Identification of Gene Targets to Prove Useful in Treating Fatty Liver Disease

Published By : 22 Aug 2017 | Published By : QYRESEARCH

Effective treatment strategies for fatty liver disease and its likely vociferous descendent liver cancer have eluded researchers for long. A silver lining seems to surface recently when researchers at Sweden identified several drug targets that are will prove useful in the development of an effective treatment strategy in managing various chronic liver diseases. A team of scientist at KTH Royal Institute of Technology in Stockholm, Sweden have identified a number of liver-specific gene targets and inferred that inhibiting these won’t harm any noticeable side effects to other human tissues.

The results of the study was published in Molecular Systems Biology, a Journal by EMBO Press.

Inhibiting Liver Genes Decreases Fat Content Causing Common Chronic Liver Problems

Non-alcoholic fatty liver disease (NAFLD), along with its type non-alcoholic steatohepatitis (NASH), is considered as one of the most prevalent chronic liver ailments among adults and adolescents world over. Caused by factors such as obesity, diabetes, or excessive alcohol consumption, the condition may further lead to cirrhosis and liver cancer. However, treatments are few and mostly include measures that mitigate risk exposures.

According to the lead researcher, the team followed a network modeling approach on the data gathered by Human Protein Atlas and The Genotype-Tissue Expression project consortia. This vast network offered clinical data from as many as 46 major human tissues. The team then conducted numerous experiments with mouse liver samples, primary human hepatocytes, and human cancer cell lines to establish crucial relationship among these genes and validate the approach. They concluded that their inhibition results in reducing fat content in liver cells.

The genes identified were primarily linked to NAFLD pathogenesis or to HCC pathogenesis, which helped them develop various potential drug targets. These will be used for finding therapies with minimal side-effects. 

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