DLM, 02-07-2018

Learning by example:

How T cells learn to discriminate "self" from "foreign" during negative selection

Inge Wortel
inge.wortel@radboudumc.nl
Department of Tumor Immunology, Radboudumc

T cell diversity & negative selection: a problem of numbers

The immune system's T cell repertoire has to:

• recognize >10¹⁵ foreign peptides ... ¹
• ... while tolerating hundreds of thousands of self peptides

¹ Sewell (2012). Nature Reviews Immunology.

T cell diversity & negative selection: a problem of numbers

The immune system's T cell repertoire has to:

• recognize >10¹⁵ foreign peptides ... ¹
• ... while tolerating hundreds of thousands of self peptides

Diversity: random TCR

Tolerance: negative selection

¹ Sewell (2012). Nature Reviews Immunology.

Does negative selection achieve tolerance?

Healthy humans have many autoreactive T cells! ¹

$\rightarrow$ Negative selection is incomplete!

¹ Yu et al. (2015). Immunity.

Approach: Artificial Immune System (AIS)

Use AIS to investigate the purpose of incomplete negative selection:

• Make repertoires with millions of different TCRs
• Simulate negative selection in silico
• General classification algorithm

A classic classification problem: languages

Can you see the difference between English and Xhosa?

A simple model of TCR-sequence recognition

Three ingredients needed:

A simple model of TCR-sequence recognition

Three ingredients needed:

A simple model of TCR-sequence recognition

Three ingredients needed:

A simple model of TCR-sequence recognition

Three ingredients needed:

Simulating negative selection in silico

Example: Xhosa recognition after negative selection on English

Negative selection allows discrimination

Negative selection on 500 English strings (t = 3),
Compare recognition of different English and Xhosa strings

Before:	After:	Most frequently recognized:

$\rightarrow$ TCRs distinguish strings they have not seen!

Why discrimination on unseen strings?

TCRs rarely react to both English and Xhosa:

Nodes:

• English
• Xhosa

Edge if >10,000 TCRs in common

Concordance (same-language neighbors): 81%

Why discrimination on unseen strings?

This only works if strings are truly different:

Nodes:

• English
• More English

Edge if >10,000 TCRs in common

Concordance (same-language neighbors): 50%

Negative selection works only if self and foreign are dissimilar

Summary: what have we learned from languages?

Negative selection allows self-foreign discrimination:

• Even on "unseen" sequences
• Even when tolerance is incomplete

But self and foreign should be sufficiently dissimilar.

Back to self versus foreign peptides

Back to self versus foreign peptides

Use the same model for peptides instead of strings:

Peptides from "self" and "foreign" proteomes are similar

Peptides from "self" and "foreign" proteomes are similar

 HIV peptides are embedded in
 clusters of self peptides.

  Xhosa/English: separate clusters.

$\rightarrow$ self-foreign discrimination will be difficult!

Self-foreign discrimination is difficult

... but remains possible:

What if thymic self peptides are non-random?

• random self peptides are often similar and will delete the same TCRs
  $\rightarrow$ this is inefficient
• some peptides are exchangeable, others are not
  $\rightarrow$ a training set with more non-exchangeable peptides might do better:

"Optimal" training peptides improve self-foreign discrimination

Removal of self-reactivity $\neq$ self-foreign discrimination! Why does this work?

Why better self-foreign discrimination?

Self peptides with low exchangeability less often resemble foreign peptides:

$\rightarrow$ non-exchangeable peptides efficiently remove self-reactive TCRs, but preserve foreign-reactive ones!

How could the thymus be "optimal"?

Computed "optimal" set is enriched in rare AAs, depleted of common AAs.
Peptides with rare AAs tend to be less exchangeable:

$\rightarrow$ Could enrichment of rare AAs help self-foreign discrimination?

How could the thymus be "optimal"?

Simple bias already improves self-foreign discrimination.

The same is true for other pathogens

• Poor self-foreign discrimination after selection on random peptides
• Improved self-foreign discrimination after selection on biased peptides

Self-foreign dissimilarity is no longer necessary

Randomly split the self peptides into "self" and "foreign":

$\rightarrow$ T cells can discriminate, no matter how much a virus resembles self!

Summary: self-foreign discrimination in the immune system

Negative selection remains useful...

• Even incomplete negative selection can allow T cells
  to discriminate "self" from "foreign".
• This reconciles textbook theory with
  surprising experimental data.

...but self-foreign discrimination is hard:

• high self-foreign similarity hampers discrimination after selection
• Prediction: we can overcome this with training peptides biased for rare AAs
  $\rightarrow$ because of AA bias in peptide presentation pathway?

Acknowledgements

Theoretical Biology, Utrecht

• Rob de Boer
• Can Kesmir

Physiology, McGill University Montreal

• Judith Mandl