A computational approach based on the colored Petri net formalism for studying multiple sclerosis

Multiple Sclerosis (MS) is an immune-mediated inflammatory disease of the Central Nervous System (CNS) which damages the myelin sheath enveloping nerve cells thus causing severe physical disability in patients. Relapsing Remitting Multiple Sclerosis (RRMS) is one of the most common form of MS in adults and is characterized by a series of neurologic symptoms, followed by periods of remission. Recently, many treatments were proposed and studied to contrast the RRMS progression. Among these drugs, daclizumab (commercial name Zinbryta), an antibody tailored against the Interleukin-2 receptor of T cells, exhibited promising results, but its efficacy was accompanied by an increased frequency of serious adverse events. Manifested side effects consisted of infections, encephalitis, and liver damages. Therefore daclizumab has been withdrawn from the market worldwide. Another interesting case of RRMS regards its progression in pregnant women where a smaller incidence of relapses until the delivery has been observed

Plasma cytokines, glomerular filtration rate and adipose tissue cytokines gene expression in chronic kidney disease (CKD) patients

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Multiple sclerosis disease: A computational approach for investigating its drug interactions

Multiple Sclerosis (MS) is a chronic and potentially highly disabling disease that can cause permanent damage and deterioration of the central nervous system. In Europe it is the leading cause of non-traumatic disabilities in young adults, since more than 700,000 EU people suffer from MS. Although recent studies on MS pathophysiology have been performed, providing interesting results, MS remains a challenging disease. In this context, thanks to recent advances in software and hardware technologies, computational models and computer simulations are becoming appealing research tools to support scientists in the study of such disease. Motivated by this consideration, we propose in this paper a new model to study the evolution of MS in silico, and the effects of the administration of the daclizumab drug, taking into account also spatiality and temporality of the involved phenomena. Moreover, we show how the intrinsic symmetries of the model we have developed can be exploited to drastically reduce the complexity of its analysis

Genome-wide activity of unliganded estrogen receptor-\u3b1\ua0 in breast cancer cells

Estrogen receptor-\u3b1 (ER\u3b1) has central role in hormone-dependent breast cancer and its ligand-induced functions have been extensively characterized. However, evidence exists that ER\u3b1 has functions that are independent of ligands. In the present work, we investigated the binding of ER\u3b1 to chromatin in the absence of ligands and its functions on gene regulation. We demonstrated that in MCF7 breast cancer cells unliganded ER\u3b1 binds to more than 4,000 chromatin sites. Unexpectedly, although almost entirely comprised in the larger group of estrogen-induced binding sites, we found that unliganded-ER\u3b1 binding is specifically linked to genes with developmental functions, compared with estrogen-induced binding. Moreover, we found that siRNA-mediated down-regulation of ER\u3b1 in absence of estrogen is accompanied by changes in the expression levels of hundreds of coding and noncoding RNAs. Down-regulatedmRNAs showed enrichment in genes related to epithelial cell growth and development. Stable ER\u3b1 down-regulation using shRNA, which caused cell growth arrest, was accompanied by increased H3K27me3 at ER\u3b1 binding sites. Finally, we found that FOXA1 and AP2\u3b3 binding to several sites is decreased upon ER\u3b1 silencing, suggesting that unliganded ER\u3b1 participates, together with other factors, in the maintenance of the luminal-specific cistrome in breast cancer cell