Tolerogenic IL-10-engineered dendritic cell-based therapy to restore antigen-specific tolerance in T cell mediated diseases.

Tolerogenic dendritic cells play a critical role in promoting antigen-specific tolerance via dampening of T cell responses, induction of pathogenic T cell exhaustion and antigen-specific regulatory T cells. Here we efficiently generate tolerogenic dendritic cells by genetic engineering of monocytes with lentiviral vectors co-encoding for immunodominant antigen-derived peptides and IL-10. These transduced dendritic cells (designated DCIL-10/Ag) secrete IL-10 and efficiently downregulate antigen-specific CD4+ and CD8+ T cell responses from healthy subjects and celiac disease patients in vitro. In addition, DCIL-10/Ag induce antigen-specific CD49b+LAG-3+ T cells, which display the T regulatory type 1 (Tr1) cell gene signature. Administration of DCIL-10/Ag resulted in the induction of antigen-specific Tr1 cells in chimeric transplanted mice and the prevention of type 1 diabetes in pre-clinical disease models. Subsequent transfer of these antigen-specific T cells completely prevented type 1 diabetes development. Collectively these data indicate that DCIL-10/Ag represent a platform to induce stable antigen-specific tolerance to control T-cell mediated diseases.

AKUImg: A database of cartilage images of Alkaptonuria patients.

ApreciseKUre is a multi-purpose digital platform facilitating data collection, integration and analysis for patients affected by Alkaptonuria (AKU), an ultra-rare autosomal recessive genetic disease. We present an ApreciseKUre plugin, called AKUImg, dedicated to the storage and analysis of AKU histopathological slides, in order to create a Precision Medicine Ecosystem (PME), where images can be shared among registered researchers and clinicians to extend the AKU knowledge network. AKUImg includes a new set of AKU images taken from cartilage tissues acquired by means of a microscopic technique. The repository, in accordance to ethical policies, is publicly available after a registration request, to give to scientists the opportunity to study, investigate and compare such precious resources. AKUImg is also integrated with a preliminary but accurate predictive system able to discriminate the presence/absence of AKU by comparing histopatological affected/control images. The algorithm is based on a standard image processing approach, namely histogram comparison, resulting to be particularly effective in performing image classification, and constitutes a useful guide for non-AKU researchers and clinicians.