Scientific heroes

My first scientific hero was Sir David Attenborough. From the youngest age, his documentaries guided me into thinking like a scientist. To try to understand life through the prism of evolution, to understand living systems as complex positive and negative feedback loops. David Attenborough inspired me to become a biologist, and shaped my politics and my ethics. I recently wrote to him to thank him for the profound effect he has had on me, and was incredibly pleased to receive a letter from him this week. #tellyourheroes

New horizons for Immunology

by Becky Allen

New group leaders bring new skills, new expertise and new perspectives, and 2018 saw three new group leaders join the Institute’s Immunology programme. Professor Adrian Liston, Dr Claudia Ribeiro de Almeida and Dr Sarah Ross talk about their research, their ambitions and what makes the Institute such a special place to work.

They come from VIB in Belgium, Oxford’s Proudfoot lab and the University of Dundee, they focus on different areas of immunology and bring new interests and expertise, but Professor Adrian Liston, Dr Claudia Ribeiro de Almeida and Dr Sarah Ross are all hugely excited to have recently joined the Institute.

Liston, already an established group leader, works on the specialist population of immune cells known as CD4T cells. These cells effectively coordinate and ‘turbo charge’ our immune response. They are also the cells that are targeted by HIV, explaining why the disease causes immune system suppression and illustrating how crucial CD4T cells are to our overall health.

At VIB, Liston specialised in translational immunology, understanding and then developing ways to treat children with rare immune diseases. “These diseases are incredibly severe, but once you understand them mechanistically you can work out ways to treat them,” he explains. “It was very rewarding because these kids that would otherwise often die can go on to lead long, healthy lives once you’ve found out what’s wrong and how to fix it.”

At the Institute, Liston wants to answer three key questions: how the millions of CD4T cells in our bodies communicate and cooperate; how they switch between a ramping up and damping down cell type; and what they do in our tissues. Understanding how these cells modulate our immune system means that they can potentially be used as a tool to fine tune the immune system to help overcome age-related decline.

With its world-leading Immunology programme and cutting-edge facilities the Institute is a great fit for Liston’s ambitions. But what sets the Institute apart is how it nurtures scientific innovation and champions equality and inclusion.

“Great research requires fantastic people who think as differently as possible, which means having an environment that celebrates equality and inclusion. The Institute has a great reputation for this internationally – it’s setting the gold standard for equality and inclusion. You can feel the difference here,” says Liston. “The Institute provides an environment where you’re going to be stimulated and have the chance to explore the limits of your imagination.”

Ribeiro de Almeida and Ross both believe the Institute’s culture will help them build their first research groups. “It’s a very friendly, supportive environment. Everyone is ready to help with time and feedback – they want you to succeed and that’s really special,” says Ross.

Ribeiro de Almeida’s work centres on B lymphocytes and the rare ability they have to rearrange antibody genes by cutting and pasting DNA in order to fight the plethora of pathogens to which we are exposed. “I’m interested in understanding how this mechanism is regulated throughout cell development,” she explains. “It’s a fundamental question, because mistakes in this process can result in leukaemia and lymphoma. To understand these diseases and wider age-related immune dysfunction it’s important to understand how these molecular mechanisms are regulated.”

As a postdoc in Oxford, she worked in a lab that studied gene expression rather than B cells, so she brings a more molecular approach to the programme. She also discovered an RNA-binding protein that plays an important part in B lymphocytes’ cut and paste process, something she’s keen to follow up: “The research I want to do next is to identify which proteins are implicated in this mechanism of gene rearrangement and how they modulate B cell responses.”

Ross specialises in T lymphocytes and the impact that hypoxia – or low levels of oxygen – has on the way they work. Because T cells commonly encounter hypoxic environments, they can adapt to low oxygen environments by changing the proteins they express. While this helps them survive, it can also make them less effective killers of disease cells.

