In a recent publication in Nucleic Acids Research, researchers from the Systems Biology group at the Human Protein Atlas investigated anomalies in regulation of lipid metabolism in the liver, in association with hepatocellular carcinoma.

Hepatocellular carcinoma has a high mortality rate and early detection of the disease is crucial for the application of effective treatment strategies. Several lines of evidence imply that lipid anomalies underlie the hepatocellular carcinoma pathogenesis.

Here, the researchers applied a tailored network-based approach to identify signaling hubs associated with regulation of this part of the metabolism.

First author of the study, Sunjae Lee is a post doc in the Systems Biology group at Science for Life Laboratory in Stockholm.

– I did my PhD in the Korea Advanced Institute of Science and Technology and there I started to study biological components using computational models.

Already during his PhD studies Sunjae Lee worked with proteins involved in the regulation of homeostasis of different metabolites – metabolic sensors and metabolic enzymes.

Metabolic sensors recognize the metabolic information during the regulation of homeostasis by detecting the levels of intracellular metabolites. Metabolic enzymes catalyze the reactions of metabolites, altering their intracellular levels.

– In a previous study I proposed a network-based approach that identifies prognostic markers among proteins that play a critical role possibly linking sensors and enzymes, relating to known biological hypothesis. These proteins, referred to as bridge proteins, can be assessed systematically based on information about molecular interactions recorded in several databases. To this end, I have defined a ”bridgeness” metric, representing the degrees of connection between sensors and enzymes, Sunjae Lee explains.

During his PhD he followed the work of Jens Nielsen, and decided to join his group in Gothenburg for a post doc. When Adil Mardinoglu was recruited to Science for Life Laboratory Sunjae Lee joined him, and is now combining his method on bridgeness with Adil Mardinoglu´s deep knowledge on metabolic networks to analyze omics data obtained from subjects with complex diseases to find effective treatment strategies.

In the present study, Sunjae Lee and his co-workers reveal that not only lipid enzymes, but also signaling hub genes show dysregulated expression, and even synergistically leading to lipid anomalies in hepatocellular carcinoma. They further found that there are distinct hub genes and enzymes associated with HCC pathogenesis caused by viral hepatitis. Lastly, the network-based approach uncovered catabolic and anabolic signaling hub genes that could serve as novel drug targets for treatment of hepatocellular carcinoma, by alleviating lipid anomalies during its pathogenesis.

– This is such an interesting field of research! With new methods, so much data is accumulating and I believe that these kinds of systems to analyze them will become more and more common, Sunjae Lee concludes.

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