Some sporadic insights into academia.
Science is Fascinating.
Scientists are slightly peculiar.
Here are the views of one of them.

Thursday, 25 May 2017

What actually prevents viral lung infection?

The protection provided by our immune system against infection is multi-layered. Each individual cell has a degree of self-defence where it is able to recognise and kill infectious pathogens, this is called intrinsic immunity. Then there is a rapid response called the innate immune system that recognises infection in general. Finally there is a pathogen specific response tailored to each individual virus subtype called adaptive immunity. In turn the adaptive immunity has several elements to it there is a cellular arm made up of two flavours of T cells (CD4 and CD8) and an antibody arm which is also divided into 5 different subtypes based on the structures of the immunoglobulin molecule produced, these are called IgA, IgD, IgE, IgG and IgM. Why they are not called IgA,B,C,D and E is unclear to me, but then again much of immunology nomenclature is opaque (think of the HLA/MHC gene numbering system – or don’t): some might say is deliberately difficult to keep out interlopers from other fields.

HAI

Whilst we know that these different components exist, what produces them and how they work to kill infections, we don’t have a complete picture of the relative contributions each component makes. Thanks to studies performed in the 1970’s in the common cold unit, Porton Down (in the rolling Wiltshire countryside of the UK), we do know that antibodies in the blood protect against influenza infection. In these studies, volunteers were deliberately infected with influenza and the rate of infection compared with antibody levels in the blood. The researchers found that volunteers whose blood scored greater than 40 on a particular test called Haemagglutination inhibition (HAI), which measures the functional activity of antibodies, were significantly less likely to get infected. This benchmark number of 1:40, is now used to assess new vaccines. However, the HAI test only assesses one of the arms of the immune system – IgG. We were interested in the role of other components.

IgA

In order to assess the role of another antibody subtype, IgA, in our recently published study we went back to human challenge studies. Working with a biotech company – Altimmune – volunteers were deliberately infected with influenza. However in this study, individuals were deliberately selected who had a sub-protective HAI titre. This enabled us to look at the role of other components without the masking effect of blood IgG. Having screened the patients to have low levels of functional antibody in the blood, one prediction might be that they should all get infected. However of the 47 volunteers infected, fifteen had no recoverable virus or symptoms of infection. This suggests that there are indeed other factors that can protect against infection. We measured influenza specific antibody and found that volunteers with high levels of flu binding IgA antibody in their nose or their blood produced less virus over the course of the study. This suggests that IgA can also protect against flu.

CD8

However, there were patients with low IgA and low IgG who didn’t get infected, suggesting that there are additional factors contributing to protection. We have data that suggest that CD8 T cells could also be playing a role. CD8 T cells are also called cytotoxic T cells, they work by recognising little bits of virus that are displayed on the surface of infected cells as little flags of infection. Recently it has been shown that there is a special population of T cells that live in the lungs and are primed to recognise and prevent infections. We found high levels of these cells in the lung after a viral infection (Respiratory Syncytial Virus: RSV, which has a very large burden of disease in children). What was really striking was that by transferring these cells alone from one animal that had been exposed to RSV to another animal who hadn’t we could also transfer protection against infection. This means that CD8 T cells are also able to protect against infection, the full study is described in our paper in Mucosal Immunology.

A model: 
So where does this leave us? We think there is a layered defence against infection. IgA, which is mostly found in the upper airway, forms a barrier to the virus getting into cells in the first place. If this barrier is breached, then the IgG prevents the virus from moving from the upper to the lower airway. If the IgG fails to prevent infection of the lungs, CD8 T cells resident in the lungs rapidly kill the infected cells reducing the burden of disease. What this means is that when designing vaccines for these infections, we need to target all three components of the immune response for the best protection.


Friday, 21 April 2017

How to turn 19,000 data points into 1 graph.



Science is stories.

Good stories move science forwards. The stories come from the data and turning data into a story is a long and iterative process. The more data you have the longer it can take, as our tools get better at producing more data per sample it is getting harder to find the story. In our recently published study (Inflammatory Responses to Influenza Vaccination at the Extremes of Age) we were measuring 27 different mediators after giving 2 different vaccines 3 times to 3 different ages of mouse, sampling at 8 timepoints after vaccination with 5 replicate animals at each timepoint leading to 19,440 data points. This was a tricky knot to unpick.

Inflammatory responses

The aim of the study was to investigate whether age changed the immune response to vaccination. In particular we were interested in whether age affected inflammation after immunisation. Inflammation sounds bad, but we actually need a small amount to kick the immune system and make the vaccine work. We know that vaccines work less well at the extremes of age and wanted to determine whether the initial reaction to the vaccine shaped how well it worked. To investigate the inflammatory response, we used a tool called Luminex. Luminex measures chemical messengers in the blood called cytokines; these chemical messengers recruit cells of the immune system to the site of vaccination, activate them and shape the type of response they generate. However, as mentioned, Luminex generates LOTS of data: 19,440 data points. The first time we had the complete dataset, we had to book a study room to have sufficient space to spread out all the bits of paper with the data on. So how did we move it from there into a story?

