S IN Soluzioni Informatiche

Vicenza, Italy

S IN Soluzioni Informatiche

Vicenza, Italy
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Zamboni A.,University of Verona | di Carli M.,New Energy Technologies | Guzzo F.,University of Verona | Stocchero M.,S IN Soluzioni Informatiche | And 10 more authors.
Plant Physiology | Year: 2010

The analysis of grapevine (Vitis vinifera) berries at the transcriptomic, proteomic, and metabolomic levels can provide great insight into the molecular events underlying berry development and postharvest drying (withering). However, the large and very different data sets produced by such investigations are difficult to integrate. Here, we report the identification of putative stage-specific biomarkers for berry development and withering and, to our knowledge, the first integrated systems-level study of these processes. Transcriptomic, proteomic, and metabolomic data were integrated using two different strategies, one hypothesis free and the other hypothesis driven. A multistep hypothesis-free approach was applied to data from four developmental stages and three withering intervals, with integration achieved using a hierarchical clustering strategy based on the multivariate bidirectional orthogonal projections to latent structures technique. This identified stage-specific functional networks of linked transcripts, proteins, and metabolites, providing important insights into the key molecular processes that determine the quality characteristics of wine. The hypothesis-driven approach was used to integrate data from three withering intervals, starting with subdata sets of transcripts, proteins, and metabolites. We identified transcripts and proteins that were modulated during withering as well as specific classes of metabolites that accumulated at the same time and used these to select subdata sets of variables. The multivariate bidirectional orthogonal projections to latent structures technique was then used to integrate the subdata sets, identifying variables representing selected molecular processes that take place specifically during berry withering. The impact of this holistic approach on our knowledge of grapevine berry development and withering is discussed. © 2010 American Society of Plant Biologists.


Consonni R.,CNR Institute for Macromolecular Studies | Cagliani L.R.,CNR Institute for Macromolecular Studies | Stocchero M.,S IN Soluzioni Informatiche | Porretta S.,Stazione Sperimentale per lIndustria Delle Conserve Alimentari
Journal of Agricultural and Food Chemistry | Year: 2010

Nuclear magnetic resonance (NMR) is nowadays largely used as valid tool in metabolomic applications. In this study, the metabolite content of Italian and Chinese tomato paste at different concentration rates of two production years (2007 and 2008) was investigated with the aim of building a robust geographical differentiation statistical model. A total of 119 tomato paste samples were analyzed by 1H NMR and multivariate data analysis tools, in particular using bidirectional orthogonal projection to latent structures-discriminant analysis (02PLS - DA). This technique is well-suited for noisy and correlated variables and was recently adopted to obtain robust classification models, having a clear interpretation of the systematic variation useful to characterize each class. In the present study, the analysis of latent space underlying the classification model allowed us to understand the role played by the production year on geographical discrimination. The 02PLS-DA model performed considering only tomato paste samples of 2007 was capable of predicting the geographical origin of all analyzed samples. The effect of the production year therefore resulted in not affecting the geographical origin discrimination. © 2010 American Chemical Society.


Mattarucchi E.,European Commission - Joint Research Center Ispra | Stocchero M.,S IN Soluzioni Informatiche | Moreno-Rojas J.M.,European Commission - Joint Research Center Ispra | Giordano G.,European Commission - Joint Research Center Ispra | And 3 more authors.
Journal of Agricultural and Food Chemistry | Year: 2010

The aim of this study was to asses the applicability of LC-MS profiling to authenticate a selected Trappist beer as part of a program on traceability funded by the European Commission. A total of 232 beers were fingerprinted and classified through multivariate data analysis. The selected beer was clearly distinguished from beers of different brands, while only 3 samples (3.5% of the test set) were wrongly classified when compared with other types of beer of the same Trappist brewery. The fingerprints were further analyzed to extract the most discriminating variables, which proved to be sufficient for classification, even using a simplified unsupervised model. This reduced fingerprint allowed us to study the influence of batch-to-batch variability on the classification model. Our results can easily be applied to different matrices and they confirmed the effectiveness of LC-MS profiling in combination with multivariate data analysis for the characterization of food products. © 2010 American Chemical Society.


Carraro S.,University of Padua | Giordano G.,University of Padua | Reniero F.,European Commission - Joint Research Center Ispra | Carpi D.,European Commission - Joint Research Center Ispra | And 3 more authors.
Allergy: European Journal of Allergy and Clinical Immunology | Year: 2013

