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,
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Molecular Statistics and Bioinformatics Section, Biometric Research Branch, National Cancer Institute, and
Neuroimmunology Branch, National Institute of Neurological Disorder and Stroke, National Institutes of Health, Bethesda, MD 20892;
Torrey Pines Institute for Molecular Studies and Mixture Sciences, San Diego, CA 92121; and
Clinical Neuroimmunology Group, Department of Neurology, Philipps-University Marburg, Marburg, Germany
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Abstract |
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 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Because it has been difficult to describe the trimolecular complex in its entirety, experiments initially focused on the interaction between peptide and MHC molecules. Structural studies of MHC class I and class II molecules complexed with antigenic peptides disclosed that the latter bind in a linear fashion (3). Sequencing of peptide pools and of individual self peptides eluted from MHC molecules (4, 5) together with systematic binding analyses (6, 7) have provided experimental data for the definition of MHC-binding motifs (8, 9, 10, 11, 12) and the development of MHC peptide-binding models. A combination of positive and negative influences from amino acid side chains in the antigenic peptide has been shown to determine the interaction between peptide and MHC molecules (13). Indeed, the assumption of independent contribution of each amino acid side chain in the peptide sequence to MHC binding has been used to develop quantitative methods that predict peptide binding to MHC alleles (8, 14, 15, 16). More recently, elegant neural network approaches have been used to further refine the prediction of peptide binding to MHC (17, 18, 19, 20). Based on the fact that a subset of MHC-binding peptides are also T cell epitopes (21, 22), MHC binding has been used to predict candidate T cell epitopes in bulk T cell populations, such as those contained in the peripheral blood (12, 19). However, to dissect and predict precisely the interaction of all three components of the trimolecular complex has until now been a difficult undertaking. Therefore, the quantitative study of MHC peptide recognition by single TCR has remained a largely unsettled issue.
The specificity of the trimolecular complex interaction has been studied using individual substitution analogues. Although initial studies showed that some amino acids in the antigenic peptide sequence are necessary for recognition by the TCR (primary TCR contacts) and others can tolerate conservative substitutions (secondary contacts) (23, 24), the systematic use of single and multiple amino acid-substituted peptides has shown that all amino acid side chains can contribute to peptide recognition in a largely independent manner (25). In extreme cases, this can lead to recognition of peptides with entirely different amino acid sequences by the same TCR (25).
The development of soluble- and bead-bound combinatorial peptide libraries in various formats representing millions to trillions of peptides has emerged as a powerful approach to both T cell epitope determination and the analysis of TCR specificity and flexibility, as recently reviewed (26, 27). Recent studies (28, 29, 30, 31, 32) of T cell clones (TCC)5 demonstrated the efficacy of using positional scanning synthetic combinatorial libraries (PS-SCL) for identifying target Ags and highly active peptide mimics. However, it was technically impossible to fully use this technology without the development of quantitative methods for predicting the stimulatory potential of peptides based on data from these complex libraries.