“I want to understand how oxygen regulates T cells from a signalling and gene expression perspective,” she explains. “If we could identify factors that we could target therapeutically to overcome the effects of low oxygen and boost the ability of T cells to perform their protective function, that would be amazing.”

We know that as we age, our immune system becomes less effective and poorer oxygenation is also connected with ageing, so what Ross discovers about hypoxia could have important implications, both for our understanding of the ageing immune system and in making immunotherapies more successful.

The arrival of all three is an exciting opportunity for the Institute, the Immunology programme and its three new group leaders. “It’s amazing – I’m still pinching myself,” Ross concludes. “It’s great to be able to make new plans and work out how to turn them into reality utilising the know-how and the facilities we have access to here. And it’s exciting to be here because my plans cover all three programmes – Immunology, Signalling and Epigenetics – adding my own to all the expertise here makes it hugely exciting.”

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When should I apply for a fellowship?

To develop an academic career in research you need to develop two sides of your CV - one is your publication record and the other is a record of prizes, fellowships and grants. Publications are hard. Years of research and a brutal revision process make accumulating a publication record slow and painful. Prizes, fellowships and grants are, in principle, easier. You write up a proposal and submit. Often you can even submit the same proposal in multiple places. It is very rare that prizes are awarded without you applying for it, so sending out a lot of applications just makes sense.

For poster prizes, departmental awards, travel grants, the equation is quite simple: apply for them. There are a lot of small prizes that are usually under-subscribed, they can earn you a little money and these small prizes build up to greater things. For fellowships, on the other hand, I have started to lean in the other direction. I used to suggest that all post-docs apply for a fellowship - if successful, it is a nice income and a highlight in your CV, and if unsuccessful, well... it is good practice. Now, however, I tend to discourage fellowship applications very early on in the career. Newly graduated PhD students are often very keen to apply for fellowships, and there are so many opportunities that you can easily spend a year applying for one after the other.

The question that you need to ask yourself is whether applying for fellowships is a a good investment of time. A successful fellowship application normally merges a great idea (the science) and a proven record to deliver that idea (most importantly, your publication record). A lot of post-docs make the mistake of applying for fellowships too early, before their publication record is ready to support it. It is easy to think that there is no cost to doing so, but applying for fellowships takes a non-trivial amount of time. As a scientist you always need to weigh up the opportunity cost: time spent on fellowships is time not spent on experiments. There are times when applying for a fellowship is sensible, and other times when you are better off investing all of your energy in experiments (building that publication record for future fellowships). Here are a few factors that you can consider:

Factors that should encourage you to apply for fellowships:

• You are a senior post-doc looking to become independent
• The fellowship allows you to take a key mobility step (eg, Marie Curie to return to Europe)
• You just published a highlight paper
• Your PI says you need a fellowship to stay in the lab (but consider moving labs instead)
• You don't expect your CV to improve in the next year
• You will not be eligible next year

Factors that should discourage you from applying for fellowships:

• Your PI has a grant that covers the full period of your project
• You have a top story cooking (it deserves all your attention!)
• Your publication record is relatively thin, without any highlight papers (but watch out for being excessively self-critical! A neutral faculty member is a useful independent judge)
• You expect your CV to improve in the next year

My suggestion: rather than automatically write a fellowship, because that is what post-docs do, carefully consider the factors above, consult with faculty and then rationally decide where to invest your effort. Make sure that you don't talk yourself out of applying just for reasons of self-confidence, but also don't be afraid to concentrate on the science this year instead of applying, to put yourself in a better position to apply next year.

Golden Pipette won by Dr Wenson Karunakaran

Congratulations to Dr Wenson Karunakaran! 

It was tough competition for the sixth Golden Pipette at the Cambridge-Leuven joint lab retreat. The final prize had to go to Dr Karunakaran for his work on brain CD4 T cells.

Many neuroscientists assume there are no CD4 T cells inside the healthy brain, but there are in fact around 5000 per gram of brain tissue. How do we know? Wenson imaged and counted them, one by one. 

That is what it takes to win the Golden Pipette.

