Biomedical subjects

Kasper Lamberth

Publications and source records attributed to Kasper Lamberth.

8 recordsLinked to original sources

PubMed2005

An integrative approach to CTL epitope prediction: a combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions.

Reverse immunogenetic approaches attempt to optimize the selection of candidate epitopes, and thus minimize the experimental effort needed to identify new epitopes. When predicting cytotoxic T cell epitopes, the main focus has been on the highly specific MHC class I binding event. Methods have also been developed for predicting the antigen-processing steps preceding MHC class I binding, including proteasomal cleavage and transporter associated with antigen processing (TAP) transport efficiency. Here, we use a dataset obtained from the SYFPEITHI database to show that a method integrating predictions of MHC class I binding affinity, TAP transport efficiency, and C-terminal proteasomal cleavage outperforms any of the individual methods. Using an independent evaluation dataset of HIV epitopes from the Los Alamos database, the validity of the integrated method is confirmed. The performance of the integrated method is found to be significantly higher than that of the two publicly available prediction methods BIMAS and SYFPEITHI. To identify 85% of the epitopes in the HIV dataset, 9% and 10% of all possible nonamers in the HIV proteins must be tested when using the BIMAS and SYFPEITHI methods, respectively, for the selection of candidate epitopes. This number is reduced to 7% when using the integrated method. In practical terms, this means that the experimental effort needed to identify an epitope in a hypothetical protein with 85% probability is reduced by 20-30% when using the integrated method. The method is available at http://www.cbs.dtu.dk/services/NetCTL. Supplementary material is available at http://www.cbs.dtu.dk/suppl/immunology/CTL.php.

ATP-Binding Cassette Transporters↗

PubMed2004

Definition of supertypes for HLA molecules using clustering of specificity matrices.

Major histocompatibility complex (MHC) proteins are encoded by extremely polymorphic genes and play a crucial role in immunity. However, not all genetically different MHC molecules are functionally different. Sette and Sidney (1999) have defined nine HLA class I supertypes and showed that with only nine main functional binding specificities it is possible to cover the binding properties of almost all known HLA class I molecules. Here we present a comprehensive study of the functional relationship between all HLA molecules with known specificities in a uniform and automated way. We have developed a novel method for clustering sequence motifs. We construct hidden Markov models for HLA class I molecules using a Gibbs sampling procedure and use the similarities among these to define clusters of specificities. These clusters are extensions of the previously suggested ones. We suggest splitting some of the alleles in the A1 supertype into a new A26 supertype, and some of the alleles in the B27 supertype into a new B39 supertype. Furthermore the B8 alleles may define their own supertype. We also use the published specificities for a number of HLA-DR types to define clusters with similar specificities. We report that the previously observed specificities of these class II molecules can be clustered into nine classes, which only partly correspond to the serological classification. We show that classification of HLA molecules may be done in a uniform and automated way. The definition of clusters allows for selection of representative HLA molecules that can cover the HLA specificity space better. This makes it possible to target most of the known HLA alleles with known specificities using only a few peptides, and may be used in construction of vaccines. Supplementary material is available at http://www.cbs.dtu.dk/researchgroups/immunology/supertypes.html.

Amino Acid Motifs↗

PubMed2004

Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach.

MOTIVATION: Prediction of which peptides will bind a specific major histocompatibility complex (MHC) constitutes an important step in identifying potential T-cell epitopes suitable as vaccine candidates. MHC class II binding peptides have a broad length distribution complicating such predictions. Thus, identifying the correct alignment is a crucial part of identifying the core of an MHC class II binding motif. In this context, we wish to describe a novel Gibbs motif sampler method ideally suited for recognizing such weak sequence motifs. The method is based on the Gibbs sampling method, and it incorporates novel features optimized for the task of recognizing the binding motif of MHC classes I and II. The method locates the binding motif in a set of sequences and characterizes the motif in terms of a weight-matrix. Subsequently, the weight-matrix can be applied to identifying effectively potential MHC binding peptides and to guiding the process of rational vaccine design. RESULTS: We apply the motif sampler method to the complex problem of MHC class II binding. The input to the method is amino acid peptide sequences extracted from the public databases of SYFPEITHI and MHCPEP and known to bind to the MHC class II complex HLA-DR4(B1*0401). Prior identification of information-rich (anchor) positions in the binding motif is shown to improve the predictive performance of the Gibbs sampler. Similarly, a consensus solution obtained from an ensemble average over suboptimal solutions is shown to outperform the use of a single optimal solution. In a large-scale benchmark calculation, the performance is quantified using relative operating characteristics curve (ROC) plots and we make a detailed comparison of the performance with that of both the TEPITOPE method and a weight-matrix derived using the conventional alignment algorithm of ClustalW. The calculation demonstrates that the predictive performance of the Gibbs sampler is higher than that of ClustalW and in most cases also higher than that of the TEPITOPE method.

Algorithms↗

PubMed2003

CCR3 expression induced by IL-2 and IL-4 functioning as a death receptor for B cells.

