For decades, herpesviruses have played a subtle, often frustrating game with the human immune system – establishing initial infections, then retreating into a dormant state, only to reactivate unexpectedly. While the genes involved in these viral lifecycles have been known, understanding when and how these viruses switch between states has remained a significant challenge. Now, a new study published in in the journal Nature Communications suggests that artificial intelligence may be providing a crucial key to unlocking these viral secrets.
Researchers at Bar-Ilan University in Israel have developed an AI tool, dubbed ENHAvir, capable of identifying the “switches” – more formally known as enhancers – that control gene activity within herpesviral DNA. These enhancers don’t directly produce proteins, but instead regulate when and how strongly other genes are turned on or off, essentially dictating the virus’s behavior.
The difficulty in mapping these enhancers traditionally stems from the compact and multi-functional nature of viral DNA, coupled with the need for slow, complex laboratory work, often limited to a single cell type at a time. The Bar-Ilan team, led by Professor Meir Shamay, took a novel approach, treating DNA sequences as a form of language. Just as human language follows grammatical rules, DNA exhibits discernible patterns. The researchers trained ENHAvir on a remarkably small dataset – just six known enhancer sequences from a single herpesvirus.
“Usually, AI tools are introduced to millions of genetic sequences so they can train,” explains Professor Shamay. “We, introduced it to only six sequences.”
The crucial question then became: could this AI, trained on such limited data, recognize the underlying “grammar” of enhancers across different herpesviruses? The answer, according to the study, is a resounding yes. ENHAvir successfully identified novel enhancers in a range of herpesviruses, including Epstein-Barr virus (EBV), Cytomegalovirus (CMV), and both HSV-1 (oral herpes) and HSV-2 (genital herpes).
Herpesviruses are remarkably common. The vast majority – 90 to 95 percent
– of adults will be infected with at least one herpesvirus during their lifetime. These viruses are adept at evading the immune system. They initially infect cells, replicate, and then enter a dormant state, hiding within nerve cells or immune system lymphocytes. This latency allows them to persist for decades, reactivating during periods of stress, illness, or weakened immunity.
Understanding the mechanisms that trigger this switch from latency to active infection is paramount to developing effective treatments and preventative strategies. The identification of these enhancers, and the ability to potentially manipulate their activity, offers a promising new avenue for intervention. Researchers believe that by “extinguishing” or “activating” these enhancers, it may be possible to control viral activity and prevent illness.
Professor Shamay’s lab has previously identified enhancers in Kaposi’s sarcoma-associated herpesvirus (KSHV) and EBV, viruses linked to certain cancers. The current study builds on this work, demonstrating the power of AI to accelerate the discovery process. “Locating the relevant enhancers is a challenging task because they can be located at different positions relative to the gene they regulate, and this task is particularly challenging in the small genomes of viruses,” Shamay noted.
The implications of this research extend beyond herpesviruses themselves. The study highlights the potential of AI to decipher complex biological systems and identify key regulatory elements within the human genome. “The control mechanisms of enhancers also exist in the human genome,” Shamay explains. “By studying viral enhancers, we can also learn about human enhancers. This relates to evolution: viruses have existed for millions of years, and, genetically, they extract only the most essential sequences from human cells to survive. Researching viral genes by means of AI opens the door for understanding biological systems in the human body.”
This breakthrough comes amidst growing interest in leveraging AI for advancements in virology and infectious disease management. A review published in in PubMed highlights the increasing role of AI in improving herpesvirus detection, modeling transmission, and identifying potential treatments. Researchers are employing machine learning, deep learning, and natural language processing to address diagnostic limitations and forecast outbreaks.
recent discoveries, including one reported in , have identified specific amino acids critical for herpes viral entry, potentially offering another target for therapeutic intervention. These advancements, coupled with the AI-driven enhancer identification, suggest a paradigm shift in our approach to combating herpesviruses and, potentially, other viral infections.
While the research is promising, it’s important to remember that these are early findings. Further research is needed to fully understand the function of these newly identified enhancers and to develop targeted therapies based on this knowledge. However, the study represents a significant step forward in our understanding of these pervasive viruses and offers a glimmer of hope for more effective treatments in the future.
