Homeostatic expansion and phenotypic conversion of naïve T cells in response to self peptide/MHC ligands

Recent data suggest that survival of resting, naïve T cells requires an interaction with self MHC molecules. From analysis of the class I MHC-restricted T cell receptor transgenic strain OT-I, we report a different response. Rather than merely surviving, these T cells proliferated slowly after transfer into T-depleted syngeneic hosts. This expansion required both T cell “space” and expression of normal levels of self class I MHC molecules. Furthermore, we demonstrate that during homeostatic expansion in a suitable environment, naïve phenotype (CD44low) OT-I T cells converted to memory phenotype (CD44med/high), despite the absence of foreign antigenic stimulation. On the other hand, cells undergoing homeostatic expansion did not acquire cytolytic effector function. The significance of these data for reactivity of T cells with self peptide/MHC ligands and the implications for normal and abnormal T cell homeostasis are discussed.

The paradox of alloreactivity and self MHC restriction: Quantitative analysis and statistics

Although 1–24% of T cells are alloreactive, i.e., respond to MHC molecules encoded by a foreign haplotype, it is generally believed that T cells cannot recognize foreign peptides binding foreign MHC molecules. We show using a quantitative model that, if T cell selection and activation are affinity-driven, then an alloreactivity of 1–24% is incompatible with the textbook notion that self MHC restriction is absolute. If an average of 1% of clones are alloreactive, then according to our model, at most 20-fold more clones should, on average, be activated by antigens presented on self MHC than by antigens presented on foreign MHC. This ratio is at best 5 if alloreactivity is 5%. These results describe average properties of the murine immune system, but not the outcome of individual experiments. Using supercomputer technology, we simulated 100,000 MHC restriction experiments. Although the average restriction ratio was 7.1, restriction was absolute in 10% of the simulated experiments, greater than 100, although not absolute, in 29%, and below 6 in 24%. This extreme variability agrees with experimental estimates. Our analysis suggests that alloreactivity and average self MHC restriction both cannot be high, but that a low average restriction level is compatible with high levels in a significant number of experiments.

Antigenic peptides containing large PEG loops designed to extend out of the HLA-A2 binding site form stable complexes with class I major histocompatibility complex molecules.

Recognition of peptides bound to class I major histocompatibility complex (MHC) molecules by specific receptors on T cells regulates the development and activity of the cellular immune system. We have designed and synthesized de novo cyclic peptides that incorporate PEG in the ring structure for binding to class I MHC molecules. The large PEG loops are positioned to extend out of the peptide binding site, thus creating steric effects aimed at preventing the recognition of class I MHC complexes by T-cell receptors. Peptides were synthesized and cyclized on polymer support using high molecular weight symmetrical PEG dicarboxylic acids to link the side chains of lysine residues substituted at positions 4 and 8 in the sequence of the HLA-A2-restricted human T-lymphotrophic virus type I Tax peptide. Cyclic peptides promoted the in vitro folding and assembly of HLA-A2 complexes. Thermal denaturation studies using circular dichroism spectroscopy showed that these complexes are as stable as complexes formed with antigenic peptides.

Biosynthesis of major histocompatibility complex molecules and generation of T cells in Ii TAP1 double-mutant mice.

Major histocompatibility complex (MHC) class I and II molecules are loaded with peptides in distinct subcellular compartments. The transporter associated with antigen processing (TAP) is responsible for delivering peptides derived from cytosolic proteins to the endoplasmic reticulum, where they bind to class I molecules, while the invariant chain (Ii) directs class II molecules to endosomal compartments, where they bind peptides originating mostly from exogenous sources. Mice carrying null mutations of the TAP1 or Ii genes (TAP10) or Ii0, respectively) have been useful tools for elucidating the two MHC/peptide loading pathways. To evaluate to what extent these pathways functionally intersect, we have studied the biosynthesis of MHC molecules and the generation of T cells in Ii0TAP10 double-mutant mice. We find that the assembly and expression of class II molecules in Ii0 and Ii0TAP10 animals are indistinguishable and that formation and display of class I molecules is the same in TAP10 and Ii0TAP10 animals. Thymic selection in the double mutants is as expected, with reduced numbers of both CD4+ CD8- and CD4- CD8+ thymocyte compartments. Surprisingly, lymph node T-cell populations look almost normal; we propose that population expansion of peripheral T cells normalizes the numbers of CD4+ and CD8+ cells in Ii0TAP10 mice.