Immune profiling ‘will be a revolution in medicine’

A revolution in medicine is coming.

It could aid the diagnosis of diseases, guide the way patients are treated and inform the discovery of new therapies.

Immune profiling seeks to explain how our body’s own defences are affected by and are responding to disease.

At the Babraham Institute, Professor Adrian Liston is working on the translation of this technique from the laboratory to the clinic.

“The immune profile is much more powerful than genomic data, but it’s much easier to get genomic data,” he tells the Cambridge Independent. “You can take blood, send it overnight and get it sequenced off-site. We are not at that stage with immune system data.

“But the more we know about different diseases, the more we realise there are inflammatory, or immune-mediated, components.

“It can be a revolution in medicine. Once the infrastructure is set up and hospitals are doing the analysis routinely, we will see an explosion in utility. Right now, it’s a research tool only.”

Read the full article at the Cambridge Independent

Dokter Algoritme

Algoritmen kunnen inzichten bereiken waar een mens moeilijk toe komt. Computeralgoritmen kunnen almaar beter moeilijke diagnosen stellen, soms zelfs beter dan artsen. Immunologe Erika Van Nieuwenhove van de Leuvense tak aan het Vlaams Instituut voor Biotechnologie (VIB) en haar collega’s melden in Annals of the Rheumatic Diseases dat ze een zelflerend algoritme hebben ontwikkeld dat met bijna 90 procent zekerheid artritis bij kinderen kan vaststellen, louter op basis van een bloedtest.

Het gaat om de vaakst voorkomende vorm van reuma bij kinderen, maar omdat de ernst en de evolutie van de symptomen sterk kunnen variëren, is een diagnose stellen niet altijd gemakkelijk. Het algoritme evalueert alleen de samenstelling van het immuunsysteem van de patiënten. Het zal nuttig zijn om te bepalen welke behandeling aangewezen is.

Knack - 24 Apr. 2019 - Page 86

Is the Scandinavian model the solution for STEM parents?

The Scandinavian model for parental leave is often touted as the world’s best. 12 months parental leave, split between both parents. This is a great model for a lot of careers, but is it actually a part of the solution to the issues that women face in the STEM fields?  In some cases, perhaps, but as a blanket solution I find it lacking:

1)     First, it must be stated that extended parental leave is only part of the Scandinavian model. When it was first legislated, the leave was typically taken almost exclusively by the mother, with the father just taking the mandated “Daddy’s two weeks”. Essentially, it reinforced the traditional model that put parenting on to the woman, often truncating women’s careers. This has changed substantially over the years, but those changes are due to the evolution of Scandinavian culture and the increasing normality of equality in the Scandinavian countries. Implementing just extended parental leave will not recreate the full advances seen in women’s careers across Scandinavia in the past decade

2)     Extended parental leave is ideal for workplaces where workers can be readily replaced during this period. Large employers are capable of hiring extra staff which can shift between different positions, and employees that only need days or weeks of training to get up to speed are easier to replace. Academic science generally fulfils neither of these categories. First, while universities are large, labs are essentially independent small businesses. Few if any labs are large enough to have standing rotating staff that substitute in for parental leave. Second, for scientific staff, their skills require months or years of training. As an employer, I generally write off the first 3 months of a post-doc’s time as just getting up to scratch of new techniques and the project. For a Masters student starting a PhD, often the entire first year is spent mastering the field without actual productive experiments being performed. That level of expertise is just not readily replaceable, which means the science suffers. This will then leave a negative mark on the applicants' CV beyond the one-year gap.

3)     Scientific funding and scientific projects rarely have the flexibility to make this work. Consider a PI who hires a post-doc to work on a 3 year project. One year into the project, the post-doc goes on maternity leave for a year. The PI cannot put the project on hold for a year – since the funding clock is still running. Instead they need to transfer the project to a new person. Is it fair or reasonable for that project to be transferred again when the parental leave post-doc comes back? Potentially, but it is something that needs to be solved on a case-by-case basis. Even if the funding could be put on hold for parental leave (as in some fellowships), scientific careers are built on advancing science. If the work is scooped in the meantime, original work becomes confirmatory work – which would be a negative for the lab, the PI and the post-doc.

