Pyrolysis-Gas Chromatography-Mass Spectometry and Chemometric Analysis for the Characterization of Complex Matrices

The present PhD thesis was focused on the development and application of chemical methodology (Py-GC-MS) and data-processing method by multivariate data analysis (chemometrics). The chromatographic and mass spectrometric data obtained with this technique are particularly suitable to be interpreted by chemometric methods such as PCA (Principal Component Analysis) as regards data exploration and SIMCA (Soft Independent Models of Class Analogy) for the classification. As a first approach, some issues related to the field of cultural heritage were discussed with a particular attention to the differentiation of binders used in pictorial field. A marker of egg tempera the phosphoric acid esterified, a pyrolysis product of lecithin, was determined using HMDS (hexamethyldisilazane) rather than the TMAH (tetramethylammonium hydroxide) as a derivatizing reagent. The validity of analytical pyrolysis as tool to characterize and classify different types of bacteria was verified. The FAMEs chromatographic profiles represent an important tool for the bacterial identification. Because of the complexity of the chromatograms, it was possible to characterize the bacteria only according to their genus, while the differentiation at the species level has been achieved by means of chemometric analysis. To perform this study, normalized areas peaks relevant to fatty acids were taken into account. Chemometric methods were applied to experimental datasets. The obtained results demonstrate the effectiveness of analytical pyrolysis and chemometric analysis for the rapid characterization of bacterial species. Application to a samples of bacterial (Pseudomonas Mendocina), fungal (Pleorotus ostreatus) and mixed- biofilms was also performed. A comparison with the chromatographic profiles established the possibility to: • Differentiate the bacterial and fungal biofilms according to the (FAMEs) profile. • Characterize the fungal biofilm by means the typical pattern of pyrolytic fragments derived from saccharides present in the cell wall. • Individuate the markers of bacterial and fungal biofilm in the same mixed-biofilm sample.

SMO inhibitor specifically targets the Hedgehog Pathway and reverts the drug-resistance of Leukemic Stem Cells

Abnormal Hedgehog signaling is associated with human malignancies. Smo, a key player of that signaling, is the most suitable target to inhibit this pathway. To this aim several molecules, antagonists of Smo, have been synthesized, and some of them have started the phase I in clinical trials. Our hospital participated to one of these studies which investigated the oral administration of a new selective inhibitor of Smo (SMOi). To evaluate ex vivo SMOi efficacy and to identify new potential clinical biomarkers of responsiveness, we separated bone marrow CD34+ cells from 5 acute myeloid leukemia (AML), 1 myelofibrosis (MF), 2 blastic phases chronic myeloid leukemia (CML) patients treated with SMOi by immunomagnetic separation, and we analysed their gene expression profile using Affimetrix HG-U133 Plus 2.0 platform. This analysis, showed differential expression after 28 days start of therapy (p-value ≤ 0.05) of 1,197 genes in CML patients and 589 genes in AML patients. This differential expression is related to Hedgehog pathway with a p-value = 0.003 in CML patients and with a p-value = 0.0002 in AML patients, suggesting that SMOi targets specifically this pathway. Among the genes differentially expressed we observed strong up-regulation of Gas1 and Kif27 genes, which may work as biomarkers of responsiveness of SMOi treatment in CML CD34+ cells whereas Hedgehog target genes (such as Smo, Gli1, Gli2, Gli3), Bcl2 and Abca2 were down-regulated, in both AML and CML CD34+ cells. It has been reported that Bcl-2 expression could be correlated with cancer therapy resistance and that Hedgehog signaling modulate ATP-binding (ABC) cassette transporters, whose expression has been correlated with chemoresistance. Moreover we confirmed that in vitro SMOi treatment targets Hedgehog pathway, down-regulate ABC transporters, Abcg2 and Abcb1 genes, and in combination with tyrosine kinase inhibitors (TKIs) could revert the chemoresistance mechanism in K562 TKIs-resistant cell line.

Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.