Articular cartilage glycosaminoglycans inhibit the adhesion of endothelial cells

Articular cartilage undergoes severe loss of proteoglycan and its constituent glycosaminoglycans (GAGs) in osteoarthritis. We hypothesize that the low GAG content of osteoarthritic cartilage renders the tissue susceptible to pathological vascularization. This was investigated using an in vitro angiogenesis model assessing endothelial cell adhesion to GAG-depleted cartilage explants. Bovine cartilage explants were treated with hyaluronidase to deplete GAG content and then seeded with fluorescently tagged human endothelial cells (HMEC-1). HMEC-1 adherence was assessed after 4 hr and 7 days. The effect of hyaluronidase treatment on GAG content, chondrocyte viability, and biochemical composition of the extracellular matrix was also determined. Hyaluronidase treatment reduced the GAG content of cartilage explants by 78 ± 3% compared with that of controls (p <0.0001). GAG depletion was associated with significantly more HMEC-1 adherence on both the surface (superficial zone) and the underside (deep zone) of the explants (both p <0.0001). The latter provided a more favorable environment for extended culture of HMEC-1 compared with the articulating surface. Hyaluronidase treatment altered the immunostaining for chondroitin sulfate epitopes, but not for lubricin. Our results support the hypothesis that articular cartilage GAGs are antiadhesive to endothelial cells and suggest that chondroitin sulfate and/or hyaluronan are responsible. The loss of these GAGs in osteoarthritis may allow osteochondral angiogenesis resulting in disease progression.

Computer aided selection of candidate vaccine antigens

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.

MHC class II binding prediction:a little help from a friend

Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonetheless use of computational models of class II binding can still offer viable help to the work of the immunologist and vaccinologist.

Unexpected role of surface transglutaminase type II in celiac disease

Background & Aims: In celiac disease (CD), transglutaminase type II (TG2) has 2 fundamental roles: (1) as the autoantigen recognized by highly specific autoantibodies and (2) the modifier of pathogenic gliadin T-cell epitopes. It follows that inhibition of TG2 might represent an attractive strategy to curb the toxic action of gliadin. Here we studied the validity of this strategy using the organ culture approach. Methods: Duodenal biopsy specimens from 30 treated patients with CD, 33 untreated patients with CD, and 24 controls were cultured with or without gliadin peptides p31-43, pα-9, and deamidated pα-9 for 20 minutes, 3 hours, and 24 hours. In 31 patients with CD and 16 controls, TG2 inhibitor R283 or anti-TG CUB 7402 or anti-surface TG2 (6B9) mAbs were used in cultures. T84 cells were also cultured with or without peptides with or without TG inhibitors. Mucosal modifications after culture were assessed by immunofluorescence, in situ detection of TG activity, confocal microscopy, and fluorescence-activated cell sorter analysis. Results: The enzymatic inhibition of TG2 only controlled gliadin-specific T-cell activation. The binding of surface TG2 contained gliadin-specific T-cell activation and p31-43-induced actin rearrangement, epithelial phosphorylation, and apoptosis, both in organ cultures and T84 cells. Conclusions: These data indicate a novel and unexpected biological role for surface TG2 in the pathogenesis of CD suggesting a third role for TG2 in CD. These results have a specific impact for celiac disease, with wider implications indicating a novel biologic function of TG2 with possible repercussions in other diseases. © 2005 by the American Gastroenterological Association.

AntigenDB:an immunoinformatics database of pathogen antigens

The continuing threat of infectious disease and future pandemics, coupled to the continuous increase of drug-resistant pathogens, makes the discovery of new and better vaccines imperative. For effective vaccine development, antigen discovery and validation is a prerequisite. The compilation of information concerning pathogens, virulence factors and antigenic epitopes has resulted in many useful databases. However, most such immunological databases focus almost exclusively on antigens where epitopes are known and ignore those for which epitope information was unavailable. We have compiled more than 500 antigens into the AntigenDB database, making use of the literature and other immunological resources. These antigens come from 44 important pathogenic species. In AntigenDB, a database entry contains information regarding the sequence, structure, origin, etc. of an antigen with additional information such as B and T-cell epitopes, MHC binding, function, gene-expression and post translational modifications, where available. AntigenDB also provides links to major internal and external databases. We shall update AntigenDB on a rolling basis, regularly adding antigens from other organisms and extra data analysis tools. AntigenDB is available freely at http://www.imtech.res.in/raghava/antigendb and its mirror site http://www.bic.uams.edu/raghava/antigendb.