We report in this study a new strategy that combines data acquisition with PS-SCL and analysis with a quantitative scoring matrix to identify agonist peptides for clonotypic TCR of known and unknown specificity. Peptides can be identified from database searches with unprecedented efficiency and ranked according to a score that is predictive of their stimulatory potency. To our knowledge, this is by far the most efficient available approach to identify stimulatory peptides for individual TCR and predict their actual stimulatory potency with relatively high accuracy. While further improvements of this strategy will be pursued, we have developed a tool for the identification of potential T cell epitopes, the design of vaccines, and the quantitative analysis of TCR degeneracy. Finally, we demonstrate how the search results from the above prediction strategy can be related to tissue-specific expression profiles determined by cDNA microarray assays to identify candidate peptides that are derived from proteins that are overexpressed in a diseased tissue, i.e., the brain in multiple sclerosis (MS), and are thus available for the expansion of autoreactive T cells.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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TCC were established from peripheral blood or cerebrospinal
fluid (CSF) lymphomononuclear cells by a split-well technique, as
previously described (33, 34). TCC GP5F11 was established
from PBMC of a patient with MS using influenza virus hemagglutinin (HA)
peptide (306–318) (PKYVKQNTLKLAT, single letter amino acid code) as an
Ag. The TCC is restricted by DRB1*0404. TCC TL3A6 was established with
myelin basic protein (MBP) from PBMC of a patient with MS and
recognizes the immunodominant epitope MBP87–99
(VHFFKNIVTPRTP) in the context of DR2a (DR
+ DRB5*0101). The TCC has
been extensively characterized for recognition of numerous altered
peptides derived from MBP87–99 as well as other
molecular mimics (25, 31, 32, 35, 36). The TCR usage is
TCRAV18 and TCRBV5S1. TCC CSF-3 was established with a lysate of
Borrelia burgdorferi from the CSF of a patient with chronic
Lyme disease, as described (34). The TCC recognizes
several B. burgdorferi-derived as well as human peptides in
the context of DR2b (DR + DRB1*1501). The TCR usage is TCRAV13S2 and
TCRBV14S1.
Peptides and peptide combinatorial libraries
Peptides were synthesized by the simultaneous multiple peptide synthesis method (37) and characterized using HPLC and mass spectrometry. A synthetic N-acetylated, C-amide L-amino acid combinatorial peptide library in a positional scanning format (PS-SCL; 200 mixtures in the OX9 format, in which O represents one of the 20 L-amino acids, and X represents all of the natural L-amino acids, except cysteine) was prepared as described (38).
Proliferative assays
The proliferation of TCC in response to PS-SCL or individual peptides was tested by seeding in duplicate 2 x 104 T cells, 5 x 104 irradiated PBMC with or without mixtures from PS-SCL or peptide. Proliferation was measured by [3H]thymidine (Amersham, Arlington Heights, IL) incorporation (32).
Statistical analysis and model building
A positional scoring matrix was generated by assigning a value
of the stimulatory potential to each of the 20 defined amino acids in
each position. The score Sij for each amino acid
i at each position j was calculated as follows:
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In an alternative score called stimulation index (S-index), we generated the score in each position by using the mean of duplicate cpm values in the presence of mixtures from the PS-SCL fractions divided by the mean of duplicate values in the absence of mixtures from the PS-SCL. The S-index score appeared preferable when the PS-SCL spectrum of the cpm value was more clearly defined.
Under the assumption of independent contribution to stimulation, the
predicted stimulatory potential of given peptide is the sum of the
scores in each position. A 10-mer peptide sequence can be represented
by a 20 x 10 matrix of 0s and 1s (pij),
where pij = 1 if the ith amino acid
(using the same order as for the rows of the scoring matrix) is in
position j. Let Sij denote the
components of the positional scoring matrix. Then the score for the
peptide is:
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Database search
We wrote a Perl script to systematically search the GenPept database. A window with the same length of peptide as used in the PS-SCL was applied to slide over the available translated protein-coding sequences. The sum of the scores within the window was used as a ranking criterion. All peptides with scores higher than a threshold were output into a file. The threshold was chosen based on the statistical significance of the peptide score, compared with that for a random peptide. Those peptides were then sorted. Redundant peptides were removed. The database search can also be restricted to specific organisms (e.g., Homo sapiens or Influenza virus).
Statistical significance
We developed a statistical significance test of the hypothesis
that the score for a peptide is no greater than would be expected if
the peptide were obtained from 10 random draws of amino acids. Under
the null hypothesis, it is not assumed that all amino acids are equally
likely, but rather the relative frequencies
f1, f2, ...
f20 are derived from the database being
searched. Under the null hypothesis, the distribution of S will be
approximately normally distributed. The mean and the variance of this
null distribution can be expressed as
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denotes the standard normal distribution function.
