Proteomic fingerprinting of mistletoe (Viscum album L.) via combinatorial peptide ligand libraries and mass spectrometry analysis.

Combinatorial peptide ligand libraries (CPLLs), coupled to mass spectrometry (MS) analysis, have been used to investigate in depth the proteome of Viscum album L. (VA), commonly named European mistletoe, in order to provide a first proteomic fingerprinting. For this purpose, the proteins were captured via CPLLs at two different pH values (acidic and neutral). A total of 648 non-redundant proteins were identified by using two different databases. The two pH values, chosen for bead incubations, have contributed to increment the capture ability: 56% and 31% of CPLLs species were respectively recognized at pH7.2 and at pH2.2. Finally the biological function of identified proteins was evaluated in order to understand their role on human health and the potential benefits of mistletoe extracts in medicine. SIGNIFICANCE: Viscum album L. (VA) extracts are recently used as supporting medicine for cancer therapy, improving patients' survival and increasing their quality of life in medicine. These anticancer effects are investigated and they are probably due to mistletoe's capability to favor tumor cell's death and to modulate the immune system. Although the increasing interest in VA medical benefits, the role of its components in human health remains unclear. In order to exploit this aspect, it is important to comprehensively study proteins present in Viscum album L. (VA) extracts. Nevertheless, since plant proteomics analysis is in most cases handicapped by the presence of high-abundance proteins masking the detection of the low-abundance ones, it is important to overcome this challenge. In this sense, combinatorial peptide ligand libraries (CPLLs) have been used to reduce the dynamic protein concentration range to enable the identification of a higher amount of proteins than employing conventional methods. In this work, a total of 648 non-redundant proteins were identified: 56% and 31% of CPLLs species were respectively recognized at pH7.2 and at pH2.2. This deep proteome identification was useful to investigate the biological functions of proteins in order to evaluate their potential role in human health.

According to the CPLL proteome sheriffs, not all aperitifs are created equal!

Combinatorial peptide ligand libraries (CPLLs) have been adopted for investigating the proteome of a popular aperitif in Northern Italy, called "Amaro Branzi", stated to be an infusion of a secret herbal mixture, of which some ingredients are declared on the label, namely Angelica officinalis, Gentiana lutea and orange peel, sweetened by a final addition of honey. In order to assess the genuineness of this commercial liqueur, we have prepared extracts of the three vegetable ingredients, assessed their proteomes, and compared them to the one found in the aperitif. The amaro's proteome was identified via prior capture with CPLLs at two different pH values (2.2 and 4.8). Via mass spectrometry analysis of the recovered fractions, after elution of the captured populations in 4% boiling SDS, we could confirm the presence of the following: six proteins originating from honey, 11 from orange peels, 29 from G. lutea and 46 from A. officinalis (including shared species), plus 33 species which could not be attributed to the other secret ingredients, due to paucity of genomic data on plant proteins, for a total of 93 unique gene products (merging shared proteins). This fully confirmed the genuineness of the product. Considering that most of these species could be present in trace amounts, undetectable by conventional techniques, the CPLL methodology, due to its ability to enhance the signal of trace components up to 3 to 4 orders of magnitude, could represent a powerful tool for investigating the genuineness and natural origin of commercial beverages in order to protect consumers from adulterated products.

Tannin analysis of chestnut bark samples (Castanea sativa Mill.) by HPLC-DAD-MS.

In the present investigation, an HPLC-DAD/ESI-MS method for the complete analysis of tannins and other phenolic compounds of different commercial chestnut bark samples was developed. A total of seven compounds (vescalin, castalin, gallic acid, vescalagin, 1-O-galloyl castalagin, castalagin and ellagic acid) were separated and quantified, being 1-O-galloyl castalagin tentatively identified and found for the first time in chestnut bark samples. Thus, this method provided information regarding the composition and quality of chestnut bark samples, which is required since these samples are commercialised due to their biochemical properties as ingredients of food supplements.

Capillary electrophoresis of free fatty acids by indirect ultraviolet detection: application to the classification of vegetable oils according to their botanical origin.

A method for the determination of fatty acids in vegetable oils by capillary electrophoresis with indirect UV-vis detection has been developed. The separation of fatty acids was optimized in terms of Brij surfactant nature and concentration and organic modifier (2-propanol) percentage. The optimal background electrolyte consisted of 10 mM p-hydroxybenzoate, 5 mM Tris at pH 8.8, 80 mM Brij 98, 40% acetonitrile, and 10% 2-propanol. Under these conditions, vegetable oils from five botanical origins (avocado, corn, extra virgin olive, hazelnut, and soybean) were analyzed and the fatty acid contents established. Linear discriminant analysis (LDA) models were constructed using fatty acid peak areas as predictors. An excellent resolution among all category pairs was obtained, and all samples were correctly classified with assignment probabilities of >95%.

