Innovative methods to identify specific epitopes and associated antigens from a variety of different disease
applications and strategies to apply this technological framework to the study of alopecia areata.
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Large Scale Epitope Identification Screen and Its Potential Application to the Study of Alopecia Areata
1. Technology is available to generate
targeted experimental data
⢠Technology advancement allow high throughput
antigen and epitope identification
⢠Combination of bioinformatics, proteomics, next
generation sequencing and high throughput
assays
⢠Several NIAD contracts tackled biodefense
targets, emerging and reemerging diseases, and
allergens
⢠Field moving past anecdotal evidence, into
population based studies
2. Large scale epitope identification
suggests new vaccine targets
2
Lindestam Arlehamn et al. PLoS Pathog. 2013
4. Large scale epitope identification enables
definition of correlates of protection
⢠High resolution map of T cell
responses in the general
population of an endemic
area (Sri Lanka)
⢠408 epitopes described, 80%
novel
⢠Over 700 patients from
hyperendemic areas
⢠CD8 T cells are associated
with protection from DENV
4
Weiskopf et al. PNAS 2013
Magnitude per responder
0 5 10 15 20
0
1000
2000
3000
4000
5000
p= 0.04
Protection ------- Susceptibility
AverageSFC/responder
Frequency of responses
20
30
40
p= 0.36
ofresponders[%]
Magnitude per Epitope
0 5 10 15 20
0
100
200
300
400
500
p= 0.02
Protection ------- Susceptibility
AverageSFC/epitope
Breadth of response
15
20
25
p= 0.2
numberof
/responder
5. Novel protein identification
immunoproteomics
⢠Pollen extract separated on
2D gel
⢠Spots picked, based on
antibody or protein staining
⢠Spots cut out from gel,
analyzed in mass
spectrometer
⢠83 new proteins from 2D gel
+ 10 proteins from whole
extract mass spec were
chosen for further studies
Schulten et al. PNAS 2013
6. T cell antigen identification based on
HLA class II binding predictions
⢠Predict peptides binding to a panel of 25 HLA
class II molecules (DR, DP, DQ)
⢠Synthesize 822 peptides that bind
promiscuously (>12 HLA variants)
⢠Test peptides as pools for IL-5 production in
PBMC from TG allergic donors
8. Towards large-scale screen of potential
targets for T cell recognition in AA
⢠The issue of large versus small
⢠Donor recruitment
⢠HLA typing of donor cohort
9. The issue of large versus small for
large-scale screens
⢠According to one approach, it is most relevant to
study in situ T cells during acute episodes
⢠High in biological relevance - but not suited to
high throughput epitope/antigen identification
⢠Memory and resident T cells, especially away
from acute phases, recirculate in the periphery
and they are readily detected in PBMC
⢠Examples from TB, allergies, influenza, herpesâŚ
10. Donor recruitment
⢠Based on these considerations we moved to
enroll AA donors through community
outreach
⢠The AA community is in general eager to help,
amenable to full unit donations
11. Reported HLA associations in AA
⢠Increased frequency of DQB1*03, coding for DQ7
heterodimers in patients when compared with
controls British Journal of Dermatology 165(4):823-7
⢠HLA-DR4, DR11 and DQ*03 alleles increased in
unrelated AA patients compared with controls. Journal of
Inv. Derm. Symp.Proc. Vol 4;3, December 1999
⢠Most recent metanalysis Betz RC et al. Nat Commun. 2015
Jan 22;6:5966
⢠Class II association, but CD8 infiltrate -> a conservative
approach would target both
13. A list of over 300 potential targets
⢠Compiled from published proteomic studies, gene
expression data (genes down in AA vs. control scalp),
and several additional hair follicle proteins
⢠Many keratins and keratin-associated proteins
â Because of protein homology a more limited set of
peptides maybe required
⢠Trichohyalin and keratins are heavily modified
â We do not know which proteins are modified and where
/how
â we focused on unmodified versions, hoping to detect
reactivity against non-modified peptides
14. Peptide selection strategy
311 unique protein sequences (UniProt) â
Clustered at 50% identity threshold
(UCLUST)
15-mers overlapping by 10aa +
variants from alignment
Predictions for general Class
II DR & A*02:01
2278 MHC class II peptides
(10%-ile +DQB1*03:01)
2000 MHC class I
(1%-ile)
www.iedb.org
MHC binding tool
v. 2.15.1
15. Overall message
⢠Technology is available to generate targeted
experimental data
⢠We have recruited an initial donor cohort (and
age matched controls)
⢠Assembled a target set of over 300 proteins
⢠The IEDB analysis resource can be used to
predict epitopes
16. Acknowledgments
⢠Sinu Paul
⢠John Sidney
⢠April Frazier
⢠Cecilia Lindestam Arlehamn
⢠AA donors
⢠LJI clinical coordination team
⢠Angela Christiano
⢠Annemieke De Jong