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(The FASEB Journal. 2000;14:431-438.)
© 2000 FASEB

Compositional bias and mimicry toward the nonself proteome in immunodominant T cell epitopes of self and nonself antigens

GIOVANNI RISTORI*,1, MARCO SALVETTI*,12, GRAZIANO PESOLE{dagger},1,3, MARCELLA ATTIMONELLI{ddagger}, CARLA BUTTINELLI*, ROLAND MARTIN§ and PAOLO RICCIO{dagger}

* Dipartimento di Scienze Neurologiche, Università ‘La Sapienza’, Rome, Italy;
{dagger} Dipartimento di Biologia D.B.A.F., Università della Basilicata, Potenza, Italy;
{ddagger} Dip. Biochimica e Biologia Molecolare, University of Bari, Bari, Italy; and
§ Cellular Immunology Section, Neuroimmunology Branch, NINDS, National Institutes of Health, Bethesda, Maryland 20892-1400, USA

2Correspondence: Dipartimento di Scienze Neurologiche, Università ‘La Sapienza’, v.le dell’Università 30, 00185-Rome, Italy. E-mail: md0914{at}mclink.it


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
We investigated whether and how molecular mimicry affects the shaping of the helper T cell repertoire. We implemented an algorithm that measures the probability of mimicry between epitopes of known immunogenicity and self or nonself proteomes. This algorithm yields ‘similarity profiles’, which represent the probability of matching between all contiguous overlapping peptides of the antigen under examination and those in the proteome(s) considered. Similarity profiles between helper T cell epitopes (of self or microbial antigens and allergens) and human or microbial SWISSPROT collections were produced. For each antigen, both collections yielded largely overlapping profiles, demonstrating that self-nonself discrimination does not rely on qualitative features that distinguish human from microbial peptides. However, epitopes whose probability of mimicry with self or nonself prevails are, respectively, tolerated or immunodominant and coexist within the same (auto-)antigen regardless of its self/nonself nature. Epitopes (on self and nonself antigens) can cross-stimulate T cells at increasing potency as their similarity with nonself augments. Mimicry, rather than complicating self-nonself discrimination, assists in the shaping of the immune repertoire and helps define the defensive or autoreactive potential of a T cell. Being a predictor of epitope immunogenicity, it bears relevance to vaccine design.—Ristori, G., Salvetti, M., Pesole, G., Attimonelli, M., Buttinelli, C., Martin, R., Riccio, P. Compositional bias and mimicry toward the nonself proteome in immunodominant T cell epitopes of self and nonself antigens.


Key Words: T lymphocytes • tolerance • molecular mimicry • immunogenicity • vaccine design


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
THE PROBLEM OF self-nonself discrimination is a key question in immunology. Burnet (1) postulated that the immune system is structured to ignore everything that is self while reacting against ‘foreign’ antigens. We now know that Burnet’s paradigm, centered on the elimination of autoreactive lymphocytes, is insufficient since T cells specific for self antigens are part of the healthy immune repertoire (2) . Other studies have now added another level of complexity (3 4 5 6 7 8) : the T cell receptor (TCR) -mediated immune response to self epitopes is not only present in the normal repertoire, but also has a potential of cross-reactivity against microbial epitopes that may be much greater than previously thought.

These observations raise two problems for tolerance maintenance. The first is the possibility of activation of self-reactive clones through microbial mimicry (9 , 10) . The other one deals more generally with the shaping of the T cell repertoire. It seems almost impossible to predict whether a T cell is primarily specific for a self or for a nonself epitope. If, in principle, T cells are neither autoreactive nor pathogen reactive, how is the decision about their fate (tolerance induction or clonal expansion) made and how is it possible to distinguish autoreactive T cells from pathogen-reactive ones? Regulatory events such as costimulatory signals, the cytokine network, and the instructive role of innate immunity in the adaptive response (11 12) contribute to a qualitative discrimination between self and nonself. The decision about a T cell’s fate is therefore chosen depending on the context in which a given peptide is encountered. However, the highly degenerate binding between TCR and processed peptides implies many possible encounters in numerous contexts, including dangerous ones [though protective mechanisms are effective in limiting the occurrence of this possibility (13 14 15) ]. Thus, the autoreactive or pathogen-reactive potential of a T cell may depend not only on its contingent specificity for an (auto-)antigen as a whole, but also on the overall probability that its TCR has to bind self or nonself peptides. This led us to hypothesize that quantitative differences in terms of probability of cross-reactivity with self or nonself epitopes may affect T cell tolerance or immunodominance.

As a potential autoantigen in multiple sclerosis (MS), myelin basic protein (MBP) is the best-characterized determinant in humans in terms of helper T cell epitopes. Various studies have agreed on the existence of regions of this antigen that are recognized at a high precursor frequency and regions that appear to be relatively ignored by T cells (16 17 18 19) . We chose MBP as a prototype determinant to investigate how the probability of mimicry with self and with nonself varies in different epitopes of the same antigen and how this affects the shaping of the T cell repertoire. The results obtained with MBP were verified and confirmed on other determinants chosen from among allergens, microbial antigens, and murine autoantigens whose helper T epitopes are known as well.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Construction of similarity profiles
The probability of molecular mimicry between an antigen and a given protein collection can be expressed in terms of the overall probability of matching between all the oligopeptides contained in the antigen sequence and the protein collection under examination. The matching probability (fixing the peptide length and number of allowed mismatches) was calculated from the frequency of occurrence of the 20 amino acids in the protein collection under examination. If we consider a protein sequence of L residues, the matching probability of the i-th oligopeptide (i=1, ... , L-w+1) of length w, siw = aiai+1 ... ai+w-1 , with a given protein collection can be calculated with a zero-order Markov chain (20) using the residue frequencies f(aj), j=1,..,20, which are calculated on the protein collection under examination:

If we allow up to m mismatches, which can be located in N = (mw) different positions in the oligopeptide generating (20 21) m.N different oligopeptides, the oligopeptide matching probability can be calculated as

where ax,y is the y-th mismatched amino acid in the x-th arrangement of the m possible total mismatches. To better clarify the above formula, let us calculate the probability of matching with any tetrapeptide, allowing exactly two mismatches. If we consider, for example, the tetrapeptide AYWF, the two mismatches can occur in six (n=6) different arrangements (e.g., **WF, *Y*F, *YW*, A**F, A*W*, AY**). Then the matching probability can be calculated by the following equation:

which can be also written as:

and generalized as:

deriving the same formula above.

After calculating the occurrence probability for all L-w+1 overlapping oligopeptides contained in the protein considered, allowing a certain number of mismatches, the similarity profile (SP) of this protein with a given protein collection can be constructed by plotting the occurrence probability of each oligopeptide as a function of its position in the protein. The relevant position of each oligopeptide corresponds to that of its central residue. For example, matching probabilities to construct SP plots with w=15 are calculated from position 8 (the central residue of the first 15-mer) to L-7. Analogously, positions 5 and 3 are used for nonamers and hexamers, respectively. High and low values of matching probabilities should correspond respectively to protein regions with a higher and lower probability of mimicry with the protein sequence collection being considered. SP were constructed by the program EXPECTPATTERN (S. Brunetta and G. Pesole, unpublished results).

Construction of actual matching profiles
An alternative way to estimate the probability of mimicry of a given antigen is to calculate the oligopeptides shared, allowing a certain number of mismatches, between the antigen under examination and the protein sequences contained in the public databases. This approach differs from the probabilistic model used to construct SP in that it considers actual oligopeptide sequence matches instead of expected ones, calculated using the frequency of single amino acids of the oligopeptide in the relevant protein collection. After searching all possible oligopeptides of a given length contained in the antigen considered in the human and microbial collection (allowing a certain number of mismatches), the actual matching profile can be obtained by plotting the number of matches for each w-mer oligopeptide as a function of its position along the relevant antigen. The oligopeptide matches were determined with the program FINDPATTERNS (GCG, Program manual for the GCG Package; Genetic Computer Group, Madison, Wis.), which is able to identify short sequence patterns that can be defined ambiguously, allowing a certain number of mismatches in a given protein collection.

Protein collections
The sequence collections for human and microbial proteins were generated from the SWISSPROT database (release 34) by using the ACNUC retrieval program (21) . To remove redundant sequences that might have biased further analyses, we used the CLEANUP program (22) adapted to deal with amino acid sequences. The four nonredundant protein sequence collections used for further analyses were 1) human (7445 sequences, 3463933 amino acids); 2) mouse (4275 sequences, 1861570 amino acids); 3) prokaryotic (36458 sequences, 11294372 amino acids); and 4) viral (14464 sequences, 4682692 amino acids). The microbial protein collection included both prokaryotic and viral sequences (phages excluded); in all preliminary analyses, both produced largely overlapping SP profiles (not shown). Microbial SP (nonself SP) was calculated as the mean of prokaryotic and viral SP.

Dose titration experiments with MBP-specific T cell clones
The MBP-specific T cell clones (TCC) were derived from peripheral blood leukocytes using a limiting dilution ‘split well’ technique as described (23) . The molecular mimics and superagonists for TCC TLF6 and TL5G7 were identified by combinatorial peptide libraries in the positional scanning format alone (TL5G7) (5) or in combination with single amino acid mutational approach (TL5F6). Defined peptides were synthesized by multiple peptide synthesis (24) , and their identity and purity were confirmed by electrospray mass spectroscopy and high-performance liquid chromatography. T cell proliferation assays were performed measuring standard [3H]thymidine-incorporation. Different concentration of peptides were used to define their stimulatory capacity (potency) as a log step of EC50 [micromolar concentration range (upper limit considered) of a peptide resulting in half-maximal proliferation of TCC].


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Similarity profiles between an antigen and human or microbial protein collections are comparable
Similarity profiles, which represent the site-by-site probability of mimicry of a given antigen, were constructed by plotting the matching probability, derived from equation (2) in Materials and Methods, between MBP oligopeptides of different length and human or microbial (prokaryotic and viral) proteins as a function of their position along the antigen sequence. We plotted the matching probabilities of all MBP 15-mers (7 mismatched allowed) (Fig. 1A ), 9-mers (2 mismatches allowed) (Fig. 1B ), and 6-mers (no mismatches allowed) (Fig. 1C ) with the human and microbial protein collections as defined in Materials and Methods. The results indicate that MBP does not differ substantially in its SP from either human or microbial proteomes (Fig. 1) , with no significant dependence on peptide length. The following work was then performed considering 15-mers (7 mismatches allowed) as an operational approach. The peptide length takes into account the expected length of a peptide presented by a major histocompatibility complex (MHC) class II molecule, while the number of mismatches allows the prediction of a functionally significant fraction of mimics (obviously not all mimics can be identified since cross-reactivity between peptides showing virtually no sequence homology has been described) (6) .



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  		Figure 1. Similarity profile of A) MBP 15-mer oligopeptides (up to 7 mismatches allowed), B) MBP 9-mer oligopeptides (2 mismatches allowed), C) MBP 6-mer oligopeptides (no mismatches allowed) with the human (thin line) and microbial (thick line) protein collections from SWISSPROT database. Microbial SP was calculated as the mean of prokaryotic and viral SP. High and low values correspond respectively to protein regions with a higher and lower probability of mimicry with the protein sequence collection being considered.

To test the reliability of the probabilistic model used to obtain SP, we obtained the MBP actual matching profile as defined in Materials and Methods. We compared all overlapping MBP 15-mer peptides with the human or microbial protein collections and then plotted the actual number of matching 15-mer oligopeptides (up to 7 mismatches allowed) as a function of their position along the antigen sequence (Fig. 2 ). The lack of major differences between the plot in Fig. 1 (SP) and that in Fig. 2 (actual matching profiles) validates the probabilistic model used to construct SP. Moreover, the potentially biased content of the public protein databases, which still represent a very partial sample of all proteins, makes the use of SP even more reliable and may account for the slight differences observed between SP and actual profiles.



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  		Figure 2. Actual matching profiles calculated by plotting the actual number of matching 15-mer oligopeptides (up to 7 mismatches allowed) between the human or microbial protein collection and MBP.

On these grounds, the SP approach was applied to mouse cytochrome c (cyt c; Fig. 3A ), hepatitis C virus core protein (HCVC; Fig. 3B ) and allergen bee phospholipase A2 (PLA; Fig. 3C ). As for MBP, each antigen did not differ in its SP from self or nonself protein collections either, implying no major qualitative difference in amino acid usage between self and nonself protein repertoire. However, quantitative fluctuations in SP were evident, with regions in which the probability of mimicry either with self or with nonself prevailed (Figs. 1 and 3) . The above results were confirmed also when these analyses were restricted to collections of protein sequences from human pathogens (not shown).



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  		Figure 3. Probability of mimicry (expressed as SP) between (auto-)antigens other than MBP and the human (mouse, in the case of cyt c), prokaryotic, or viral protein collections. SP were obtained as for MBP. A) Mouse cyt c, B) HCVC, C) PLA.

The probability of mimicry varies from one region to another of the same antigen and can be independent of the self or nonself nature of the protein as a whole
We calculated for each antigen the ratio between the probability of mimicry with nonself and the probability of mimicry with self protein collections, as obtained from the SP (nonself/self SP ratio). If this ratio equals one, the antigen has an identical probability of mimicry with both collections. The expected ratio is lower than one for self antigens and higher for nonself ones. The mean ratio did not differ greatly between one antigen and another (cyt c 1.18, HCVC 0.95, PLA 0.93, MBP 0.88). However, clear differences were detected between distinct regions of each antigen, regardless of its self or nonself nature (i.e., all the antigens contained regions that were significantly more ‘self-like’ or ‘nonself-like’; Fig. 4 ).



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  		Figure 4. Nonself/self SP ratios. The ratio between the probability of mimicry with nonself and the probability of mimicry with self protein collections, obtained from the SP, was calculated for each antigen. A) MBP. B) cyt c. C) HCVC. D) PLA.

The probability of mimicry affects the immunodominance of helper T cell epitopes
To investigate whether such fluctuations in the probability of mimicry with self or nonself could affect the immunogenicity of helper T cell epitopes we considered the three largest studies of the pattern of T cell reactivity to MBP (16 17 18) and calculated the mean recognition frequency of each residue of the protein sequence. The resulting immunodominance profile overlapped strikingly with the nonself/self SP ratio shown in Fig. 4A (Fig. 5 ), confirming that the probability of mimicry affects the immunodominance of helper T cell epitopes.



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  		Figure 5. Comparison between the immunodominance profile (IP) and the nonself/self SP ratio of MBP. IP of MBP (thick line) was calculated by considering the three largest studies on the T cell fine specificity (13 14 15) . They shared the use of the split well method to establish antigen-specific T lines and of a complete panel of overlapping peptides. The IP value assigned to each residue corresponds to the mean frequency of antigen-specific T cell lines which recognized the peptide containing that residue (MS patients and controls were considered together because of the lack of major differences in their IP). The MBP nonself/self SP ratio (thin line) is superimposed.

This was confirmed with another approach: we calculated the distribution of nonself/self SP ratios (above or below one) for all the 15-mers within the immunodominant regions of MBP compared with that of the entire protein. The analysis was extended to the other (auto-)antigens, notwithstanding certain limitations (methodological differences, low numbers, and lack of fine epitope mapping) if compared with the work on MBP. The most informative studies of the pattern of T cell reactivity to each protein were once again considered (25 26 27 28 29 30) . For HCVC, two studies were analyzed separately because of differences in their results. In all cases, with the exception of the immunodominant epitopes of one of the two studies of HCVC, the immunodominant regions fell significantly within regions with a nonself/self SP ratio higher than one (Table 1 ). Predictive algorithms (31 32 33 34 35 36) did not reach the same level of accuracy in defining the immunodominant regions of the above antigens (not shown).


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  		Table 1. Distribution of 15-mers with nonself/self SP ratio respectively above (ns/s SP > 1) or below (ns/s SP < 1) one, calculated for the immunodominant regions and for the entire protein

The stimulatory capacity of helper T cell epitopes is related to their nonself/self SP ratio
To confirm the in vivo relevance of these findings, we took advantage of T cell clones specific for the immunodominant 86–99 residues of MBP, known to cross-react with a set of self (other than MBP), microbial, or hypothetical peptide sequences. For each peptide, the probability of mimicry with the human, prokaryotic, and viral protein collections as well as the nonself/self SP ratio were calculated and the results compared with the stimulatory capacity (potency) of each ligand as derived from published (5) as well as de novo dose titration experiments (Table 2 ). Peptides listed in Table 2 can be classified as superagonists (SA) or suboptimal ligands (SO) for potencies respectively higher or lower than 1 (which corresponds to the stimulatory capacity of the native 86–99 peptide of MBP). In accordance to our model, most of the SA and SO peptides had nonself/self SP ratios respectively above or below 1.8 (which is the nonself/self SP ratio of the native MBP peptide). The null hypothesis of a homogeneous distribution of peptide nonself/self SP ratios above or below 1.8, which would invalidate our model, can be significantly rejected by a Fischer exact test (P=0.025). Further, a clear relation between the stimulatory capacity of the ligands and their nonself/self SP ratios emerged (Fig. 6 ), indicating that the optimal ligands for these T cells were those at high nonself/self SP ratio (i.e., these T cell clones, rather than being primarily autoreactive, are committed to react against epitopes that are more frequent within foreign proteomes).


