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HETEROGENEOUSLY WITH PLASMA LIPID PROFILE IN SUBJECTS WITH CORONARY ARTERY DISEASE Carmela Chiarielloa, Annalisa Castagnaa, Nicola Martinellia, Patrizia Guarinia, Marcello Manfredib, Elia Ranzatoc, Simona Martinottic, Emilio Marengoc, Daniela Cecconid, Oliviero Olivieria. aDepartment of Medicine, Section of Internal Medicine B, University of Verona, Italy. bISALIT S.r.l., Novara, Italy. cDepartment of Sciences and Technological Innovation, University of Piemonte Orientale, Alessandria, Italy. dProteomics and Mass Spectrometry Laboratory, Department of Biotechnology, University of Verona, Verona, Italy.

Two approaches to the analysis of 2-D maps are available: the first one involves a step of spot detection on each gel image; the second one is based instead on the direct differential analysis of 2-D map images, following a pixel-based procedure. Both approaches strongly depend on the proper alignment of the gel images, but the pixel-based approach allows to solve important drawbacks of the spot-volume procedure, i.e., the problem of missing data and of overlapping spots. However, this approach is quite computationally intensive and requires the use of algorithms able to separate the information (i.e., spot-related information) from the background. Here, the most recent pixel-based approaches are described.

Pterocarpus marsupium is a well-known plant due to its healing properties, in particular, the use of its aqueous extract is able to reduce blood sugar levels and blood triglyceride concentrations. Although this plant has already been widely studied, a complete characterization of its aqueous extract has not been reported. The present study deals with the characterization of the aqueous extract of P. marsupium in order to obtain a full fingerprint of the volatile and nonvolatile constituents. The volatile constituents were identified by CG-MS, whereas the nonvolatile fraction was characterized by UHPLC-MS/MS using a nontarget approach. Several compounds were identified, in particular, polyphenolic species belonging to the class of proanthocyanidins. Cytotoxicity tests were carried out on four different cancer cell lines and three different non-tumoral cell lines. Preliminary results indicate a selective cytotoxicity of the aqueous extract towards the cancer cells. The potential cytotoxicity due to the presence of metals in the aqueous extract was ruled out by testing an aqueous mixture of the metals at the same concentration found in the P. marsupium extract.

In higher plants, photosystem II (PSII) is a multi-subunit pigment-protein complex embedded in the thylakoid membranes of chloroplasts, where it is present mostly in dimeric form within the grana. Its light-harvesting antenna system, LHCII, is composed of trimeric and monomeric complexes, which can associate in variable number with the dimeric PSII core complex in order to form different types of PSII-LHCII supercomplexes. Moreover, PSII-LHCII supercomplexes can laterally associate within the thylakoid membrane plane, thus forming higher molecular mass complexes, termed PSII-LHCII megacomplexes (Boekema et al. 1999a, in Biochemistry 38:2233–2239; Boekema et al. 1999b, in Eur J Biochem 266:444–452). In this study, pure PSII-LHCII megacomplexes were directly isolated from stacked pea thylakoid membranes by a rapid single-step solubilization, using the detergent n-dodecyl-α-d-maltoside, followed by sucrose gradient ultracentrifugation. The megacomplexes were subjected to biochemical and structural analyses. Transmission electron microscopy on negatively stained samples, followed by single-particle analyses, revealed a novel form of PSII-LHCII megacomplexes, as compared to previous studies (Boekema et al.1999a, in Biochemistry 38:2233–2239; Boekema et al. 1999b, in Eur J Biochem 266:444–452), consisting of two PSII-LHCII supercomplexes sitting side-by-side in the membrane plane, sandwiched together with a second copy. This second copy of the megacomplex is most likely derived from the opposite membrane of a granal stack. Two predominant forms of intact sandwiched megacomplexes were observed and termed, according to (Dekker and Boekema 2005 Biochim Biophys Acta 1706:12–39), as (C2S2)4 and (C2S2 + C2S2M2)2 megacomplexes. By applying a gel-based proteomic approach, the protein composition of the isolated megacomplexes was fully characterized. In summary, the new structural forms of isolated megacomplexes and the related modeling performed provide novel insights into how PSII-LHCII supercomplexes may bind to each other, not only in the membrane plane, but also between granal stacks within the chloroplast.

Software-based image analysis of 2-D PAGE maps is an important step for the investigation of proteome. Warping algorithms, which are employed to register spots among gels, are able to overcome the difficulties due to the low reproducibility of this analytical technique. Over the years, the research of new matching and warping mathematical methods has allowed the development of several routine applications of easy-to-use software. This chapter describes common and basic spatial transformations used for the alignment of protein spots present in different gel maps; some recently new approaches are also presented.

