http://www.immunome-research.com The most accessed research articles published by Immunome Research 2010-11-19T00:00:00Z Background: Tuberculosis, caused by the bacterium Mycobacterium tuberculosis, remains a leading cause of infectious disease morbidity and mortality, and is responsible for more than 2 million deaths a year. Reports about extremely drug resistant (XDR) strains have further heightened the sense of urgency for the development of novel strategies to prevent and treat TB. Detailed knowledge of the epitopes recognized by immune responses can aid in vaccine and diagnostics development, and provides important tools for basic research. The analysis of epitope data corresponding to M. tuberculosis can also identify gaps in our knowledge, and suggest potential areas for further research and discovery. The Immune Epitope Database (IEDB) is compiled mainly from literature sources, and describes a broad array of source organisms, including M. tuberculosis and other Mycobacterial species.DescriptionA comprehensive analysis of IEDB data regarding the genus Mycobacteria was performed. The distribution of antibody/B cell and T cell epitopes was analyzed in terms of their associated recognition cell type effector function and chemical properties. The various species, strains and proteins which the epitope were derived, were also examined. Additional variables considered were the host in which the epitopes were defined, the specific TB disease state associated with epitope recognition, and the HLA associated with disease susceptibility and endemic regions were also scrutinized. Finally, based on these results, standardized reference datasets of mycobacterial epitopes were generated. Conclusion: All current TB-related epitope data was cataloged for the first time from the published literature. The resulting inventory of more than a thousand different epitopes should prove a useful tool for the broad scientific community. Knowledge gaps specific to TB epitope data were also identified. In summary, few non-peptidic or post-translationally modified epitopes have been defined. Most importantly epitopes have apparently been defined from only 7% of all ORFs, and the top 30 most frequently studied protein antigens contain 65% of the epitopes, leaving the majority of M. tuberculosis genome unexplored. A lack of information related to the specific strains from which epitopes are derived is also evident. Finally, the generation of reference lists of mycobacterial epitopes should also facilitate future vaccine and diagnostic research. http://www.immunome-research.com/content/3/1/10 Martin Blythe Qing Zhang Kerrie Vaughan Romulo de Castro Nima Salimi Huynh-Hoa Bui David Lewinsohn Joel Ernst Bjoern Peters Alessandro Sette Immunome Research 2007, null:10 2007-12-14T00:00:00Z doi:10.1186/1745-7580-3-10 /content/figures/1745-7580-3-10-toc.gif Immunome Research 1745-7580 ${item.volume} 10 2007-12-14T00:00:00Z XML Identification of epitopes that invoke strong responses from B-cells is one of the key steps in designing effective vaccines against pathogens. Because experimental determination of epitopes is expensive in terms of cost, time, and effort involved, there is an urgent need for computational methods for reliable identification of B-cell epitopes. Although several computational tools for predicting B-cell epitopes have become available in recent years, the predictive performance of existing tools remains far from ideal. We review recent advances in computational methods for B-cell epitope prediction, identify some gaps in the current state of the art, and outline some promising directions for improving the reliability of such methods. http://www.immunome-research.com/content/6/S2/S2 Immunome Research 2010, null:S2 2010-11-03T00:00:00Z doi:10.1186/1745-7580-6-S2-S2 /content/figures/1745-7580-6-S2-S2-toc.gif Immunome Research 1745-7580 ${item.volume} S2 2010-11-03T00:00:00Z 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: B-cell epitopes are the sites of molecules that are recognized by antibodies of the immune system. Knowledge of B-cell epitopes may be used in the design of vaccines and diagnostics tests. It is therefore of interest to develop improved methods for predicting B-cell epitopes. In this paper, we describe an improved method for predicting linear B-cell epitopes. Results: In order to do this, three data sets of linear B-cell epitope annotated proteins were constructed. A data set was collected from the literature, another data set was extracted from the AntiJen database and a data sets of epitopes in the proteins of HIV was