However, this significance level does not account for the number of
10-mer sequences contained in the database. Analysis of gene expression using cDNA microarrays
Brain tissue was obtained at autopsy from two MS patients. Patient W was a 46-year-old male with primary progressive MS (39); patient R was a 46-year-old female with relapsing-remitting MS. Normal white matter was dissected, postmortem, from three nondiseased brains. RNA extracted from these three normal white matter samples was pooled, in equal amounts, for use in hybridization experiments. Lesions were identified by H&E and Luxol fast blue-periodic acid Schiff staining of paraffin-embedded sections. Further characterization of lesions was performed using immunohistochemistry for cell-specific Ags. All staging of lesions was performed as previously described (40). From the first patient, patient W, one acute (W1) and one chronically active lesion (W2) were studied. From the second patient, R, 16 chronic lesions were studied. These lesions had inflammatory cells present, but the inflammatory cells were not participating in any form of ongoing demyelination.
The detailed methodology of cDNA microarray analysis has been described in detail elsewhere (41) Arrays for this study contained 2889 human cDNAs that were primarily derived from I.M.A.G.E. consortium cDNA libraries (42). A list of genes present on the arrays can be found at http://intra.ninds.nih.gov/Biddison/cDNA_microarray.asp. [33P]dCTP-labeled cDNAs were produced by reverse transcriptase from RNAs obtained from individual MS lesions, pooled normal white matter, experimental allergic encephalomyelitis, and normal mouse brains, and hybridized to the cDNA microarrays. Hybridizations of RNA obtained from MS lesions and experimental allergic encephalomyelitis brains were performed in two independent experiments, except for lesions R10, R11, and R16, in which enough RNA was obtained for only one hybridization. Quantitation of radioactivity bound to the arrays was performed on a Molecular Dynamics STORM PhosphorImager (Molecular Dynamics, Sunnyvale, CA) at 50 µm resolution. All data were analyzed from the PhosphorImager images using Pscan (Ref. 43 , see also http://abs.cit.nih.gov/pscan). Pscan calculates spot intensities and compares spot intensities between samples, giving a ratio of gene expression between comparative samples. Using Pscan, spot intensities between arrays were automatically normalized to the median of all spot intensities on each individual array. Ratios of gene expression that were greater than 2-fold were considered significant based on a 99% confidence interval (44).
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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In this study, we sought to develop an approach that would combine the information generated from the screening of a decapeptide PS-SCL with all protein sequences in public databases. This strategy should allow the identification of the entire spectrum of stimulatory peptide ligands for a given TCC and the ranking of naturally occurring peptides with regard to predicted stimulation. The ultimate goal is to develop a methodology for identifying biologically relevant peptides for TCC of unknown specificity that have been isolated, e.g., from a tissue.
Three CD4+ TCC were tested in proliferative assays with the 200 mixtures of the decapeptide PS-SCL. Two TCC had known specificity, one specific for influenza HA (Flu-HA) (306–318) (TCC GP5F11), and one for MBP83–99 (TCC TL3A6). We also studied one clone of unknown specificity that recognizes B. burgdorferi, the causative organism of Lyme disease (TCC CSF-3).
Data obtained with combinatorial peptide libraries suggest
different levels of TCR degeneracy for different
CD4+ TCC. The stimulation profiles for TCC GP5F11
and TL3A6 are shown in Fig. 1
, A and B, respectively. The profile for CSF-3 is
shown previously (34). The profile of TL3A6 shows that
more than one mixture in several positions of the PS-SCL generated a
clear proliferative response. The amino acids of
MBP89–98 are marked by diamonds (FFKNIVTPRT).
Although the target amino acids correspond to the defined amino acid in
the most stimulatory mixtures in most positions, this is not observed
in certain positions, such as N in position 4 and P in position 8. In
contrast, the profiles for GP5F11 and CSF-3 show a very different
pattern with fewer, but more differential activity between stimulatory
and not stimulatory mixtures.
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Motif searches are widely used to search protein databases in a nonquantitative manner. However, this approach was not successful for identifying the known target peptides of the TL3A6 and GP5F11 clones. Motifs searches are generated from screening results of PS-SCL, and contained in each position are amino acids corresponding to mixtures with S-index greater than a specified threshold (see Materials and Methods for definition of S-index). Thresholds of 2 and 3 were used to generate the search motifs. The resulting motifs were then used to search the SwissProt and GenPept databases.