Emerging research has demonstrated that pancreatic ductal adenocarcinoma (PDAC) contains a sub-population of cancer stem cells (CSCs) characterized by self-renewal, anchorage-independent-growth, long-term proliferation and chemoresistance. The secretome analysis of pancreatic CSCs has not yet been performed, although it may provide insight into tumour/microenvironment interactions and intracellular processes, as well as to identify potential biomarkers. To characterize the secreted proteins of pancreatic CSCs, we performed an iTRAQ-based proteomic analysis to compare the secretomes of Panc1 cancer stem-like cells (Panc1 CSCs) and parental cell line. A total of 72 proteins were found up-/down-regulated in the conditioned medium of Panc1 CSCs. The pathway analysis revealed modulation of vital physiological pathways including glycolysis, gluconeogenesis and pentose phosphate. Through ELISA immunoassays we analysed the presence of the three proteins most highly secreted by Panc1 CSCs (ceruloplasmin, galectin-3, and MARCKS) in sera of PDAC patient. ROC curve analysis suggests ceruloplasmin as promising marker for patients negative for CA19-9. Overall, our study provides a systemic secretome analysis of pancreatic CSCs revealing a number of secreted proteins which participate in pathological conditions including cancer differentiation, invasion and metastasis. They may serve as a valuable pool of proteins from which biomarkers and therapeutic targets can be identified. Biological significance The secretome of CSCs is a rich reservoir of biomarkers of cancer progression and molecular therapeutic targets, and thus is a topic of great interest for cancer research. The secretome analysis of pancreatic CSCs has not yet been performed. Recently, our group has demonstrated that Panc-1 CSCs isolated from parental cell line by using the CSC selective medium, represent a model of great importance to deepen the understanding of the biology of pancreatic adenocarcinoma. To our knowledge, this is the first proteomic study of pancreatic CSC secretome. We performed an iTRAQ-based analysis to compare the secretomes of Panc1 CSCs and Panc1 parental cell line and identified a total of 43 proteins secreted at higher level by pancreatic cancer stem cells. We found modulation of different vital physiological pathways (such as glycolysis and gluconeogenesis, pentose phosphate pathway) and the involvement of CSC secreted proteins (for example 72 kDa type IV collagenase, galectin-3, alpha-actinin-4, and MARCKS) in pathological conditions including cancer differentiation, invasion and metastasis. By ELISA verification we found that MARCKS and ceruloplasmin discriminate between controls and PDAC patients; in addition ROC curve analyses indicate that MARCKS does not have diagnostic accuracy, while ceruloplasmin could be a promising marker only for patients negative for CA19-9. We think that the findings reported in our manuscript advance the understanding of the pathways implicated in tumourigenesis, metastasis and chemoresistance of pancreatic cancer, and also identify a pool of proteins from which novel candidate diagnostic and therapeutic biomarkers could be discovered.

Mixtures of chemicals can have additive, synergistic or antagonistic interactions. We investigated the effects of the exposure to nickel, the organophosphate insecticide chlorpyrifos at effect concentrations (EC) of 25% and 50% and their binary mixture (Ec25 + EC25) on Dictyostelium discoideum amoebae based on lysosomal membrane stability (LMS). We treated D. discoideum with these compounds under controlled laboratory conditions and evaluated the changes in protein levels using a two-dimensional gel electrophoresis (2DE) proteomic approach. Nickel treatment at EC25 induced changes in 14 protein spots, 12 of which were down-regulated. Treatment with nickel at EC50 resulted in changes in 15 spots, 10 of which were down-regulated. Treatment with chlorpyrifos at EC25 induced changes in six spots, all of which were down-regulated, treatment with chlorpyrifos at EC50 induced changes in 13 spots, five of which were down-regulated. The mixture corresponding to EC25 of each compound induced changes in 19 spots, 13 of which were down-regulated. The data together reveal that a different protein expression signature exists for each treatment, and that only a few proteins are modulated in multiple different treatments. For a simple binary mixture, the proteomic response does not allow for the identification of each toxicant. The protein spots that showed significant differences were identified by mass spectrometry, which revealed modulations of proteins involved in metal detoxification, stress adaptation, the oxidative stress response and other cellular processes.

The secretome is the complex set of molecules secreted by cells; these molecules play a key role in cell signaling, communication and migration. Secretomics has been already used to discover new potential diagnostic biomarkers and therapeutic agents and to elucidate key autocrine pathways. Malignant mesothelioma (MMe), an extremely aggressive tumor, is characterized by a long latency period (20–30 years), a poor prognosis, and limited effective therapies. MMe has a highly secretory cell type, and the factors released by cells may act in an autocrine or paracrine fashion on tumor and stroma, where they may modulate the extracellular environment. The aim of this work is to characterize the secretome of two MMe cell lines, MM98 and REN, in comparison with a mesothelial cell line Met5A, in order to evaluate differences and similarities of these two different MMe cancer model systems, and to identify potential biomarkers. We performed quantitative shotgun proteomics using SWATH-MS technology and we identified a total of 421 proteins, 112 expressed in the secretome of REN cells, 208 expressed in the secretome of MM98 cells and 189 secreted by mesothelial cells; 25 proteins are shared by the two mesothelioma cell lines. Biological significance This study characterizes the secretome signature of the REN and MM98 cell lines, confirming the availability of a cell-culture based model in order to describe the cell-specific properties, and to provide a list of putative cancer biomarkers. This work constitutes the first qualitative and quantitative proteomic approach performed on MMe secretome. Moreover, since the data were acquired in SWATH-MS acquisition mode, they can be successively re-mined without performing a new analysis of the sample, which is extremely useful for retrospective analyses. The overall aim was to identify novel tumor-derived protein biomarkers with the potential to be applied for early diagnosis, prognosis, therapy prediction and/or disease monitoring of MMe.

An on-line solid phase extraction (SPE) ultra high performance liquid chromatography tandem mass spectrometry method has been developed for the simultaneous identification and determination of thirteen polycyclic aromatic hydrocarbons (PAHs) and twelve aldehydes (derivatized with 2,4-dinitrophenylhydrazine). The chromatographic conditions have been optimized to obtain the maximum of sensitivity and resolution taking into account the different retention interactions and the different ionization conditions of PAHs and derivatized aldehydes. LOD values ranging from 0.028 to 0.768 μg L −1 for PAHs and from 0.002 to 0.125 μg L −1 for aldehydes were obtained. The resolution permitted the separation of four couples of PAH isomers. Sample pre-treatment and SPE were optimized in order to apply the whole methodology to the analysis of different food matrices as salmon, frankfurter, steak, and pork chop, subjected to different cooking modes (smoked, grilled, cooked in oil or in butter). Particular attention was devoted to the evaluation of matrix effect that was significantly reduced through the on-line SPE treatment. For each food matrix the method detection limits, the method quantitation limits, and the recovery R were evaluated. R was shown not to depend on analyte concentration in the explored concentration range (LOQ – 50.000 μg L −1 ): the average R percent ranges from 70.6% to 120.0%.

Standard automatic tuning for mass spectrometry usually exploits a one-variable-at-a-time approach. This method suffers from important drawbacks: the target function selected for optimization improves the signal of a single channel or a pool of channels without considering noise; the interactions between the parameters are not evaluated. The optimization of the experimental settings of an ESI IT mass spectrometer is carried out here by a multivariate procedure exploiting a target function representing the S/N ratio calculated by principal component analysis and a regular simplex optimization algorithm. A preliminary feasibility study was performed since the target function must be sensitive to the changes in the experimental conditions applied during the iterative tuning and be free from drifts. The feasibility study was carried out to evaluate: the presence of memory effects; the size of the variations in the S/N ratio; the number of scans needed to generate a reliable S/N ratio; the concentration of the multi-standard mixture to use during tuning; the experimental duration required to achieve S/N stability when the experimental settings are modified. The feasibility study led to the identification of the best protocol to accomplish the tuning, while simplex optimization allowed the S/N ratio to be improved by about 70% with respect to the default conditions suggested by the manufacturer.

A new algorithm for generating simulated sodium dodecil sulfate two-dimensional polyacrylamide gel electrophoresis (SDS 2D-PAGE) map images was developed. To choose the simulation strategy able to provide realistic 2D-PAGE maps, several parameters that characterize the statistical features of the images and data sets of images were taken into account, such as the distribution of size, intensity, and volume of the spots and their changes of position and volume along different replications of the same 2D-PAGE map. In this way, also the low reproducibility of replications of the same SDS 2D-PAGE maps was taken into account. The present algorithm can be usefully employed for the development of new classification and/or image analysis algorithms applied to bidimensional electrophoretic data sets, given the usually small number of experimental replications available.

A reversed-phase high-performance liquid chromatography (HPLC) method was developed for the simultaneous determination in food of biogenic amines and their precursor amino acids after a precolumn derivatization with dansyl chloride. The chromatographic conditions, selected to be suitable for mass spectrometry detection, were optimized through experimental design and artificial neural networks. The HPLC-UV method was validated by comparing the separation results with those obtained through a HPLC method, working under the same chromatographic conditions but employing mass spectrometry detection. The HPLC-UV method was then applied to the analysis of different food samples, namely, cheese, clams, salami, and beer. For all of the matrices, recoveries (relative standard deviation always 92% were obtained. The results are discussed as a function of the total biogenic amine content and of the concentration ratio between amines and precursor amino acids

When mass spectrometry is not combined to separation techniques, the evaluation of signal and noise in a complex mass spectrum is not trivial. The tuning of the spectrometer based only on the increase of the signal of a selected number of m/z values does not ensure the achievement of the best experimental conditions: signal could improve and noise could increase as well. The scope of this work is the development of a function separating signal and noise (for evaluating the S/N) from complex mass spectra for potential use as target function for the automatic tuning of the instrument. Two different methods were applied: the first is based on the separation of a pool of m/z values attributable to the signal from the m/z values due to the noise, while the second is based on the application of principal component analysis to separate the signal (present in the significant components) from the noise (present in the residuals). The comparison of the two methods was carried out by the evaluation of the stability of the signal and the target functions obtained, and the evaluation of the variation of the target functions as a function of concentration.

This paper reports the development of calibration models for quality control in the production of ethylene/propylene/1-butene terpolymers by the use of multivariate tools and FT-IR spectroscopy. 1-Butene concentration prediction is achieved in terpolymers by coupling FT-IR spectroscopy to multivariate regression tools. A dataset of 26 terpolymers (14 coming from a constrained experimental design for mixtures, plus 12 terpolymers used for external validation) was analysed by FT-IR spectroscopy. An internal method of "Polimeri Europa" plant, based on (13)C NMR spectroscopy is used to determine the percentage of 1-butene in the samples. Then, different multivariate tools are used for 1-butene concentration prediction based on the FT-IR spectra recorded. Different multivariate calibration methods were explored: principal component regression (PCR), partial least squares (PLS), stepwise OLS regression (SWR) and artificial neural networks (ANNs). The model obtained by back-propagation neural networks turned out to be the best one. The performances of the BP-ANN model were further improved by variable selection procedures based on the calculation of the first derivative of the network. The proposed approach allows the monitoring in real time of the polymer synthesis and the estimation of the characteristics of the product attainable from the concentration of 1-butene.

SIMCA classification can be applied to 2D-PAGE maps to identify changes occurring in cellular protein contents as a consequence of illnesses or therapies. These data sets are complex to treat due to the large number of proteins detected. A method for identifying relevant proteins from SIMCA discriminating powers is proposed, based on the Box-Cox transformation coupled to probability papers. The method successfully allowed the identification of the relevant spots from 2D maps.

29 chemical and physical–chemical features of water samples from 44 wells were analysed by multivariate statistical tools to provide the characterisation of the ground water distribution of the North–West area of Alessandria province (Piedmont—Italy). The 29 parameters determined include: temperature, pH, conductivity, dissolved oxygen, suspended solids, COD, total hardness, total phenols, fats and oils, non-ionic surfactants, chlorinated solvents, total chromium, P 2 O 5 and several cations and anions (NO 3 − , NH 4 + , Ca 2 + , Na + , K + , Mg 2+ , Cl − , SO 4 2− , SO 3 2− , S 2− , Fe 3+ , Mn 2+ , Zn 2+ , Al 3+ , Ni 2+ , Pb 2+ ). The sampling took place during the spring and summer 2001. The use of Principal Component Analysis together with refined mapping tools (kriging methods) provided a description of the area investigated with respect to the characteristics of the subterranean waters. A significant difference in water composition has been highlighted between small villages (with a probable human polluting effect) and zones far from the built-up areas. Cluster analysis was also performed in order to evaluate the different wells similarity and confirmed the results obtained by Principal Component Analysis.

