http://www.immunome-research.com The latest research articles published by Immunome Research 2010-12-06T00:00:00Z Background: The extraordinary diversity characterizing the antibody repertoire is generated by both evolution and lymphocyte development. Much of this diversity is due to the existence of immunoglobulin (Ig) variable region gene segment libraries, which were diversified during evolution and, in higher vertebrates, are used in generating the combinatorial diversity of antibody genes. The aim of the present study was to address the following questions: What evolutionary parameters affect the size and structure of gene libraries? Are the number of genes in libraries of contemporary species, and the corresponding gene locus structure, a random result of evolutionary history, or have these properties been optimized with respect to individual or population fitness? If a larger number of genes or different genome structures do not increase the fitness, then the current structure is probably optimized. Results: We used a simulation of variable region gene library evolution. We measured the effect of different parameters on gene library size and diversity, and the corresponding fitness. We found compensating relationships between parameters, which optimized Ig library size and diversity. Conclusions: We conclude that contemporary species' Ig libraries have been optimized by evolution in terms of Ig sequence lengths, the number and diversity of Ig genes, and antibody-antigen affinities. http://www.immunome-research.com/content/6/1/13 Michal Barak Guy Eilat Ron Unger Ramit Mehr Immunome Research 2010, null:13 2010-12-06T00:00:00Z doi:10.1186/1745-7580-6-13 /content/figures/1745-7580-6-13-toc.gif Immunome Research 1745-7580 ${item.volume} 13 2010-12-06T00:00:00Z XML Background: Toxoplasmosis causes loss of life, cognitive and motor function, and sight. A vaccine is greatly needed to prevent this disease. The purpose of this study was to use an immmunosense approach to develop a foundation for development of vaccines to protect humans with the HLA-A03 supertype. Three peptides had been identified with high binding scores for HLA-A03 supertypes using bioinformatic algorhythms, high measured binding affinity for HLA-A03 supertype molecules, and ability to elicit IFN-γ production by human HLA-A03 supertype peripheral blood CD8+ T cells from seropositive but not seronegative persons. Results: Herein, when these peptides were administered with the universal CD4+T cell epitope PADRE (AKFVAAWTLKAAA) and formulated as lipopeptides, or administered with GLA-SE either alone, or with Pam2Cys added, we found we successfully created preparations that induced IFN-γ and reduced parasite burden in HLA-A*1101(an HLA-A03 supertype allele) transgenic mice. GLA-SE is a novel emulsified synthetic TLR4 ligand that is known to facilitate development of T Helper 1 cell (TH1) responses. Then, so our peptides would include those expressed in tachyzoites, bradyzoites and sporozoites from both Type I and II parasites, we used our approaches which had identified the initial peptides. We identified additional peptides using bioinformatics, binding affinity assays, and study of responses of HLA-A03 human cells. Lastly, we found that immunization of HLA-A*1101 transgenic mice with all the pooled peptides administered with PADRE, GLA-SE, and Pam2Cys is an effective way to elicit IFN-γ producing CD8+ splenic T cells and protection. Immunizations included the following peptides together: KSFKDILPK (SAG1224-232); AMLTAFFLR (GRA6164-172); RSFKDLLKK (GRA7134-142); STFWPCLLR (SAG2C13-21); SSAYVFSVK(SPA250-258); and AVVSLLRLLK(SPA89-98). This immunization elicited robust protection, measured as reduced parasite burden using a luciferase transfected parasite, luciferin, this novel, HLA transgenic mouse model, and imaging with a Xenogen camera. Conclusions: Toxoplasma gondii peptides elicit HLA-A03 restricted, IFN-γ producing, CD8+ T cells in humans and mice. These peptides administered with adjuvants reduce parasite burden in HLA-A*1101 transgenic mice. This work provides a foundation for immunosense based vaccines. It also defines novel adjuvants for newly identified peptides for vaccines to prevent toxoplasmosis in those with HLA-A03 supertype alleles. http://www.immunome-research.com/content/6/1/12 Hua Cong Ernest Mui