95 research outputs found
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 Therefore daclizumab has been withdrawn from the market worldwide.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
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
- …