collected from the Los Alamos HIV database. An unbiased validation of the methods was made by testing on data sets on which they were neither trained nor optimized on. We have measured the performance in a non-parametric way by constructing ROC-curves. Conclusion: The best single method for predicting linear B-cell epitopes is the hidden Markov model. Combining the hidden Markov model with one of the best propensity scale methods, we obtained the BepiPred method. When tested on the validation data set this method performs significantly better than any of the other methods tested. The server and data sets are publicly available at http://www.cbs.dtu.dk/services/BepiPred. http://www.immunome-research.com/content/2/1/2 Jens Erik Pontoppidan Larsen Ole Lund Morten Nielsen Immunome Research 2006, null:2 2006-04-24T00:00:00Z doi:10.1186/1745-7580-2-2 /content/figures/1745-7580-2-2-toc.gif Immunome Research 1745-7580 ${item.volume} 2 2006-04-24T00:00:00Z 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 Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens. http://www.immunome-research.com/content/6/S2/S1 Immunome Research 2010, null:S1 2010-11-03T00:00:00Z doi:10.1186/1745-7580-6-S2-S1 /content/figures/1745-7580-6-S2-S1-toc.gif Immunome Research 1745-7580 ${item.volume} S1 2010-11-03T00:00:00Z XML AntiJen is a database system focused on the integration of kinetic, thermodynamic, functional, and cellular data within the context of immunology and vaccinology. Compared to its progenitor JenPep, the interface has been completely rewritten and redesigned and now offers a wider variety of search methods, including a nucleotide and a peptide BLAST search. In terms of data archived, AntiJen has a richer and more complete breadth, depth, and scope, and this has seen the database increase to over 31,000 entries. AntiJen provides the most complete and up-to-date dataset of its kind. While AntiJen v2.0 retains a focus on both T cell and B cell epitopes, its greatest novelty is the archiving of continuous quantitative data on a variety of immunological molecular interactions. This includes thermodynamic and kinetic measures of peptide binding to TAP and the Major Histocompatibility Complex (MHC), peptide-MHC complexes binding to T cell receptors, antibodies binding to protein antigens and general immunological protein-protein interactions. The database also contains quantitative specificity data from position-specific peptide libraries and biophysical data, in the form of diffusion co-efficients and cell surface copy numbers, on MHCs and other immunological molecules. The uses of AntiJen include the design of vaccines and diagnostics, such as tetramers, and other laboratory reagents, as well as helping parameterize the bioinformatic or mathematical in silico modeling of the immune system. The database is accessible from the URL: http://www.jenner.ac.uk/antijen. http://www.immunome-research.com/content/1/1/4 Christopher Toseland Debra Clayton Helen McSparron Shelley Hemsley Martin Blythe Kelly Paine Irini Doytchinova Pingping Guan Channa Hattotuwagama Darren Flower Immunome Research 2005, null:4 2005-10-06T00:00:00Z doi:10.1186/1745-7580-1-4 /content/figures/1745-7580-1-4-toc.gif Immunome Research 1745-7580 ${item.volume} 4 2005-10-06T00:00:00Z XML Background: Monocytes and macrophages express an extensive repertoire of G Protein-Coupled Receptors (GPCRs) that regulate inflammation and immunity. In this study we performed a systematic micro-array analysis of GPCR expression in primary mouse macrophages to identify family members that are either enriched in macrophages compared to a panel of other cell types, or are regulated by an inflammatory stimulus, the bacterial product lipopolysaccharide (LPS). Results: Several members of the P2RY family had striking expression patterns in macrophages; P2ry6 mRNA was essentially expressed in a macrophage-specific fashion, whilst P2ry1 and P2ry5 mRNA levels were strongly down-regulated by LPS. Expression of several other GPCRs was either restricted to macrophages (e.g. Gpr84) or to both macrophages and neural tissues (e.g. P2ry12, Gpr85). The GPCR repertoire expressed by bone marrow-derived macrophages and thioglycollate-elicited peritoneal macrophages had some commonality, but there were also several GPCRs preferentially expressed by either cell population. Conclusion: The constitutive or regulated expression in macrophages of several GPCRs identified in this study has not previously been described. Future studies on such GPCRs and their agonists are likely to provide important insights into macrophage