Understanding the immune systemA Challenge or impossible dream

What do we (think we) understand

• Class I pathway• Proteasomal cleavage• TAP transport• Binding to MHC• Supertype clustering and epitope selection

 

http://www.nki.nl/nkidep/h4/neefjes/neefjes.htm

Processing of intracellular proteins

• Class I pathway• Proteasomal cleavage• TAP transport• Binding to MHC• Supertype clustering and epitope selection

• Exceptions• K epitopes• Alternative translocation to ER• Alternative epitope splicing• Supertypes do not binding identical set of peptides• Some alleles can not be supertype clustered

What do we NOT understand

We don’t under

stand

anything

P9K ligands

• P9 of MHC ligands is generated by the proteasome!!• Frequency of amino acids at P9 in MHC ligands should reflect preference for proteasomal cleavage•This is not the case for all amino acids

P9K ligands

P9K ligands

• P9 of MHC ligands is generated by the proteasome!!• Frequency of amino acids at P9 should reflect preference for proteasomal cleavage•This is not the case for all amino acids• Suggests a protease other than the proteasome is likely involved in the generation of the C-termini of P9K ligands.

Peptides from endogenous proteins are presented at the cell surface in

complex with MHC class I

TAP independent epitope presentation

Proteasome

TAP

exopeptidase

MHC-I

protein

Hydrophobicpeptides

Sec61

Unknownentry route

furin

regurgitationER

How to gain access to MHC-I

-Normal entry throug TAP

-Peptides within SP gain entry through Sec61 translocon.

-Unknown ER proteases cleave proteins in ER membrane or lumen

-Furin cleave proteins in Post-ER compartment

-Simple diffusion across membranes by hydrophobic peptides

-Regurgitation

-Unknown entry route

Datasets

From Andreas Weinzierl, Tübingen University: 40 MHC-I epitopes eluted from the surface the human .174 cell line that doesn’t express TAP

For comparison:From SYFPEITHI (http://syfpeithi.bmi-heidelberg.com/): 308 MHC-I epitopes eluted from the surface of normal, TAP-containing human cells

A2

-Normal entry throug TAP

-Peptides within SP gain entry through Sec61 translocon.

-Unknown ER proteases cleave proteins in ER - membrane or lumen

-Furin cleave proteins in Post-ER compartment

-Simple diffusion across membranes by hydrophobic peptides

-Regurgitation

-Unknown entry route

?Epitope

Ex for name of sourceprotein (acc. to sp or nr)

Startposepitope

Signal-peptide

SPase cleavage (NN/HMM)

SPase cleavage site - Endpos

.174/.45

LLSAEPVPA CD79B_HUMAN 20 Yes 28 0 557

LLGPRLVLA TMP21_HUMAN 23 Yes 31 0 253

ALSAYDLVL Q6LCB5_HUMAN 29 yes 38 1 186

SLWGQPAEA CO4A5_HUMAN 18 Yes 26 0 124

VLAPRVLRA RCN1_HUMAN 21 Yes 29 0 120

ALVVQVAEA HEXB_HUMAN 34 Yes 28/42 0 116

LLAAWTARA A4_HUMAN 9 Yes 17 0 92

VLLKARLVPA gb|AAY24258.1 19 Yes 32/28 0 48

KMDASLGNLFA FAM3C_HUMAN 30 Yes 24 -16 36

LLFSHVDHVIA NAC1_HUMAN 25 Yes 35 0 28

FLGPWPAAS AMRP_HUMAN 22 Yes 28/32 -2/2 24

SLYALHVKA VKOR1_HUMAN 23 Yes 26/34 -5/3 23

LLLSAEPVPA CD79B_HUMAN 19 Yes 28 0 22

AMAPPSHLLL gb|AAC17709.1 473 Yes 21 -461 18

FLLGPRLVLA TMP21_HUMAN 22 Yes 31 0 18

LLLDVPTAAV GILT_HUMAN 26 Yes 37 2 18

LLLDVPTAAVQA GILT_HUMAN 26 Yes 37 0 15

LLDVPTAAV GILT_HUMAN 27 Yes 37 2 14

VLFRGGPRGLLAVA SSRA_HUMAN 19 Yes 20 -12 13

LLSAEPVPAA CD79B_HUMAN 20 Yes 28 -1 13

AVLALVLAPAGA NRP1_HUMAN 10 Yes 21 0 13

LAPRVLRA RCN1_HUMAN 22 Yes 29 0 4

AALLDVRSVP GDF5_MOUSE 269 Yes 27 -251 4

LLATLAAAML CLP24_HUMAN 177 Yes 25 -161 0,05

ribosome

Sec61

ER membrane

Cytosol ER lumen

Epitopes present in the N-terminal part of the SP

N’ C’

A2, cont.

