Biomarker discovered for flu susceptibility

Researchers have found a way to predict who’s most likely to get the flu.

Not everyone exposed to the influenza virus ends up with the flu. And with an estimated 30.9 million people getting sick with influenza during the 2016–2017 season in the US alone, scientists are eager to find ways of knowing who’ll get sick and why. Now, researchers at Stanford University have a solid lead. They’ve discovered a biomarker (a physical biological indicator) for flu susceptibility. The blood-based biomarker is a gene called KLRD1, which is expressed by antiviral cells. Unlike previous flu biomarkers, which only work after infection but prior to the appearance of symptoms, the new biomarker can predict who is susceptible to the flu before they are exposed to the virus.

We spoke to two of the authors of the study Purvesh Khatri and Erika Bongen to learn more.

ResearchGate: What motivated this study?

Erika Bongen: There is huge variation in who gets the flu each year. We wanted to understand what immune factors might play a role in why people get sick. We hypothesized that there may be an immune state that protects from infection upon exposure and reduces susceptibility.

RG: What is the association between KLRD1 and lower flu susceptibility?

Bongen: People with higher expression of KLRD1 are less likely to become infected when exposed to influenza. We believe this is most likely because higher expression of KLRD1 indicates that presence of more natural killer cells, which are a type of anti-viral white blood cell.

RG: Can tell us briefly how you reached these findings?

Bongen: Influenza challenge studies are an important method to understand our immune responses to influenza and to test whether a new vaccine works. In a challenge study, healthy volunteers are infected with influenza using a nasal spray and their immune response is measured for the next few days. We used blood gene expression from baseline, before the volunteers were infected, and estimated the immune cell proportions present in the volunteers' blood. We found that volunteers who did not get sick had more natural killer cells prior to infection. We then found that a gene that is expressed by natural killer cells, KLRD1, is also higher expressed by volunteers who did not get sick.

RG: What was the most challenging aspect of the study?

Purvesh Khatri: We compared people who got sick to people who did not get sick at baseline, before anyone was infected. So, we had to compare one group of healthy people to another group of healthy people. The differences are much subtler and thus harder to detect. Another challenge was that was the fact that we had 52 healthy subjects and >20,000 genes measured in ​these subjects. We were looking for a needle in a haystack. We couldn't find a set of genes that would robustly and reproducibly predict who will get infected.

RG: How does your biomarker differ from those that others have investigated in the past?

Khatri: To the best of our knowledge this is the first biomarker to predict who will get infected before infection. Past work has been on predicting who is infected after infection, but before symptom onset.

RG: I’ve seen there are other efforts to look into flu biomarkers, like the BERG Sanofi partnership. Is this part of a larger movement to do this? Why is it beneficial?

Bongen: Since influenza is a major public health problem, there is a great deal of work focused on understanding the immune response, developing better vaccines, and creating new drugs. Having biomarkers to predict influenza susceptibility, like we did, or biomarkers to predict vaccine response, like BERG Sanofi is working on, will help scientists develop better flu vaccines.

RG: What do your findings mean for drug or vaccine development?

Khatri: Our work indicates that natural killer cells are an important cell type in our body's response to influenza. It may be helpful to develop vaccines or drugs that can boost natural killer cell responses to flu.

RG: What’s next for your research?

Khatri: Although we validated our findings in a completely independent cohort, our study used gene expression in a relatively small number of samples. We need to confirm our findings in a larger cohort. Also, our results are a strong association. The next step is to find the mechanism behind it.

Featured image courtesy of James Gathany.