NVTB Schoorl meeting, April 5th 2018
How T cells learn to discriminate "self" from "foreign" during negative selection
Inge Wortel
inge.wortel@radboudumc.nl
Department of Tumor Immunology, Radboudumc
Adapted from National Cancer Institute (NIH)
The immune system's T cell repertoire has to:
1 Sewell (2012). Nature Reviews Immunology.
The immune system's T cell repertoire has to:
Diversity: random TCR
Tolerance: negative selection
1 Sewell (2012). Nature Reviews Immunology.
Healthy humans have many autoreactive T cells! 1
$\rightarrow$ Negative selection is incomplete!
1 Yu et al. (2015). Immunity.
What is the purpose of negative selection
if it is incomplete?
AISs:
Use AIS to investigate the purpose of incomplete negative selection:
Can you see the difference between English and Xhosa?
ceived kuqale erness
kubang lwaban rkness
ceived kuqale erness
kubang lwaban rkness
Three ingredients needed:
1 | Sequence | |
2 | TCR | |
3 | Affinity |
Three ingredients needed:
1 | Sequence | 6-letter strings of text in a certain language |
2 | TCR | |
3 | Affinity |
Three ingredients needed:
1 | Sequence | 6-letter strings of text in a certain language |
2 | TCR | binding motif |
3 | Affinity |
Three ingredients needed:
1 | Sequence | 6-letter strings of text in a certain language |
2 | TCR | binding motif |
3 | Affinity | Longest stretch of adjacent "matches" $\rightarrow$ binding if affinity $\geq$ threshold t |
Example: Xhosa recognition after negative selection on English
Negative selection on 500 English strings (t = 3),
Compare recognition of different English and Xhosa strings
Before: | After: | Best-recognized: |
$\rightarrow$ TCRs distinguish strings they have not seen!
English and Xhosa are rarely recognized by the same TCRs:
Nodes:
Edge if >10,000 TCRs in common
Concordance (same-language neighbors): 81%
This only works if strings are truly different:
Nodes:
Edge if >10,000 TCRs in common
Concordance (same-language neighbors): 50%
Complete specificity (t = 6) | Low specificity (t = 1) | Intermediate specificity (t = 3) |
Negative selection allows self-foreign discrimination:
This works well when:
Use the same model for peptides instead of strings:
Model at t = 4 closely matches experimental estimates
Model (t = 4) | Literature | |
Cross-reactivity: | 1 : 55,000 peptides | 1 : 30,000 peptides 1 |
Typical peptide recognition: | 0 - 20 TCRs / million | 0 - 100 TCRs / million 2-4 |
1 Ishizuka et al (2009). J Immunol.
2 Legoux et al (2010). J Immunol.
3 Blattman et al (2002). J Exp Med.
4 Alanio et al (2010). Blood.
HIV peptides are embedded in large
clusters of self peptides.
Xhosa/English: separate clusters.
$\rightarrow$ self-foreign discrimination will be difficult!
... but possible if TCRs are specific enough:
Computed "optimal" set is enriched in rare AAs, depleted of common AAs
$\rightarrow$ Could enrichment of rare AAs help self-foreign discrimination?
Choose "training" self peptides with a bias for peptides with rare AAs:
Simple bias already improves self-foreign discrimination.
Negative selection remains useful...
...but self-foreign discrimination is hard:
When "self" and "foreign" are similar: