Hoebert S. Hiemstra

Cytomegalovirus in autoimmunity: T cell crossreactivity to viral antigen and autoantigen glutamic acid decarboxylase

Antigens of pathogenic microbes that mimic autoantigens are thought to be responsible for the activation of autoreactive T cells. Viral infections have been associated with the development of the neuroendocrine autoimmune diseases type 1 diabetes and stiff-man syndrome, but the mechanism is unknown. These diseases share glutamic acid decarboxylase (GAD65) as a major autoantigen. We screened synthetic peptide libraries dedicated to bind to HLA-DR3, which predisposes to both diseases, using clonal CD4+ T cells reactive to GAD65 isolated from a prediabetic stiff-man syndrome patient. Here we show that these GAD65-specific T cells crossreact with a peptide of the human cytomegalovirus (hCMV) major DNA-binding protein. This peptide was identified after database searching with a recognition pattern that had been deduced from the library studies. Furthermore, we showed that hCMV-derived epitope can be naturally processed by dendritic cells and recognized by GAD65 reactive T cells. Thus, hCMV may be involved in the loss of T cell tolerance to autoantigen GAD65 by a mechanism of molecular mimicry leading to autoimmunity.

Molecular Mimicry in Type 1 Diabetes Immune Cross-Reactivity between Islet Autoantigen and Human Cytomegalovirus but Not Coxsackie Virus

Abstract: Type 1 diabetes is caused by a T cell‐mediated autoimmune destruction of the pancreatic beta cells. Molecular mimicry between viral pathogens and beta cell protein has been a popular theory to explain loss of tolerance in type 1 diabetes. However, functional data in support of this hypothesis have been lacking, presumably because the homologies were defined on the basis of linear similarities in peptide sequences, which ignores the criteria of HLA versus T cell receptor contact residues in peptide epitopes required for T cell recognition. We applied a HLA‐binding dedicated peptide microarray analysis using autoreactive T cell clones specific for the autoantigen GAD65 to determine the algorithm of T cell recognition by this given T cell clone. The subsequent database search identified a 100% fit with cytomegalovirus peptide, which was subsequently shown to be recognized by these clonal T cells. However, T cell clones reactive with linear homologies previously described as putative candidates for T cell cross‐reactivity between GAD65 and Coxsackie virus peptide were unable to recognize the homologous counterparts.

Quantitative determination of TCR cross-reactivity using peptide libraries and protein databases

A single T cell clone can be activated by many different peptides in the context of a particular HLA molecule. To quantify the number of peptides that can be recognized by a CD4+ T cell clone, we screened a one‐bead‐one‐peptide synthetic peptide library and a protein database for peptides that stimulate an HLA‐DR3‐restricted, human glutamic acid decarboxylase (GAD65)‐reactive CD4+ T cell clone. Both the library screening and the database analysis indicated that this T cell clone is able to recognize approximately 106 11‐mer peptides at low nanomolar concentration. Furthermore, we determined that the frequency of cross‐reactivity increased only 1.5‐3 times when the peptide concentration increased 10 times, in the range of 0.01 – 1 μM. These data imply that there is a considerable potential for T cell cross‐reactivity and are useful for studies on the role of molecular mimicry in the etiology of T cell‐mediated disease.

Antigen arrays in T cell immunology.

The screening of compound arrays in in vitro bioassays has developed into a powerful tool for the identification of biologically active substances. In the past decade, this technology has increasingly been applied to immunology. As the specificity of the immune system is determined by antigen detection via receptors on B and T cells, targeting the specificity of these immune receptors with random arrays is unique in its ability to generate general and quantitative information on cellular (cross-)reactivity. Synthetic array studies have been useful for identification of epitopes and antigens from databases by defining recognition patterns, isolation of synthetic peptides capable of modulating T cell responsiveness, characterisation of TCR promiscuity, and identification of functionally cross-reacting peptides that are potentially involved in molecular mimicry.

Limitations of homology searching for identification of T-cell antigens with library derived mimicry epitopes.

Mimicry epitopes that are recognized by T-cells can be identified through screening of synthetic peptide libraries. We have shown that these mimicry epitopes share sequence similarity with the corresponding natural epitopes and that mimicry sequences can be used for the definition of protein derived T-cell epitopes from databases. This can be done by either homology searching or pattern searching. Here we discuss the advantages and disadvantages of homology searching as an alternative for the generally applicable recognition pattern approach. We show that only for part of the library derived mimicry epitopes, the degree of similarity to the natural epitope may be high enough for successful homology searching in small databases.

An HLA class II restricted T-cell is able to recognize about a million different peptides

Class II restricted T-cells are able to recognize peptides in the context of MHC class II molecules on antigen presenting cells via their T-cell receptors. In order to obtain effective protection against bacterial and viral infections and against tumor growth, the presence of a large number of different T-cells is required. A particular ligand can be recognized by various (slightly) different T-cell receptors. On the other hand, to better understand T-cell cross-reactivity, it is important to know how many different peptides can be recognized by a particular T-cell. This number is expressed as P(t). In the literature P(t) values up to 10 have been reported [1,2]. Here we use two independent ways to determine P(t). The first makes use of dedicated peptide libraries and the second uses pattern searching with T-cell recognition motifs in databases. Both approaches indicate that P(t) is about 10.