Tiziana Pacchiarotta

Gas chromatography-atmospheric pressure chemical ionization-time of flight mass spectrometry for profiling of phenolic compounds in extra virgin olive oil.

A new analytical approach based on gas chromatography coupled to atmospheric pressure chemical ionization-time of flight mass spectrometry was evaluated for its applicability for the analysis of phenolic compounds from extra-virgin olive oil. Both chromatographic and MS parameters were optimized in order to improve the sensitivity and to maximize the number of phenolic compounds detected. We performed a complete analytical validation of the method with respect to its linearity, sensitivity, precision, accuracy and possible matrix effects. The LODs ranged from 0.13 to 1.05ppm for the different tested compounds depending on their properties. The RSDs for repeatability test did not exceed 6.07% and the accuracy ranged from 95.4% to 101.5%. To demonstrate the feasibility of our method for analysis of real samples, we analyzed the extracts of three different commercial extra-virgin olive oils. We have identified unequivocally a number of phenolic compounds and obtained quantitative information for 21 of them. In general, our results show that GC-APCI-TOF MS is a flexible platform which can be considered as an interesting tool for screening, structural assignment and quantitative determination of phenolic compounds from virgin olive oil.

Ultra high performance liquid chromatography-time of flight mass spectrometry for analysis of avocado fruit metabolites: Method evaluation and applicability to the analysis of ripening degrees

We have developed an analytical method using UHPLC-UV/ESI-TOF MS for the comprehensive profiling of the metabolites found in the methanolic extracts of 13 different varieties of avocado at two different ripening degrees. Both chromatographic and detection parameters were optimized in order to maximize the number of compounds detected and the sensitivity. After achieving the optimum conditions, we performed a complete analytical validation of the method with respect to its linearity, sensitivity, precision, accuracy and possible matrix effects. The LODs ranged from 1.64 to 730.54 ppb (in negative polarity) for benzoic acid and chrysin, respectively, whilst they were found within the range from 0.51 to 310.23 ppb in positive polarity. The RSDs for repeatability test did not exceed 7.01% and the accuracy ranged from 97.2% to 102.0%. Our method was then applied to the analysis of real avocado samples and advanced data processing and multivariate statistical analysis (PCA, PLS-DA) were carried out to discriminate/classify the examined avocado varieties. About 200 compounds belonging to various structural classes were tentatively identified; we are certain about the identity of around 60 compounds, 20 of which have been quantified in terms of their own commercially available standard.

1H NMR-based metabolic profiling of urinary tract infection: combining multiple statistical models and clinical data

Urinary tract infection (UTI) encompasses a variety of clinical syndromes ranging from mild to life-threatening conditions. As such, it represents an interesting model for the development of an analytically based scoring system of disease severity and/or host response. Here we test the feasibility of this concept using 1H NMR based metabolomics as the analytical platform. Using an exhaustively clinically characterized cohort and taking advantage of the multi-level study design, which opens possibilities for case–control and longitudinal modeling, we were able to identify molecular discriminators that characterize UTI patients. Among those discriminators a number (e.g. acetate, trimethylamine and others) showed association with the degree of bacterial contamination of urine, whereas others, such as, for instance, scyllo-inositol and para-aminohippuric acid, are more likely to be the markers of morbidity.

Fibrinogen alpha chain O-glycopeptides as possible markers of urinary tract infection

Urinary tract infection (UTI) is the most common bacterial infection leading to substantial morbidity and considerable health care expenditures across all ages. Here we present an exploratory UPLC-MS study of human urine in the context of febrile, complicated urinary tract infection aimed to reveal and identify possible markers of a host response on infection. A UPLC-MS based workflow, taking advantage of Ultra High Resolution (UHR) Qq-ToF-MS, and multivariate data handling were applied to a carefully selected group of 39 subjects with culture-confirmed febrile Escherichia coli UTI. Using a combination of unsupervised and supervised multivariate modeling we have pinpointed a number of peptides specific for UTI. An unequivocal structural identification of these peptides, as O-glycosylated fragments of the human fibrinogen alpha 1 chain, required MS2 and MS3 experiments on two different MS platforms: ESI-UHR-Qq-ToF and ESI-ion trap, a blast search and, finally, confirmation was achieved by matching experimental tandem mass spectra with those of custom synthesized candidate-peptides. In conclusion, exploiting non-targeted UPLC-MS based approach for the investigation of UTI related changes in urine, we have identified and structurally characterized unique O-glycopeptides, which are, to our knowledge, the first demonstration of O-glycosylation of human fibrinogen alpha 1-chain.

