparative evaluation of the different samples. Furthermore, an in-depth statistical survey by Principal Components Analysis (PCA) and HeatMap, Hierarchical luster Analysis (HCA) and Partial Least Squares Discriminant Analysis (PLS-DA-VIP), was conducted to consider any correlations between the investigated cultivars. The findings of this study may help to provide more information on the C. sativa inflorescences useful for potential applications of their metabolites in scientific research.

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The antitumor activity of polyphenols derived from extra virgin olive oil and, in particular the biological activity of HTyr, has been studied extensively. However, the use of HTyr as a therapeutic agent for clinical applications is limited by its low bioavailability and rapid excretion in humans. To overcome these limitations, several synthetic strategies have been optimized to prepare lipophenols and new compounds derived from HTyr to increase lipophilicity and bioavailability. One very promising ester is hydroxytyrosyl oleate (HTyr-OL) because the chemical structure of HTyr, which is responsible for several biological activities, is linked to the monounsaturated chain of oleic acid (OA), giving the compound high lipophilicity and thus bioavailability in the cellular environment. In this study, the in vitro cytotoxic, anti-proliferative, and apoptotic induction activities of HTyr-OL were evaluated against SH-SY5Y human neuroblastoma cells, and the effects were compared with those of HTyr and OA. The results showed that the biological activity of HTyr was maintained in HTyr-OL treatments at lower dosages. In addition, the shotgun proteomic approach was used to study HTyr-OL-treated and untreated neuroblastoma cells, revealing that the antioxidant, anti-proliferative and anti-inflammatory activities of HTyr-OL were observed in the unique proteins of the two groups of samples.

The amyloid-β precursor protein (APP) is a ubiquitous membrane protein often associated with Alzheimer’s disease (AD) and cerebral amyloid angiopathy (CAA). Despite its role in the development of the pathogenesis, APP exerts several physiological roles that have been mainly investigated in neuronal tissue. To date, the role of APP in vasculature and endothelial cells has not been fully elucidated. In this study, we used molecular and proteomic approaches to identify and investigate major cellular targets of APP down-regulation in endothelial cells. We found that APP is necessary for endothelial cells proliferation, migration and adhesion. The loss of APP alters focal adhesion stability and cell–cell junctions’ expression. Moreover, APP is necessary to mediate endothelial response to the VEGF-A growth factor. Finally, we document that APP propagates exogenous stimuli and mediates cellular response in endothelial cells by modulating the Scr/FAK signaling pathway. Thus, the intact expression and processing of APP is required for normal endothelial function. The identification of molecular mechanisms responsible for vasoprotective properties of endothelial APP may have an impact on clinical efforts to preserve and protect healthy vasculature in patients at risk of the development of cerebrovascular disease and dementia including AD and CAA.