Tables I and II show the number of peptides that
satisfied the motif searches, and indicates whether the target peptide
was identified. The target peptide was not found with either of the
motifs for TL3A6 in either database. The target peptide for GPF11 was
identified only when the search criterion was so permissive/lax that
over 500 other peptides were also selected. Furthermore, the inability
of motif searches to rank peptides renders it almost impossible to
identify the most likely epitopes in a rational way and without
synthesizing and testing very large numbers of individual peptides.
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It is clear that a more systematic approach that employs all the
data generated from the screening of PS-SCL needs to be developed for
the search of databases. Our strategy is outlined in the flow diagram
(Fig. 2).
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Our algorithm provides a predicted stimulatory score for the peptide of
the same length as used in PS-SCL libraries. Based on the above
assumptions, the peptide score is the sum of position-specific scores
of the component amino acids. The scoring is accomplished by
calculation of a matrix in which the columns represent positions, and
the rows the 20 aa used in PS-SCL libraries. The scoring matrix entry
for a particular amino acid in a specific position is based on the
stimulation assay results for the mixture of PS-SCL corresponding to
that amino acid defined in that position (Fig. 3A). The scoring matrix entry
can either use the S-index or use the Z-index, which takes into account
the experimental errors (see Materials and Methods).
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B), a
stimulatory score is calculated for all published 10-mer peptides, and
then they are ranked accordingly. This strategy offers important
advantages compared with motif searches: 1) all the information derived
from the PS-SCL screening is used, and the selection based on a cutoff
of activity is not required; 2) peptides are now ranked according to
their predicted stimulatory score.
An example of a score matrix for one of the
CD4+ TCC (GP5F11) is shown in Fig. 3A.
The amino acids of the Flu-HA308–317 peptide are
boxed. Note that the amino acids of the target peptide sequence L in
position P7 and A in P10 are below an S-index value of 3,
thus explaining the failure of the motif search to find the target
influenza peptide. The principle of the sliding decamer scoring window
that is moved across a protein sequence in 1-aa increments is
shown in Fig. 3B. Three decamer peptides within the
Flu-HA304–321 sequence are scored by adding
the stimulatory values of the respective 10 aa. Note the drastic
changes in stimulatory scores when the scoring window is moved 1 aa to
the left (score 51.98) or to the right (13.7) as compared with the
optimal register that is shown in the middle (score 256.01). These
changes of the scores indicate that, as soon as both MHC and TCR
contact positions that contribute most of the stimulatory activity are
out of the correct register, the peptide may lose binding to the MHC
and/or fail to stimulate the clone because the TCR contacts are not
positioned properly.
Testing the score matrix-based approach using clones with known specificity and with synthesized peptides
The effectiveness of this approach is demonstrated in Table III. When the score matrices for clones
TL3A6 and GP5F11 were used to score all peptides in the GenPept
database, both the target peptides (MBP89–98
peptide for TL3A6, and Flu-HA309–318 for GP5F11)
were correctly identified. The GenPept database
(ftp://ftp.ncifcrf.gov/pub/genpept) was searched because it is
substantially larger than SwissProt (http://www.expasy.ch/sprot). The
relative ranks obtained for the target peptides are given in Table III.
For GP5F11, the rank among viral peptides is given; for TL3A6, we show
the rank among human peptides. Consistent with previous observations
with another autoreactive clone (45),
MBP89–98 was far from optimal, i.e., it ranked
only 202nd in the set of human peptides using the S-index matrix. In
contrast, the target peptide Flu-HA309–318
ranked as the sixth highest scoring peptide for GP5F11 among viral
proteins, and 24th when not only viral, but also human proteins were
scored. This also suggests that molecular mimics that are potentially
more stimulatory than the native foreign peptide can be identified.