Toward the atomistic simulation of T cell epitopes automated construction of MHC:peptide structures for free energy calculations

Epitopes mediated by T cells lie at the heart of the adaptive immune response and form the essential nucleus of anti-tumour peptide or epitope-based vaccines. Antigenic T cell epitopes are mediated by major histocompatibility complex (MHC) molecules, which present them to T cell receptors. Calculating the affinity between a given MHC molecule and an antigenic peptide using experimental approaches is both difficult and time consuming, thus various computational methods have been developed for this purpose. A server has been developed to allow a structural approach to the problem by generating specific MHC:peptide complex structures and providing configuration files to run molecular modelling simulations upon them. A system has been produced which allows the automated construction of MHC:peptide structure files and the corresponding configuration files required to execute a molecular dynamics simulation using NAMD. The system has been made available through a web-based front end and stand-alone scripts. Previous attempts at structural prediction of MHC:peptide affinity have been limited due to the paucity of structures and the computational expense in running large scale molecular dynamics simulations. The MHCsim server (http://igrid-ext.cryst.bbk.ac.uk/MHCsim) allows the user to rapidly generate any desired MHC:peptide complex and will facilitate molecular modelling simulation of MHC complexes on an unprecedented scale.

SVRMHC prediction server for MHC-binding peptides

The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.

In silico prediction of peptide-MHC binding affinity using SVRMHC

The binding between peptide epitopes and major histocompatibility complex (MHC) proteins is a major event in the cellular immune response. Accurate prediction of the binding between short peptides and class I or class II MHC molecules is an important task in immunoinformatics. SVRMHC which is a novel method to model peptide-MHC binding affinities based on support rector machine regression (SVR) is described in this chapter. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide-MHC binding.

Predicting class II MHC-Peptide binding:a kernel based approach using similarity scores

Background - Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epitopes. For Class II MHC alleles, the binding groove is open at both ends, causing ambiguity in the positional alignment between the groove and peptide, as well as creating uncertainty as to what parts of the peptide interact with the MHC. Moreover, the antigenic peptides have variable lengths, making naive modelling methods difficult to apply. This paper introduces a kernel method that can handle variable length peptides effectively by quantifying similarities between peptide sequences and integrating these into the kernel. Results - The kernel approach presented here shows increased prediction accuracy with a significantly higher number of true positives and negatives on multiple MHC class II alleles, when testing data sets from MHCPEP [1], MCHBN [2], and MHCBench [3]. Evaluation by cross validation, when segregating binders and non-binders, produced an average of 0.824 AROC for the MHCBench data sets (up from 0.756), and an average of 0.96 AROC for multiple alleles of the MHCPEP database. Conclusion - The method improves performance over existing state-of-the-art methods of MHC class II peptide binding predictions by using a custom, knowledge-based representation of peptides. Similarity scores, in contrast to a fixed-length, pocket-specific representation of amino acids, provide a flexible and powerful way of modelling MHC binding, and can easily be applied to other dynamic sequence problems.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Transporter associated with antigen processing preselection of peptides binding to the MHC:a bioinformatic evaluation

TAP is responsible for the transit of peptides from the cytosol to the lumen of the endoplasmic reticulum. In an immunological context, this event is followed by the binding of peptides to MHC molecules before export to the cell surface and recognition by T cells. Because TAP transport precedes MHC binding, TAP preferences may make a significant contribution to epitope selection. To assess the impact of this preselection, we have developed a scoring function for TAP affinity prediction using the additive method, have used it to analyze and extend the TAP binding motif, and have evaluated how well this model acts as a preselection step in predicting MHC binding peptides. To distinguish between MHC alleles that are exclusively dependent on TAP and those exhibiting only a partial dependence on TAP, two sets of MHC binding peptides were examined: HLA-A*0201 was selected as a representative of partially TAP-dependent HLA alleles, and HLA-A*0301 represented fully TAP-dependent HLA alleles. TAP preselection has a greater impact on TAP-dependent alleles than on TAP-independent alleles. The reduction in the number of nonbinders varied from 10% (TAP-independent) to 33% (TAP-dependent), suggesting that TAP preselection is an important component in the successful in silico prediction of T cell epitopes.