Immunoinformatic evaluation of multiple epitope ensembles as vaccine candidates:E coli 536

Epitope prediction is becoming a key tool for vaccine discovery. Prospective analysis of bacterial and viral genomes can identify antigenic epitopes encoded within individual genes that may act as effective vaccines against specific pathogens. Since B-cell epitope prediction remains unreliable, we concentrate on T-cell epitopes, peptides which bind with high affinity to Major Histacompatibility Complexes (MHC). In this report, we evaluate the veracity of identified T-cell epitope ensembles, as generated by a cascade of predictive algorithms (SignalP, Vaxijen, MHCPred, IDEB, EpiJen), as a candidate vaccine against the model pathogen uropathogenic gram negative bacteria Escherichia coli (E-coli) strain 536 (O6:K15:H31). An immunoinformatic approach was used to identify 23 epitopes within the E-coli proteome. These epitopes constitute the most promiscuous antigenic sequences that bind across more than one HLA allele with high affinity (IC50 <50nM). The reliability of software programmes used, polymorphic nature of genes encoding MHC and what this means for population coverage of this potential vaccine are discussed.

Antibody detection of Burkholderia pseudommallei and Burkholderia mallei

Monoclonal and polyclonaI antibodies have been produced for use in immunological assays for the detection of Burkholderia pseudomallei and Burkholderia mallei. Monoclonal antibodies recognising a high molecular weight polysaccharide material found in some strains of both species have been shown to be effective in recognising B. pseudomallei and B. mallei and distinguishing them from other organisms. The high molecular weight polysaccharide material is thought to be the capsule of B. pseudomallei and B. mallei and may have important links with virulence. B. pseudomallei and B. mallei are known to be closely related, sharing many epitopes, but antigenic variation has been demonstrated within both the species. The lipopolysaccharide from strains of B. pseudomal/ei and B. mallei has been isolated and the silver stain profiles found to be visually very similar. A monoclonal antibody raised to B. mallei LPS has been found to recognise both B. mallei and B. pseudomallei strains. However, in a small number of B. pseudomallei strains a visually atypical LPS profile has been demonstrated. A monoclonal ant ibody rai sed against this atypical LPS showed no recognition of the typical LPS profile of either B. mallei or B. pseudomallei. This atypical LPS structure has not been reported and may be immunologically distinct from the typical LPS. Molecular biology and antibody engineering techniques have been used in an attempt to produce single-chain antibody fragments reactive to B. pseudomallei. Sequencing of one of the single-chain antibody fragments produced showed high homology with murine immunoglobulin genes, but none of the single-chain antibody fragments were found to be specific to B. pselldomallei.

The spectrum and severity of FUS-immunoreactive inclusions in the frontal and temporal lobes of ten cases of neuronal intermediate filament inclusion disease

Neuronal intermediate filament inclusion disease (NIFID), a rare form of frontotemporal lobar degeneration (FTLD), is characterized neuropathologically by focal atrophy of the frontal and temporal lobes, neuronal loss, gliosis, and neuronal cytoplasmic inclusions (NCI) containing epitopes of ubiquitin and neuronal intermediate filament proteins. Recently, the 'fused in sarcoma' (FUS) protein (encoded by the FUS gene) has been shown to be a component of the inclusions of familial amyotrophic lateral sclerosis with FUS mutation, NIFID, basophilic inclusion body disease, and atypical FTLD with ubiquitin-immunoreactive inclusions (aFTLD-U). To further characterize FUS proteinopathy in NIFID, and to determine whether the pathology revealed by FUS immunohistochemistry (IHC) is more extensive than a-internexin, we have undertaken a quantitative assessment of ten clinically and neuropathologically well-characterized cases using FUS IHC. The densities of NCI were greatest in the dentate gyrus (DG) and in sectors CA1/2 of the hippocampus. Anti-FUS antibodies also labeled glial inclusions (GI), neuronal intranuclear inclusions (NII), and dystrophic neurites (DN). Vacuolation was extensive across upper and lower cortical layers. Significantly greater densities of abnormally enlarged neurons and glial cell nuclei were present in the lower compared with the upper cortical laminae. FUS IHC revealed significantly greater numbers of NCI in all brain regions especially the DG. Our data suggest: (1) significant densities of FUS-immunoreactive NCI in NIFID especially in the DG and CA1/2; (2) infrequent FUS-immunoreactive GI, NII, and DN; (3) widely distributed vacuolation across the cortex, and (4) significantly more NCI revealed by FUS than a-internexin IHC.