The aim of this work was to obtain the correct classification of a set of two-dimensional polyacrylamide gel electrophoresis map images using the Zernike moments as discriminant variables. For each 2D-PAGE image, the Zernike moments were computed up to a maximum p order of 100. Partial least squares discriminant analysis with variable selection, based on a backward elimination algorithm, was applied to the moments calculated in order to select those that provided the lowest error in cross-validation. The new method was tested on four datasets: (1) samples belonging to neuroblastoma; (2) samples of human lymphoma; (3) samples from pancreatic cancer cells (two cell lines of control and drug-treated cancer cells); (4) samples from colon cancer cells (total lysates and nuclei treated or untreated with a histone deacetylase inhibitor). The results demonstrate that the Zernike moments can be successfully applied for fast classification purposes. The final aim is to build models that can be used to achieve rapid diagnosis of these illnesses.

2D gel electrophoresis is a tool for measuring protein regulation, involving image analysis by dedicated software (PDQuest, Melanie, etc.). Here, partial least squares discriminant analysis was applied to improve the results obtained by classic image analysis and to identify the significant spots responsible for the differences between two datasets. A human colon cancer HCT116 cell line was analyzed, treated and not treated with a new histone deacetylase inhibitor, RC307. The proteins regulated by RC307 were detected by analyzing the total lysates and nuclear proteome profiles. Some of the regulated spots were identified by tandem mass spectrometry. The preliminary data are encouraging and the protein modulation reported is consistent with the antitumoral effect of RC307 on the HCT116 cell line. Partial least squares discriminant analysis coupled with backward elimination variable selection allowed the identification of a larger number of spots than classic PDQuest analysis. Moreover, it allows the achievement of the best performances of the model in terms of prediction and provides therefore more robust and reliable results. From this point of view, the multivariate procedure applied can be considered a good alternative to standard differential analysis, also taking into account the interdependencies existing among the variables.

The effect of exposure of paper samples to UV light was monitored by use of ATR-FT-IR spectroscopy and multivariate statistical tools. Three types of paper were tested: common laser-printer paper, news print, and thermal fax paper. The samples were first characterised by ATR-FT-IR spectroscopy to determine natural experimental variability. They were then exposed to UV light for 30 h and the effects of the exposure were monitored by use of the same spectroscopic technique. Finally, multivariate statistical tools were applied to the final dataset, coupled with construction of multivariate control charts, to identify the effects of UV light on the sample surfaces.

2-D gel electrophoresis usually provides complex maps characterized by a low reproducibility: this hampers the use of spot volume data for the identification of reliable biomarkers. Under these circumstances, effective and robust methods for the comparison and classification of 2-D maps are fundamental for the identification of an exhaustive panel of candidate biomarkers. Multivariate methods are the most suitable since they take into consideration the relationships between the variables, i.e., effects of synergy and antagonism between the spots. Here the most common multivariate methods used in spot volume datasets analysis are presented. The methods are applied on a sample dataset to prove their effectiveness.

Software-based image analysis of 2-D PAGE maps is an important step for the investigation of proteome. Warping algorithms, which are employed to register spots among gels, are able to overcome the difficulties due to the low reproducibility of this analytical technique. Over the years, the research of new matching and warping mathematical methods has allowed the development of several routine applications of easy-to-use software. This chapter describes common and basic spatial transformations used for the alignment of protein spots present in different gel maps; some recently new approaches are also presented.

Two approaches to the analysis of 2-D maps are available: the first one involves a step of spot detection on each gel image; the second one is based instead on the direct differential analysis of 2-D map images, following a pixel-based procedure. Both approaches strongly depend on the proper alignment of the gel images, but the pixel-based approach allows to solve important drawbacks of the spot-volume procedure, i.e., the problem of missing data and of overlapping spots. However, this approach is quite computationally intensive and requires the use of algorithms able to separate the information (i.e., spot-related information) from the background. Here, the most recent pixel-based approaches are described.

The comparison of 2-D maps is not trivial, the main difficulties being the high complexity of the sample and the large experimental variability characterizing 2-D gel electrophoresis. The comparison of maps from control and treated samples is usually performed by specific software, providing the so-called spot volume dataset where each spot of a specific map is matched to its analogous in other maps, and they are described by their optical density, which is supposed to be related to the underlying protein amount. Here, a different approach is presented, based on the direct comparison of 2-D map images: each map is decomposed in terms of moment functions, successively applying the multivariate tools usually adopted in image analysis problems. The moments calculated are then treated with multivariate classification techniques. Here, two types of moment functions are presented (Legendre and Zernike moments), while linear discriminant analysis and partial least squares discriminant analysis are exploited as classification tools to provide the classification of the samples. The procedure is applied to a sample dataset to prove its effectiveness.

Gel electrophoresis is usually applied to identify different protein expression profiles in biological samples (e.g., control vs. pathological, control vs. treated). Information about the effect to be investigated (a pathology, a drug, a ripening effect, etc.) is however generally confounded with experimental variability that is quite large in 2-DE and may arise from small variations in the sample preparation, reagents, sample loading, electrophoretic conditions, staining and image acquisition. Obtaining valid quantitative estimates of protein abundances in each map, before the differential analysis, is therefore fundamental to provide robust candidate biomarkers. Normalization procedures are applied to reduce experimental noise and make the images comparable, improving the accuracy of differential analysis. Certainly, they may deeply influence the final results, and to this respect they have to be applied with care. Here, the most widespread normalization procedures are described both for what regards the applications to 2-DE and 2D Difference Gel-electrophoresis (2-D DIGE) maps.