Coupling in silico and in vitro analysis of peptide-MHC binding:a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes

The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions.

Identifiying human MHC supertypes using bioinformatic methods

Classification of MHC molecules into supertypes in terms of peptide-binding specificities is an important issue, with direct implications for the development of epitope-based vaccines with wide population coverage. In view of extremely high MHC polymorphism (948 class I and 633 class II HLA alleles) the experimental solution of this task is presently impossible. In this study, we describe a bioinformatics strategy for classifying MHC molecules into supertypes using information drawn solely from three-dimensional protein structure. Two chemometric techniques–hierarchical clustering and principal component analysis–were used independently on a set of 783 HLA class I molecules to identify supertypes based on structural similarities and molecular interaction fields calculated for the peptide binding site. Eight supertypes were defined: A2, A3, A24, B7, B27, B44, C1, and C4. The two techniques gave 77% consensus, i.e., 605 HLA class I alleles were classified in the same supertype by both methods. The proposed strategy allowed “supertype fingerprints” to be identified. Thus, the A2 supertype fingerprint is Tyr9/Phe9, Arg97, and His114 or Tyr116; the A3-Tyr9/Phe9/Ser9, Ile97/Met97 and Glu114 or Asp116; the A24-Ser9 and Met97; the B7-Asn63 and Leu81; the B27-Glu63 and Leu81; for B44-Ala81; the C1-Ser77; and the C4-Asn77. action fields calculated for the peptide binding site. Eight supertypes were defined: A2, A3, A24, B7, B27, B44, C1, and C4. The two techniques gave 77% consensus, i.e., 605 HLA class I alleles were classified in the same supertype by both methods. The proposed strategy allowed “supertype fingerprints” to be identified. Thus, the A2 supertype fingerprint is Tyr9/Phe9, Arg97, and His114 or Tyr116; the A3-Tyr9/Phe9/Ser9, Ile97/Met97 and Glu114 or Asp116; the A24-Ser9 and Met97; the B7-Asn63 and Leu81; the B27-Glu63 and Leu81; for B44-Ala81; the C1-Ser77; and the C4-Asn77.

Estudio en aves silvestres de canto ("Passeriformes") de las moléculas MHC y reclasificación molecular de la familia "Carduelini"

The Major Histocompatibility Complex (MHC) comprises the most polymorphic loci in animals. MHC plays an important role during the first steps of the immune response in vertebrates. In humans, MHC molecules (also named human leukocyte antigens, HLA) were initially regarded as class I or class II molecules. Each of them, presents to different T cells subsets. MHC class I molecules, are heterodimers in which the heavy chain (alpha) has three extracellular domains, two of which (alpha 1 and alpha 2) are polymorphic and conform the antigen recognition sites (ARS). The ARS is thought to be subjected to balancing selection for variability, which is the cause of the very high polymorphism of the MHC molecules. Different pathogenic epitopes would be the evolutionary force causing balancing selection. MHC class I genes have been completely sequenced (α1 and α2 protein domains) and thoroughly studied in Gallus gallus (chicken) as well as in mammals. In fact, the MHC locus was first defined in chicken, specifically in the highly consanguineous variety „Leghorn‟. It has been found that, in the case of chickens the MHC genetic region is considerably smaller than it is in mammals (remarkably shorter introns were found in chickens), and is organized quite differently. The noteworthy presence of short introns in chickens; supported the hypothesis that chicken‟s MHC represented a „minimal essential MHC‟. Until now, it has been assumed that chicken (order Galliformes) MHC was similar to all species included in the whole class Aves...

