Predictive Models to Shed Light on How Mucus Impacts Health
UVA researchers are leveraging predictive models to better understand bacterial infections that involve mucus, such as cystic fibrosis.
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By Jessica Kent
- A team from the University of Virginia (UVA) has received a five-year, $3.2 million NIH grant to build predictive models that analyze interactions between mucus and bacteria.
The research will focus on how these interactions impact human health, and could have important implications for bacterial infections involving mucus like cystic fibrosis.
Cystic fibrosis affects more than 70,000 children and young adults around the world, the researchers stated. With this condition, the gene that usually triggers a certain protein to move chloride to cell surfaces malfunctions, so mucus becomes thick and sticky. This can result in clogged airways in the lungs, making it difficult to breathe.
Mucus is an important biological material that helps humans fight off illness. Normally, mucus acts as a protective coating in our sinuses, lungs, intestines, stomach, and throat.
“Mucosal layers are such an important barrier between us and the outside world, and we know that any kind of disruption to that is a big source of problems, but really there’s very little known about the interactions between mucins – those primary components of mucus – and bacteria,” said Jason Papin, a professor in the University of Virginia’s Department of Biomedical Engineering.
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The study builds on a pilot grant at UVA Engineering several years ago to develop proposals for large engineering research centers. The pilot grant laid the foundation for the current collaboration and research.
“We came at it from the pathogen, from the microbes, the bacteria,” said Papin. “My lab has done a lot of work with Pseudomonas aeruginosa. This bacterium is a huge problem in the cystic fibrosis community. But it’s a really big problem in lots of other areas like urinary tract infections and burn wounds, and people who are immunocompromised, like AIDS patients and cancer patients on chemotherapy.”
The research team has worked to build computer models of the metabolism of these bacteria, leading to a better understanding of how Pseudomonas aeruginosa survives in difficult environments.
“They are these recurrent, long-term lung infections that you can treat with antibiotics,” said Joanna Goldberg, a bacterial geneticist from Emory University, who was formerly a professor in the UVA Department of Microbiology, Immunology and Cancer Biology.
“But unlike a typical infection that goes away, this bacterium will continue to come back again and again. And so patients are constantly undergoing treatment with antibiotics.”
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The researchers have also been studying how bacteria move in mucus and what kind of chemical signaling might be taking place.
“My role is primarily to look at modeling of chemical transport and bacterial transport at what’s called a macroscopic scale,” said Roseanne Ford, a professor in UVA’s Department of Chemical Engineering.
“Within mucus, the distribution of bacteria in different chemicals affects things like how they might change their phenotype. Depending on those external chemical cues, the cells internally can respond differently by turning on and off different genes, which might make them more or less virulent.”
The NIH grant will allow the researchers to develop a multi-scale computational model that can guide the group’s experimental design with the goal of gaining a better understanding of the relationship between microbes and the mucin.
“Even though we’re focusing on what happens in Pseudomonas aeruginosa, there are millions of other bacteria in the same space, living in the same neighborhood, swimming around in that same mucus,” said Shayn Peirce-Cottler, who is co-director of the UVA Center for Advanced Biomanufacturing and an affiliated faculty of UVA’s Fibrosis Initiative.
“And so there are interactions between Pseudomonas aeruginosa and other helpful bacteria that live in the mucus to help defend us and combat Pseudomonas aeruginosa. It’s mind-blowingly complicated with respect to these different bugs and these different proteins, but that’s exactly why we must use computational modeling.”
With this information, researchers will be able to build comprehensive predictive models that lead to a better understanding of cystic fibrosis and other mucus infections.
“Over millions of years, it seems like mucus has evolved the ability to keep problematic pathogens in check. And that was the starting point of my research group, really, to understand how mucus affects the behavior and life of microbes that live inside it,” said Katharina Ribbeck, Hyman Career Development Career Professor in the Department of Biological Engineering at the Massachusetts Institute of Technology.
