$1.75 million grant awarded to researchers including UofM’s Jim Adelman to study host variation

Sept. 30, 2021 — UofM professor Jim Adelman and researchers from Virginia Tech, University of San Diego and University of Connecticut received a $1.75 million grant to study variation in host susceptibility to infectious disease.

The four-year study is funded by the National Institute of General Medical Sciences within a multi-agency program, the Ecology and Evolution of Infectious Diseases (EEID). 

“As we’ve all seen recently, there’s a ton of variation in who gets sick after exposure to a pathogen,” said Adelman, assistant professor in the Department of Biological Sciences and co-investigator on the project. “Those differences in susceptibility are what this project is all about: how that variation arises and what its consequences are for pathogen spread and evolution.”

Every individual in a population is different in some way. These differences — age, genetics, acquired immunity, the coarseness of your nose hairs — make individuals either more or less susceptible to a pathogen infection. Prior exposure to a pathogen, the prime focus of the study, also can affect this susceptibility. Collectively, the differences among individuals can lead to a certain level of variation among a population as a whole. Individuals in a population will not all have the same protection.

“It’s not just about how well protected your population is on average,” said Dana Hawley, principal investigator of the study and professor of Biological Sciences at Virginia Tech. “It also matters how much variability there is among individuals. You can have two populations that each have the same level of vaccination, but one population may have a lot more variability than another. We don’t really know how that affects both the degree to which an outbreak might occur and how the pathogen might be pressured to evolve.”

By studying house finch populations, this new project will test and model how different degrees of prior exposure to a common conjunctival pathogen will affect the variation in their populations. The research will not only apply to house finches but expand to human-pathogen relationships as well.

“The idea with this particular grant program is that you generate theory that is broadly relevant,” said Hawley. “Even though we’re using birds as a model, I think that the house finch-bacteria system really is a good model for any kind of human pathogen where you get these reinfection dynamics, which appears to be more and more the case for COVID-19.”

Like a wall protecting a village from invaders, the variability of a population can protect against pathogen outbreaks and community transmission. Having an outer wall of vaccination only improves this protection. 

What is not known is what level of variability best protects a population, and if invaders had hopped the walls before, how much prior exposure in a population would make a difference in that variability.

Getting an idea of what this relationship looks like could allow researchers and decision-makers to better predict and prepare for potential outbreaks and variants.

“Our study system is well-matched to these kinds of questions: we can test existing theory using finches from natural populations, in safely controlled settings, and then translate those findings into new mathematical models – models that could prove critical for predicting outbreaks not only in wildlife, but also in domestic animals and even humans,” said Adelman.

The COVID-19 pandemic has emphasized this point. Uncertainty, misjudgment and relentless change have made it especially difficult to predict the course of epidemics. A changing climate can exacerbate the problem, especially with mosquito-borne pathogens such as West Nile virus and Zika.

The need for more comprehensive and explicit information is here, and that information comes from better models that consider variation in immunity and susceptibility.

Repeated modeling and remodeling after experiments define the study. The loop of testing and rebuilding models will lead to more precise and accurate results that can better predict how different variations will impact outbreaks and pathogen evolution.

The EEID grant gives the opportunity to truly uncover what part variation and prior exposure play in disease dynamics. When another pandemic or disease outbreak does arise, we will be better prepared.