We report that CCR3 is not expressed on freshly isolated peripheral and germinal B cells, but is up-regulated after stimulation with IL-2 and IL-4 (approximately 98% CCR3(+)). Ligation of CCR3 by eotaxin/chemokine ligand (CCL) 11 induces apoptosis in IL-2- and IL-4-stimulated primary CD19(+) (approximately 40% apoptotic cells) B cell cultures as well as B cell lines, but has no effect on chemotaxis or cell adhesion. Freshly isolated B cells express low levels of CD95 and CD95 ligand (CD95L) (19 and 21%, respectively). Expression is up-regulated on culture in the presence of a combination of IL-2, IL-4, and eotaxin/CCL11 (88% CD95 and 84% CD95L). We therefore propose that ligation of such newly induced CCR3 on peripheral and germinal B cells by eotaxin/CCL11 leads to the enhanced levels of CD95 and CD95L expression. Ligation of CD95 by its CD95L expressed on neigboring B cells triggers relevant death signaling pathways, which include an increase in levels of Bcl-2 expression, its functional activity, and the release of cytochrome c from the mitochondria into the cytosol. These events initiate a cascade of enzymatic processes of the caspase family, culminating in programmed cell death. Interaction between CCR3 and eotaxin/CCL11 may, besides promoting allergic reactions, drive activated B cells to apoptosis, thereby reducing levels of Ig production, including IgE, and consequently limit the development of the humoral immune response. The apoptotic action of eotaxin/CCL11 suggests a therapeutic modality in the treatment of B cell lymphoma.

Apoptosis↗

PubMed2003

Breaking tolerance in hepatitis B surface antigen (HBsAg) transgenic mice by vaccination with cross-reactive, natural HBsAg variants.

Processing exogenous hepatitis B surface antigen (HBsAg) of the hepatitis B virus (HBV) generates the K(b)-binding S(208-215) epitope 1; processing endogenous HBsAg generates the K(b)-binding S(190-197) epitope 2. Cross-reactive CD8(+) T cell responses were primed to epitope 1 but not epitope 2 when mice were immunized with natural HBsAg(ayw), or HBsAg(adw2) variants differing within both epitopes by one or two residues. Expression of HBsAg(ayw) from a transgene in the liver renders (HBs-tg) mice tolerant to epitope 1 of HBsAg(ayw). CD8(+) T cells specific for epitope 1 could be primed in HBs-tg mice by HBsAg(adw2); these specific CD8(+) T cells cross-reacted with epitope 1 processed from the transgene-encoded HBsAg(ayw). The liver of vaccinated HBsAg(ayw) transgenic mice showed severe histopathology and contained functional (IFNgamma-producing), cross-reactive CD8(+) T cells, and vaccinated HBs-tg mice showed reduced antigenemia. Hence, vaccination with natural HBsAg variants from different HBV sero/genotypes can prime cross-reactive, specific CD8(+) T cell immunity that breaks tolerance to HBsAg.

Adoptive Transfer↗

PubMed2003

Ca2+ response in neutrophils after exposure to bacterial N-formyl-methionyl-leucyl-phenylalanine: delayed response in ulcerative colitis.

OBJECTIVE: In acute stages of ulcerative colitis (UC), neutrophils migrate from the circulation into inflamed colonic tissue, initiated by yet unknown stimuli. The bacterial peptide N-formyl-methionyl-leucyl-phenylalanine (FMLP) is a component of the surface membrane of colonic bacteria such as Escherichia coli and stimulates Ca2+ influx into neutrophils, reflecting the fact that ionized calcium is an important secondary messenger for several neutrophil functions, including locomotion, phagocytosis and free oxygen radical production. Recent studies have revealed that Ca2+ dependent ICAM-1/beta 2-integrin mediated neutrophil migration is impaired in UC patients. The aim of the present work was to study the influx of Ca2+ into peripheral blood neutrophils of UC patients after exposure to FMLP and after binding of either beta 2-integrins or intercellular adhesion molecule-1 (ICAM-1). METHODS: The relative intracellular Ca2+ levels ([Ca2+]i ) were measured spectrofluorometrically in neutrophils isolated from eight UC patients and eight controls. The cells were exposed to 1 nm FMLP, 5 pm free ICAM-1, or antibodies binding ICAM-1 or the beta 2-integrins CD11a, CD11b, CD11c and CD18. RESULTS: A pronounced increase in [Ca2+]i was observed by exposure of cells to FMLP, and neutrophils from UC patients showed a consistent and significant delayed response as compared to cells from control subjects (P < 0.01). Antibody mediated cross-linking of CD18 triggered a small but detectable increase in [Ca2+]i, which did not differ between patients and controls. CONCLUSION: A delayed response to bacterial peptides appears to be a phenotypic trait for neutrophils of UC patients. A connection between FMLP stimulated Ca2+ influx and CD11/CD18 upregulation is discussed.

Adult↗

PubMed2003

Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.

In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.

Amino Acid Sequence↗

PubMed2003

Selecting informative data for developing peptide-MHC binding predictors using a query by committee approach.

Strategies for selecting informative data points for training prediction algorithms are important, particularly when data points are difficult and costly to obtain. A Query by Committee (QBC) training strategy for selecting new data points uses the disagreement between a committee of different algorithms to suggest new data points, which most rationally complement existing data, that is, they are the most informative data points. In order to evaluate this QBC approach on a real-world problem, we compared strategies for selecting new data points. We trained neural network algorithms to obtain methods to predict the binding affinity of peptides binding to the MHC class I molecule, HLA-A2. We show that the QBC strategy leads to a higher performance than a baseline strategy where new data points are selected at random from a pool of available data. Most peptides bind HLA-A2 with a low affinity, and as expected using a strategy of selecting peptides that are predicted to have high binding affinities also lead to more accurate predictors than the base line strategy. The QBC value is shown to correlate with the measured binding affinity. This demonstrates that the different predictors can easily learn if a peptide will fail to bind, but often conflict in predicting if a peptide binds. Using a carefully constructed computational setup, we demonstrate that selecting peptides with a high QBC performs better than low QBC peptides independently from binding affinity. When predictors are trained on a very limited set of data they cannot be expected to disagree in a meaningful way and we find a data limit below which the QBC strategy fails. Finally, it should be noted that data selection strategies similar to those used here might be of use in other settings in which generation of more data is a costly process.

Algorithms↗