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.

Time and how to organise it better

Organisation

Time, next to grant funding, is the thing for which most academics would sell their soul. Everything is a PRIORITY that needs doing NOW. This can be overwhelming, especially if you are caring for someone at home who also needs all of your time NOW.

The self-help literature shelves are awash with advice on how to better organise your time. So much so you’d need a whole second life just reading them. Luckily I have read the abstracts, looked at the front covers, chatted to a mate and condensed all this collected knowledge down to 3 easy steps and 5 superpowers.

Time being short, let’s begin:

Step 1, The List. Everyone has a list, be it on scraps of recycled paper or blue roll, lab gloves, google notes or a specially designed pad. The list may only exist in the ether, your head or your PA’s head (if you are lucky enough to have one). But somehow, somewhere, you need to register everything that needs to be done. If you are not even doing this, then you are truly lost. Make a list: write down everything, big or small, home or work, trivial or world changing.

Step 2, The Main Effort. Having written your list, step back and decide what it is you want to achieve. Not only by the end of today, but at the end of the year, in 5 years’ time and if possible over your whole career. Whatever your goal is, from finishing your PhD on time, through getting that paper out and becoming a PI to winning the Nobel Prize, define the activities you need to achieve it and put all your effort into those activities. In military circles, this is called the main effort (or Schwerpunkt): a centre of gravity, where a decisive result is to be achieved.

Step 3, Triage. Inherent in the main effort approach is the need to sacrifice other fronts to achieve the goal. You therefore need to prioritise your work into 4 categories (I use an adaptation of triage).

Red (Immediate): These tasks directly contribute to you achieving the main effort; spend as much time as possible doing them.

Yellow (Delayed): It’s helping, but indirectly; or someone is really screaming at you to get it done.

Green (Minor): It is not on the main effort but needs doing.

Dead: There are some tasks that a) seem urgent but aren’t or b) even if you tick them off the list come back to bite you at the end of the day. Don’t do them!

For example, my goal is to get promoted, to do this I need to publish papers and get grants (my main effort). I split my list into papers, grants, lab and admin (i.e. everything else). Where possible I prioritise doing papers and grants.

So now that you defined your goal and prioritised the tasks that will help you achieve it, you still need to find the time to do them. And this is where my timefighting superpowers come in.

·         Bullet Dodge. Say no. If it doesn’t get you where you need to be, don’t do it. Use this superpower sparingly. Part of being an academic is being a good citizen. If you are a superstar, you can be as selfish as you like, but the rest of us need to keep our jobs in order to achieve our goals and part of keeping your job is saying yes to things you don’t want to do. EO Wilson in his letters advises us to “avoid departmental level-administration…make excuses, dodge, plead, trade”.

·         Hyper-alert. There are parts of the day in which you will do your best work. For me it is in the morning up to lunch (strictly at 12 noon) and after 3 pm. So I schedule deep-thought for those periods and busy work for 1230-3. (If you are in my group and reading this and I have scheduled a meeting for 2…of course I value our time together).

·         Hyperfocus. Time flies when you’re truly engaged in a piece of work. Sometimes you get into a magical state where the ideas crystallise into words on the page. This Zen state of intense focus/ productivity is named as ‘flow’ by Mihály Csíkszentmihályi. Do everything you can to attain and remain in this state, for as long as possible.

·         Immunity to internet. Having identified your priorities and found your perfect time of the day, don’t let others or yourself come between you and flow. Some tips: divide your time into 45 minute chunks and in those segments, turn off the email, turn off the internet, block social media accounts, hide your phone, have a note pad so if something urgent but not task related comes up you can write it down and move on and focus. If you can’t get away from distractions in your office, work in the library, work from home, put on headphones, get away from that person who always ‘just needs’ to ask you one thing.

·         Let go your over-critical self. Some tasks are never going to be finished to perfection, there are times when you have to accept that 80% and submitted is better than perfect and still on your C drive as the final-final-final.doc. Not everything is achievable in a day; if you have pushed a task as far as it will go, put a mark next to it to remind you more is needed or you are waiting for someone else. One of the best pieces of advice to reduce stress about incomplete tasks is to go home at a sensible time every day and if things are not finished to repeat the mantra ‘I’ll just try harder tomorrow’.

