A Novel Bioinformatic Approach to Problems in Neurological Disorders: A Kind of MAGIC

Epilepsy is the third most prevalent neurological disorder after stroke and Alzheimer’s Disease with an incidence of 1 in 26 individuals. It is estimated that 65 million worldwide currently live with epilepsy. Epileptogenesis refers to the progressive decrease in seizure threshold that results in unprovoked, spontaneous seizures that increase in frequency, severity and duration. Though there are a number of anti-convulsant drugs available, there are no anti-epileptogenic drugs that mitigate disease progression. Epileptogenesis is associated with a plethora of changes in the brain including alterations in plasticity, cell death, neurogenesis, inflammation and axonal sprouting. These changes can occur from minutes to months, but the orchestrating mechanisms underlying their manifestation are completely unknown. Long-term changes in gene expression that are associated with epileptogenesis imply that one or more master regulators of transcription may be coordinating these brain alterations.

In order to uncover these genetic mechanisms, we turned to our recently published genome-wide expression dataset generated by the Epilepsy Microarray Consortium. The datasets consist of mRNA expression profiles of mouse dentate granule cells assayed at various time points after Status Epilepticus (SE) across multiple pre-clinical epilepsy models, laboratories and at various time-points. This provides an opportunity to discern model-independent and lab-independent alterations in gene networks. We used a novel bioinformatics tool (MAGIC) developed by my lab to reveal the transcription factors and cofactors that drive large scale gene changes observed in the Epilepsy Consortium dataset. We find that a handful of nuclear proteins drive the majority of gene changes after SE with the principle driver being EZH2, a core enzymatic subunit of the Polycomb complex. Our work shows that Polycomb induction after SE is a protective response. Thus, pharmacological inhibition of Polycomb results in accelerated disease progression and reduced time to spontaneous seizures post SE. In summary, using Big Data approaches we have uncovered a major protective mechanism that is launched by the brain after seizures. I will present MAGIC as a tool to mine transcriptomes for transcription factors and cofactors that drive programmatic gene changes in health and disease and its utility in defining drivers of long term gene changes in epilepsy.

Bio: Avtar Roopra is an Associate Professor in Neuroscience at the University of Wisconsin at Madison. His lab studies the role of chromatin biology in breast cancer, autism and epilepsy. He focusses on the coordinating mechanisms behind large scale gene changes that occur in disease. To that end, the lab has developed bioinformatics tools to condense the problem of studying thousands of gene changes in disease to a few nuclear proteins that orchestrate their regulation. In sum, the lab studies the impact of signal transduction, metabolism and environment on gene networks using Big Data approaches in cancer and neurological disorders.