Vaccine Science Goes Holistic

Vol. 12, No. 01 - January 2014

As a growing number of researchers are studying biological systems in their entirety to better understand—and even predict—immune responses to vaccines, there is hope that such “systems biology” approaches might one day help accelerate clinical trials.

By Andreas von Bubnoff

A system, according to Merriam Webster’s online dictionary, is a “group of related parts that move or work together.” The term also evokes thoughts of looking at something in its entirety.

That’s certainly true for systems biology, an emerging branch of biology that involves studying all parts of biological systems such as the immune system at once. One common approach systems biologists use to study the immune system is to measure changes in the activity of not just some, but all or most of the genes of an organism in response to certain stimuli that are known to cause an immune response, such as vaccination. This “systems” approach has grown a lot in popularity in recent years, and is already starting to yield novel biological insights.

Researchers are even starting to use it to predict the future: A few years ago, they measured changes in the activity of genes a few days after vaccination with the yellow fever vaccine. They found that a certain “signature” of such gene activity changes could be used to predict the level of the later adaptive antibody and cellular immune responses to that vaccine (see VAX Feb. and Mar. 2004 Primers on Understanding the Immune System, Part I and II). That study was the first that used a systems approach to identify signatures that could predict immune responses to a vaccine.

The yellow fever vaccine is just one of many different types of vaccines: As a so-called live viral vaccine, it contains a weakened version of the actual pathogen that causes the disease, while certain other vaccines only contain parts of the pathogen they are protecting against. Interestingly, systems biologists have found that the combination of gene activity changes that predict adaptive immune responses to different vaccine types differ from each other: For example, the gene activity signatures that predict later immune responses to two meningococcal vaccines (which contain certain sugars) are similar to each other, but differ from signatures that predict immune responses to yellow fever and other live-viral vaccines.

One day, it might even be possible to use this approach to predict immune responses to candidate HIV vaccines. Research teams have already shown that this might be possible for simian immunodeficiency virus (SIV), the monkey version of HIV that infects rhesus macaques. They measured gene activity changes within two weeks of the animals receiving a vaccine that contained SIV proteins. One year later, they infected the animals with SIV and checked the impact of the vaccine on virus levels.

They found that the gene activity measurements from two weeks after vaccination could predict—with about 85% accuracy—whether the vaccine reduced the viral load after the SIV challenge one year later. To efficiently process the complex data, the researchers used techniques from machine learning, where computers identify hidden patterns and relationships in enormous datasets to focus on the most relevant information. 

Systems biologists are also trying to predict potential adverse effects of vaccines. For example, almost half of the almost 400 children who were vaccinated in a Japanese clinical trial that tested an inactivated whole H5N1 flu virus vaccine in the 2007/2008 flu season developed fever after the first of two vaccinations. As a result, the vaccine was not approved by the Japanese health authorities.

But the trial was useful in another way: Japanese researchers studied the vaccine recipients to identify molecular markers that could predict whether they would develop fever. They measured the levels of most of the 2,000 known microRNAs—small RNA molecules that regulate gene activity—in serum samples that had been taken from 85 of the children before the vaccination. They found that the levels of 73 microRNAs differed between the children who developed fever and the ones who didn’t. They hope that these microRNAs can be used to predict whether vaccinees will develop fever in other vaccine trials.

The systems approach could also be helpful in predicting whether people with latent tuberculosis (TB) infection are likely to develop active TB disease. Being able to make such a prediction is very important: One third of the world’s population, or about two billion people, are estimated to have latent TB infection; of those, about 10% will develop active TB at some point in their lives. Every year, nine million people develop the active disease, and 1.5 million die.

Just why some people come out of latency while others don’t is not understood. But researchers are trying to at least identify markers that can help predict whether latently infected people will develop active infection, which would make prevention and treatment of TB much easier. To see if this is possible, they measured gene activity changes in blood cells taken from more than 6,000 adolescents with latent TB in South Africa five times over a period of two years.
They found more than 1,200 genes that showed different activity in 35 people who developed TB disease during that time, compared with 70 people who didn’t. The researchers could use this information to predict the development of active TB six months in advance with up to 80% accuracy. The accuracy was lower for earlier time points, but still had excellent predictive value up to 18 months before active TB developed. They hope to use the analysis to identify people at high risk of developing TB disease for prophylactic treatment or to enroll them into efficacy trials of TB vaccines or treatments.
But it’s not all about predicting the future. Systems biologists are also trying to better understand why some people respond to vaccines better than others. They have found, for example, that higher activity of inflammation-related genes in elderly people before vaccination corresponds with lower immune responses to flu vaccination. Age-related systemic inflammation could therefore be one reason why vaccines have less of an effect in elderly people. The researchers now want to see if reducing inflammation before vaccination can improve immune responses to yellow fever and hepatitis B vaccines in elderly people.

Systems biology can also help to better understand results from clinical trials of HIV candidate vaccines. In a trial called Step, researchers tested an HIV vaccine candidate called MRKAd5, which uses a common cold virus (adenovirus serotype 5, or Ad5) as a vector to deliver fragments of HIV to the immune system. MRKAd5 didn’t protect from infection; in fact, people with preexisting immune responses to Ad5 showed increased HIV infection risk.

Recent measurements of gene expression changes in people who received the vaccine suggest a possible explanation for this: One day after vaccination with MRKAd5, people with preexisting immunity to Ad5 activated fewer inflammation-related genes. This suggests that insufficient activation of appropriate “danger signals” by the vaccine may have something to do with the increased HIV infection risk in this population.

Because the “systems” approach involves measuring everything, researchers are not constrained by their preconceptions of what to expect. That’s why systems biology can also lead to unexpected insights. When researchers measured global gene expression changes in response to flu vaccination, they found that the upregulation of a gene called TLR5 one week after vaccination correlated with the level of the subsequent antibody response to the vaccine.

That surprised the researchers, because TLR5 is a receptor that senses bacterial flagellin, which is not present in viruses. At first, they thought the flu vaccine they were studying might be contaminated with bacterial products. But there was no evidence for any such contaminants, and further investigation revealed that mice without TLR5, or without bacteria in their gut, had fewer cells that produce antibodies. This suggests that the sensing of our own gut bacteria by TLR5 might help induce the antibody response to vaccines, and that things that disturb bacteria, like antibiotics, might be harmful to some vaccine responses.

If recent trends are any indication, systems biology has a lot of promise in helping researchers to develop better vaccines. Some researchers believe that one day, the approach might even be able to help accelerate the process of testing vaccine candidates in clinical trials, which can take as many as 15 years.

While clinical trials today test, say, 10 parameters in 10,000 people, systems biologists might be able to find ways to do the same thing with much less effort, says Rino Rappuoli, a vaccinologist at the company Novartis. “If we could use [this] technology [to test] ten people [with] 40,000 data points per person to predict what’s going to happen, probably vaccine development would be much faster.”