Download Host institution: Bioinformatics Laboratory Academic Medical Center

Host institution:
Bioinformatics Laboratory
Academic Medical Center (AMC)
University of Amsterdam (UvA)
Meibergdreef 9
1105 AZ Amsterdam
The Netherlands
Contact:
Prof. dr. A.H.C. van Kampen
http://www.bioinformaticslaboratory.nl
[email protected]
+31-20-5667096
Title:
Integration of T cell and B cell models to study disease related to the
adaptive immune response
Short topic description (max. 1000 words)
Background
B cells are lymphocytes involved in the humoral immunity of the adaptive immune system.
These cells are activated by antigens (eg, bacterial proteins; non-self antigen) or auto-antigens
(self antigen) as is the case in autoimmune disorders. B cells bind antigens through their B cell
receptors (BCRs). Each B cell has a unique BCR. Consequently, the full population of B cells
recognizes a broad range of antigens. The activation of B cells leads to the formation of
germinal centers (GC; Victoria, 2012) that facilitate affinity maturation. This comprises
iterative cycles of cell proliferation, differentiation to B memory and plasma cells, and somatic
hypermutation (SHM) to increase to affinity of the BCR for the antigen. Affinity maturation
has been modelled by several groups through the use of ordinary differential equations
(ODEs). Figure 1 shows the affinity maturation model of Oprea (1997). This model comprises
B cell blasts (B), centroblasts (CB), centrocytes (CC), and output cells (plasma and memory
cells). T follicular help cells (TFH) are not explicitly modelled but included through the
parameter ρ.
Figure 1. Model of B cell affinity maturation in the germinal center (Oprea and Perelson (1997)
Other models have focussed on T lymphocytes. For example, Khailaie (2013) developed an
ODE model for immune activation in which self antigens and nonself antigens are not
distinguished (Figure 2). Their model considers the dynamic interplay of conventional T cells,
regulatory T cells (Tregs), and interleukin 2 (IL-2) signalling molecules. The model shows that
the renewal rate ratio of resting Tregs to naïve T cells as well as the proliferation rate of
activated T cells determine the probability of immune stimulation. The actual initiation of an
immune response, however, relies on the absolute renewal rate of naïve T cells. This result
suggests that thymic selection reduces the probability of autoimmunity by increasing the Agstimulation threshold of self reaction which is established by selection of a low number of
low-avidity autoreactive T cells balanced with a proper number of Tregs. In this model, self and
nonself appear as a result of shifted Ag-stimulation thresholds which delineate different
regimes of immune activation.
Figure 2. T cell model from Khailaie (2013).
Objective
In this proposed CASyM exchange project we aim to integrate the T cell model from Khailaie
and the B cell model of Oprea to obtain a more comprehensive model of the adaptive immune
response. This implies two steps. First we need to extend the Oprea model by explicitly
including TFH cells. Secondly, we need to model the TFH differentiation from naïve T cells to
link the two models. TFH differentiation is reviewed by, for example, Crotty (2014).
Subsequently, we will investigate how this model (and modifications/extensions thereof) can
be used to study immune-related disorders in which the underlying adaptive immune system
is not functioning normal. For example, how does the ratio of TFH to naïve T cells affect the B
cell response? Examples, of such disease provide autoimmune disorders such as rheumatoid
arthritis, and cancers such as leukemia and lymphoma.
This project will be a collaboration with Dr. Niek de Vries (dept. of Rheumatology and Clinical
Immunology), and Dr. Jeroen Guikema (department of pathology) who’s main interest lies
with somatic hypermutation and somatic recombination in leukemia/lymphoma. As part of
the collaboration with De Vries we use Next Generation Sequencing (NGS) data to determine
T and B cell responses in autoimmune disorders (e.g., Klarenbeek, 2010; Doorenspleet, 2014).
We will investigate if this data can be used to parameterize the integrated model.
The project comprises the following steps:
1. Extension of the Oprea model which we already implemented in R, with T follicular help
cells.
2. Implementation and reproduction of results of the Khailaie model. We will implement the
model in R (www.r-project.org) using the package dsolve (http://desolve.r-forge.rproject.org/).
3. Brief literature review to find (a) get a better understanding of the relation between the
T cell and B cell response, and (b) get a better understanding of the defects in the adaptive
immune system underlying autoimmune disorders and/or leukemia/lymphoma.
4. Integration of the T and B cell model. Once integrated, we will investigate how the
integrated model can be used to study immune-related disease.
References

Crotty S. (2014) T follicular helper cell differentiation, function, and roles in disease. Immunity. 41(4):529.

Doorenspleet ME, Klarenbeek PL, de Hair MJH, van Schaik BDC, Esveldt REE, van Kampen aHC, Gerlag DM,
Musters a, Baas F, Tak PP, de Vries N (2014) Rheumatoid arthritis synovial tissue harbours dominant B-cell
and plasma-cell clones associated with autoreactivity. Annals of the rheumatic diseases 73:756-762.

Khailaie S, Bahrami F, Janahmadi M, Milanez-Almeida P, Huehn J, Meyer-Hermann M (2013) A mathematical
model of immune activation with a unified self-nonself concept. Frontiers in immunology 4:474.

Klarenbeek PL, Tak PP, van Schaik BDC, Zwinderman AH, Jakobs ME, Zhang Z, van Kampen AHC, van Lier
RAW, Baas F, de Vries N (2010) Human T-cell memory consists mainly of unexpanded clones. Immunology
letters 133:42-48.

Oprea M, Perelson AS (1997) Somatic mutation leads to efficient affinity maturation when centrocytes
recycle back to centroblasts. Journal of immunology, 158:5155-5162

Victora GD, Nussenzweig MC (2012) Germinal centers. Annual review of immunology 30:429-457.
The preferred background of the fellow
The ideal candidate has knowledge/skills about computational/mathematics modelling,
bioinformatics, systems biology. Knowledge and working knowledge of Ordinary Differential
Equations. Knowledge of the R statistical package. Knowledge of immunology.
The expected duration of the research exchange (1 – 3 months)
3 months