William Witola John Sidney Jeff Alexander Alessandro Sette Ajesh Maewal Rima McLeod Immunome Research 2010, null:12 2010-12-03T00:00:00Z doi:10.1186/1745-7580-6-12 /content/figures/1745-7580-6-12-toc.gif Immunome Research 1745-7580 ${item.volume} 12 2010-12-03T00:00:00Z XML Background: Macrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively. Results: We show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest. Conclusions: The computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without compromise in image quality or information loss in associated spectra. These results motivate further use of label free microscopy techniques in real-time imaging of live immune cells. http://www.immunome-research.com/content/6/1/11 Robert Milewski Yutaro Kumagai Katsumasa Fujita Daron Standley Nicholas Smith Immunome Research 2010, null:11 2010-11-19T00:00:00Z doi:10.1186/1745-7580-6-11 /content/figures/1745-7580-6-11-toc.gif Immunome Research 1745-7580 ${item.volume} 11 2010-11-19T00:00:00Z XML Background: The advent of Systems Biology has been accompanied by the blooming of pathway databases. Currently pathways are defined generically with respect to the organ or cell type where a reaction takes place. The cell type specificity of the reactions is the foundation of immunological research, and capturing this specificity is of paramount importance when using pathway-based analyses to decipher complex immunological datasets. Here, we present DC-ATLAS, a novel and versatile resource for the interpretation of high-throughput data generated perturbing the signaling network of dendritic cells (DCs). Results: Pathways are annotated using a novel data model, the Biological Connection Markup Language (BCML), a SBGN-compliant data format developed to store the large amount of information collected. The application of DC-ATLAS to pathway-based analysis of the transcriptional program of DCs stimulated with agonists of the toll-like receptor family allows an integrated description of the flow of information from the cellular sensors to the functional outcome, capturing the temporal series of activation events by grouping sets of reactions that occur at different time points in well-defined functional modules. Conclusions: The initiative significantly improves our understanding of DC biology and regulatory networks. Developing a systems biology approach for immune system holds the promise of translating knowledge on the immune system into more successful immunotherapy strategies. http://www.immunome-research.com/content/6/1/10 Duccio Cavalieri Damariz Rivero Luca Beltrame Sonja Buschow Enrica Calura Lisa Rizzetto Sandra Gessani Maria Gauzzi Walter Reith Andreas Baur Roberto Bonaiuti Marco Brandizi Carlotta De Filippo Ugo D'Oro Sorin Draghici Isabelle Dunand-Sauthier Evelina Gatti Francesca Granucci Michaela Gundel Matthijs Kramer Mirela Kuka Arpad Lanyi Cornelis Melief Nadine van Montfoort Renato Ostuni Philippe Pierre Razvan Popovici Eva Rajnavolgyi Stephan Schierer Gerold Schuler Vassili Soumelis Andrea Splendiani Irene Stefanini Maria Torcia Ivan Zanoni Raphael Zollinger Carl Figdor Jonathan Austyn Immunome Research 2010, null:10 2010-11-19T00:00:00Z doi:10.1186/1745-7580-6-10 /content/figures/1745-7580-6-10-toc.gif Immunome Research 1745-7580 ${item.volume} 10 2010-11-19T00:00:00Z XML Background: Binding of peptides to Major Histocompatibility class II (MHC-II) molecules play a central role in governing responses of the adaptive immune system. MHC-II molecules sample peptides from the extracellular space allowing the immune system to detect the presence of foreign microbes from this compartment. Predicting which peptides bind to an MHC-II molecule is therefore of pivotal importance for understanding the immune response and its effect on host-pathogen interactions. The experimental cost associated with characterizing the binding motif of an MHC-II molecule is significant and large efforts have therefore been placed in developing accurate computer methods capable of predicting this binding event. Prediction of peptide binding to MHC-II is complicated by the open binding cleft of the MHC-II molecule, allowing binding of peptides extending out of the binding groove. Moreover, the genes encoding the MHC molecules are immensely diverse leading to a large set of different MHC molecules each potentially