Krishnaswamy kasturirangan, Chancellor of Jawaharlal Nehru University, Delhi

Washington DC [US], September 2 : Researchers used a network-based approach to reclassify around 200 uncommon disorders. Initial comparisons with clinical data show how this can improve therapy efficacy prediction.Furthermore, the study demonstrates for the first time the striking similarities in molecular pathways between uncommon diseases and autoimmune and autoinflammatory ailments such as chronic inflammatory bowel disease, multiple sclerosis, and some types of diabetes. The findings have been reported in Science Advances.Network-based approaches frequently reveal what was previously hidden; this is also true in medical research. CeMM Adjunct Principal Investigators Kaan Boztug, Director of St. Anna Children's Cancer Research Institute, and Jörg Menche, a professor at the University of Vienna and Max Perutz Labs, have been using network-based methods for several years to gain better systemic and molecular understanding of rare diseases, congenital immune disorders, and congenital inflammatory disorders.The researchers successfully identified new molecular and mechanistic similarities between rare immune system disorders in their latest study, led by the study's first author Julia Guthrie, by examining the high degree of interconnectedness of molecular interactions, leading to their reclassification.The researchers established that individuals with disorders within the same classification group responded to the same drugs by comparing their findings to clinical data.The researchers analysed roughly 200 uncommon immunological diseases with inflammatory characteristics for their study. The network-based analysis of protein-protein interactions found parallels in the biological pathways underlying various illnesses. The disorders were reclassified as a result of these assessments, and the researchers predicted which medicines would produce the best results for each category.“Compared to existing clinical data, the new disease classification allows for a much better prediction of promising therapies compared to the previous approach. Network biology allows us to gain deeper insights into the intricate interplay between the immune system and diseases.This, in turn, enables us to develop more targeted and personalized approaches for diagnosis and treatment” explained co-study leader Kaan Boztug.The results also indicate that numerous autoimmune and autoinflammatory diseases such as chronic inflammatory disorders, multiple sclerosis, systemic lupus erythematosus, and type 1 diabetes are closely linked. The study’s first author Julia Guthrie explained, “We were able to identify a group of key genes and their interaction partners that are central to homeostasis. We refer to this network of key genes as ‘AutoCore’. In autoimmune and autoinflammatory diseases, the AutoCore resides right at the centre of the associated genes. Additionally, we identified 19 other subgroups that are intended to provide us with better insights into homeostasis and immune system deregulation.”While conventional approaches often categorize immune system disorders according to specific body regions and thus view them in isolation, a systemic approach aims to offer a more detailed picture of underlying mechanisms. Co-study leader Jörg Menche explained, “We increasingly recognized the conceptual and practical limitations of the traditional paradigm of ‘one gene, one disease’ in the research of rare diseases.""This hinders our understanding of the complex molecular network through which the components of the immune system are orchestrated. Therefore, we developed a visualization in the form of a multidimensional network that depicts all currently known monogenic immune defects underlying autoimmunity and autoinflammation, as well as their molecular interactions. As a result, we can see how closely genes are interconnected in rare diseases.”Disclaimer: This post has been auto-published from an agency feed without any modifications to the text and has not been reviewed by an editor

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