Spatial patterns of FUS-immunoreactive neuronal cytoplasmic inclusions (NCI) in neuronal intermediate filament inclusion disease (NIFID)

Neuronal intermediate filament inclusion disease (NIFID), a rare form of frontotemporal lobar degeneration (FTLD), is characterized neuropathologically by focal atrophy of the frontal and temporal lobes, neuronal loss, gliosis, and neuronal cytoplasmic inclusions (NCI) containing epitopes of ubiquitin and neuronal intermediate filament (IF) proteins. Recently, the 'fused in sarcoma' (FUS) protein (encoded by the FUS gene) has been shown to be a component of the inclusions of NIFID. To further characterize FUS proteinopathy in NIFID, we studied the spatial patterns of the FUS-immunoreactive NCI in frontal and temporal cortex of 10 cases. In the cerebral cortex, sectors CA1/2 of the hippocampus, and the dentate gyrus (DG), the FUS-immunoreactive NCI were frequently clustered and the clusters were regularly distributed parallel to the tissue boundary. In a proportion of cortical gyri, cluster size of the NCI approximated to those of the columns of cells was associated with the cortico-cortical projections. There were no significant differences in the frequency of different types of spatial patterns with disease duration or disease stage. Clusters of NCI in the upper and lower cortex were significantly larger using FUS compared with phosphorylated, neurofilament heavy polypeptide (NEFH) or a-internexin (INA) immunohistochemistry (IHC). We concluded: (1) FUS-immunoreactive NCI exhibit similar spatial patterns to analogous inclusions in the tauopathies and synucleinopathies, (2) clusters of FUS-immunoreactive NCI are larger than those revealed by NEFH or ???, and (3) the spatial patterns of the FUS-immunoreactive NCI suggest the degeneration of the cortico-cortical projections in NIFID.

EpiTOP—a proteochemometric tool for MHC class II binding prediction

Motivation: T-cell epitope identification is a critical immunoinformatic problem within vaccine design. To be an epitope, a peptide must bind an MHC protein. Results: Here, we present EpiTOP, the first server predicting MHC class II binding based on proteochemometrics, a QSAR approach for ligands binding to several related proteins. EpiTOP uses a quantitative matrix to predict binding to 12 HLA-DRB1 alleles. It identifies 89% of known epitopes within the top 20% of predicted binders, reducing laboratory labour, materials and time by 80%. EpiTOP is easy to use, gives comprehensive quantitative predictions and will be expanded and updated with new quantitative matrices over time.

Toward an atomistic understanding of the immune synapse:large-scale molecular dynamics simulation of a membrane-embedded TCR-pMHC-CD4 complex

T-cell activation requires interaction of T-cell receptors (TCR) with peptide epitopes bound by major histocompatibility complex (MHC) proteins. This interaction occurs at a special cell-cell junction known as the immune or immunological synapse. Fluorescence microscopy has shown that the interplay among one agonist peptide-MHC (pMHC), one TCR and one CD4 provides the minimum complexity needed to trigger transient calcium signalling. We describe a computational approach to the study of the immune synapse. Using molecular dynamics simulation, we report here on a study of the smallest viable model, a TCR-pMHC-CD4 complex in a membrane environment. The computed structural and thermodynamic properties are in fair agreement with experiment. A number of biomolecules participate in the formation of the immunological synapse. Multi-scale molecular dynamics simulations may be the best opportunity we have to reach a full understanding of this remarkable supra-macromolecular event at a cell-cell junction.

Innate recognition of apoptotic cells:novel apoptotic cell-associated molecular patterns revealed by crossreactivity of anti-LPS antibodies

Cells dying by apoptosis are normally cleared by phagocytes through mechanisms that can suppress inflammation and immunity. Molecules of the innate immune system, the pattern recognition receptors (PRRs), are able to interact not only with conserved structures on microbes (pathogen-associated molecular patterns, PAMPs) but also with ligands displayed by apoptotic cells. We reasoned that PRRs might therefore interact with structures on apoptotic cells-apoptotic cell-associated molecular patterns (ACAMPs)-that are analogous to PAMPs. Here we show that certain monoclonal antibodies raised against the prototypic PAMP, lipopolysaccharide (LPS), can crossreact with apoptotic cells. We demonstrate that one such antibody interacts with a constitutively expressed intracellular protein, laminin-binding protein, which translocates to the cell surface during apoptosis and can interact with cells expressing the prototypic PRR, mCD14 as well as with CD14-negative cells. Anti-LPS cross reactive epitopes on apoptotic cells colocalised with annexin V-and C1q-binding sites on vesicular regions of apoptotic cell surfaces and were released associated with apoptotic cell-derived microvesicles (MVs). These results confirm that apoptotic cells and microbes can interact with the immune system through common elements and suggest that anti-PAMP antibodies could be used strategically to characterise novel ACAMPs associated not only with apoptotic cells but also with derived MVs. © 2013 Macmillan Publishers Limited All rights reserved.

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.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

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.