Predicted Indirectly Recognizable HLA Epitopes Presented by HLA-DRB1 Are Related to HLA Antibody Formation During Pregnancy.

Pregnancy can prime maternal immune responses against inherited paternal HLA of the fetus, leading to the production of child-specific HLA antibodies. We previously demonstrated that donor-specific HLA antibody formation after kidney transplantation is associated with donor-derived HLA epitopes presented by recipient HLA class II (predicted indirectly recognizable HLA epitopes presented by HLA class II [PIRCHE-II]). In the present study, we evaluated the role of PIRCHE-II in child-specific HLA antibody formation during pregnancy. A total of 229 mother-child pairs were HLA typed. For all mismatched HLA class I molecules of the child, we subsequently predicted the number of HLA epitopes that could be presented by maternal HLA class II molecules. Child-specific antigens were classified as either immunogenic or nonimmunogenic HLA based on the presence of specific antibodies and correlated to PIRCHE-II numbers. Immunogenic HLA contained higher PIRCHE-II numbers than nonimmunogenic HLA. Moreover, the probability of antibody production during pregnancy increased with the number of PIRCHE-II. In conclusion, our data suggest that the number of PIRCHE-II is related to the formation of child-specific HLA antibodies during pregnancy. Present confirmation of the role of PIRCHE-II in antibody formation outside the transplantation setting suggests the PIRCHE-II concept is universal.

Systemic inflammation in childhood obesity: circulating inflammatory mediators and activated CD14++ monocytes.

AIMS/HYPOTHESIS: In adults, circulating inflammatory mediators and activated CD14(++) monocytes link obesity to its metabolic and cardiovascular complications. However, it is largely unknown whether these inflammatory changes already occur in childhood obesity. To survey inflammatory changes during the early stages of obesity, we performed a comprehensive analysis of circulating inflammatory mediators, monocyte populations and their function in childhood obesity. METHODS: In lean and obese children aged 6 to 16 years (n = 96), 35 circulating inflammatory mediators including adipokines were measured. Hierarchical cluster analysis of the inflammatory mediator profiles was performed to investigate associations between inflammatory mediator clusters and clinical variables. Whole-blood monocyte phenotyping and functional testing with the toll-like receptor 4 ligand, lipopolysaccharide, were also executed. RESULTS: First, next to leptin, the circulating mediators chemerin, tissue inhibitor of metalloproteinase 1, EGF and TNF receptor 2 were identified as novel inflammatory mediators that are increased in childhood obesity. Second, cluster analysis of the circulating mediators distinguished two obesity clusters, two leanness clusters and one mixed cluster. All clusters showed distinct inflammatory mediator profiles, together with differences in insulin sensitivity and other clinical variables. Third, childhood obesity was associated with increased CD14(++) monocyte numbers and an activated phenotype of the CD14(++) monocyte subsets. CONCLUSIONS/INTERPRETATION: Inflammatory mediator clusters were associated with insulin resistance in obese and lean children. The activation of CD14(++) monocyte subsets, which is associated with increased development of atherosclerosis in obese adults, was also readily detected in obese children. Our results indicate that inflammatory mechanisms linking obesity to its metabolic and cardiovascular complications are already activated in childhood obesity.

Quantifying how MHC polymorphism prevents pathogens from adapting to the antigen presentation pathway.

The classical antigen presentation pathway consists of two monomorphic (proteasome and TAP) and one polymorphic components (MHC Class I). Viruses can escape CTL responses by mutating an epitope so that it is no longer correctly processed by the pathway. Whereas escape mutations that affect MHC binding are typically no longer under selection pressure in the next host of the virus (as hosts differ in their MHC alleles), escape mutations that affect the antigen processing of epitope precursors prevent the use of those epitope precursors by any of the MHC alleles in a host population. Viruses might therefore be under selection pressure to adapt to the monomorphic proteasome and TAP. We designed an agent-based model of a host population, in which an HIV-1 like virus adapts to the antigen presentation pathway of individual hosts, as the virus spreads through the population. We studied how the polymorphism of the MHC and the monomorphism of the proteasome and TAP affected the level of adaptation to the host population that the virus could reach. We found that due to the polymorphism and high specificity of the MHC class I molecules, the CTL epitopes that are targeted by the CTL responses of different hosts do not share many epitope precursors. Therefore, escape mutations in epitope precursors are frequently released from immune selection pressure, and can revert back to the virus wildtype sequence. As a result, the selection pressure on the virus to adapt to the proteasome and TAP is relatively small, which explains the low level of adaptation of the virus to the monomorphic steps in the antigen presentation pathway.

Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach.

We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict binding vs non-binding peptides. Furthermore, quantitative ANN allowed a straightforward application of a 'Query by Committee' (QBC) principle whereby particularly information-rich peptides could be identified and subsequently tested experimentally. Iterative training based on QBC-selected peptides considerably increased the sensitivity without compromising the efficiency of the prediction. This suggests a general, rational and unbiased approach to the development of high quality predictions of epitopes restricted to this and other HLA molecules. Due to their quantitative nature, such predictions will cover a wide range of MHC-binding affinities of immunological interest, and they can be readily integrated with predictions of other events involved in generating immunogenic epitopes. These predictions have the capacity to perform rapid proteome-wide searches for epitopes. Finally, it is an example of an iterative feedback loop whereby advanced, computational bioinformatics optimize experimental strategy, and vice versa.

Prediction of human protein function from post-translational modifications and localization features.

We have developed an entirely sequence-based method that identifies and integrates relevant features that can be used to assign proteins of unknown function to functional classes, and enzyme categories for enzymes. We show that strategies for the elucidation of protein function may benefit from a number of functional attributes that are more directly related to the linear sequence of amino acids, and hence easier to predict, than protein structure. These attributes include features associated with post-translational modifications and protein sorting, but also much simpler aspects such as the length, isoelectric point and composition of the polypeptide chain.

Evolutionary and immunological implications of contemporary HIV-1 variation.

Evolutionary modelling studies indicate less than a century has passed since the most recent common ancestor of the HIV-1 pandemic strains and, in that time frame, an extraordinarily diverse viral population has developed. HIV-1 employs a multitude of schemes to generate variants: accumulation of base substitutions, insertions and deletions, addition and loss of glycosylation sites in the envelope protein, and recombination. A comparison between HIV and influenza virus illustrates the extraordinary scale of HIV variation, and underscores the importance of exploring innovative HIV vaccine strategies. Deeper understanding of the implications of variation for both antibody and T-cell responses may help in the effort to rationally design vaccines that stimulate broad cross-reactivity. The impact of HIV-1 variation on host immune response is reviewed in this context.

Identification of barley and rye varieties using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks.

Cereal varieties are normally identified using time-consuming methods such as visual examination of either the intact grain or one-dimensional electrophoretic patterns of the grain storage proteins. A fast method for identification of wheat (Triticum aestivum L.) varieties has previously been developed, which combines analysis of alcohol-soluble wheat proteins (gliadins) using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks. Here we have applied the same method for the identification of both barley (Hordeum vulgare L.) and rye (Secale cereale L.) varieties. For barley, 95% of the mass spectra were correctly classified. This is an encouraging result, since in earlier experiments only a grouping into subsets of varieties was possible. However, the method was not useful in the classification of rye, due to the strong similarity between mass spectra of different varieties.

Identifying cytotoxic T cell epitopes from genomic and proteomic information: "The human MHC project.".

Complete genomes of many species including pathogenic microorganisms are rapidly becoming available and with them the encoded proteins, or proteomes. Proteomes are extremely diverse and constitute unique imprints of the originating organisms allowing positive identification and accurate discrimination, even at the peptide level. It is not surprising that peptides are key targets of the immune system. It follows that proteomes can be translated into immunogens once it is known how the immune system generates and handles peptides. Recent advances have identified many of the basic principles involved. The single most selective event is that of peptide binding to MHC, making it particularly important to establish accurate descriptions and predictions of peptide binding for the most common MHC variants. These predictions should be integrated with those of other steps involved in antigen processing, as these become available. The ability to translate the accumulating primary sequence databases in terms of immune recognition should enable scientists and clinicians to analyze any protein of interest for the presence of potentially immunogenic epitopes. The computational tools to scan entire proteomes should also be developed, as this would enable a rational approach to vaccine development and immunotherapy. Thus, candidate vaccine epitopes might be predicted from the various microbial genome projects, tumor vaccine candidates from mRNA expression profiling of tumors ("transcriptomes") and auto-antigens from the human genome.

A mathematical model on germinal center kinetics and termination.