THE IMPORTANCE OF MHC-INDEPENDENT NATURAL KILLER CELL RECEPTORS IN THE IMMUNOREGULATION OF MURINE PREGNANCY

Numerous leukocyte populations are essential for pregnancy success. Uterine natural killer (uNK) cells are chief amongst these leukocytes and represent a unique lineage with limited cytotoxicity but abundant angiokine production. They possess a distinct phenotype of activating and inhibitory receptors that recognize major histocompatibility complex (MHC) molecules, such as the killer immunoglobulin like receptors (KIRs; mouse Ly49), and MHC-independent activating receptors, including the aryl hydrocarbon receptor (AHR) and natural cytotoxicity receptor 1 (NCR1). While the roles of MHC-dependent receptors are widely addressed in pregnancy, MHC-independent receptors are relatively unstudied. This thesis investigated the roles of MHC-independent receptors in promotion of mouse pregnancy and characterized early leukocyte interactions in the presence and absence of NCR1. It was hypothesized that loss of MHC-independent receptors impairs uNK cell development resulting in aberrations in leukocyte function and decidual vasculature. Implantation sites from Ahr-/- and Ncr1Gfp/Gfp mice were assessed using whole mount in situ immunohistochemistry (WM-IHC) and histochemical techniques. Leukocyte interactions identified during preliminary WM-IHC studies were confirmed as immune synapses. The novel identification of immune synapses in early mouse pregnancy compelled further examination of leukocyte conjugates in wildtype C57BL/6 and Ncr1Gfp/Gfp mice. In Ahr-/- and Ncr1Gfp/Gfp mice, receptor loss resulted in reduced uNK cell diameters, impaired decidual vasculature, and failures in spiral artery remodeling. Ahr-/- mice had severe fertility deficits whereas Ncr1Gfp/Gfp mice had increased fetal resorption indicating differing receptor requirements in pregnancy success. NCR1 loss primarily affected uNK cell maturation and function as identified by alterations in granule ultrastructure, lytic protein expression, and angiokine production. Leukocyte conjugates were frequent in early C57BL/6 decidua basalis and included uNK cells conjugating first with antigen presenting cells and then with T cells. Overall conjugate formation was reduced in the absence of NCR1, but specific uNK cell conjugations were unaffected by receptor loss. While KIR-MHC interactions are associated with numerous pregnancy complications in humans, the role of other uNK cell receptors are not well characterized. These results illustrate the importance of MHC-independent receptors in uNK cell activation during early pregnancy in mice and encourage further studies of pregnancy complications that may occur independently of maternal KIR-MHC contributions.

The Spleen CD4(+) T Cell Response to Blood-Stage Plasmodium chabaudi Malaria Develops in Two Phases Characterized by Different Properties

The pivotal role of spleen CD4(+) T cells in the development of both malaria pathogenesis and protective immunity makes necessary a profound comprehension of the mechanisms involved in their activation and regulation during Plasmodium infection. Herein, we examined in detail the behaviour of non-conventional and conventional splenic CD4(+) T cells during P. chabaudi malaria. We took advantage of the fact that a great proportion of CD4(+) T cells generated in CD1d(-/-) mice are I-A(b)-restricted (conventional cells), while their counterparts in I-Ab(-/-) mice are restricted by CD1d and other class IB major histocompatibility complex (MHC) molecules (non-conventional cells). We found that conventional CD4(+) T cells are the main protagonists of the immune response to infection, which develops in two consecutive phases concomitant with acute and chronic parasitaemias. The early phase of the conventional CD4(+) T cell response is intense and short lasting, rapidly providing large amounts of proinflammatory cytokines and helping follicular and marginal zone B cells to secrete polyclonal immunoglobulin. Both TNF-alpha and IFN-gamma production depend mostly on conventional CD4(+) T cells. IFN-gamma is produced simultaneously by non-conventional and conventional CD4(+) T cells. The early phase of the response finishes after a week of infection, with the elimination of a large proportion of CD4(+) T cells, which then gives opportunity to the development of acquired immunity. Unexpectedly, the major contribution of CD1d-restricted CD4(+) T cells occurs at the beginning of the second phase of the response, but not earlier, helping both IFN-gamma and parasite-specific antibody production. We concluded that conventional CD4(+) T cells have a central role from the onset of P. chabaudi malaria, acting in parallel with non-conventional CD4(+) T cells as a link between innate and acquired immunity. This study contributes to the understanding of malaria immunology and opens a perspective for future studies designed to decipher the molecular mechanisms behind immune responses to Plasmodium infection.