B51

-Normal entry throug TAP

-Peptides within SP gain entry through Sec61 translocon.

-Unknown ER proteases cleave proteins in ER - membrane or lumen

-Furin cleave proteins in Post-ER compartment

-Simple diffusion across membranes by hydrophobic peptides

-Regurgitation

-Unknown entry route

?

Protein with SP

Protein without SP

Epitope

Ex for name of sourceprotein (acc. to sp or nr)

Startosepitope

Signal-Peptide (SignalP)

SPase cleavage

SPase cleavage site - Endpos

.174/.45

ALLSSLNDF NIF3L_HUMAN 5 No   na 13

LLHPPPPPPPA RANB9_HUMAN 68 No   na 13

QLQEGKNVIGL TAGL2_HUMAN 165 No   na 8

SLPKKLALL L10K_HUMAN 72 No   na 3

Epitope

Ex for name of sourceprotein (acc. to sp or nr)

Startpos epitope

Signal-peptide

(SignalP)

SPase cleavage-site

SPase cleavage site - Endpos

.174/.45

HGVFLPLV K0247_HUMAN 21 Yes 39 11 92

MAPLALHLL FIG1_HUMAN 1 Yes 21 12 18

MASRWGPLIG CAB45_HUMAN 8 Yes 36 19 5

MAPRTLVL 1A02_HUMAN 4 Yes 24 13 0,5

MAPRTLIL 1C03_HUMAN 4 Yes 24 13 0,2

GSHSMRYF 1A01_HUMAN 25 Yes 24 -8 0,2

ILAPAGSLPKIref|XP_514384.1| 328 No   na 6

KAPVTKVAA PDLI1_HUMAN 240 No   na 2

NPLPSKETI TYB4_HUMAN 26 No   na 1

NPYDSVKKI FAT10_HUMAN 25 No   na 0,2

DALDVANKIGII RL23A_HUMAN 145 No   na 0,07

YPFKPPKV UB2E3_HUMAN 120 No   na 0,04

Proteasome

TAP

exopeptidase

MHC-I

protein

Hydrophobicpeptides

Sec61

Unknownentry route

furin

regurgitationER

How to gain access to MHC-I

-Normal entry throug TAP

-Peptides within SP gain entry through Sec61 translocon.

-Unknown ER proteases cleave proteins in ER membrane or lumen

-Furin cleave proteins in Post-ER compartment

-Simple diffusion across membranes by hydrophobic peptides

-Regurgitation

-Unknown entry route

Presentation of alternatively spliced epitopes

Presentation of Noncontiguous peptides

• The conventional approach to epitope discovery is to use overlapping peptides

• What if splicing of noncontiguous peptides occure?

HLA-A3 Antigen produced by splicing of Noncontiguous peptides

Warren et al. Science, 313, p 1444, 2006

HLA-A3 Antigen produced by splicing of Noncontiguous peptides

Warren et al. Science, 313, p 1444, 2006

NetMHC version 3.0. Prediction using Neural Networks. Allele A0301.Strong binder threshold 50.00. Weak binder threshold 500.00.