Background Asthma is a heterogeneous disease and its different phenotypes need to be better characterized from a biochemical-inflammatory standpoint. This study aimed to apply the metabolomic approach to exhaled breath condensate (breathomics) to discriminate different asthma phenotypes, with a particular focus on severe asthma in children. Methods In this cross-sectional study, we recruited 42 asthmatic children (age, 8-17 years): 31 with nonsevere asthma (treated with inhaled steroids or not) and 11 with severe asthma. Fifteen healthy children were enrolled as controls. Children performed exhaled nitric oxide measurement, spirometry, exhaled breath condensate (EBC) collection. Condensate samples were analyzed using a metabolomic approach based on mass spectrometry. Results A robust Bidirectional-Orthogonal Projections to Latent Structures-Discriminant Analysis (O2PLS-DA) model was found for discriminating both between severe asthma cases and healthy controls (R2 = 0.93; Q2 = 0.75) and between severe asthma and nonsevere asthma (R 2 = 0.84; Q2 = 0.47). The metabolomic data analysis leads to a robust model also when the 3 groups of children were considered altogether (K = 0.80), indicating that each group is characterized by a specific metabolomic profile. Compounds related to retinoic acid, adenosine and vitamin D (Human Metabolome Database) were relevant for the discrimination between groups. Conclusion The metabolomic profiling of EBC could clearly distinguish different biochemical-metabolic profiles in asthmatic children and enabled the severe asthma phenotype to be fully discriminated. The breathomics approach may therefore be suitable for discriminating between different asthma metabolic phenotypes. © 2012 John Wiley & Sons A/S.


Carraro S.,University of Padua | Giordano G.,University of Padua | Pirillo P.,University of Padua | Maretti M.,University of Padua | And 5 more authors.
Journal of Pediatrics | Year: 2015

Objectives To assess a group of adolescents with bronchopulmonary dysplasia (BPD) from a biochemical-metabolic standpoint, applying the metabolomic approach to studying their exhaled breath condensate (EBC). Study design Twenty adolescents with BPD (mean age 14.8 years) and 15 healthy controls (mean age 15.2 years) were recruited for EBC collection, exhaled nitric oxide measurement, and spirometry. The EBC samples were analyzed using a metabolomic approach based on mass spectrometry. The obtained spectra were analyzed using multivariate statistical analysis tools. Results A reliable Orthogonal Projections to Latent Structures-Discriminant Analysis model showed a clear discrimination between cases of BPD and healthy controls (R2 = 0.95 and Q2 = 0.92). The search for putative biomarkers identified an altered complex lipid profile in the adolescents with BPD. Conclusions The metabolomic analysis of EBC distinguishes cases of BPD from healthy individuals, suggesting that the lung of survivors of BPD is characterized by long-term metabolic abnormalities. The search for putative biomarkers indicated a possible role of an altered surfactant composition, which may persist far beyond infancy. © 2015 Elsevier Inc.


PubMed | University of Padua, S IN Soluzioni Informatiche, Bambino Gesu Pediatric Hospital and European Commission - Joint Research Center Ispra
Type: Journal Article | Journal: The Journal of pediatrics | Year: 2015

To assess a group of adolescents with bronchopulmonary dysplasia (BPD) from a biochemical-metabolic standpoint, applying the metabolomic approach to studying their exhaled breath condensate (EBC).Twenty adolescents with BPD (mean age 14.8 years) and 15 healthy controls (mean age 15.2 years) were recruited for EBC collection, exhaled nitric oxide measurement, and spirometry. The EBC samples were analyzed using a metabolomic approach based on mass spectrometry. The obtained spectra were analyzed using multivariate statistical analysis tools.A reliable Orthogonal Projections to Latent Structures-Discriminant Analysis model showed a clear discrimination between cases of BPD and healthy controls (R(2) = 0.95 and Q(2) = 0.92). The search for putative biomarkers identified an altered complex lipid profile in the adolescents with BPD.The metabolomic analysis of EBC distinguishes cases of BPD from healthy individuals, suggesting that the lung of survivors of BPD is characterized by long-term metabolic abnormalities. The search for putative biomarkers indicated a possible role of an altered surfactant composition, which may persist far beyond infancy.


Zanon C.,San Bortolo Hospital | Stocchero M.,S IN Soluzioni Informatiche | Albiero E.,San Bortolo Hospital | Albiero E.,Fondazione Progetto Ematologia Hematology Project Foundation | And 6 more authors.
Cytometry Part B - Clinical Cytometry | Year: 2014

Background Cytokine-induced killer (CIK) cells, obtained after mononucleated cell stimulation with interferon-γ, interleukin-2, and anti-CD3 antibody, are constituted by CD3+CD56+ (CIK) cells and a minority of natural killer (NK; CD3-CD56+) cells and T-lymphocytes (CD3+CD56-) with antitumor effect against hematological malignancies, thus representing a promising immunotherapy strategy. To ensure in vivo antitumor activity it is mandatory to maximize the percentage of CD3+56+ effector cells, which is highly variable depending on the starting sample and the harvesting day. Based on cytofluorimetric data, we have retrospectively applied multivariate statistical data analysis (MVDA) to 30 expansions building mathematical models able to predict the expansion fate and the optimal CIK harvesting day. Methods Cell phenotype was monitored during culture; multivariate batch statistical process control was applied to monitor cell expansion and orthogonal projections to latent structures to predict CIK percentage. Results Ten expansions had CD3 +CD56+ cells ≥40% (good batches) and 20 had CD3 +CD56+ cells ≤40%. In 36.7%, CD3+CD56 + cells reached the highest concentration at day 17 and the others at day 21. We built a highly predictive regression model for estimating CD3 +CD56+ cells during culture. Three variables resulted highly informative: NK % at day 0, cytotoxic T-lymphocytes % (CTLs, CD3 +CD8+) at day 4, and CIK % at day 7. "Good batches" are characterized by a high percentage of CTLs and CD3 +CD56+ cells at day 4 and day 7, respectively. Conclusion By applying MVDA it is possible to optimize CIK expansion, deciding the optimal cell harvesting day. A predictive role for CTL and CIK was evidenced. © 2013 International Clinical Cytometry Society © 2013 Clinical Cytometry Society.


