A step forwards in immunomodulation and immunetolerance knowledge. HLA-G expression in co-cultures of peripheral blood mononuclear cells and stem cells after in vitro NGAL stimulation

NGAL (Neutrophil Gelatinase-associated Lipocalin ) is a protein of lipocalin superfamily. Recent literature focused on its biomarkers function in several pathological condition (acute and chronic kidney damage, autoimmune disease, malignancy). NGAL biological role is not well elucidated. Several are the demonstration of its bacteriostatic role. Recent papers have indeed highlight NGAL role in NFkB modulation. The aim of this study is to understand whether NGAL may exert a role in the activation (modulation) of T cell response through the regulation of HLA-G complex, a mediator of tolerance. From 8 healthy donors we obtained peripheral blood mononuclear cells (PBMCs) and we isolated by centrifugation on a Ficoll gradient. Cells were then treated with four concentrations of NGAL (40-320 ng/ml) with or without iron. We performed flow cytometry analysis and ELISA test. NGAL increased the HLA-G expression on CD4+ T cells, with an increasing corresponding to the dose. Iron effect is not of unique interpretation. NGAL adiction affects regulatory T cells increasing in vitro expansion of CD4+ CD25+ FoxP3+ cells. Neutralizing antibody against NGAL decreased HLA-G expression and reduced significantly CD4+ CD25+ FoxP3+ cells percentage. In conclusion, we provided in vitro evidence of NGAL involvement in cellular immunity. The potential role of NGAL as an immunomodulatory molecule has been evaluated: it has been shown that NGAL plays a pivotal role in the induction of immune tolerance up regulating HLA-G and T regulatory cells expression in healthy donors. As potential future scenario we highlight the in vivo role of NGAL in immunology and immunomodulation, and its possible relationship with immunosuppressive therapy efficacy, tolerance induction in transplant patients, and/or in other immunological disorders.

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.