Matteo Stocchero

Identification of Putative Stage-Specific Grapevine Berry Biomarkers and Omics Data Integration into Networks

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.

Asthma severity in childhood and metabolomic profiling of breath condensate

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.

Novel aspects of grape berry ripening and post-harvest withering revealed by untargeted LC-ESI-MS metabolomics analysis

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.

Untargeted metabolomics: an emerging approach to determine the composition of herbal products.

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).

An NMR-based metabolomic approach to identify the botanical origin of honey

NMR can be used in food analysis for origin discrimination and biomarker discovery using a metabolomic approach. Here, we present an example of this strategy to discriminate honey samples of different botanical origins. The NMR spectra of 353 chloroform extracts of selected honey samples were analyzed to detect possible markers of their floral origin. Six monofloral Italian honey types (acacia, linden, orange, eucalyptus, chestnut, and honeydew) were analyzed together with polyfloral samples. Specific markers were identified for each monofloral origin: two markers for acacia (chrysin and pinocembrin), one for chestnut (γ-LACT-3-PKA), two for orange (8-hydroxylinalool and caffeine), one for eucalyptus (dehydrovomifoliol), one for honeydew (a diacylglycerilether) and two for linden (4-(1-hydroxy-1-methylethyl)cyclohexa-1,3-diene-carboxylic acid and 4-(1-methylethenyl)cyclohexa-1,3-diene-carboxylic acid). An NMR-based metabolomic approach that used O2PLS-DA multivariate data analysis allowed us to discriminate the different types of honey. Two different classifiers were built based on different multivariate techniques. The high precision of the classification obtained suggests that this approach could be useful to develop generally applicable metabolomic tools to discriminate the origin of honey samples.

Authentication of trappist beers by LC-MS fingerprints and multivariate data analysis.

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.

Towards a scientific interpretation of the terroir concept: plasticity of the grape berry metabolome

BackgroundThe definition of the terroir concept is one of the most debated issues in oenology and viticulture. The dynamic interaction among diverse factors including the environment, the grapevine plant and the imposed viticultural techniques means that the wine produced in a given terroir is unique. However, there is an increasing interest to define and quantify the contribution of individual factors to a specific terroir objectively. Here, we characterized the metabolome and transcriptome of berries from a single clone of the Corvina variety cultivated in seven different vineyards, located in three macrozones, over a 3-year trial period.ResultsTo overcome the anticipated strong vintage effect, we developed statistical tools that allowed us to identify distinct terroir signatures in the metabolic composition of berries from each macrozone, and from different vineyards within each macrozone. We also identified non-volatile and volatile components of the metabolome which are more plastic and therefore respond differently to terroir diversity. We observed some relationships between the plasticity of the metabolome and transcriptome, allowing a multifaceted scientific interpretation of the terroir concept.ConclusionsOur experiments with a single Corvina clone in different vineyards have revealed the existence of a clear terroir-specific effect on the transcriptome and metabolome which persists over several vintages and allows each vineyard to be characterized by the unique profile of specific metabolites.

Unbiased Metabolomic Investigation of Alzheimer’s Disease Brain Points to Dysregulation of Mitochondrial Aspartate Metabolism

Alzheimers disease (AD) is the most common cause of adult dementia. Yet the complete set of molecular changes accompanying this inexorable, neurodegenerative disease remains elusive. Here we adopted an unbiased lipidomics and metabolomics approach to surveying frozen frontal cortex samples from clinically characterized AD patients (n = 21) and age-matched controls (n = 19), revealing marked molecular differences between them. Then, by means of metabolomic pathway analysis, we incorporated the novel molecular information into the known biochemical pathways and compared it with the results of a metabolomics meta-analysis of previously published AD research. We found six metabolic pathways of the central metabolism as well as glycerophospholipid metabolism predominantly altered in AD brains. Using targeted metabolomics approaches and MS imaging, we confirmed a marked dysregulation of mitochondrial aspartate metabolism. The altered metabolic pathways were further integrated with clinical data, showing various degrees of correlation with parameters of dementia and AD pathology. Our study highlights specific, altered biochemical pathways in the brains of individuals with AD compared with those of control subjects, emphasizing dysregulation of mitochondrial aspartate metabolism and supporting future venues of investigation.

Untargeted Metabolomic Analysis of Amniotic Fluid in the Prediction of Preterm Delivery and Bronchopulmonary Dysplasia

Objective Bronchopulmonary dysplasia (BPD) is a serious complication associated with preterm birth. A growing body of evidence suggests a role for prenatal factors in its pathogenesis. Metabolomics allows simultaneous characterization of low molecular weight compounds and may provide a picture of such a complex condition. The aim of this study was to evaluate whether an unbiased metabolomic analysis of amniotic fluid (AF) can be used to investigate the risk of spontaneous preterm delivery (PTD) and BPD development in the offspring. Study design We conducted an exploratory study on 32 infants born from mothers who had undergone an amniocentesis between 21 and 28 gestational weeks because of spontaneous preterm labor with intact membranes. The AF samples underwent untargeted metabolomic analysis using mass spectrometry combined with ultra-performance liquid chromatography. The data obtained were analyzed using multivariate and univariate statistical data analysis tools. Results Orthogonally Constrained Projection to Latent Structures-Discriminant Analysis (oCPLS2-DA) excluded effects on data modelling of crucial clinical variables. oCPLS2-DA was able to find unique differences in select metabolites between term (n = 11) and preterm (n = 13) deliveries (negative ionization data set: R2 = 0.47, mean AUC ROC in prediction = 0.65; positive ionization data set: R2 = 0.47, mean AUC ROC in prediction = 0.70), and between PTD followed by the development of BPD (n = 10), and PTD without BPD (n = 11) (negative data set: R2 = 0.48, mean AUC ROC in prediction = 0.73; positive data set: R2 = 0.55, mean AUC ROC in prediction = 0.71). Conclusions This study suggests that amniotic fluid metabolic profiling may be promising for identifying spontaneous preterm birth and fetuses at risk for developing BPD. These findings support the hypothesis that some prenatal metabolic dysregulations may play a key role in the pathogenesis of PTD and the development of BPD.

Monitoring liver alterations during hepatic tumorigenesis by NMR profiling and pattern recognition

Human hepatocellular carcinoma (HCC) is the most recurrent malignancy of the liver and represents one of the main causes of cancer death worldwide. Furthermore, the liver is the most frequent site of metastatic colonization, and hepatic metastases are far more common than primary cancers in Western countries. A possible way of investigating liver diseases is to study the tissue metabolic profiles. High-resolution nuclear magnetic resonance (NMR) spectroscopy of hepatic tissue extracts was combined with pattern-recognition and visualization techniques to uncover metabolic differences among analyzed tissue types. Extracts were from primary HCC, chronic hepatitis-C-virus related cirrhotic tissues, hepatic metastases from colorectal carcinomas, and non-cirrhotic normal liver tissues adjacent to metastases as controls. We identified all metabolites present in the NMR spectra, and after statistical evaluation of all spectral classes, we were able to define the metabolic changes underlying the different liver conditions and diseases. In particular, the lactate and the glucose tissue signals were found to primarily discriminate the different histological samples. We followed the biochemical changes of human hepatic lesions through primary (HCC) and secondary (metastases from colorectal carcinoma) liver tumors, cirrhotic tissues, and non-cirrhotic histologically-confirmed normal liver tissues adjacent to metastases, achieving a metabolic differentiation of the various pathological states based upon the variation of the intracellular lactate/glucose ratio. It is suggested that such a signal pattern may act as a potential marker for assessing pathological hepatic lesions.