Consider two hypothetical cases, and whether extended parental leave helps or hinders a woman’s career in science.

Scenario 1. A young female PI goes on extended parental leave. What happens to her lab? You can’t shut the lab down for a year. PIs have responsibilities to their students and post-docs, they have responsibilities to their grant funders. So either those students get shuffled to another PI, or they have to work independently (and sub-optimally), or the PI on leave actually spends a chunk of unrecognised time managing the lab remotely. After the return, authorship on papers can often become murky and grants have been spent inefficiently.

Scenario 2. A young post-doc employed on a grant goes on extended parental leave. The PI hires a replacement post-doc to continue the project (the grant and science must go on). A year later the post-doc returns and (best case scenario) the PI manages to find enough funding to keep both staff on for a year to finish up. The paper may end up with a joint-first authorship, or maybe the new post-doc was able to push things fast enough that the original post-doc becomes second author. Afterall, the year back after parental leave is hardly your most productive. There is no easy fix – the PI needs to consider the contribution of both staff members in making a decision.

In short, I think that the Scandinavian model is excellent, and an incredible advance for some careers. However the particular aspect of extended parental leave is not suitable for all people (not everyone wants it), and it can have a negative effect on STEM careers. I would suggest a more flexible approach to STEM researchers who have children. This approach would allow researchers to make the choice to take extended leave, or make the choice to stay active in their field:

• Extended parental leave should be an option available to all
• Implement broad structural changes that promote equality in STEM, most importantly hiring women at senior levels and normalising a healthy life balance
• Infant daycare and before/after-hours childcare should be cheap and readily available for parents who chose that pathway.
• A “parental sabbatical” should be available to PIs who have children, where the PI becomes excused from all teaching and committee duties for a year, but is still able to work on research
• Grants should be automatically extended by a year if the PI has a child or if the staff paid on those grants go on leave
• Ethics protocols, biosafety protocols, etc should be automatically extended by a year if the PI has a child or the staff working under those protocols go on leave
• Review board decisions should be delayed by two years if a PI has a child
• Please comment if you have additional suggestions

If you are interested in how my family handled having a child and a career in STEM, you can read an interview I had with eLife on being a scientist parent. 

Using machine learning to diagnose disease

Profiling the immune system in paediatric arthritis patients offers hope for improved diagnosis and treatment

A team of scientists from VIB and KU Leuven has developed a machine learning algorithm that identifies children with juvenile arthritis with almost 90% accuracy from a simple blood test. The new findings, published this week in Annals of the Rheumatic Diseases, pave the way for the use of machine learning to improve diagnosis and to predict which juvenile arthritis patients may respond best to different treatment options. The work was led by Professor Adrian Liston, a group leader at the Babraham Institute in Cambridge, UK and at VIB and KU Leuven in Leuven, Belgium.

Juvenile idiopathic arthritis is the most common rheumatic disease in children, but it presents in many different severities and forms. This diversity makes clinical assessment and patient classification difficult.

A team of researchers at Belgian research organisations VIB, KU Leuven and UZ Leuven undertook a detailed biological characterisation of the immune system of hundreds of children with and without juvenile arthritis to help the diagnosis or treatment decisions for this disease.

“Essentially, we took blood samples from more than 100 children, two thirds of whom had childhood arthritis,” explains Erika Van Nieuwenhove (VIB-KU Leuven), and first author of the study. “We analysed their immune system at a greater level of detail than was ever done before for this disease, and simply using this data we then used machine learning to see if we could tell which children had arthritis.”