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Table IV shows the relationship between
stimulatory potential predicted by the scoring matrices and actual
measurement of TCC stimulation. Thresholds for matrix score prediction
were based on relative operating characteristic analysis
(46) to balance sensitivity and specificity. For clone
CSF-3, for example, of the 62 peptides predicted to be stimulatory
(have scores above the threshold of 47.5), 58 did stimulate the TCC (a
positive predictive value of 58/62, or 93.5%). Of the 26 peptides
predicted to be nonstimulatory, only 5 stimulated the TCC (negative
predictive value: 21/26, 80.8%). The sensitivity for predictions with
this clone was 92%; that is, of the 63 peptides that actually
stimulated the TCC, 58 were correctly predicted. The specificity was
84%; that is, of the 25 peptides that did not stimulate the TCC, 21
were correctly predicted. Although the sets of synthesized peptides are
small compared with the number of peptides that would be predicted to
be stimulatory, Table IV documents the excellent sensitivity,
specificity, and negative predictive values for the three TCC.
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shows the information on the 10
highest scoring peptides derived from B. burdorferi database
analysis for TCC CSF-3 with the half-maximal stimulatory value that was
determined by dose-titration, proliferative experiments. Examples of
the stimulatory activity of peptides predicted to activate TCC GP5F11
are shown in Fig. 4. Note that a
predicted stimulatory peptide with optimal amino acids in each position
(WMKQNIGRFL) and a higher score than the target peptide is in fact two
orders of magnitude more potent than the target sequence. One of the
shown peptides with a score of 132.40 ranks much lower than the
putative stimulatory threshold for TCC GP5F11, and consequently it did
not stimulate the clone. However, even a few high scoring peptides
(data not shown) are not stimulatory from reasons that are currently
under further investigation.
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The novel strategy described in this work allows us to find peptides from every known source that have stimulatory activity for the clone that was tested with PS-SCL. This leads to the problem of how one identifies from this wealth of data which peptides may be biologically relevant. In cases in which the target Ag for the clone is not known or molecular mimics with potential relevance for an organ-specific disease are of interest, several strategies may be used.
One approach to identify proteins involved in autoimmune diseases
is to examine the expression of genes that are overexpressed in the
target organ using cDNA microarray technology (41). We
examined gene expression in 18 lesions from two MS patients and
compared them with levels of gene expression in pooled normal white
matter from three individuals with cDNA microarrays containing 2889
human genes. One of the genes that was overexpressed (>2-fold) in 17
of the 18 MS lesions examined was titin (Fig. 5A), a giant muscle protein
(47). When we asked which genes that are overexpressed in
MS plaques are also identified as candidate epitopes/molecular mimics
for CD4+ TCC that were tested with the PS-SCL
(Fig. 5B), we identified peptides derived from the same
interesting candidate, titin, among the highest scoring peptides for
both a CD4+ TCC recognizing the immunodominant
MBP peptide (83–99) in the context of the MS-associated DR allele
DRB5*0101, but also for the B. burgdorferi-specific TCC
CSF-3 (Fig. 5C). Titin, a giant muscle protein
(47), is surprisingly overexpressed in MS brain tissue,
and the identification of titin-derived peptides as candidate molecular
mimics for two TCC that are potentially pathogenic in two different CNS
inflammatory/autoimmune disorders, i.e., MS and chronic CNS Lyme
disease, offers unique opportunities to study the involvement of such
candidate Ags in the pathogenesis of these diseases.
