The research was a collaboration between two teams; one at Cosbi (Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology), which is specialized in IT and big data, and one at Cibio Department, which is focused on biology and genomics.
The research group at The Microsoft Research - University of Trento Centre for Computational and Systems Biology. Silvia Parolo, Pranami Bora, Lorena Leonardelli, Enrico Domenici. ©AlessioCoser.
Metabolic Syndrome is a common pathological condition defined by increased risk of cardiovascular diseases and type 2 diabetes. Risk factors for the condition include obesity, hyperglycemia, hypertension, elevated levels of triglycerides and low levels of high-density lipoprotein (HDL) cholesterol.
While lifestyle changes are highly effective in the early phase of Metabolic Syndrome, pharmacological treatments are frequently required in more advanced stages. Currently, pharmacological treatments address only single components of the multifactorial disease. There is an urgent need to study the underlying biological pathways of this condition in order to develop more effective treatment strategies.
The analytical workflow of the network-based approach includes three interconnected parts. Frist, trait-associated genes were established from genome-wide association studies (GWAS) and literature on Metabolic Syndrome. Second, the genes were mapped to biological networks to develop tissue-specific modules. When pathway enrichment analysis was applied to modules, the tissue-specific disease enriched modules were determined specifically for Metabolic Syndrome. Lastly, the impact of existing drugs on Metabolic Syndrome was determined by mapping drug targets and genes on the network to build tissue-specific drug modules. To investigate the relationship between the drug modules and the disease-specific modules, a proximity score was determined that combines network-based distance with semantic similarity. High scores indicate high similarity among modules.
"To carry out such a complex study we adopted a new computational approach that measures the 'distance' between the proteins targeted by the drug and those found in the networks affected by the disease," said Enrico Domenici, president of Cosbi.
Drug repositioning is the process by which new therapeutic applications are identified for already approved drugs. This field has grown out of increased availability of high-throughput data which allows researchers to establish new computational approaches to systematically investigate drugs.
The results obtained from the analysis of the adipose network show the key role of inflammation in obesity and suggest the anti-inflammatory drugs may be used in the treatment of obesity. Moreover, the network-based drug module analysis identified ibrutinib as the most promising candidate for drug repurposing. This was determined from the analysis of genetic and literature data to identify immune-related pathways as significantly enriched in disease genes. Ibrutinib is a small molecule that inhibits BTK, a protein well known for its essential role in B cell development and maturation with a role in chronic inflammation and macrophage immune response.
In vivo validation
To validate the potential benefits of ibrutinib for the treatment of Metabolic Syndrome, the researchers used high-fat diet zebrafish models. The in vivo zebrafish experiments support the potential of ibrutinib in reducing obesity-related inflammation. They found that ibrutinib administration reduced the number of macrophages in the high-fat diet-induced inflammation zebrafish model. This was also accompanied by reduced expression of molecular markers of lipid metabolism and inflammation, suggesting that ibrutinib may have long-term effects on lipid accumulation and associated inflammation.
"Testing the effectiveness of a drug that is already on the market makes it possible to skip a number of long and complex stages of the approval process for new medical products, because their tolerance and safety have already been demonstrated. Our data are just a start and more in-depth studies and clinical tests are required, but they demonstrate that combining experience in big data analysis and the ability to develop models for biological validation can boost drug repurposing research," stated Maria Caterina Mione, head of research at Cibio.
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