The results were quite remarkable: the algorithm was about 90% accurate at identifying the children with the disease. “Using only information on the immune system, and no clinical data at all, we could design a machine learning algorithm that was about 90% accurate at spotting which kids had arthritis,” says Professor Adrian Liston (Babraham Institute, Cambridge, UK and VIB-KU Leuven). “This result is a proof-of-principle demonstration that immune phenotyping combined with machine learning holds huge potential to diagnose disease. Similar approaches could be applied to improve patient selection for treatments and clinical trials.”

The researchers are hopeful about the impact of this research in improving patient outcomes. “The tool needs further validation but otherwise there are no scientific barriers to this approach being quickly translated to the clinic,” comments Professor Carine Wouters (UZ Leuven), who was the clinical lead for this study. “Down the line, we could use this kind of detailed classification information—and machine learning analysis—to identify which patients will respond best to specific treatment options.”

Interview with "The Optimist" magazine

Read the article at The Optimist

Thinking back to your PhD, how would you describe this experience.

I quite enjoyed my PhD. The key success in a PhD is to find a match between supervisor and student. I only spoke to my supervisor every 3-4 months, and it was always about concepts and strategy rather than trouble-shooting. For me, I loved the independence that this gave me, and the amazing post-docs in the lab gave me more than enough technical advice. However, some of the other students around me did not like this approach to mentoring, and would have preferred weekly meetings going into the detail of their experiments. This was pure luck on my behalf - I could easily have ended up in a lab that I found stifling, because I didn't ask the right questions going in. This independence let me mould my PhD to my strengths. I learned just a few basic techniques and then applied them to novel questions. It was an approach that let me generate data and papers quickly, and led to an "easy PhD". 

To ask the famous question, is there anything you would like to change retrospectively regarding your PhD?

The flip side of having an "easy PhD" is that I never really had to leave my comfort zone. Since I didn't spend months (or years) painfully learning and optimising new techniques, I never became as technically skilled as the other PhD students around me. Science is so fast moving, that the best strategy is to learn how to learn, which you only get the hard way. Instead, I had my couple of techniques and I had learned how to plan experiments and writing papers. This made my post-doc really difficult - I didn't have the versatility or skill of other post-docs around me, who were picking up and using the latest techniques with trained ease, earned by blood, sweat and tears earlier on. Now as a PI, this deficit is not so important, since my job is all planning and writing, but even now I regret never learning to become a great experimentalist.   

 Which advice or tips would you give us PhDs on our way?

1. Analyse experiments as you go. I started the habit very early on of always analysing experiments once I finished them. By this I mean a full analysis, including a publication level graph, a figure legend and a few lines of text describing the result. It takes a little time, but it means you get real-time feedback on the quality of your experiments - give you have all the right controls, were the numbers high enough to make conclusions, are my conclusions solid enough to plan the next stage, etc. It also made writing papers and a thesis very simple - I just cut and paste my analysed data in, and I was half-way there. Since editing is much less intimidating than writing, I never developed that writing paralysis that some students get.

2. Don't stress about careers! The infamous "bottleneck" in the academic career is mostly illusional. In Flanders, perhaps 15-20% of PhD students go on to a long-term academic career, but even in countries with lower rates (2-5% would be normal) this this not due to a bottleneck. A PhD in biomedical science is one of the most desirable training programs possible for a modern career. The vast majority of people who leave academia are not pushed out; instead, biomed PhDs are leaving academic because of pull-factors - they find highly desirable jobs that they believe they will enjoy more. When I look around at the PhDs that I trained with or that I have trained, I can honestly say that not one has had a career failure. Yes, very few are now research professors, but that is because almost all of them found something else they liked more. Doctor, CEO, start-up company, scientific writer, senior public servant - all great jobs. Very, very few of the 100+ academic careers that I have followed have ended with someone getting pushed out of academia (i.e., timing out of the post-doc fellowship system), and those that did landed on their feet and found a great career that they now say is better suited to them. So.... don't stress about your future career. Concentrate on doing well in your PhD, and start planning your career a year in advance of any decision, but don't make yourself unhappy about uncertainty in which successful career path you will end up taking.