Psoriasis is a chronic inflammatory disease that affects skin and is associated with systemic inflammation and many serious comorbidities ranging from metabolic syndrome to cancer. Important discoveries about psoriasis pathogenesis have enabled the development of effective biological treatments blocking the T helper 17 pathway. However, it has not been settled whether psoriasis is a T cell-mediated autoimmune disease or an autoinflammatory disorder that is driven by exaggerated innate immune signalling. Our comparative gene expression and hierarchical cluster analysis reveal important gene circuits involving innate receptors. Innate immune activation is indicated by increased absent in melanoma 2 (AIM2) inflammasome gene expression and active caspase 1 staining in psoriatic lesional skin. Increased eomesodermin (EOMES) expression in lesional and non-lesional skin is suggestive of innate-like virtual memory CD8+ T cell infiltration. We found that signs of systemic inflammation were present in most of the patients, correlated with the severity of the disease, and pointed to IL-6 involvement in the pathogenesis of psoriatic arthritis. Among the circulating T cell subpopulations, we identified a higher proportion of terminally differentiated or senescent CD8+ T cells, especially in patients with long disease duration, suggesting premature immunosenescence and its possible implications for psoriasis co-morbidities.
Alzheimer’s disease and other types of dementia are the top cause for disabilities in later life and various types of experiments have been performed to understand the underlying mechanisms of the disease with the aim of coming up with potential drug targets. These experiments have been carried out by scientists working in different domains such as proteomics, molecular biology, clinical diagnostics and genomics. The results of such experiments are stored in the databases designed for collecting data of similar types. However, in order to get a systematic view of the disease from these independent but complementary data sets, it is necessary to combine them. In this study we describe a heterogeneous network-based data set for Alzheimer’s disease (HENA). Additionally, we demonstrate the application of state-of-the-art graph convolutional networks, i.e. deep learning methods for the analysis of such large heterogeneous biological data sets. We expect HENA to allow scientists to explore and analyze their own results in the broader context of Alzheimer’s disease research.
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