Virus World

Biologists at Columbia University Vagelos College of Physicians and Surgeons have leveraged a computational method to map protein-protein interactions between all known human-infecting viruses and the cells they infect. The method, along with the data that it generated, has generated a wealth of information about how viruses manipulate the cells that they infect and cause disease. Among the study’s findings are the role of estrogen receptors in regulating Zika virus infection and how human papillomavirus (HPV) causes cancer.  The study, led by Sagi Shapira, Ph.D., assistant professor of systems biology at Columbia University Vagelos College of Physicians and Surgeons, was published today in the journal Cell.

 

At the molecular level, viruses invade cells and manipulate them to replicate, survive, and cause disease. Since they depend on human cells for their life cycle, one way viruses co-opt cellular machinery is through protein-protein interactions within their cell host. Similarly, cells respond to infection by initiating immune responses that control and limit viral replication – these too, depend on protein-protein interactions. To date, considerable effort has been invested in identifying these key interactions – and many of these efforts have resulted in many fundamental discoveries, some with therapeutic implications. However, traditional methods are limited in terms of scalability, efficiency, and even access. To address this challenge, Dr. Shapira and his collaborators developed and implemented a computational framework, P-HIPSTER, that infers interactions between pathogen and human proteins–the building blocks of viruses and cells.

 

Until now, our knowledge about many viruses that infect people is limited to their genome sequences. Yet for most viruses, little has been uncovered about the underlying biological interactions that drive these relationships and give rise to disease. “There are over 1,000 unique viruses that are known to infect people,” says Dr. Shapira. “Yet, despite their unquestionable public health importance, we know virtually nothing about the vast majority of them. We just know they infect human cells. The idea behind this effort was to systematically catalog the interactions that viruses have with the cells they infect. And, by doing so, also reveal some really interesting biology and provide the scientific community with a resource that they can use to make interesting observations of their own.” Using a novel algorithm, P-HIPSTer exploits protein structural information to systematically interrogate virus-human protein-protein interactions with remarkable accuracy. Dr. Shapira and his collaborators applied P-HIPSTer to all 1,001 human-infecting viruses and the approximately 13,000 proteins they encode. The algorithm predicted roughly 280,000 likely pairs of interacting proteins that represent a comprehensive catalog of human virus protein-protein interactions with an accuracy rate of almost 80 percent....

 

Findings publisehd on August 29, 2019 in the journal Cell:

https://doi.org/10.1016/j.cell.2019.08.005