Hierarchical grid transformation is a powerful hierarchical approach to 2-D map warping, able to model both global and local deformations. The algorithm can be stopped when a desired degree of accuracy in the images alignment is obtained. The deformed image is warped and aligned to the target image using a grid where the number of nodes increases in each step of the algorithm. The numerical optimization of the position of the nodes of the grid can be efficiently solved by genetic algorithms, ensuring the achievement of the optimal position of the nodes with a low computational cost with respect to other methods. Here, the optimization of the position of the nodes is carried out by GENOCOP (genetic algorithm for numerical optimization of constrained problems), refined by the following conjugate gradient optimization step. The modeling of the warped space is then achieved by a spline model where some constraints are introduced in the choice of the nodes that are moved. The whole procedure can be intended as an evolutionary method that models the deformation of the gel map at different levels of detail.

Background Parkinson’s disease (PD) is a chronic progressive neurodegenerative disorder that is clinically defined in terms of motor symptoms. These are preceded by prodromal non-motor manifestations that prove the systemic nature of the disease. Identifying genes and pathways altered in living patients provide new information on the diagnosis and pathogenesis of sporadic PD. Methods Changes in gene expression in the blood of 40 sporadic PD patients and 20 healthy controls ("Discovery set") were analyzed by taking advantage of the Affymetrix platform. Patients were at the onset of motor symptoms and before initiating any pharmacological treatment. Data analysis was performed by applying Ranking-Principal Component Analysis, PUMA and Significance Analysis of Microarrays. Functional annotations were assigned using GO, DAVID, GSEA to unveil significant enriched biological processes in the differentially expressed genes. The expressions of selected genes were validated using RT-qPCR and samples from an independent cohort of 12 patients and controls ("Validation set"). Results Gene expression profiling of blood samples discriminates PD patients from healthy controls and identifies differentially expressed genes in blood. The majority of these are also present in dopaminergic neurons of the Substantia Nigra, the key site of neurodegeneration. Together with neuronal apoptosis, lymphocyte activation and mitochondrial dysfunction, already found in previous analysis of PD blood and post-mortem brains, we unveiled transcriptome changes enriched in biological terms related to epigenetic modifications including chromatin remodeling and methylation. Candidate transcripts as CBX5, TCF3, MAN1C1 and DOCK10 were validated by RT-qPCR. Conclusions Our data support the use of blood transcriptomics to study neurodegenerative diseases. It identifies changes in crucial components of chromatin remodeling and methylation machineries as early events in sporadic PD suggesting epigenetics as target for therapeutic intervention. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2058-3) contains supplementary material, which is available to authorized users.

Mass spectrometry (MS)-based methods have become popular in recent years for the quantitation of biomolecules, especially peptides and proteins. High Mobility Group Box-1 protein (HMGB1) is a nuclear DNA-binding protein that resides inside the nucleus and can be released to the extracellular space under specific conditions. HMGB1 has been reported as circulating mechanistic indicators of cell death mode in animal models and in clinical studies. There is accumulating evidence that HMGB1 contributes to the pathogenesis of inflammatory and autoimmune diseases. Moreover, HMGB1 has been found to play an important role in tumor development, growth, and spread. Circulating HMGB1 level could be a useful and specific marker for evaluating different disease status and predicting prognosis in patients. The label-free quantitative comparison without any standard reference or normalization of the target peptide signal is the simplest Liquid Chromatographic-MS (LC-MS) method for the quantitation of a protein. The aim of the research was the development of an analytical method for the quantitation of HMGB1 by a label-free Mass Spectrometry technique. In this paper are presented the preliminary results: the quantitation of HMGB1 was assessed in a cell lysate sample through the use of the standard concentration curve of purified HMGB1 protein.

A new ultra high performance liquid chromatography coupled with tandem mass spectrometry method for a fast and sensitive determination of eight polyphenols (hydroxytyrosol, catechin, epicatechin, epigallocatechin gallate, oleuropein, quercetin, rutin, tyrosol) and panthotenic acid in extra-virgin olive oil was developed. The method does not require long sample pre-treatment and presents the lowest limit of detection and limit of quantitation values present in literature. Inter- and intra-day variability, linear dynamic range of the calibration curve, recovery and matrix effect were also determined and investigated. The method was applied to several oil samples of different type and origin. Given its accuracy, precision and rapidity, the method is characterized by an interestingly high throughput, reliability, and sensitivity.

This study is focused on the characterisation of typical salami produced in Alessandria province (North West of Italy). Seventeen small or medium salami producers from this area were involved in the study and provided the samples investigated. The aim is double and consists in obtaining a screening of the characteristics of different products and following their evolution along ripening. The study involved five types of typical salami that were characterised for aroma components and nutritional features. This approach could provide a basis for a possible PDO or PGI label request. Principal Component Analysis and cluster analysis were used as multivariate statistical tools for data treatment. The overall results obtained point out that the products investigated do not deviate from analogous European products and show the possibility of characterising by specific parameters three main groups of samples:Salamini di Mandrogne,Muletta, andNobile Giarolo; moreover some considerations can also be drawn with respect to the nutritional characterization considering the biogenic amines profile.