We devise a mathematical model to study germinal center (GC) kinetics. Earlier models for GC kinetics are extended by explicitly modeling 1) the cell division history of centroblasts, 2) the Ag uptake by centrocytes, and 3) T cell dynamics. Allowing for T cell kinetics and T-B cell interactions, we study the role of GC T cells in GC kinetics, GC termination, and B cell selection. We find that GC T cells play a major role in GC formation, but that the maintenance of established GC reactions requires very few T cells only. The results therefore suggest that the termination of a GC reaction is largely caused by lack of Ag on the follicular dendritic cells and is hardly influenced by Th cells. Ag consumption by centrocytes is the major factor determining the decay rate of the antigenic stimulus during a GC reaction. Investigating the effect of the Ag dose on GC kinetics, we find that both the total size of the GC and its duration are hardly influenced by the initial amount of Ag. In the model this is due to a buffering effect by competition for limited T cell help and/or competition between proliferating centroblasts.

Identification of wheat varieties using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and an artificial neural network

A novel tool for variety identification of wheat (Triticum aestivum L.) has been developed: an artificial neural network (ANN) is used to classify the gliadin fraction analysed by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOFMS). The robustness of this novel method with respect to various experimental parameters has been tested. The results can be summarised: (i) With this approach 97% of the wheat varieties can be classified correctly with a corresponding correlation coefficient of 1.0, (ii) The method is fast since the time of extracting gliadins from flour can be reduced to 20 min without significant decrease in overall performance, (iii) The storage of flour or extracts under standard conditions does not influence the classification ability (i. e. the generalisation ability) of the method, and (iv) The classification obtained is not influenced by the identity of the operator making the analysis. This study demonstrates that a combination of an ANN and MALDI-TOFMS analysis of the gliadin fraction provides a fast and reliable tool for the variety identification of wheat. Copyright 1999 John Wiley & Sons, Ltd.

How does cytopathicity affect the production of neutralizing antibody?

Cytopathic viruses evoke an earlier neutralizing antibody (nAb) response than noncytopathic viruses do. This was previously explained by the elimination of infected B cells by the cytotoxic T cells (CTLs), which predominate during infections with noncytopathic viruses. Using a simple mathematical model we provide a much simpler explanation for this difference in the kinetics of neutralizing antibody production. The analysis of the model shows that the delay in nAb production during infections with noncytopathic viruses is a simple consequence of the cytopathic effect alone: noncytopathic viruses infect a larger fraction of nAb-producing B cells and as a result nAb response is delayed. Extending the model with CTLs, we find that a major effect of CTLs is to limit the antigenic stimulus of the nAb-producing B cells. Thus, by reducing the proliferation rate of nAb-producing B cells, CTLs further delay the production of neutralizing antibodies.

From image processing to classification: IV. Classification of electrophoretic patterns by neural networks and statistical methods enable quality assessment of wheat varieties for breadmaking.

The end-use quality of products made from doughs consisting of wheat flour and water is often dependent upon the storage (gluten) proteins of the grain endosperm. Today the electrophoretic patterns of the high molecular weight (HMW) glutenin subunits are used for quality selections in wheat breeding programs in several countries. In this study, we used two multivariate techniques to classify digitized patterns from isoelectric focusing of gliadins and glutenins: a two-layered neural network architecture consisting of a self-organizing feature map and a feed-forward classifier [1], and discriminant analysis [2,3]. Three groups of seven wheat varieties (Triticum aestivum L.), associated with poor, medium or good properties in relation to bread-making quality, were used. The best classification results were obtained by the neural network model, based on data from the gliadin fraction: it was possible to classify varieties associated with poor or good quality, with recognition rates of 70 and 69%, respectively. The statistical method was better suited to solve the classification problem when the data was based on the glutenin fraction: if a specific variety was already known to be non-poor, this method enabled us to classify the medium- and good-quality classes with recognition rates of 90 and 88%, respectively. The results obtained were confirmed by correlation coefficients.

From image processing to classification: II. Classification of electrophoretic patterns using self-organizing feature maps and feed-forward neural networks.

In a recent study, isoelectric focusing patterns were classified with a neural network using the back-propagation algorithm [1]. In order to further study the classification process and to generalize the presentation of electrophoretic patterns, Kohonen's self-organizing feature maps [2] were applied in this study. Although these feature maps are very efficient in many pattern recognition tasks, our data proved to be too complex for classification with an unsupervised system. Therefore, a second supervised network on top of the feature map was necessary. As in [3], a feed-forward network trained by the back-propagation algorithm was used. The final system allows us to correctly classify 90% of all wheat varieties. Moreover, the system proved to be reliable, reasonable in training time and shows the same accuracy in different experimental setups.