Data Compression
 It took four things –perseverance, perspective, peer review and bio-informatics.

Perseverance: With any dataset, but large ones in particular, time is the most critical factor in finding the story. You need to spend time with the dataset, getting to know it, formatting and reformatting: sorting by size, time, alphabetically, into classes of cytokines. Analysis can’t be done piecemeal; several times I would get close to understanding the data but then have to take time off to do something else and when I came back to the data would have forgotten the trends I had been close to identifying and have to start from scratch. There were several dead ends and times when I wanted to give up as there was no discernible pattern in the data.

Perspective: That said, analysis can’t all be done in one sitting. You need time for the subconscious to churn it through, you need to read around the subject to see what other people have seen, you need conversations with colleagues and chance insights when on the loo. The creative process can’t be rushed.

Peer-review: Exposing your precious story to the slings and arrows of outrageous review is often frustrating and can be soul-destroying. However, in this case (and I grudgingly admit quite frequently for other studies) peer review significantly improved the paper. It gave us time and perspective to rethink the conclusions and suggested new ways of analysing and thinking about the dataset.

Bioinformatics: It turns out that, whilst easy and accessible, excel may not be the most effective tool for looking at big datasets. There are a range of other bioinformatic tools, which can help in the analysis. In this case we used principal component analysis. Now I have no idea how the maths behind this actually works, but I do know it squishes the 19,000 or so variables into 2 so that you can then see broad trends in the data and then from there go back and look for individual variables of interest.

So what did we learn?                        

Having spent time staring at the data, a number of patterns did emerge. First of all, age is a major factor in the inflammatory response to vaccination; with different cytokines being produced in young, adult and elderly animals. Secondly adjuvants can shape the response. Adjuvants are compounds that improve vaccine efficacy; the addition of an adjuvant called MF59 reduced age associated differences, inducing higher levels of the cytokines IL-5, G-CSF, KC, and MCP-1. The level of these four cytokines correlated with the level of antibody produced after vaccination. This is important because it shows that poor responses at the extremes of age can be overcome through the addition of adjuvants; it also gives us some insight into what response to a vaccine can lead to the best results. Taking a complex (and large) dataset and turning it into a story was a lengthy process, but has helped us understand more about the immune response to vaccines.

Tuesday, 28 February 2017

From Great sweetness came forth infection.

Bacteria, like all living things, need food to grow. The bacteria that infect us are no exception to this and their food source is us! The airways are surprisingly rich in nutrients for bacterial growth, some of this comes from the food we ate (micro-inhalation) and some leaks out from the blood or cells lining the airways. We know that underlying lung diseases increase the risk of bacterial infection and have recently shown that this is related to the levels of glucose in the airways. We think that this works a little like leaving a jam jar open – bacteria will colonise and grow on the available sugar.

New Treatments for Bad Bugs

Antibiotic resistance bacteria (bacteria that are not killed by antibiotics) are a crisis in global health. If antibiotics stop working, as well as an increase in the severity infections that are treatable, much of the medical advances of the last 50 years including surgery and transplant also become ineffective. We therefore need new ways of killing bacteria. This could either be by finding drugs that directly attack the bacteria, or by changing strategies.

War on bugs


Our finding that bacteria grow better when sugar is high opens up new treatment strategies – to starve the bug, rather than attacking it. In our recent study, we investigated whether an anti-diabetic drug (Dapagliflozin, made by AstraZeneca) could prevent bacterial lung infection. Treating diabetic mice with Dapagliflozin reduced the blood sugar; critically it also reduced the airway sugar levels. The reduction in airway sugar led to a reduction in bacterial infection in the drug treated mice. We have seen a similar effect using another anti-diabetic drug – metformin. These studies suggest that reducing blood and lung sugar will reduce the number of infections seen in people with diabetes.

Sunday, 1 January 2017

New Year's Resolution 2017

My first resolution is a work-centric one. It is not dissimilar to the resolution I made in 2016 (and 2015, 2014 and 2013). It is to publish 10 papers in the same year and to get promoted! In some ways, this is the academic equivalent of saying that I will quit smoking and lose 2st (12kg) in weight: it is aspirational, but lacks the detail needed to achieve it.
The second resolution is a political call to arms, to myself and the whole academic community. I think it is fair to say that we, the experts, lost 2016. Somewhere in post-truth politics, our voices stopped being heard. In the next four years, the truths I hold to be self-evident – that vaccines work, evolution happens and the climate is changing – will be under attack and no amount of clever Facebook posts that I make to my like-minded friends will help defend them. I need to come up with better ways to get the message across: fighting rhetoric with reason, fear with facts and populism with pragmatism.
It’s going to be a long year.

This post first appeared on Times Higher Education on the 5th Jan 2017