biology, as well as novel inflammatory pathways that could be future targets for drug discovery. http://www.immunome-research.com/content/4/1/5 Jane Lattin Kate Schroder Andrew Su John Walker Jie Zhang Tim Wiltshire Kaoru Saijo Christopher Glass David Hume Stuart Kellie Matthew Sweet Immunome Research 2008, null:5 2008-04-29T00:00:00Z doi:10.1186/1745-7580-4-5 /content/figures/1745-7580-4-5-toc.gif Immunome Research 1745-7580 ${item.volume} 5 2008-04-29T00:00:00Z XML Background: Dendritic cells (DCs) are the sentinels of the mammalian immune system, characterized by a complex maturation process driven by pathogen detection. Although multiple studies have described the analysis of activated DCs by transcriptional profiling, recent findings indicate that mRNAs are also regulated at the translational level. A systematic analysis of the mRNAs being translationally regulated at various stages of DC activation was performed using translational profiling, which combines sucrose gradient fractionation of polysomal-bound mRNAs with DNA microarray analysis. Results: Total and polysomal-bound mRNA populations purified from immature, 4 h and 16 h LPS-stimulated human monocyte-derived DCs were analyzed on Affymetrix microarrays U133 2.0. A group of 375 transcripts was identified as translationally regulated during DC-activation. In addition to several biochemical pathways related to immunity, the most statistically relevant biological function identified among the translationally regulated mRNAs was protein biosynthesis itself. We singled-out a cluster of 11 large ribosome proteins mRNAs, which are disengaged from polysomes at late time of maturation, suggesting the existence of a negative feedback loop regulating translation in DCs and linking ribosomal proteins to immuno-modulatory function. Conclusion: Our observations highlight the importance of translation regulation during the immune response, and may favor the identification of novel protein networks relevant for immunity. Our study also provides information on the potential absence of correlation between gene expression and protein production for specific mRNA molecules present in DCs. http://www.immunome-research.com/content/5/1/5 Maurizio Ceppi Giovanna Clavarino Evelina Gatti Enrico Schmidt Aude de Gassart Derek Blankenship Gerald Ogola Jacques Banchereau Damien Chaussabel Philippe Pierre Immunome Research 2009, null:5 2009-11-27T00:00:00Z doi:10.1186/1745-7580-5-5 /content/figures/1745-7580-5-5-toc.gif Immunome Research 1745-7580 ${item.volume} 5 2009-11-27T00:00:00Z XML Background: Using a combined in silico approach, we investigated the glycosylation of T cell epitopes and autoantigens. The present systems biology analysis was made possible by currently available databases (representing full proteomes, known human T cell epitopes and autoantigens) as well as glycosylation prediction tools. Results: We analyzed the probable glycosylation of human T cell epitope sequences extracted from the ImmuneEpitope Database. Our analysis suggests that in contrast to full length SwissProt entries, only a minimal portion of experimentally verified T cell epitopes is potentially N- or O-glycosylated (2.26% and 1.22%, respectively). Bayesian analysis of entries extracted from the Autoantigen Database suggests a correlation between N-glycosylation and autoantigenicity. The analysis of random generated sequences shows that glycosylation probability is also affected by peptide length. Our data suggest that the lack of peptide glycosylation, a feature that probably favors effective recognition by T cells, might have resulted in a selective advantage for short peptides to become T cell epitopes. The length of T cell epitopes is at the intersection of curves determining specificity and glycosylation probability. Thus, the range of length of naturally occurring T cell epitopes may ensure the maximum specificity with the minimal glycosylation probability. Conclusion: The findings of this bioinformatical approach shed light on fundamental factors that might have shaped adaptive immunity during evolution. Our data suggest that amino acid sequence-based hypo/non-glycosylation of certain segments of proteins might be substantial for determining T cell immunity/autoimmunity. http://www.immunome-research.com/content/5/1/4 Tamas Szabo Robin Palotai Peter Antal Itay Tokatly Laszlo Tothfalusi Ole Lund Gyorgy Nagy Andras Falus Edit Buzas Immunome Research 2009, null:4 2009-09-24T00:00:00Z doi:10.1186/1745-7580-5-4 /content/figures/1745-7580-5-4-toc.gif Immunome Research 1745-7580 ${item.volume} 4 2009-09-24T00:00:00Z XML