Download Modeling homeostatic T cells responses Benedict Seddon MRC

Modeling homeostatic T cells responses
Benedict Seddon
MRC National Institute for Medical Research
London, U.K.
T cell homeostasis : keeping the immune system the right size
Activated/Effector
T cells
Naïve T cell pool
Thymus
Ag
INPUT
Resting
memory
Resource
competition
Death
The homeostatic T cell response to lymphopenia
Activated/Effector
T cells
Naïve T cell pool
Thymus
Ag
INPUT
Memory
Lymphopenia
• Infection, stress, HIV
• Thymectomy/old age
• Clinical intervention (chemo and radio therapy)
Different signals for different subsets
Naïve T cells
Cytokines + TCR
Memory T cells
Cytokines
CD4
(s)pMHC
spMHC
IL-7
dc
dc
Stromal cell
CD8
IL-7
IL-15
IL-7 - survival
IL-15- proliferation
More division but more death also
Measuring homeostatic T cell responses in vivo
Lymphopenia Induced Proliferation (LIP)
Dye label T cells
Hosts with no T cells
14 d
CFSE
1. Proliferation (CFSE)
2. Survival (cell recovery)
Heterogeneous LIP response of polyclonal CD8 T cells
Day 13
Day 6
Memory/effector
41%
91%
Naive
59%
CD44
9%
CFSE
Proliferative responses by different clones are heterogenous
F5 - low avidity
10
10
10
5
10
4
10
3
10
5
4
3
0
0
0
OT1- high avidity
10
10
10
10
3
10
4
10
5
5
10
4
10
3
10
0
10
0
10
3
10
4
10
5
5
4
3
0
0
CD44
CD122
0
CFSE
10
3
10
4
10
5
CFSE
3
10
4
10
5
Antigen and homeostatic induced proliferation
Foreign
Homeostatic
Self-pMHC
TCR signal
pMHC
IL-7R
IL-2R
γc cytokine
IL-2
growth factor
proliferative
IL-7
Trying to understand cell cycle control during LIP
d3
d7
How can modeling help
understand LIP ?
Define cellular events underlying
- Flu
d14 the
- flu
response.
Does LIP follow the same rules as
antigen responses ?
Are they governed by the same
biological programme ?
d3 Creating
+ flu successful models can
+ Flu
identify key parameters that can
help understand the biological
CD44
processes
Flu specific F5 TCR
transgenic T cells
CFSE
Autopilot divisions model
Stochastic divisions
Generating time series data to model
d7
d3
210
d0=91%
43 2 1 0
d0=31%
d11
d14
> 6 6 543 2 1 0
> 6 6 543 2 1 0
d0=12%
d0=7%
Time series data for F5 TCR transgenic T cells
2.5
Mean divisions
2
1.5
1
0.5
0
0
2
4
6
8
10
Time (d)
12
14
Cell division + cell survival = expansion
Observed expansion
Predicted expansion from CFSE
10
10
Total
Adjusted
8
Fold Expansion
Fold Expansion
8
6
4
6
4
2
2
0
0
0
2
4
6
8
10
Time (d)
12
14
0
no/little cell death
5
10
Time (d)
15
20
Comparing the Autopilot and Single divisions models - mean divisions
Autopilot
Single divisions
Comparing the Autopilot and Single divisions models - mean divisions
Autopilot
Single divisions
Time dependent reduction in λ - µ
λ(t) = λ0 exp(− µ t)
Comparing the Autopilot and Single divisions models - CFSE profiles
d3
d4
d6
d7
Frequency of cells in each division
Observed
Model prediction
d11
d14
Parameters for model
Autopilot
Weibull distribution
T (lagtime) = 0
∆=2.88 d
Single divisions
Const λ :
Reducing λ :
T = 2.03d
T = 2.44d
λ= 0.21
λ0= 0.455 day-1
∆ =6.65h
∆=6.19h
µ=0.115
Testing the model
•
Parameter values are biological reasonable
•
Important predictions of the model ?
– Single stochastic divisions
– Lag time ~ 2 days to division onset
– Slowing in division rate
‘Autopilot’ antigen induced proliferation
TCR signal
Naïve
T cells
IL-2 - γc cytokine
- growth factor
- proliferative
Autocrine loop
Homeostatic proliferation
TCR signal
Triggered by
Self-pMHC
IL-7 - γc cytokine
- growth factor
- proliferative
Naïve
T cells
competition
IL-7
Acknowledgements
Present:
Georgina Cornish
Claire Pearson
Manoj Saini
Charles Sinclair
Sim Tung
LAB
Past :
Andrew Yates,
Emory University, Atlanta, U.S.
Robin Callard
Institute for Child Health, UCL
Rodolphe Thiébaut
INSERM U875, Université Bordeaux
Mark Coles
Department of Biology and HYMS
University of York, U.K.
Anne Mathiot