So what are you waiting for? Identify your goal and don’t let anything (including yourself) get in the way!

This first appeared on Digital Science blog, May 2017

No scientist is too junior to fix the system

This first appeared in Nature: May 2017

The March for Science filled the streets on a Saturday afternoon. The next steps should come at research institutions and universities, says John Tregoning

                Last month thousands of researchers took to the streets. It is time to channel this collective energy to shape the culture of science.

                We all love to complain how the system for doing science thwarts ideal practice. Prestigious publications are rewarded more than sound work. Everyone ends up chasing trends and asking the same questions. Broader multidisciplinary research might achieve more, but it is harder to publish and less well rewarded. We end up sticking to the path of the prestigious paper and big grant at the expense of worthier endeavors.

                Why don’t we just change the system to something better? After all, science is uniquely self-regulating. The people who set the science agenda are scientists, the people who allocate funding are scientists, and the people who decide what gets published are scientists. The tool we hold in highest regard is peer-review: we are judge, jury and executioner.

One reason for stasis is that scientists value consistency. The scientific process requires controlling variables as tightly as possible, even down to those unlikely to have any impact on an experiment. I know people who won’t change the order in which they use pipette tips; they are unlikely to change the research system.

Another reason is that we’re too busy just getting by in the current system to pause to fix its flaws. Grant submissions and experimental timepoints—tasks that reward the individual and have strict deadlines--will always win against some nebulous effort for the common good.

But most of all there’s the sad reality that those who most feel the need for change have the least power to create it. It’s all too easy to justify putting off activism. The time to fix the system, we tell ourselves, is after we have gained actual influence. If a PhD student shouts in frustration, are things going to change, or will she just be marginalized as a rabble rouser?

This leads to a pernicious inertia: moving up the ladder shifts your perspective. Making tenure puts you in a position to make change, but can inure you to the status quo. The principal investigator tells the postdoc that finding a permanent position is nothing compared with the angst of getting a grant. The postdoc tells the PhD student that defending a thesis is nothing compared with the angst of finding a permanent position. The higher you rise, the smaller the problems of those in the levels below seem. In other words, research traps young scientists in a suboptimal system, but if they plan to advance their careers before setting it right, nothing will change.

Within the last twelve months, separate groups of researchers have made headlines [http://www.nature.com/news/the-mathematics-of-science-s-broken-reward-system-1.20987] by applying evolutionary fitness metaphors to show that scientists are driven to less rigorous but more ‘productive’ practices. They portray science as a zero-sum game: everyone is so busy competing that no one revises the rules. Those who spend their time lobbying for change rather than collecting data will find themselves scooped of the recognition required for resources.

But evolutionary theory also suggests a potential way out: reciprocal altruism. The key is to use whatever influence you do have to help your peers, and to trust that your peers will do the same. I have reaped the benefits. One example was relinquishing a key authorship position on a paper in order to maintain a productive collaboration. At the time, I felt I was losing out, not fighting hard enough in the struggle for the scarce resource of credit to which I felt justified. But the small sacrifice paid off. I continued to work with my co-authors, and we wrote a successful grant together. The immediate reward of prime authorship would have been less beneficial in the long run.

More broadly, I am collecting a group of like-minded colleagues that consciously try to be less self-focused and support each other. In practice, this comes down to small things that even a pipetting-compulsive can handle: we read each other’s drafts, accept a fair share of committee posts so no one has an undue burden, take the time to forward relevant grant announcements, or just to go out for a drink. We just each try to work a bit more toward a collective good: I happen to be enthusiastic about identifying broken stuff in the building that everyone else ignores (burnt out lights, squeaky doors, blocked sinks) and seeing that they get repaired.