binding a unique set of peptides. Characterizing each MHC-II molecule using peptide-screening binding assays is hence not a viable option. Results: Here, we present an MHC-II binding prediction algorithm aiming at dealing with these challenges. The method is a pan-specific version of the earlier published allele-specific NN-align algorithm and does not require any pre-alignment of the input data. This allows the method to benefit also from information from alleles covered by limited binding data. The method is evaluated on a large and diverse set of benchmark data, and is shown to significantly out-perform state-of-the-art MHC-II prediction methods. In particular, the method is found to boost the performance for alleles characterized by limited binding data where conventional allele-specific methods tend to achieve poor prediction accuracy. Conclusions: The method thus shows great potential for efficient boosting the accuracy of MHC-II binding prediction, as accurate predictions can be obtained for novel alleles at highly reduced experimental costs. Pan-specific binding predictions can be obtained for all alleles with know protein sequence and the method can benefit by including data in the training from alleles even where only few binders are known. The method and benchmark data are available at http://www.cbs.dtu.dk/services/NetMHCIIpan-2.0 http://www.immunome-research.com/content/6/1/9 Morten Nielsen Sune Justesen Ole Lund Claus Lundegaard Soren Buus Immunome Research 2010, null:9 2010-11-13T00:00:00Z doi:10.1186/1745-7580-6-9 /content/figures/1745-7580-6-9-toc.gif Immunome Research 1745-7580 ${item.volume} 9 2010-11-13T00:00:00Z XML Background: Improving our understanding of the immune response is fundamental to developing strategies to combat a wide range of diseases. We describe an integrated epitope analysis system which is based on principal component analysis of sequences of amino acids, using a multilayer perceptron neural net to conduct QSAR regression predictions for peptide binding affinities to 35 MHC-I and 14 MHC-II alleles. Results: The approach described allows rapid processing of single proteins, entire proteomes or subsets thereof, as well as multiple strains of the same organism. It enables consideration of the interface of diversity of both microorganisms and of host immunogenetics. Patterns of binding affinity are linked to topological features, such as extracellular or intramembrane location, and integrated into a graphical display which facilitates conceptual understanding of the interplay of B-cell and T-cell mediated immunity.Patterns which emerge from application of this approach include the correlations between peptides showing high affinity binding to MHC-I and to MHC-II, and also with predicted B-cell epitopes. These are characterized as coincident epitope groups (CEGs). Also evident are long range patterns across proteins which identify regions of high affinity binding for a permuted population of diverse and heterozygous HLA alleles, as well as subtle differences in reactions with MHCs of individual HLA alleles, which may be important in disease susceptibility, and in vaccine and clinical trial design. Comparisons are shown of predicted epitope mapping derived from application of the QSAR approach with experimentally derived epitope maps from a diverse multi-species dataset, from Staphylococcus aureus, and from vaccinia virus. Conclusions: A desktop application with interactive graphic capability is shown to be a useful platform for development of prediction and visualization tools for epitope mapping at scales ranging from individual proteins to proteomes from multiple strains of an organism. The possible functional implications of the patterns of peptide epitopes observed are discussed, including their implications for B-cell and T-cell cooperation and cross presentation. http://www.immunome-research.com/content/6/1/8 Robert Bremel E. Homan Immunome Research 2010, null:8 2010-11-02T00:00:00Z doi:10.1186/1745-7580-6-8 /content/figures/1745-7580-6-8-toc.gif Immunome Research 1745-7580 ${item.volume} 8 2010-11-02T00:00:00Z XML Background: Operation of the immune system is multivariate. Reduction of the dimensionality is essential to facilitate understanding of this complex biological system. One multi-dimensional facet of the immune system is the