Human colorectal cancer: upregulation of the adaptor protein Rai in TILs leads to cell dysfunction by sustaining GSK-3 activation and PD-1 expression

Background: The immunosuppressive tumor microenvironment (TME) of colorectal cancer (CRC) is a major hurdle for immune checkpoint inhibitor-based therapies. Hence characterization of the signaling pathways driving T cell exhaustion within TME is a critical need for the discovery of novel therapeutic targets and the development of effective therapies. We previously showed that (i) the adaptor protein Rai is a negative regulator of T cell receptor signaling and T helper 1 (Th1)/Th17 cell differentiation; and (ii) Rai deficiency is implicated in the hyperactive phenotype of T cells in autoimmune diseases. Methods: The expression level of Rai was measured by qRT-PCR in paired peripheral blood T cells and T cells infiltrating tumor tissue and the normal adjacent tissue in CRC patients. The impact of hypoxia-inducible factor (HIF)-1α on Rai expression was evaluated in T cells exposed to hypoxia and by performing chromatin immunoprecipitation assays and RNA interference assays. The mechanism by which upregulation of Rai in T cells promotes T cell exhaustion were evaluated by flow cytometric, qRT-PCR and western blot analyses. Results: We show that Rai is a novel HIF-1α-responsive gene that is upregulated in tumor infiltrating lymphocytes of CRC patients compared to patient-matched circulating T cells. Rai upregulation in T cells promoted Programmed cell Death protein (PD)-1 expression and impaired antigen-dependent degranulation of CD8+ T cells by inhibiting phospho-inactivation of glycogen synthase kinase (GSK)-3, a central regulator of PD-1 expression and T cell-mediated anti-tumor immunity. Conclusions: Our data identify Rai as a hitherto unknown regulator of the TME-induced exhausted phenotype of human T cells

Prevalence study of mental disorders in an Italian region. Preliminary report

Background Mental disorders are a major public health problem. However, over the last few years, there have been few studies aimed at evaluating their diffusion. Therefore, this study aimed at evaluating: the prevalence of the most frequent psychiatric disorders in the general population residing in Tuscany using a clinical scale administered by trainee in psychiatry. Methods The study was carried out on a representative sample of the general population aged > 18 years, randomly extracted from the register of patients in the Tuscany region, adopting a proportional sampling method stratified by gender, age group and Local Health Units (LHU). Each person was contacted by letter followed by a phone call from an operator who makes an appointment with the trainee in psychiatry. The diagnostic interview conducted was the Mini-International Neuropsychiatric Interview (MINI). Point and lifetime prevalence by gender and age group were calculated. Differences and associations were considered statistically significant if their p-values were less than 0.05. Results Of the 408 people involved, 390 people were enrolled (of which 52.6% female). The 28.5% of the sample had been affected by a psychiatric disorder during their lifetime. In their lifetime, the most represented psychiatric disorders were major depressive episode (20.4%), major depressive disorder (17.0%) and panic disorder (10.3%), more frequent in the female than the male group. Current conditions were predominantly major depressive episode (3.1%) and agoraphobia (2.8%). A 5.9% rate of current suicidal ideation was also found. Conclusions In the general population, 28.5% of people reported a psychiatric disorder during their lifetime. This prevalence is considerably higher than that reported in a previous study carried out in central Italy

The small GTPase Rab29 is a common regulator of immune synapse assembly and ciliogenesis

Acknowledgements We wish to thank Jorge Galán, Gregory Pazour, Derek Toomre, Giuliano Callaini, Joel Rosenbaum, Alessandra Boletta and Francesco Blasi for generously providing reagents and for productive discussions, and Sonia Grassini for technical assistance. The work was carried out with the financial support of Telethon (GGP11021) and AIRC.Peer reviewedPostprin

Regulation of Selective B Cell Autophagy by the Pro-oxidant Adaptor p66SHC

p66SHC is a pro-oxidant member of the SHC family of protein adaptors that acts as a negative regulator of cell survival. In lymphocytes p66SHC exploits both its adaptor and its reactive oxygen species (ROS)-elevating function to antagonize mitogenic and survival signaling and promote apoptosis. As a result, p66SHC deficiency leads to the abnormal expansion of peripheral T and B cells and lupus-like autoimmunity. Additionally, a defect in p66SHC expression is a hallmark of B cell chronic lymphocytic leukemia, where it contributes to the accumulation of long-lived neoplastic cells. We have recently provided evidence that p66SHC exerts a further layer of control on B cell homeostasis by acting as a new mitochondrial LC3-II receptor to promote the autophagic demise of dysfunctional mitochondria. Here we discuss this finding in the context of the autophagic control of B cell homeostasis, development, and differentiation in health and disease

An incremental least squares algorithm for large scale linear classification

In this work we consider the problem of training a linear classifier by assuming that the number of data is huge (in particular, data may be larger than the memory capacity). We propose to adopt a linear least-squares formulation of the problem and an incremental recursive algorithm which requires to store a square matrix (whose dimension is equal to the number of features of the data). The algorithm (very simple to implement) converges to the solution using each training data once, so that it effectively handles possible memory issues and is a viable method for linear large scale classification and for real time applications, provided that the number of features of the data is not too large (say of the order of thousands). The extensive computational experiments show that the proposed algorithm is at least competitive with the state-of-the-art algorithms for large scale linear classification. © 2012 Elsevier B.V. All rights reserved