PubMed | University of Padua and S IN Soluzioni Informatiche
Type: | Journal: Pharmacological research | Year: 2016

Recurrent respiratory infections (RRI) represent a widespread condition which has a severe social and economic impact. Immunostimulants are used for their prevention. It is crucial to better characterize children with RRI to refine their diagnosis and identify effective personalized prevention strategies. Metabolomics is a high-dimensional biological method that can be used for hypothesis-free biomarker profiling, examining a large number of metabolites in a given sample using spectroscopic techniques. Multivariate statistical data analysis then enables us to infer which metabolic information is relevant to the biological characterization of a given physiological or pathological condition. This can lead to the emergence of new, sometimes unexpected metabolites, and hitherto unknown metabolic pathways, enabling the formulation of new pathogenetic hypotheses, and the identification of new therapeutic targets. The aim of our pilot study was to apply mass-spectrometry-based metabolomics to the analysis of urine samples from children with RRI, comparing these childrens biochemical metabolic profiles with those of healthy peers. We also compared the RRI childrens and healthy controls metabolomic urinary profiles after the former had received pidotimod treatment for 3 months to see whether this immunostimulant was associated with biochemical changes in the RRI childrens metabolic profile. 13 children (age range 3-6 yeas) with RRI and 15 matched per age healthy peers with no history of respiratory diseases or allergies were enrolled. Their metabolomic urine samples were compared before and after the RRI children had been treated with pidotimod for a period of 3 months. Metabolomic analyses on the urine samples were done using mass spectrometry combined with ultra-performance liquid chromatography (UPLC-MS). The resulting spectroscopic data then underwent multivariate statistical analysis and the most relevant variables characterizing the two groups were identified. Data modeling with post-transformation of PLS2-Discriminant Analysis (ptPLS2-DA) generated a robust model capable of discriminating the urine samples from children with RRI from those of healthy controls (R


Commisso M.,University of Verona | Strazzer P.,University of Verona | Toffali K.,University of Verona | Stocchero M.,S IN Soluzioni Informatiche | Guzzo F.,University of Verona
Computational and Structural Biotechnology Journal | Year: 2013

Natural remedies, such as those based on traditional Chinese medicines, have become more popular also in western countries over the last 10 years. The composition of these herbal products is largely unknown and difficult to determine. Moreover, since plants respond to their environment changing the metabolome, the composition of plant material can vary depending on the plant growth conditions. However, there is a growing need of a deeper knowledge on such natural remedies also in view of the growing number of reports of toxicity following the consumption of herbal supplements. Untargeted metabolomics is a useful approach for the simultaneous analysis of many compounds in herbal products. In particular, liquid chromatography/mass spectrometry (LC-MS) can determine presence, amount and sometime structures of plant metabolites in complex herbal mixtures, with significant advantages over techniques such as nuclear magnetic resonance (NMR) spectroscopy and gas chromatography/mass spectrometry (GC-MS). © 2013 Commisso et al.


Toffali K.,University of Verona | Zamboni A.,University of Verona | Anesi A.,University of Verona | Stocchero M.,S IN Soluzioni Informatiche | And 3 more authors.
Metabolomics | Year: 2011

We established a step-by-step, experiment-guided metabolomics procedure, based on LC-ESI-MS analysis, to generate a detailed picture of the changing metabolic profiles during late berry development in the important Italian grapevine cultivar Corvina. We sampled berries from four developmental time points and three post-harvest time points during the withering process, and used chromatograms of methanolic extracts to test the performance of the MetAlign and MZmine data mining programs. MZmine achieved a better resolution and therefore generated a more useful data matrix. Then both the quantitative performance of the analytical platform and the matrix effect were assessed, and the final dataset was investigated by multivariate data analysis. Our analysis confirmed the results of previous studies but also revealed some novel findings, including the prevalence of two specific flavonoids in unripe berries and important differences between the developmental profiles of flavones and flavanones, suggesting that specific individual metabolites could have different functions, and that flavones and flavanones probably play quite distinct biological roles. Moreover, the hypothesis-free multivariate analysis of subsets of the wide data matrix evidentiated the relationships between the various classes of metabolites, such as those between anthocyanins and hydroxycinnamic acids and between flavan-3-ols and anthocyanins. © 2010 Springer Science+Business Media, LLC.

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