Malignant mesothelioma is a poor prognosis cancer in urgent need of alternative therapies. Oleuropein, the major phenolic of olive tree (Olea europaeaL.), is believed to have therapeutic potentials for various diseases, including tumors. We obtained an oleuropein-enriched fraction, consisting of 60% w/w oleuropein, from olive leaves, and assessed its effects on intracellular Ca2+and cell viability in mesothelioma cells. Effects of the oleuropein-enriched fraction on Ca2+dynamics and cell viability were studied in the REN mesothelioma cell line, using fura-2 microspectrofluorimetry and MTT assay, respectively. Fura-2-loaded cells, transiently exposed to the oleuropein-enriched fraction, showed dose-dependent transient elevations of cytosolic Ca2+concentration (Ca2+i). Application of standard oleuropein and hydroxytyrosol, and of the inhibitor of low-voltage T-type Ca2+channels NNC-55-0396, suggested that the effect is mainly due to oleuropein acting through its hydroxytyrosol moiety on T-type Ca2+channels. The oleuropein-enriched fraction and standard oleuropein displayed a significant antiproliferative effect, as measured on REN cells by MTT cell viability assay, with IC50of 22 μg/mL oleuropein. Data suggest that our oleuropein-enriched fraction from olive leaf extract could have pharmacological application in malignant mesothelioma anticancer therapy, possibly by targeting T-type Ca2+channels and thereby dysregulating intracellular Ca2+dynamics.

This paper reports the study of the photodegradation reactions that tricyclazole can naturally undergo, under the action of sunlight, in aqueous solutions of standard tricyclazole and of the commercial BEAM(TM) formulation. The analyses are carried out by ultra-high performance liquid chromatography technique coupled with high-resolution tandem mass spectrometry. Analysis of both tricyclazole and BEAM(TM) water solutions undergone to hydrolysis does not evidence new chromatographic peaks with respect to the not treated solutions. On the contrary, analysis of the same samples subjected to sunlight irradiation shows a decreased intensity of tricyclazole signal and the presence of new chromatographic peaks. Two photodegradation products of tricyclazole have been identified, one of which has been also quantified, being the commercial standard available. The pattern is similar for the solutions of the standard fungicide and of the BEAM(TM) formulation. The results obtained from eco-toxicological tests show that toxicity of tricyclazole standard solutions is greater than that of the irradiated ones, whereas toxicity levels of all the BEAM(TM) solutions investigated (non-irradiated, irradiated, and hydrolyzed) are comparable and lower than those shown by tricyclazole standard solutions. Experiments performed in paddy water solution show that there is no difference in the degradation products formed.

Contamination of water resources is one of the major problems to be faced for environment preservation and sustainability. Although anti-pollution strategies taken in the last half-century have consistently reduced in surface water the amount and the presence of many recognised contaminants, other potentially hazardous chemicals are being released into the environment, together with new substances that are continuously synthesized and whose dangerous properties are not well known. Water-pollutant monitoring makes typically use of methods developed for target analysis, focused on priority pollutants. The monitoring of target compounds based on mass spectrometry (MS) and selected reaction monitoring (SRM) mode is often insuffi cient to defi nitely assess the quality of surface water, just because the presence of only a limited number of potential pollutants is considered. Also potentially harmful non-target pollutants simultaneously present must be taken into account. In the determination of semi-polar and polar pollutants, liquid chromatography coupled with tandem MS is generally the technique of choice. To obtain a complete information on water composition, full-spectrum acquisition techniques, better if with the possibility to obtain information about MS/MS spectra, are required. For this purpose, hybrid mass spectrometers like triple quadrupole/linear ion trap (QqLIT), hybrid quadrupole/time-of-fl ight (QqTOF) MS and linear ion trap/orbitrap analyzer must be used. The last two instruments are the masters of the nontarget approach, because their can offer the advantage of high resolution MS, that combined with a good MS accuracy allows to identify the unknown species. In addition, the hybrid nature of such instruments allows to acquire during a single chromatographic run also information about high resolution MS/MS spectra, that are fundamental in order to attribute to each unknown species a probable chemical structure. An interesting advantage associated with high resolution MS based methodologies consists in the possibility of performing retrospective analysis, since it allows the identifi cation of organic contaminants included in the fi rst screening: this investigation can be done at any time, without the need of new analysis or new sample injection. In addition, the elevated acquisition speed of TOF makes it compatible with ultra high performance liquid chromatography (UHPLC). UHPLC and high resolution MS provide potent analysis rich in information on sample composition. During a non-target screening analysis, all the compounds eluted from the analytical column can be detected without any kind of selection, a part the obvious limitations derived from chromatography and ionization process in the LC-MS interface. A genuine non-target analysis involves the automated component detection from the total ion current and the use of deconvolution software to detect the presence of multiple species and to produce pure spectra for each individual component. An important limitation to this approach is the lack of availability of large compound libraries similar to those used in gas chromatography-mass spectrometry (GC-MS), making the identifi cation of unknown species very complex and time-consuming. At the moment, spectral libraries for LC-MS are home-made and quite limited. This review presents an overview of published UHPLC-MS methods developed for post-target and non-target screening analysis of water emerging contaminants, such as pesticides and their degradation products, pharmaceuticals and drug side-reaction products, surfactants and illicit drugs. The different aspects of the current MS instrumentations, using tandem, hybrid and high resolution MS systems, are compared and discussed.

The method for preparing paintings varies from artist to artist. The non-destructive investigation of the preparation used to make a canvas is very important both for restoration and authentication purposes. A portable diffuse reflectance infrared Fourier transform (DRIFT) method for the non-invasive characterization of grounds in paintings is presented: with this method there is no need to sample or touch the artworks and the analysis can be performed directly on site. Sixteen of the most important canvas grounds used since the XIV to the XX century were prepared following the ancient recipes: they include grounds from Vasari, Caravaggio, and De Chirico. The grounds were analyzed by DRIFT spectroscopy and a reference spectral collection was built. The DRIFT method was then performed on the ground of a painting of Moncalvo: the infrared analysis was able to identify the ground composition. The portable DRIFT method can be applied as a non-invasive diagnostic tool in the cultural heritage field: useful information can be provided to conservators and art historians.

