The saying about the limitations of models is attributed to a British Statistician, George Box. It speaks to how we use models to understand the world around us. This reflects is a deeper philosophical discussion as to what extent we can ever reach ‘truth’. But for now the point is that the models and observations we make in medicine and biology are often flawed. And we need to be aware of these flaws in order to better utilise the models.
In our recently published paper Comparative cross-species transcriptomics during RSV infection identifies targets to treat RSV disease we combined three different approaches to understand infection with Respiratory Syncytial Virus (RSV), a significant cause of illness in babies less than six months old.
Although natural RSV infection in children is the disease of interest, it is challenging to study directly in babies. It is very difficult to collect samples and it is often unknown when the child was first infected. An alternative is to use human infection challenge studies – where volunteers are deliberately infected with RSV. This has the advantage of being in the same species (the human) but the studies use young, healthy adults not babies. There is an additional challenge that all adults in these studies will have previously been infected with RSV at some time in their lives (probably several times) which will affect the immune response to any subsequent infection. The volunteers typically experience mild-to-moderate disease and so don’t fully recapitulate the disease seen in babies. Another alternative is to use mouse models. As well as the possibility to perform biological repeats in genetically identical individuals, following infection of a mouse, you can access all tissues and you can manipulate the response experimentally. However, there are interspecies differences in physiology, behaviour, viral tropism and genetics which can limit interpretation. All experimental approaches are ethically assessed, but there is a sliding scale – more can be done in adults than babies, more in mice than humans. And so by combining these different approaches we can compensate for limitations inherent to each.
In the published study, we compared the immune response in the blood, the lungs and the nose. As with different models, sampling different sites compensates for limitations of sampling an individual site. Blood is easily accessible and it is possible to collect large volumes of material, multiple times. But for a respiratory virus like RSV, it isn’t the actual site of infection (even though the lungs are highly perfused with blood). Blood can reflect cells moving into or out of the lungs, but not the complete picture. The lungs, as the site of infection tell us what is happening where the virus is and therefore can give us much more information. However, they are much harder to sample – it takes a medical procedure called a bronchoscopy to collect the tissue. Repeat sampling over time is not possible and these kind of samples cannot be collected from babies. The nose represents a good compromise. It is easily accessible and is the entry site for infection. Newer sampling methods have enabled the collection of good quality material from the nose without causing discomfort.
Having collected material from infected individuals, we then measured changes in gene expression. Cells, when they are infected or when they are responding to a local infection produce RNA that encodes the proteins they will use to fight off the virus. Profiling the changes in the RNA in a particular sample gives us a snapshot of how the immune system is working. We used an approach called RNA-Seq which captures all of the RNA in a sample and measures how many copies of each gene have been expressed. When two samples are compared side by side, the relative amounts of RNA can be evaluated; this is then presented as differentially expressed genes (DEG for short). The idea being that genes that change in amount are the ones that are important for the response to the infection.
Overall we evaluated 209 samples. When we pooled the data, we saw a clear increase in immune system genes following infection. This was not unexpected, the question we wanted to address was whether they were beneficial. The immune system has a dual role in disease, it is vital to protect us against viruses, but sometimes it overshoots and being in in excess of that required to clear the virus it can damage the lungs and cause us to feel sick. Within the data from this study, we observed increases in genes from the interleukin 17 family (IL-17). This is a type of signalling molecule called a cytokine which shapes the flavour of the immune response. It triggers a cascade of other genes, one of which is called S100A. Both IL-17 and S100A have been shown to cause enhanced disease following other viral infections, but their role in RSV is not known. Returning to the mouse model, we were able to block the action of S100A and show that when inhibited, there is less disease. Overall the study showed that integrating different data sets provides new insight and may ultimately lead to new treatments.