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  		Table 2. Peptides sequences stimulating 2 TCC specific for the immunodominant 86–99 residues of MBP



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  		Figure 6. The linear regression between the nonself/self SP ratio and the potency of the peptides studied (Table 2) . Each point resulted from averaging groups of 6 contiguous values with step one (i.e., 1–6, 2–7, ... , 16–21); the choice of a six-sized class is arbitrary, but no appreciable differences were observed with five- or seven-sized classes. The latter approach was used to measure the overall effect of the nonself composition of the peptide ligands on the stimulatory potency, minimizing the effect of peptide-specific features. In fact, the linear regression model, which was already significant for points corresponding to values of each single peptide listed in Table 2 (F=8.24, P<0.01 with r2=0.36; not shown), became highly significant (F=54.76, P<0.0001 with r2=0.79) for points resulting from six-sized class values.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Our results deliver three messages. First, the comparability of the self and nonself SP (the absence of major qualitative differences in amino acid usage between self and nonself protein repertoire reflects general properties of microbial and human proteomes), together with the degenerate peptide recognition by at least some helper T cells (3 4 5 6 7 8) , implies that a qualitative distinction between self and nonself peptides at the TCR level would not be functional. Second, we show that instead there appears to exist a quantitative distinction, based on the probability of mimicry with nonself as opposed to self sequences (to the extent that nonself/self SP ratio may represent an index of immunodominance). Third, the quantitative distinction operates at the epitope level and is, at least to some extent, independent of the self or nonself origin of an antigen as a whole. In accord with the above concepts, in vivo data with MBP-reactive TCC shows that T cells specific for immunodominant determinants—even on self antigens—have more likely been expanded by nonself-like epitopes and will recognize as optimal ligands peptide sequences derived from microbial proteomes. This system would ensure a broader protection profile against pathogens, with reduced probability of cross-activating potentially autoreactive T cells. This danger may be further reduced by the possibility that the T cells with high-affinity receptors for self epitopes be deleted in the thymus, leaving a peripheral repertoire of potentially autoreactive T cells that respond to self peptides in the low-affinity range. A qualitative self-nonself discrimination at the TCR level is therefore unnecessary, since the compositional bias toward the nonself proteome in immunodominant T cell epitopes of self and nonself antigens optimizes the immune response against pathogens and the maintenance of tolerance.

The lack of major qualitative differences between human and microbial proteomes implies repeated encounters with cross-reactive epitopes. Apparently of hindrance for self-nonself discrimination, this may instead help the maintenance of tolerance (37) and sustain the mature repertoire of naive (38) and memory (39) subsets. Moreover, the comparability of the SP between self and nonself may help explain the paradox of thymic positive selection that occurs on a self ‘substrate’ but must serve in the response toward nonself (40 41 42 43 44 45 46) . The fact that self-like epitopes tend to be suboptimal ligands (as shown by our experiments with the MBP-specific T cell clones) confirms that mimicry can be functional both to the maintenance of memory and to positive selection.

Our findings should not be interpreted as implying that immunodominance and tolerance are solely attributable to the probability of mimicry; additional features such as the MHC haplotype, differential processing in antigen-presenting cells of different lineage, structural peculiarities of the determinant itself and others, including the self or nonself nature of the antigen as a whole, obviously are relevant (47 48) . Nonetheless, a T lymphocyte can no longer be regarded as ‘autoreactive’ or ‘pathogen-reactive’ on the basis of the stimulating antigen alone. The probability of mimicry at the epitope level should be taken into account when studying the defensive or autoreactive potential of a T lymphocyte for vaccine design or tolerance induction.


   ACKNOWLEDGMENTS
 
We thank Prof. Cesare Fieschi and Dr. Carlo Pozzilli for their support, Chiara Montesperelli for her help with MBP data analysis, Prof. Vincenzo Barnaba and Prof. Paolo Pozzilli for their comments, and Dr. Sandra Brunetta for her assistance in developing the computer programs. We also thank Dr. Margherita Fanelli for help with statistical analysis. Database searches and analyses were performed with software available at the EMBnet Italian National Node (Area di ricerca del CNR, Bari, Italy). Supported in part by EU grant BMH4-CT96–0893 (M.S. and P.R.) and BMH4-CT96–0990 (P.R.), EU grant BIO4-CT95–0130 (G.P.), and the Associazione Italiana Sclerosi Multipla (A.I.S.M.).


   FOOTNOTES
 
1 These authors contributed equally to this work.

3 Present address: Dip. Fisiologia e Biochimica Generali, University of Milan, Milan, Italy.

Received for publication May 20, 1999. Revised for publication September 29, 1999.


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 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
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