Start now. Don’t wait on your senior colleagues, and definitely don’t wait until you become the senior colleague. Build a network of like-minded people. Identify something that doesn’t work and fix it. It can be as small as leaky tap or as big as peer review. Believe that idealism can be catching.

Reciprocal altruism may seem idealistic, but focusing solely on your own advancement can come back to bite you. Academic promotions and appointments to senior positions require recommendations from colleagues, and I’m sure I’m not the only one who has heard of ambitious individuals who would never be considered for department chair because they have stabbed too many people in the back.

Let’s strive to stand together. Historians called last month’s worldwide march to defend science unprecedented in terms of its scale and breadth. That energy and optimism need not dissipate – it should be funneled into making the overall system function better. The payoff may not occur immediately, but play the long game and we all can win.

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.

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.

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

Support basic science

The promised injection of £2 billion into the UK science ecosystem is without doubt a good thing. However, there is some uncertainty as to how it will be handed out.

Since this is taxpayers’ money, there needs to be a demonstration that the money has been “well spent”: the big question, then, is what defines well-spent science funding? In the event of the government not opting for the “give it all to John Tregoning” option, I wanted to make a case for the funding of basic science.

Translation versus inspiration

While all science involves repeated testing of ideas, we artificially split the world of scientific effort into two very broad areas: basic science (pure research, learning about stuff for the sake of learning); and translational science (testing things like drugs, chemicals, devices, bridges and computers to improve the quality of human existence).

To those with a commercial mindset, the translational approach has the greater value. You put money in, you get better stuff out. So why invest in pure research?

Essentially, basic science underpins translational research: the ideas about how to make stuff better come out of pure research. Lots of modern engineering depends on us understanding how gravity works, but Newton’s aim wasn’t to put rockets on the moon. While the results are not immediately tangible, basic science underpins technologies that are the foundations of billion-dollar industries – for example cancer immunotherapy, lasers, the internet, GPS, fluorescent and luminescent proteins.

I strongly believe that we need both: funding translational science at the expense of basic science may pay off in the short term, but it damages advances in the long term.

The home of basic research

I also believe that in the current research ecosystem, universities are best placed to deliver the pure research and companies small and large are best placed to develop it into real things.

Companies utilise (and often contribute to) the basic research being performed by academia, but rarely initiate basic research programmes by themselves: though there are exceptions, the IBM Zurich Research Laboratory (which has gained two Nobels) has just celebrated its 60th year and the AT&T Bell labs earned 8 Nobel prizes.

If universities are initiating the research, it raises a question about who financially benefits from the basic research, as the money may not seem to come directly back to the originator. But it will trickle back in tax revenue, employment, better medicines, cleaner cars and other indirect benefits.

This is a strength of bringing Innovate UK and RCUK (Research Councils UK) into one umbrella organisation, enabling the flow from academic basic science to small and medium enterprise led innovation (ie, by any firm with up to 250 employees) to large company implementation.

Teaching

The other benefit of basic research is the teaching and training element.

The economy needs people with science backgrounds. A PhD provides the student with very much more than just the ability to move colourless liquids around – it gives them problem-solving, teamwork and analytical skills, tenacity, flexibility and independence. But just as no one expects doctors to train without ever seeing a patient, the best way to learn science is by doing science.

Basic research delivers this apprenticeship in science. To quote the National Science Foundation in the US: “Basic science is a gamble because it deals with the unknown, but a sure thing because it always leads to improvements in knowledge.”

Reap what you sow

The good news is that the public have repeatedly demonstrated support for basic science: a 2014 survey by the British Science Association reported that 8 out of 10 people questioned supported research with no immediate benefit. So please include basic research in the mix – not to the exclusion of work with an immediate pay-off, but as part of a long term strategy to further develop our scientific excellence.

To paraphrase John F. Kennedy: we choose to do the research we do, not because it is easy, but because it is hard; we choose to do basic science because it is there and new hopes for knowledge are there and we are going to climb these mountains.

Surely that is as uplifting a message as we can hope to end 2016 on.

This article was first published on the Times Higher Education Supplement 31/12/16