How metabolism affects gene activity via the ‘Histone code’

Scott Campit, Sriram Chandrasekaran

Each cell in our body makes tiny machines called proteins that carry out essential tasks. The instructions to make these essential molecules are stored in DNA. While the genetic code itself is vital for life, proteins work together with DNA to maintain health. One example is histones, the proteins that wrap around DNA. By tightly wrapping around a region of DNA, histone proteins control the activity of genes encoded in the DNA segment. Attaching or removing specific metabolites onto histones acts like a switch, turning genes ON or OFF. The collection of different chemical modifications on histones is called the ‘histone code’. Several diseases including Alzheimer's disease, hypertension, and cancer are predicted to occur due to alterations in the composition of the histone code.

To discover new therapies for these diseases, our lab is developing computational models of how genes, histones, and metabolites interact together in a cell. In this study, we focused on how nutrition and metabolism of a cell affects the levels of a key histone modification called acetylation. This modification results in turning ON of genes in the DNA associated with the modified histone. Using our computer model, we simulated how starving or feeding cells with glucose and other nutrients impacts histone acetylation. Our model demonstrated that the excess levels of a key metabolite -  acetyl-coA is predictive of increase in histone acetylation levels. This observation can explain how in metabolic disorders or in tumors the change in metabolism can affect gene activity.

Next, we wanted to see if we could use these computer models to identify new cancer therapies. Several drugs that are used to treat cancer kill tumors by changing histone acetylation levels. Yet, it is difficult to determine how effective these drugs for different cancer types. Our computational model accurately predicted cancer cell types that are sensitive to these drugs based on their metabolic activity. This approach will ultimately allow researchers and clinicians to identify new cancer drugs that inhibit specific types of tumors using computer models.

Reference: Shen, F., Boccuto, L., Pauly, R., Srikanth, S. and Chandrasekaran, S., 2019. Genome-scale network model of metabolism and histone acetylation reveals metabolic dependencies of histone deacetylase inhibitors. Genome biology20(1), p.49.

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