Tobias Wolff, bioinformatician at Saarland University, explains his work status analyzing DNA methylation and gene expression in HIV and Long Covid patients, the challenges of managing large datasets, and the future of bioinformatics in viral disease research.
1. EPIVINF studies how viral infections like HIV and SARS-CoV-2 leave “epigenetic footprints” in our immune system. Why is it important to look beyond genes and into these deeper regulatory layers?
While genetic sequences tell us what could happen in a cell, epigenetic layers like DNA methylation, histone modifications, and chromatin accessibility tell us how genes are being regulated in response to environmental cues, including viral infections. Viruses like HIV or SARS-CoV-2 don’t just trigger temporary gene expression, they can leave lasting marks on the immune system that persist even after the infection clears. By looking at these epigenetic footprints, we can understand mechanisms of immune memory, dysregulation, or exhaustion that wouldn’t be apparent from gene expression alone and potentially identify novel targets for therapeutic intervention.
2. The latest EPIVINF progress indicates that certain proteins named EZH2 and SUZ12, which are key epigenetic regulators, are affected during HIV infection. Why is this important for understanding how the disease progresses?
EZH2 and SUZ12 are core components of the Polycomb Repressive Complex 2, which regulates gene silencing through histone methylation. Changes in their activity during HIV infection suggest that the virus can reprogram the chromatin landscape of immune cells, potentially silencing genes involved in antiviral responses or immune regulation. Understanding these changes helps us explain why certain immune cells become dysfunctional or exhausted over time, which is critical for understanding HIV pathogenesis and could inform strategies to restore immune function.
3. How could EPIVINF’s discoveries help clinicians better monitor or predict outcomes for HIV or Long-COVID patients in the future?
By identifying specific epigenetic signatures associated with disease progression or lingering symptoms, we could potentially develop biomarkers that predict which patients are at higher risk of severe outcomes or long-term complications. For example, monitoring changes in regulators like EZH2 or SUZ12, or specific DNA methylation patterns in immune cells, could help clinicians identify patients who might benefit from early interventions. Ultimately, our work aims to move beyond static gene expression profiles and provide a more predictive and mechanistic framework for patient monitoring.