Comparing two metabolic profiling approaches (liquid chromatography and gas chromatography coupled to mass spectrometry) for extra-virgin olive oil phenolic compounds analysis: A botanical classification perspective.

Over the last decades, the phenolic compounds from virgin olive oil (VOO) have become the subject of intensive research because of their biological activities and their influence on some of the most relevant attributes of this interesting matrix. Developing metabolic profiling approaches to determine them in monovarietal virgin olive oils could help to gain a deeper insight into olive oil phenolic compounds composition as well as to promote their use for botanical origin tracing purposes. To this end, two approaches were comparatively investigated (LC-ESI-TOF MS and GC-APCI-TOF MS) to evaluate their capacity to properly classify 25 olive oil samples belonging to five different varieties (Arbequina, Cornicabra, Hojiblanca, Frantoio and Picual), using the entire chromatographic phenolic profiles combined to chemometrics (principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA)). The application of PCA to LC-MS and GC-MS data showed the natural clustering of the samples, seeing that 2 varieties were dominating the models (Arbequina and Frantoio), suppressing any possible discrimination among the other cultivars. Afterwards, PLS-DA was used to build four different efficient predictive models for varietal classification of the samples under study. The varietal markers pointed out by each platform were compared. In general, with the exception of one GC-MS model, all exhibited proper quality parameters. The models constructed by using the LC-MS data demonstrated superior classification ability.

Online spectral library for GC-atmospheric pressure chemical ionization–ToF MS

BACKGROUND Invented more than three decades ago by Horning, GC-MS under atmospheric pressure chemical ionization (GC-APCI-MS) has only recently emerged from years of obscurity. However, the general acceptance of GC-APCI-MS is certainly constrained by the lack of spectral libraries, which make the traditional GC-MS approaches so powerful. RESULTS Here we present a concept of a GC-APCI-QqToF spectral library. The library is web-based, fully searchable and at moment includes spectra of 150 compounds from the most common chemical families. The fragmentation pattern of some chemical families is explained and a protocol for de novo identification has been provided in order to facilitate the identification of unknown compounds. CONCLUSION A library for GC-APCI-QqToF is now publicly available online.

Metabolomic analysis of avocado fruits by GC-APCI-TOF MS: effects of ripening degrees and fruit varieties

In order to investigate avocado fruit ripening, nontargeted GC-APCI-TOF MS metabolic profiling analyses were carried out. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to explore the metabolic profiles from fruit samples of 13 varieties at two different ripening degrees. Mannoheptulose; pentadecylfuran; aspartic, malic, stearic, citric and pantothenic acids; mannitol; and β-sitosterol were some of the metabolites found as more influential for the PLS-DA model. The similarities among genetically related samples (putative mutants of “Hass”) and their metabolic differences from the rest of the varieties under study have also been evaluated. The achieved results reveal new insights into avocado fruit composition and metabolite changes, demonstrating therefore the value of metabolomics as a functional genomics tool in characterizing the mechanism of fruit ripening development, a key developmental stage in most economically important fruit crops.

Effect of Suboptimal Sampling and Handling Conditions on Urinary Metabolic Profiles

Collection and storage of the clinical samples are crucial factors in the metabolomic workflows. However, with the expansion of metabolomics into the clinical domain and towards the large field studies in particular, the high sampling/storage standards practiced in the tightly controlled hospital environment cannot always be guaranteed. Thus, if the samples are exposed to suboptimal conditions and their integrity is compromised should they be discarded? Or such samples retain physiologically relevant information and can be of use? To explore the options we analyzed 117 urine samples that were collected under two different conditions. Part of the samples were collected within a clinical setting under optimal conditions, another part by patients at home and shipped to the hospital by mail. All samples were analyzed by liquid chromatography–mass spectrometry (LC–MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy. Multivariate modelling revealed clear differences between the two sampling conditions for both LC–MS and 1H NMR data sets. However, the differential metabolites appeared to be platform-specific, which clearly emphasizes the complementary nature of both techniques. The analysis of the samples that were exposed to suboptimal conditions revealed that age and body mass index remain as dominant traits of the metabolic profile, although their influence was stronger for LC–MS data. In conclusion, although it is important to ensure adequate sample collection and storage conditions, urine samples that do not fulfil these criteria still retain valuable physiological information and as such thus they could be of use for metabolomic studies when no alternative is available.