PhD opportunity in the Liston lab!

Are you interested in a PhD in neuroimmunology? Want to find out how to harness the power of the immune system to cure traumatic brain injury? Check out our PhD position here. It is a rare chance to join a fantastic international team, and to learn to do high level science in a supportive and caring environment.

A successful candidate will be:

• passionate about science and the project
• experienced at failure, with a track-record in the resilience needed to pick yourself up and try again
• willing to be wrong, willing to learn, willing to improve
• driven to make a difference, discover new biology or move a promising therapeutic to the next stage
• creative and imaginative
• detail-orientated and reliable

The successful candidate does not need:

• experience at immunology or neuroscience. You are here to learn, not start as an expert!
• technical experience in X, Y or Z - as above
• a perfect CV. I'm interested in seeing that you know how to succeed in the face of adversity

 If you are submitting an application, consider a co-application to a Cambridge College, such as Peterhouse.

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

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.”

EMBO Young Investigator meeting

Great meeting with great people

Punting on the Cam

Visiting the original lab books of Rosalind Franklin

Identical twins light the way for new genetic cause of arthritis

Identical twin girls who presented with severe arthritis helped scientists to identify the first gene mutation that can single-handedly cause a juvenile form of this inflammatory joint disease. By investigating the DNA of individual blood cells of both children and then modelling the genetic defect in a mouse model, the research team led by Adrian Liston (VIB-KU Leuven) was able to unravel the disease mechanism. The findings will help to develop an appropriate treatment as well.

Juvenile idiopathic arthritis is the most common form of all childhood rheumatic diseases. It is defined as arthritis that starts at a young age and persists throughout adulthood, but which does not have a defined cause. Patients present with a highly variable clinical picture, and scientists have long suspected that different combinations of specific genetic susceptibilities and environmental triggers drive the disease.

A single gene mutation

In a new study by researchers at VIB, KU Leuven and UZ Leuven, the cause of juvenile arthritis in a young pair of identical twins was traced back to a single genetic mutation.

"Single-cell sequencing let us track what was going wrong in every cell type in the twin’s blood, creating a link from genetic mutation to disease onset,” explains Dr. Stephanie Humblet-Baron, one of the researchers involved in the study. “It was the combination of next generation genetics and immunology approaches that allowed us to find out why these patients were developing arthritis at such a young age.”

Of mice and men

Parallel studies in mice confirmed that the gene defect found in the patients’ blood cells indeed led to an enhanced susceptibility to arthritis. Prof. Susan Schlenner, first author of the study, stresses the relevance of this approach: "New genetic editing approaches bring mouse research much closer to the patient. We can now rapidly produce new mouse models that reproduce human mutations in mice, allowing us to model the disease of individual patients."

According to immunology prof. Adrian Liston such insights prove invaluable in biomedical research: “Understanding the cause of the disease unlocks the key to treating the patient.”

From cause to cure

Liston’s team collaborated closely with prof. Carine Wouters, who coordinated the clinical aspect of the research: "The identification of a single gene that can cause juvenile idiopathic arthritis is an important milestone. A parallel mouse model with the same genetic mutation is a great tool to dissect the disease mechanism in more detail and to develop more effective targeted therapies for this condition.”

And the little patients? They are relieved to know that scientists found the cause of their symptoms: "We are delighted to know that an explanation has been found for our illness and more so because we are sure it will help other children."

Thankfully, the children’s arthritis is under good control at the moment. Thanks to the new scientific findings, their doctors will be in a much better position to treat any future flare-ups.

NFIL3 mutations alter immune homeostasis and sensitise for arthritis pathology 

Schlenner et al. 2018 Annals of the Reumatic Diseases

The genetics behind immune system variation

Unlocking The Secrets Of A Rare Immune Disease

by Adrian Liston and Josselyn Garcia-Perez 

Primary immunodeficiencies (PID) are a heterogeneous group of disorders that disturb the host’s immunity, creating susceptibility to infections. PIDs are genetically diverse, with mutations in many different genes capable of causing immunodeficiency. The clinical symptoms of PIDs include, but are not limited to, susceptibility to infections, inflammation, and autoimmunity, although each gene mutated, and indeed each individual mutation, can lead to different manifestations.

Central to understanding PIDs is to understand which immune cell type is rendered defective by the mutation the patient carries. The type of infections the patient develops is often a key indicator of the underlying immunodeficiency; for example, pulmonary infections and bacterial septicemia are associated with B cell defect, whereas fungal susceptibility is associated with defects in certain types of T cells. Candidate pathways can be investigated using genetics and immune screening, and successful identification of the underlying causes allows a treatment program to be tailored to the patient.

Read the full story on Science Trends

Golden Pipette won by Dr Carly Whyte

Congratulations to Dr Carly Whyte, for winning the Golden Pipette!

Carly won the Golden Pipette for her mind-boggling data on how the cellular source of IL-2 profoundly alters the impact of this key cytokine on the cells around it. Data to be published, as soon as we understand it!

Carly will be moving over to the Babraham in January. Will the Golden Pipette be won back by team Leuven in time? Or will Cambridge take ownership of this proud trophy?