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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A) (34) and self (Fig. 1B) Ags. We
then used a matrix-based methodology for the analysis of the
experimental data generated with the PS-SCL (Fig. 2). This methodology
is based on a model of independent and additive contribution of each
amino acid in the peptide sequence to the interactions with both the
TCR and the MHC molecule (25). Although numeric matrices
(8) and other mathematical approaches based on independent
amino acid contribution to antigenicity have been previously used to
describe the interaction of antigenic peptides with specific MHC
molecules (17, 18), the present study fills the important
gap of applying a quantitative, matrix-based model to the interaction
of an MHC peptide ligand (keeping the MHC molecule constant) with a
specific, clonotypic TCR using the data generated from PS-SCL. The
biometrical analysis described in this work systematically compares the
information derived from a PS-SCL composed of trillions of decapeptides
with all the decapeptides (13, 879, 822 for a H. sapiens
database, and 20, 198, 794 for a viral database) contained in a public
protein database to rank and predict the most stimulatory peptides for
a given TCC. The predictions based on this methodology are so accurate
(Tables III and IV, Fig. 4) (34) that they actually lend
strong support to an additive, combinatorial model of peptide
antigenicity. Available TCR crystal structures indeed suggest that
peptides may modulate the preexisting affinity between MHC and TCR that
is based on a large contact surface between these two components of the
trimolecular complex (51, 52). It should be noted that
this model does not contradict, but indeed extends and develops the
concept of primary and secondary TCR contacts (23, 53). In
fact, although complex substitutions of amino acids along the entire
sequence of the peptide can lead to molecular mimicry in the absence of
any sequence homology (25), the relative weight of
different amino acids in each position of the peptides sequence is
apparent from the experimental data (Fig. 1, A and
B).
An important application of the above described model is that one can
identify peptide ligands for a specific TCR by searching public
database not only with MHC and TCR anchor motifs (54) or
motifs obtained from PS-SCL data (34, 45, 49), but also
using the scoring matrix derived from the screening of a PS-SCL
composed of trillions of peptides (Fig. 3, A and
B). We also illustrate the limitations of using motifs
derived from PS-SCL screening to identify TCR agonist peptides. Such a
strategy does not fully use the information generated by screening
specific TCR with PS-SCL. Therefore, the native ligand may not be found
if the motif is not sufficiently degenerate (Table I, S-index >
3; Table II, S-index > 3; S-index > 2), or if even one of
the positions does not contain the amino acid that appears in the
native sequence. Another advantage in the identification of T cell
epitopes is that one can rank the predicted stimulatory peptides
according to their score. This is of great practical value when the
number of candidate peptides is very high (Table II) and one needs
criteria to select which of the identified candidate peptides should be
synthesized and actually tested with the TCC. In addition to
identifying promptly the target peptide sequences (Table III), one can
then synthesize and test a feasible number (hundreds) of candidate
peptides to confirm their stimulatory activity (examples in Fig. 4; see
also Table IV). Interestingly, we confirmed our previous observation
that for autoreactive TCC, the ligand used to establish and expand the
TCC is often a suboptimal one, consistent with the notion that high
affinity self-reactive TCC are deleted in thymic selection
(55). Whereas for autoreactive TCC we often found natural
ligands derived from foreign or even self Ags whose potency was several
orders of magnitude higher than that of the native peptide
(45), for TCC GP5F11 and other TCC specific for foreign
Ags (R. Martin, B. Gran, M. Nagal, E. Borras, S. Jacobsen, W. E.
Biddison, R. Houghten, H. F. McFarland, and C. Pinilla, unpublished
observations) the native ligand was much closer to the optimal one
(Table III) (56, 57). Although more potent synthetic
ligands could be designed based on the deconvolution of the PS-SCL data
(26, 32) (e.g., peptide WMKQNIGRFL in Fig. 4), naturally
occurring superagonists were rare. The fact that foreign Ag-specific
TCC may recognize their antigenic peptides as highly potent ones is
consistent with an efficient immune response required to eliminate
infectious agents.
This study adds a new and important contribution to the definition and
prediction of T cell epitopes using synthetic combinatorial libraries
(26, 27). It should be noted that many of the previous
approaches to the identification of T cell epitopes were based on the
prediction of which peptides would be good binders for specific MHC/HLA
molecules (8, 16). Because only a fraction of the
potential MHC-binding peptides is a T cell epitope for an
individual TCR, these approaches provide information that is
specific for particular MHC molecules, but cannot predict which
fraction of the peptides that bind a restriction element is actually
stimulatory for a TCR with its unique structural features. Conversely,
TCR ligands are not always high affinity MHC binders (58).
