The discovery paves the way for the development of new intervention strategies to protect plant, and even some animal cells, from deadly fungal infections. The findings are published in the July 23 edition of the journal Cell.
The researchers have revealed how special disease-related proteins, known as effectors, blaze a trail into cells. Fungi and fungal-like microbes known as oomycetes produce effector molecules that penetrate cells and switch off the host’s defense system. Once the host’s immune system has been disabled, the fungus or oomycete swiftly follows up, breaking and entering the cell and unleashing disease.
The pathogens in question, which include the microbe that caused the Irish potato famine in the 19th century, cause billions of dollars in losses for commercial farmers worldwide in crops such as soybean. They are also responsible for potentially fatal infectious diseases in humans.
“Our breakthrough finding is that these dangerous disease-causing proteins must bind a specific lipid molecule found on the cell surface before they can enter the cell,” said Brett Tyler, professor at the Virginia Bioinformatics Institute and the leader of the project.
In a previous study, Tyler and researchers had pinpointed specific regions of the effector proteins that are intimately involved in breaking and entry of the cell. The new study shows that these regions on the effector proteins bind the lipid phosphatidylinositol 3-phosphate and that this binding is essential for the proteins to enter the cells. “The nasty proteins enter by hitching a ride on a lipid raft, a region of the cell’s outer membrane that can be internalized by the cell. The lipid acts as a bridge between the effector protein and the raft, and in doing so help to unlock the door for entry of the disease-causing proteins into the cell,” adds Tyler.
Intriguingly, the researchers have also identified two methods to block the entry process that could lead to new disease interventions against infection in medicine and agriculture. Shiv Kale of Fairfax, Va., a graduate student in the genetics, bioinformatics, and computational biology program at the Virginia Bioinformatics Institute and one of the lead authors on the study, remarked, “We were able to block the entry process of the disease-related proteins using two types of inhibitors. The first group of inhibitors covers the lipid so that the pathogen cannot get access to it. The second jams the site on the protein that normally binds the lipid.”
The scientists were also able to show that the entry process into some human cells takes place by the same mechanism. Said Virginia Bioinformatics Institute Associate Professor Chris Lawrence, who collaborated on the study, “Our finding that the entry of the effectors into human cells can be blocked with small molecules suggest that it may be possible to find new strategies to combat several debilitating human diseases, in addition to treating plant diseases.”
The research was funded by the National Science Foundation and by United States Department of Agriculture’s National Institute of Food and Agriculture.
The team of researchers included Virginia Bioinformatics Institute Associate Professor Christopher Lawrence, Assistant Professor Daniel Capelluto of the Department of Biological Sciences at Virginia Tech, and Professor Weixing Shan of China’s Northwest Agricultural and Forestry University.
The Virginia Bioinformatics Institute at Virginia Tech is a premier bioinformatics, computational biology, and systems biology research facility that uses transdisciplinary approaches to science combining information technology, biology, and medicine. These approaches are used to interpret and apply vast amounts of biological data generated from basic research to some of today’s key challenges in the biomedical, environmental, and agricultural sciences. With more than 240 highly trained multidisciplinary, international personnel, research at the institute involves collaboration in diverse disciplines such as mathematics, computer science, biology, plant pathology, biochemistry, systems biology, statistics, economics, synthetic biology, and medicine. The large amounts of data generated by this approach are analyzed and interpreted to create new knowledge that is disseminated to the world’s scientific, governmental, and wider communities.