The growing number of studies on platinum(II) complexes is stimulated by their importance as antitumour chemotherapeutics. 195 Pt NMR spectroscopy is a very useful tool for characterizing and investigating these complexes. An accurate estimation of NMR chemical shifts plays an important role in the evaluation of molecular structure. Moreover, the predictions should be fast and accurate in order to be useful as a tool for structure evaluation within a large set of compounds. In this context, ab initio calculations are time consuming and quite difficult because of the relativistic effect that must be considered for the third row transition metals. A new approach is offered here by chemometrics: by means of an artificial neural network algorithm, it is now possible to build a model to predict the chemical shift of a Pt-complex on the basis of its molecular features, thus providing a useful alternative to computational methods. We have successfully applied such a model to 185 different 195 Pt chemical shift values.

This paper concerns the improvement of a method, already applied for the conservation state monitoring of wooden and painted surfaces, to a system closely simulating a real artwork, namely a canvas painted with mixtures of three organic pigments (Alizarin, Permanent Red, Phtalocyanine Green). Ten mixtures of these pigments, according to an augmented simplex-centroid design, were prepared, mixed with linseed oil and spread on 10 cotton canvas strips. Drying ended, all the samples were analysed by ATR-FT-IR spectroscopy to describe the superficial variability in normal conditions of conservation, i.e. when no degradation is present. Successively, the samples were exposed to artificial UV light simulating the action of an aggressive portion of sunlight. The IR spectra of the surfaces were regularly acquired to monitor the superficial changes due to the UV aggression. Treatment ended, a chemometric study based on the Principal Component Analysis of the spectroscopic data collected both in normal conditions of conservation and during the artificial accelerated ageing, was performed and the multivariate Shewhart and Cusum control charts were built with the scores of the significant PCs (principal components). PCA based control charts showed to be able to identify the presence of significant changes of the painted surfaces, to identify the starting of the degradations and to provide insights about the chemical alterations induced by the UV exposure.

An investigation on the sediment composition and grain size was carried out along the Bormida river (Piedmont, Northern Italy). The samples were taken both in the riverbed and on its banks. Multivariate statistical exploratory methods permitted to identify possible sources of primary pollution. In particular, principal component analysis (PCA) showed that there are probably at least three anthropogenic main causes of pollution, one connected with Cr, Ni and Sn, the second with cadmium and the last one with mercury. Some correlations were found between heavy metal ion concentrations and organic matter and/or the sediment grain size. Differences between the samples collected in the riverbed and on the banks were identified and the relationships between the principal components and the distance of the samples from the riverbed and from the hot spot represented by the ACNA industrial site were also analyzed.

A Portland cement process was taken into consideration and monitored for one month with respect to polluting emissions, fuel and raw material physical-chemical properties, and operative conditions. Soft models, based on linear (partial least-squares, PLS, and principal component regression, PCR) and nonlinear (artificial neural networks, ANNs) approaches, were employed to predict the polluting emissions. The predictive ability of the three regression methods was evaluated by means of the partition of the dataset by Kohonen self-associative maps into both a training and a test set. Then, a "leave-more-out" approach, based on the use of a training set, a test set, and a production set, was adopted. The training set was used to build the models, the test set was used to select the number of latent variables or the neural network training endpoint, and the production set was used to produce genuine predictions. ANNs proved to be much more effective in prediction with respect to PLS and PCR and, at least in the case of SO 2 and dust, provided a predictive ability comparable with the experimental estimated uncertainty of the response. This showed that it is possible to satisfactorily predict the two responses. Such a prediction will result in the prevention of environmental and legal problems connected to the polluting emissions.

Optimization of the experimental settings of a laser probe analyzer used for monitoring the interlace level of yarns in the textile industry was performed to reduce large number of errors that concern the reading of the interlace level of internal reference materials. The aim was to obtain a repeatable and accurate reading of the interlace level for two classes of products: texturized and stretched yarns. The experimental design techniques allowed to build a regression model for each class, relating the instrument reading and the experimental parameters of the control analyzer. The use of the optimal settings suggested by the regression models ensured great improvement in the accuracy of the readings furnished by the probe. This improvement ranged from 8% to 10% in the case of the texturized threads and from 15% to 32% in the case of the stretched ones, bringing about a relevant improvement in the quality control process.

A great mean value of recovery for extraction of 3,4-dichloroaniline from a soil is calculated from individual recovery values evaluated for four different fractions of the soil. Then the uncertainty associated to this great mean recovery is calculated and used to know whether to apply or not the correction in routine analysis performed for the same kind of soil and the same analyte. The most representative fractions that, as a function of particle size, can be identified in a soil are: sand (2.000–0.063 mm), coarse silt (0.063–0.020 mm), fine silt (0.020–0.002 mm) and clay (≤0.002 mm). These fractions are here considered as sub matrices of the matrix soil. To evaluate the mean recovery and its uncertainty, as a function of the sub matrix and the analyte concentration, the four blank soil fractions were spiked with the analyte at three concentration levels (10.0, 50.0 and 100.0 mg/kg) and three replicates were performed for each experiment. The 36 samples were extracted by accelerated solvent system and the amounts of 3,4-dichloroaniline were determined by RP-HPLC analysis. From the 36 individual recovery values, the great mean and its uncertainty are calculated. Experiments performed on samples of soil of similar composition, spiked with known concentrations of the same analyte showed the goodness of the mean recovery value.