binding of epitopes to the MHC-I and MHC-II molecules by diverse populations of individuals. Prediction of such epitope binding is critical and several immunoinformatic strategies utilizing amino acid substitution matrices have been designed to develop predictive algorithms. Contemporaneously, computational and statistical tools have evolved to handle multivariate and megavariate analysis, but these have not been systematically deployed in prediction of MHC binding. Partial least squares analysis, principal component analysis, and associated regression techniques have become the norm in handling complex datasets in many fields. Over two decades ago Wold and colleagues showed that principal components of amino acids could be used to predict peptide binding to cellular receptors. We have applied this observation to the analysis of MHC binding, and to derivation of predictive methods applicable on a whole proteome scale. Results: We show that amino acid principal components and partial least squares approaches can be utilized to visualize the underlying physicochemical properties of the MHC binding domain by using commercially available software. We further show the application of amino acid principal components to develop both linear partial least squares and non-linear neural network regression prediction algorithms for MHC-I and MHC-II molecules. Several visualization options for the output aid in understanding the underlying physicochemical properties, enable confirmation of earlier work on the relative importance of certain peptide residues to MHC binding, and also provide new insights into differences among MHC molecules. We compared both the linear and non-linear MHC binding prediction tools to several predictive tools currently available on the Internet. Conclusions: As opposed to the highly constrained user-interaction paradigms of web-server approaches, local computational approaches enable interactive analysis and visualization of complex multidimensional data using robust mathematical tools. Our work shows that prediction tools such as these can be constructed on the widely available JMP® platform, can operate in a spreadsheet environment on a desktop computer, and are capable of handling proteome-scale analysis with high throughput. http://www.immunome-research.com/content/6/1/7 Robert Bremel E. Homan Immunome Research 2010, null:7 2010-11-02T00:00:00Z doi:10.1186/1745-7580-6-7 /content/figures/1745-7580-6-7-toc.gif Immunome Research 1745-7580 ${item.volume} 7 2010-11-02T00:00:00Z XML Background: One of the major challenges in the field of vaccine design is to predict conformational B-cell epitopes in an antigen. In the past, several methods have been developed for predicting conformational B-cell epitopes in an antigen from its tertiary structure. This is the first attempt in this area to predict conformational B-cell epitope in an antigen from its amino acid sequence. Results: All Support vector machine (SVM) models were trained and tested on 187 non-redundant protein chains consisting of 2261 antibody interacting residues of B-cell epitopes. Models have been developed using binary profile of pattern (BPP) and physiochemical profile of patterns (PPP) and achieved a maximum MCC of 0.22 and 0.17 respectively. In this study, for the first time SVM model has been developed using composition profile of patterns (CPP) and achieved a maximum MCC of 0.73 with accuracy 86.59%. We compare our CPP based model with existing structure based methods and observed that our sequence based model is as good as structure based methods. Conclusion: This study demonstrates that prediction of conformational B-cell epitope in an antigen is possible from is primary sequence. This study will be very useful in predicting conformational B-cell epitopes in antigens whose tertiary structures are not available. A web server CBTOPE has been developed for predicting B-cell epitope http://www.imtech.res.in/raghava/cbtope/. http://www.immunome-research.com/content/6/1/6 Hifzur Ansari Gajendra Raghava Immunome Research 2010, null:6 2010-10-20T00:00:00Z doi:10.1186/1745-7580-6-6 /content/figures/1745-7580-6-6-toc.gif Immunome Research 1745-7580 ${item.volume} 6 2010-10-20T00:00:00Z XML Background: Innate immunity is the first line of defence offered by host cells to infections. Macrophage cells involved in innate