-------------------------------------------------- pos peptide 1-log50k(aff) affinity(nM) Bind Level Identity-------------------------------------------------- 0 STPKRRHKK 0.4237 510 A3 1 TPKRRHKKK 0.1019 16598 A3 2 PKRRHKKKS 0.0071 46309 A3 3 KRRHKKKSL 0.0082 45761 A3 4 RRHKKKSLP 0.0137 43097 A3 5 RHKKKSLPR 0.1051 16035 A3 6 HKKKSLPRG 0.0085 45624 A3 7 KKKSLPRGT 0.0091 45326 A3 8 KKSLPRGTA 0.0109 44425 A3 9 KSLPRGTAS 0.0991 17110 A3 10 SLPRGTASS 0.0608 25887 A3 11 LPRGTASSR 0.0732 22656 A3--------------------------------------------------

Number of high binders 0. Number of weak binders 0. Number of peptides 12

Antigen produced by splicing of Noncontiguous peptides

Warren et al. Science, 313, p 1444, 2006

Warren et al. Science, 313, p 1444, 2006

Antigen produced by splicing of Noncontiguous peptides

Final peptide: SLPRGTSTPKA3 motif: P2:L, P9:K

HLA-A3 Antigen produced by splicing of Noncontiguous peptides

Warren et al. Science, 313, p 1444, 2006

NetMHC version 3.0. Prediction using Neural Networks. Allele A0301.Strong binder threshold 50.00. Weak binder threshold 500.00.

-------------------------------------------------- pos peptide 1-log50k(aff) affinity(nM) Bind Level Identity-------------------------------------------------- 0 SLPRGSTPK 0.5029 216 WB SLPRGSTPK--------------------------------------------------

Number of high binders 0. Number of weak binders 1. Number of peptides 1

Supertypes. What are they good for?

• Alleles within supertypes present the same set of peptides!

 

Clustering of HLA alleles

O Lund et al., Immunogenetics. 2004 55:797-810

Supertypes. What are they good for?

• Alleles with in supertypes present the same set of peptides!• Is this really so?• Less that 50% of A6802 binders will bind to A0201!

• Less than 33% of A0201 binders will bind to A6802!

The truth about supertypes!

A2

A24

A26

A3

A1

Supertypes are good for getting funding, but..

• Need to define more refined method for identifying promiscuous epitopes• Need to develop method to predict binding across all HLA alleles

• Supertypes is too simple a picture

What more do we (think we) understand• Why are epitopes 9 amino acids long?

• Why did nature not choose 15mers?

• Which class I presented peptides can bind TCR?

• Or can we estimate TCR cross reactivity?

Why 9mers?

• Why did the immune system settle on presentation of 9mer peptides?

• The proteasome generates mostly fragments of 4-7 amino acids

• TAP preference peptides of 8-18 amino acids

• MHC preference peptides of 8-12 amino acids

• So why 9?

Information processing in the immune system

• How many different self peptides do we have? • How much information is present in a 9-mer? • Can you discriminate self from non-self based on the information in 9-mers?

Burroughs, De Boer & Kesmir, Immunogenetics, 2004, 56(5):311-20

Size of self

107 << 209 : self is a small fraction of peptide space: Overlap with other “selfs” expected to be small.

Size of self - continued

Discriminating self

9-mers provide enough information to discriminate: Average overlap human and pathogens <1% (Overlap between unrelated bacteria also 1%)

Overlap with human depend on evolutionary distance

Conclusions

• Information in 9-mers sufficient to discriminate self from non-self • The chance that an immunodominant pathogenic peptide resembles self < 0.5%

TCR self-tolerance

• What determines if a MHC ligand can elicit an immune responds?• Similarity to self peptide• What is similar?• What amino acids determine similarity?

• How broad is the cross reactivity?

xx

TCR recognition of MHC:peptide complex

TCR data processing

Experimental data

Normalization & frequency conversion

Visualization (logo)

or

G10, INFg, APL

T5INFg, APL

G10Cr, APL

T5CR, APL

B7Cr, APL

2G4Cr, APL

161cr, PSCL

PBMCINFg, APL

3F4Cr, PSCL, Amididated C-terminus

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 1 2 3 4 5 6 7 8 9 10

Peptide position

Shannon Information

data

median

Shannon information

TCR and MHC specificity profile anti-correlates

HLA-A0201

HLA-A0201 restrictedHIV Gag CTL clone

Predict TRC cross reactivity

• TCR cross-reactivity can (partly) be characterized from a specificity profile, and amino acid conservation.

• Use cross reactivity model to predict cross reactivity

• Explain lacking immunogenecity of predicted CTL epitopes

• Overlap between cross reactivity space and host genome

Conclusions

• We might thinks we understand parts of the immune system, but nothing is ever always as we would like