Speaker: Aleksandra Wolczak
Department: Bionanoscience Department
Subject: Diversity of immune repertoires
Location: Building 58, TU Delft
Author: Nemo Andrea
Aleksandra Wolczak is a researcher at the Ecole Normale Supérieure (ENS) and the Centre National Recherche Scientifique (CNRS). She is known for applying statistical dynamics to cells in cases where traditional physics concept such as forces and energy are not a suitable approach. In her seminar, she covered a wide range of concepts concerning the immune system and experiments, with a particular focus on T cells.
The adaptive immune system consist of B and T cells. These cells should be able to detect and react to foreign pathogens. This is mediated through a great diversity of receptors on these cells’ membranes. It is estimated that there are around 10^9 receptors in the human body in healthy individuals. If all these different receptors were hardcoded in the DNA, it would have to be impossibly large. The way that this incredible variety is obtained is through alternative splicing of gene regions known as V, D and J. Various parts if these regions can be spliced together to create significantly more diverse set of receptors than if all the genes were simply transcribed. Additionally, random insertions and deletions in the regions where these V, D and J regions are separated allow for even greater diversity in the repertoire of receptors. So the staggering diversity in receptors comes not from the size of the DNA but from the combined effect of combinatorics and randomness.
 flow chart of the analysis pipeline of the model
They generated a probabilistic model for receptor generation, by creating a model that self learns and can calculate the probability that a certain receptor is generated. Furthermore, this model could also predict the specific mutations and splicing events that must have taken place (inference of the cause given an outcome). Additionally, they found that at the level of generation (the receptors generated in the immune system before any selection takes place) where very different between people. Their model predicted that you share as many receptors during generation with family members as complete strangers, with the exception of identical twins. The exception can be explained by the fact that identical twins share blood in the womb, thereby bringing their immune system together to some extent.
Another central question of research was what the optimal distribution of membrane receptors was. One can imagine that one might want to cover the widest range of receptors, while also producing more of the type that is compatible with the most common pathogen receptors. They modeled this with a model in which each receptors has a certain cross reactivity, which means each receptor can still bind to related receptors (albeit less strongly). Their model predicted that the optimal receptor repertoire was a peaked distribution with coverage following the antigen distribution. In practical terms this meant that the receptors were most strongly present at common antigen receptor types and the less common pathogens would be covered by cross reactivity. Such a setup still provided adequate antigen coverage, as can be seen from the image below.
 optimal receptor distribution (1D)
Another experiment conducted by her research group related to mutation and receptor effectiveness. Here, they mutated receptors and tested them against one single antigen. This way they could study the effect of mutations on receptor evolution. To asses the difference in affinity of the mutated receptor for the antigen, they used a new experimental approach called Tite-Seq. This approach determines the affinity by means of something that can be seen as analogous to a titration curve (where antigen concentration is gradually increased and fluorescence is measured), rather than a traditional measurement which is usually done at a single concentration. This method would give a more accurate assessment of concentration, as a single concentration measure could easily be deceiving. These experiments showed that most mutations were detrimental, and only a small fraction of mutation actually improved affinity.
 Effects of mutations on receptor affinity and expression
I found this seminar to be very enlightening, as it covered such a wide range of experiments and disciplines. Seeing how theory of statistical dynamics, modelling, cell biology, and evolutionary dynamics were all covered in this short seminar, I think I will certainly read some more of her research group’s work. It was also fantastic to see more about the immune system, which is something we haven’t covered in any significant detail in the nanobiology course, showing that these seminars can be complementary to the course material. Added to that, I was intrigued by the probabilistic model they created, as it seems to be a form of a Bayesian Network, which is something I’m currently trying to code as a pastime project. Lastly, it surprised me that tite-seq was a new technology, since the arguments she made in favour of this new method seemed particularly convincing to me. Maybe there are some drawbacks of this method that I don’t see with my limited practical experience, but it may be prudent for the researchers working with binding affinity even in in the bionanoscience department to consider using this method.