The approach presented in this study takes into account the whole
trimolecular complex of T cell activation by reading out a functional T
cell response. This requires a certain degree of MHC peptide binding as
well as the interaction of the MHC peptide ligand with a specific TCR.
When both are considered, the overall accuracy of T cell epitope
predictions is far superior to previously adopted methods (Table IV),
although further improvements are currently being pursued. This is
particularly helpful when the protein(s) recognized by a TCC is/are not
known (34). Indeed, less than a third of the peptides that
were identified and found to be stimulatory by the PS-SCL and scoring
matrix approach would have been predicted to be good MHC binders based
on a recently published MHC-binding prediction algorithm
(12) (data not shown).
Finally, we show that combining the above-described methodology
with the use of cDNA microarrays to assess differential gene expression
in pathological and normal tissue of two patients with MS led to an
interesting candidate molecule (titin, to date only known as an
important component of skeletal muscle (47)) that is
overexpressed in MS plaques and is recognized by a B.
burgdorferi-specific TCC (Fig. 5). Preliminary pathological
studies by immunohistochemistry indicate the expression of an isoform
of this molecule in the pathologic, as opposed to normal white matter
tissue, but further work to define its role is clearly needed. Thus,
the combination of two powerful methodologies can guide the discovery
of candidate autoantigens that would otherwise not easily be identified
by either approach.
In summary, we describe a methodology, PS-SCL-based biometrical analysis for ligand identification, which is consistent with a combinatorial model of TCR activation by antigenic peptides and allows the identification of T cell epitopes for both autoreactive and foreign Ag-specific TCC with unprecedented efficacy. The same approach has also been successfully used for the prediction and identification of Ags by CD8+ TCC (Ref. 59 and R. Martin, B. Gran, M. Nagai, E. Borras, S. Jacobson, W. E. Biddison, R. Houghten, H. F. McFarland, and C. Pinilla, unpublished results). For the first time, recognition of Ags by clones of unknown specificity can be decrypted. This is an important advance in the study of autoimmune disease, in which one tries to suppress specific immune responses, as well as for infectious and neoplastic diseases, in which a stimulation of specific responses by vaccines is pursued. Furthermore, it is important to note that this approach can be used to identify ligands within proteins in public database for any molecular interaction that has been or can be studied with PS-SCLs composed of L-amino acids.
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Acknowledgments |
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Footnotes |
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2 Y.Z. and B.G. contributed equally to this study and should be considered as co-first authors.
3 Current address: Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104.
4 Address correspondence and reprint requests to Dr. Richard Simon (for biometric analyses), National Cancer Institute, 6130 Executive Boulevard, Room 8134, MSC 7434, Bethesda, MD 20892. E-mail address: rsimon{at}mail.nih.gov; Dr. Clemencia Pinilla (for use of combinatorial peptide libraries): e-mail address: pinilla{at}tpims.org; or Dr. Roland Martin (for T cell studies): e-mail address: martinr{at}ninds.nih.gov
5 Abbreviations used in this paper: TCC, T cell clone; PS-SCL, positional scanning synthetic combinatorial libraries; CSF, cerebrospinal fluid; HA, hemagglutinin; MBP, myelin basic protein; MS, multiple sclerosis; S-index, stimulation index.
6 ••. Submitted for publication.
Received for publication February 20, 2001. Accepted for publication June 5, 2001.