This paper concerns a first attempt of application of multivariate calibration to the field of cultural heritage for the determination of the superficial pigments composition of a painting. For this purpose, 10 mixtures of three organic pigments (Alizarin, Permanent Red and Phtalocyanine Green) selected by an augmented simplex-centroid design were mixed with linseed oil and spread on 10 cotton canvas strips. Each sample was characterised with three genuine replicates of an ATR-IR spectrum. Three calibration models, responding to the relative concentration of each specific pigment in the mixtures, were built with the partial least squares (PLS1) algorithm, using the ATR-IR spectra of the surfaces as predictors. The three mixtures corresponding to the augmentation of the simplex-centroid design were used to validate the model predictive ability which proved to be very satisfactory, with very low root mean squared error of prediction (RMSEP). Finally the models were successfully applied to a real painting, to predict the concentrations of some unknown mixtures of the three studied pigments. This work might have possible applications in the determination of the composition of dyes in real paintings to obtain information on the execution technique and for restoration purposes.

In this paper, Legendre moments are calculated to extract the global information from a set of two-dimensional polyacrylamide gel electrophoresis map images. The dataset contains 18 samples belonging to two different cell lines (PACA44 and T3M4) of control (untreated) and drug-treated pancreatic ductal carcinoma cells. The aim of this work was to obtain the correct classification of the 18 samples, using the Legendre moments as discriminant variables. For each image the Legendre moments up to a maximum order of 100 were computed. The stepwise linear discriminant analysis (LDA) was performed in order to select the moments with the highest discriminating power. The results demonstrate that the Legendre moments can be successfully applied for fast classification purposes and similarity analysis.

This work presents the archaeometric characterisation of a group of ancient pottery remains discovered during the restoring of the Novalesa Abbey (Susa Valley, Turin, Italy) performed in 2000. The characterisation focuses on the achievement of information about provenance and production process of the samples. Firstly, the data concerning the multi-element characterisation of the samples by inductively coupled plasma–mass spectrometry (ICP–MS) were analysed by chemometric tools (principal component analysis and cluster analysis) in order to obtain information about their similarity and clustering. These information, integrated with the results of micro-Raman spectroscopy analysis of the inclusions shed light on differences in the production process of the samples.

Two independent methods were developed to identify and determinate synthetic dyes and to follow their degradation processes. The IP‐RP‐HPLC method was optimised by the experimental design technique and the results obtained were compared with ones obtained by direct/derivative spectrophotometry. The dyes considered in this study that are representative of soft drink components, are: Tartrazine (E102), Quinoline Yellow (E104), Sunset Yellow (E110), Carmoisine (E122), Amaranth (E123), New Coccine (E124), Patent Blue Violet (E131), and Brillant Blue FCF (E133).

Thirteen newly synthesized or resynthesized diamine-platinum(II) complexes were characterized, and their cytotoxic activities (IC50) were tested on parental and resistant ovarian cancer cell lines. They represent models of conjugates between biologically active vectors and cytotoxic Pt(II) moieties within the "drug targeting and delivery strategy". Three drugs, routinely employed in the clinical treatment of cancer, namely, cisplatin, carboplatin, and oxaliplatin, were also included in the study as controls. The quantitative structure-activity relationship approach provides simple regression models able to predict log(1/IC50) of diamine-platinum(II) complexes on both parental and resistant ovarian cancer cell lines. The 16 complexes were characterized using 197 molecular descriptors, after which the best regression models relating a subset of these descriptors to the log(1/IC50) in the two cancer cell lines were calculated. Models with four variables proved to be endowed with very good predictive ability Q2(LMO-50%) > or = 85.6%, making it possible to discard 50% of the molecules from the test set following for cross-validation procedure. A four-variable regression model also proved effective in predicting the resistance index RI, Q2(LMO-50%) = 84.4%.

This paper is devoted to the evaluation of the degradation pathway of the E133 Brilliant Blue FCF (C.I. 42090) that is largely used in the food industry. The degradation is studied in oxidation conditions obtained by addition of potassium persulfate at different persulfate to dye molar ratios under natural sunlight irradiation. The degradation pathway of the dye passes through a species coloured in dark blue and then gives rise to uncoloured species. Due to the low volatility and the poor thermal stability of the dye, reversed-phase liquid chromatography associated to mass spectrometry and tandom mass spectrometry was employed to follow the kinetics of degradation and identify some intermediates. The identification of organic species still present in the decoloured dye and the value of COD obtained in these conditions show evidence that complete decolorization does not correspond to complete mineralisation. No direct information of toxicity is available for the uncoloured degradation products but the further formation of aromatic amines can not be excluded.

Two of the most suitable analytical techniques used in the field of cultural heritage are NIR (near-infrared) and Raman spectroscopy. FT-Raman spectroscopy coupled to multivariate control charts is applied here for the development of a new method for monitoring the conservation state of pigmented and wooden surfaces. These materials were exposed to different accelerated ageing processes in order to evaluate the effect of the applied treatments on the goods surfaces. In this work, a new approach based on the principles of statistical process control (SPC) to the monitoring of cultural heritage, has been developed: the conservation state of samples simulating works-of-art has been treated like an industrial process, monitored with multivariate control charts, owing to the complexity of the spectroscopic data collected. The Raman spectra were analysed by principal component analysis (PCA) and the relevant principal components (PCs) were used for constructing multivariate Shewhart and cumulative sum (CUSUM) control charts. These tools were successfully applied for the identification of the presence of relevant modifications occurring on the surfaces. CUSUM charts however proved to be more effective in the identification of the exact beginning of the applied treatment. In the case of wooden boards, where a sufficient number of PCs were available, simultaneous scores monitoring and residuals tracking (SMART) charts were also investigated. The exposure to a basic attack and to high temperatures produced deep changes on the wooden samples, clearly identified by the multivariate Shewhart, CUSUM and SMART charts. A change on the pigment surface was detected after exposure to an acidic solution and to the UV light, while no effect was identified on the painted surface after the exposure to natural atmospheric events.