Methacrylate ester-based monolithic columns for nano-LC separation of tocopherols in vegetable oils.

The separation and determination of tocopherols (Ts) in vegetable oils by nano-LC chromatography with UV-vis detection using lauryl methacrylate ester-based monolithic columns has been developed. The separation of Ts was optimized in terms of mobile phase composition on the basis of the best compromise among efficiency, resolution and analysis time. Using a mobile phase composed of ACN/methanol/water, an excellent resolution between Ts was achieved within 18 min. The LODs were lower than 0.26 µg/mL, being repeatability values of retention time and peak area below 0.15 and 3.1%, respectively. The method was applied to the quantification of Ts and tocotrienols present in several vegetable oils from different botanical origins.

Fast separation and determination of sterols in vegetable oils by ultraperformance liquid chromatography with atmospheric pressure chemical ionization mass spectrometry detection.

A method for the determination of sterols in vegetable oils by ultraperformance liquid chromatography (UPLC) with atmospheric pressure chemical ionization mass spectrometry detection has been developed. The separation of sterols was optimized in terms of mobile phase composition, column temperature and flow rate. The optimal conditions were achieved using an Acquity UPLC BEH C18 column (50 x 2.1 mm, 1.7 microm) with a mobile phase consistent of acetonitrile/water (0.01% acetic acid) using a linear gradient, at a flow rate of 0.8 mL min(-1) and column temperature of 10 degrees C, giving a total analysis time below 5 min. The determination was performed in selective ion recording mode. The limits of detection were in all cases below 0.07 microg mL(-1), with relative standard deviation values of retention times and peak areas below 0.4 and 5%, respectively. The content of main sterols present in several vegetable oils with different botanical origins was also established.

Determination of tocopherols and tocotrienols in vegetable oils by nanoliquid chromatography with ultraviolet-visible detection using a silica monolithic column.

A method for the determination of tocopherols and tocotrienols in vegetable oils by nanoliquid chromatography with UV-vis detection has been developed. The separation of tocopherols was optimized in terms of mobile phase composition on the basis of the best compromise between efficiency, resolution, and analysis time. The optimal conditions were achieved using a C18 silica monolithic column (150 mm x 0.1 mm) with an isocratic elution of acetonitrile/methanol/water (acidified with 0.2% acetic acid) at a flow rate of 0.5 microL min(-1), giving a total analysis time below 18 min. The method has been applied to the quantification of tocopherols and tocotrienols present in several vegetable oils with different botanical origins.

Classification of extra virgin olive oils produced at La Comunitat Valenciana according to their genetic variety using sterol profiles established by high-performance liquid chromatography with mass spectrometry detection.

A method to classify extra virgin olive oils (EVOOs) according to their genetic variety using sterol profiles obtained by high-performance liquid chromatography (HPLC) with mass spectrometry (MS) detection has been developed. Sterol extracts were chromatographed on a dC18 Atlantis column (100x3 mm, 3 microm) with a gradient of acetonitrile/water (0.01% acetic acid) at a flow rate of 1.0 mL min(-1) and positive-ion mode MS detection. Using linear discriminant analysis of the HPLC-MS data (extracted ion chromatograms), EVOO samples belonging to six genetic varieties cultivated at La Comunitat Valenciana, Spain (Arbequina, Borriolenca, Canetera, Farga, Picual, and Serrana), were correctly classified with an excellent resolution among all of the categories.

Prediction of the genetic variety of extra virgin olive oils produced at La Comunitat Valenciana, Spain, by fourier transform infrared spectroscopy.

Fourier transform infrared spectroscopy (FTIR), followed by multivariate treatment of the spectral data, was used to classify extra virgin olive oils (EVOOs) according to their genetic variety. EVOO samples corresponding to seven different genetic varieties (Arbequina, Borriolenca, Canetera, Farga, Hojiblanca, Picual, and Serrana) were analyzed. The wavelength scale of the FTIR spectra of the oils was divided into 20 regions. The normalized absorbance peak areas within these regions were used as predictor variables. Classification of the EVOO samples according to their genetic variety was achieved by linear discriminant analysis (LDA). A good resolution among all categories was achieved using a LDA model constructed with only nine predictor variables. With this LDA model, 88% of the EVOOs were correctly classified, with assignment probabilities higher than 95%. This method is helpful for olive oil producers because it provides useful information related to the genetic variety of EVOOs, which is required by European Community regulations.