immunity are stimulated by lipopolysaccharide (LPS), found on bacterial cell surface, to express a complex array of gene products. Persistent LPS stimulation makes a macrophage tolerant to LPS with down regulation of inflammatory genes ("pro-inflammatory") while continually expressing genes to fight the bacterial infection ("antibacterial"). Interactions of transcription factors (TF) at their cognate TF binding sites (TFBS) on the expressed genes are important in transcriptional regulatory networks that control these pro-inflammatory and antibacterial expression paradigms involved in LPS stimulation. Results: We used differential expression patterns in a public domain microarray data set from LPS-stimulated macrophages to identify 228 pro-inflammatory and 18 antibacterial genes. Employing three different motif search tools, we predicted respectively four and one statistically significant TF-TFBS interactions from the pro-inflammatory and antibacterial gene sets. The biological literature was utilized to identify target genes for the four pro-inflammatory profile TFs predicted from the three tools, and 18 of these target genes were observed to follow the pro-inflammatory expression pattern in the original microarray data. Conclusions: Our analysis distinguished pro-inflammatory vs. antibacterial transcriptomic signatures that classified their respective gene expression patterns and the corresponding TF-TFBS interactions in LPS-stimulated macrophages. By doing so, this study has attempted to characterize the temporal differences in gene expression associated with LPS tolerance, a major immune phenomenon implicated in various pathological disorders. http://www.immunome-research.com/content/6/1/5 Rahul Kollipara Narayanan Perumal Immunome Research 2010, null:5 2010-09-21T00:00:00Z doi:10.1186/1745-7580-6-5 /content/figures/1745-7580-6-5-toc.gif Immunome Research 1745-7580 ${item.volume} 5 2010-09-21T00:00:00Z XML Background: Several arenaviruses cause severe hemorrhagic fever and aseptic meningitis in humans for which no licensed vaccines are available. A major obstacle for vaccine development is pathogen heterogeneity within the Arenaviridae family. Evidence in animal models and humans indicate that T cell and antibody-mediated immunity play important roles in controlling arenavirus infection and replication. Because CD4+ T cells are needed for optimal CD8+ T cell responses and to provide cognate help for B cells, knowledge of epitopes recognized by CD4+ T cells is critical to the development of an effective vaccine strategy against arenaviruses. Thus, the goal of the present study was to define and characterize CD4+ T cell responses from a broad repertoire of pathogenic arenaviruses (including lymphocytic choriomeningitis, Lassa, Guanarito, Junin, Machupo, Sabia, and Whitewater Arroyo viruses) and to provide determinants with the potential to be incorporated into a multivalent vaccine strategy. Results: By inoculating HLA-DRB1*0101 transgenic mice with a panel of recombinant vaccinia viruses, each expressing a single arenavirus antigen, we identified 37 human HLA-DRB1*0101-restricted CD4+ T cell epitopes from the 7 antigenically distinct arenaviruses. We showed that the arenavirus-specific CD4+ T cell epitopes are capable of eliciting T cells with a propensity to provide help and protection through CD40L and polyfunctional cytokine expression. Importantly, we demonstrated that the set of identified CD4+ T cell epitopes provides broad, non-ethnically biased population coverage of all 7 arenavirus species targeted by our studies. Conclusions: The identification of CD4+ T cell epitopes, with promiscuous binding properties, derived from 7 different arenavirus species will aid in the development of a T cell-based vaccine strategy with the potential to target a broad range of ethnicities within the general population and to protect against both Old and New World arenavirus infection. http://www.immunome-research.com/content/6/1/4 Maya Kotturi Jason Botten Matt Maybeno John Sidney Jean Glenn Huynh-Hoa Bui Carla Oseroff Shane Crotty Bjoern Peters Howard Grey Daniel Altmann Michael Buchmeier Alessandro Sette Immunome Research 2010, null:4 2010-05-17T00:00:00Z doi:10.1186/1745-7580-6-4 /content/figures/1745-7580-6-4-toc.gif Immunome Research 1745-7580 ${item.volume} 4 2010-05-17T00:00:00Z XML