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References |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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T cell receptor structure at 2.5Å and its orientation in the TCR-MHC complex. Science 274:209.This article has been cited by other articles:
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  S. Markovic-Plese Degenerate T-Cell Receptor Recognition, Autoreactive Cells, and the Autoimmune Response in Multiple Sclerosis Neuroscientist, June 1, 2009; 15(3): 225 - 231. [Abstract] [PDF]
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 J. D. Lunemann, H. Gelderblom, M. Sospedra, J. A. Quandt, C. Pinilla, A. Marques, and R. Martin Cerebrospinal Fluid-Infiltrating CD4+ T Cells Recognize Borrelia burgdorferi Lysine-Enriched Protein Domains and Central Nervous System Autoantigens in Early Lyme Encephalitis Infect. Immun., January 1, 2007; 75(1): 243 - 251. [Abstract] [Full Text] [PDF]
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 J. Lustgarten, A. L. Dominguez, and C. Pinilla Identification of Cross-Reactive Peptides Using Combinatorial Libraries Circumvents Tolerance against Her-2/neu-Immunodominant Epitope J. Immunol., February 1, 2006; 176(3): 1796 - 1805. [Abstract] [Full Text] [PDF]
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M. Sospedra, P. A. Muraro, I. Stefanova, Y. Zhao, K. Chung, Y. Li, M. Giulianotti, R. Simon, R. Mariuzza, C. Pinilla, et al. Redundancy in Antigen-Presenting Function of the HLA-DR and -DQ Molecules in the Multiple Sclerosis-Associated HLA-DR2 Haplotype J. Immunol., February 1, 2006; 176(3): 1951 - 1961. [Abstract] [Full Text] [PDF]
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 V. A. Judkowski, G. M. Allicotti, N. Sarvetnick, and C. Pinilla Peptides From Common Viral and Bacterial Pathogens Can Efficiently Activate Diabetogenic T-Cells Diabetes, September 1, 2004; 53(9): 2301 - 2309. [Abstract] [Full Text] [PDF]
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 A. W. Purcell and J. J. Gorman Immunoproteomics: Mass Spectrometry-based Methods to Study the Targets of the Immune Response Mol. Cell. Proteomics, March 1, 2004; 3(3): 193 - 208. [Abstract] [Full Text] [PDF]
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Y. Uemura, S. Senju, K. Maenaka, L. K. Iwai, S. Fujii, H. Tabata, H. Tsukamoto, S. Hirata, Y.-Z. Chen, and Y. Nishimura Systematic Analysis of the Combinatorial Nature of Epitopes Recognized by TCR Leads to Identification of Mimicry Epitopes for Glutamic Acid Decarboxylase 65-Specific TCRs J. Immunol., January 15, 2003; 170(2): 947 - 960. [Abstract] [Full Text] [PDF]
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V. Rubio-Godoy, V. Dutoit, Y. Zhao, R. Simon, P. Guillaume, R. Houghten, P. Romero, J.-C. Cerottini, C. Pinilla, and D. Valmori Positional Scanning-Synthetic Peptide Library-Based Analysis of Self- and Pathogen-Derived Peptide Cross-Reactivity with Tumor-Reactive Melan-A-Specific CTL J. Immunol., November 15, 2002; 169(10): 5696 - 5707. [Abstract] [Full Text] [PDF]
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 V. Dutoit, V. Rubio-Godoy, M. J. Pittet, A. Zippelius, P.-Y. Dietrich, F. A. Legal, P. Guillaume, P. Romero, J.-C. Cerottini, R. A. Houghten, et al. Degeneracy of Antigen Recognition as the Molecular Basis for the High Frequency of Naive A2/Melan-A Peptide Multimer+ CD8+ T Cells in Humans J. Exp. Med., July 15, 2002; 196(2): 207 - 216. [Abstract] [Full Text] [PDF]
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 V. Rubio-Godoy, C. Pinilla, V. Dutoit, E. Borras, R. Simon, Y. Zhao, J.-C. Cerottini, P. Romero, R. Houghten, and D. Valmori Toward Synthetic Combinatorial Peptide Libraries in Positional Scanning Format (PS-SCL)-based Identification of CD8+ Tumor-reactive T-Cell Ligands: A Comparative Analysis of PS-SCL Recognition by a Single Tumor-reactive CD8+ Cytolytic T-Lymphocyte Clone Cancer Res., April 1, 2002; 62(7): 2058 - 2063. [Abstract] [Full Text] [PDF]
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