A population of HLA-DR+ immature cells accumulates in the blood dendritic cell compartment of patients with different types of cancer

Dendritic cell (DC) defects are an important component of immunosuppression in cancer. Here, we assessed whether cancer could affect circulating DC populations and its correlation with tumor progression. The blood DC compartment was evaluated in 136 patients with breast cancer, prostate cancer, and malignant glioma. Phenotypic, quantitative, and functional analyses were performed at various stages of disease. Patients had significantly fewer circulating myeloid (CD11c(+)) and plasmacytoid (CD123(+)) DC, and a concurrent accumulation of CD11c(-)CD123(-) immature cells that expressed high levels of HLA-DR+ immature cells (DR+IC). Although DR+IC exhibited a limited expression of markers ascribed to mature hematopoietic lineages, expression of HLA-DR, CD40, and CD86 suggested a role as antigen-presenting cells. Nevertheless, DR+IC had reduced capacity to capture antigens and elicited poor proliferation and interferon-gamma secretion by T-lymphocytes. Importantly, increased numbers of DR+IC correlated with disease status. Patients with metastatic breast cancer showed a larger number of DR+IC in the circulation than patients with local/nodal disease. Similarly, in patients with fully resected glioma, the proportion of DR+IC in the blood increased when evaluation indicated tumor recurrence. Reduction of blood DC correlating with accumulation of a population of immature cells with poor immunologic function may be associated with increased immunodeficiency observed in cancer.

Cross-reactive recognition of human and primate cytomegalovirus sequences by human CD4 cytotoxic T lymphocytes specific for glycoprotein B and H

Although the importance of CD4(+) T cell responses to human cytonnegalovirus (HCMV) has recently been recognized in transplant and immunosuppressed patients, the precise specificity and nature of this response has remained largely unresolved. In the present study we have isolated CD4(+) CTL which recognize epitopes from HCMV glycoproteins gB and gH in association with two different HLA-DR antigens, DRA1*0101/DRB1*0701 (DR7) and DRA1*0101/DRB1*1101 (DR11). Comparison of amino acid sequences of HICMV isolates revealed that the gB and gH epitope sequences recognized by human CD4(+) T cells were not only conserved in clinical isolates from HCMV but also in CMV isolates from higher primates (chimpanzee, rhesus and baboon). Interestingly, these epitope sequences from chimpanzee, rhesus and baboon CMV are efficiently recognized by human CD4(+) CTL. More importantly, we show that gB-specific T cells from humans can also efficiently lyse pepticle-sensitized Patr-DR7(+) cells from chimpanzees. These findings suggest that conserved gB and gH epitopes should be considered while designing a prophylactic vaccine against HCMV. In addition, they also provide a functional basis for the conservation of MHC class 11 lineages between humans and Old World primates and open the possibility for the use of such primate models in vaccine development against HCMV.

Lack of association between genetic markers on chromosome 16q22-Q24 and type 1 diabetes in Russian affected families

Aim To evaluate whether the T1D susceptibility locus on chromosome 16q contributes to the genetic susceptibility to T1D in Russian patients. Method Thirteen microsatellite markers, spanning a 47-centimorgan genomic region on 16q22-q24 were evaluated for linkage to T1D in 98 Russian multiplex families. Multipoint logarithm of odds (LOD) ratio (MLS) and nonparametric LOD (NPL) values were computed for each marker, using GENEHUNTER 2.1 software. Four microsatellites (D16S422, D16S504, D16S3037, and D16S3098) and 6 biallelic markers in 2 positional candidate genes, ICSBP1 and NQO1, were additionally tested for association with T1D in 114 simplex families, using transmission disequilibrium test (TDT). Results A peak of linkage (MLS = 1.35, NPL = 0.91) was shown for marker D16S750, but this was not significant (P = 0.18). The subsequent linkage analysis in the subset of 46 multiplex families carrying a common risk HLA-DR4 haplotype increased peak MLS and NPL values to 1.77 and 1.22, respectively, but showed no significant linkage (P = 0.11) to T1D in the 16q22-q24 genomic region. TDT analysis failed to find significant association between these markers and disease, even after the conditioning for the predisposing HLA-DR4 haplotype. Conclusion Our results did not support the evidence for the susceptibility locus to T1D on chromosome 16q22-24 in the Russian family data set. The lack of association could reflect genetic heterogeneity of type 1 diabetes in diverse ethnic groups.

CandiVF - Candida albicans virulence factor database

Candida albicans is a pathogen commonly infecting patients who receive immunosuppressive drug therapy, long-term catheterization, or those who suffer from acquired immune deficiency syndrome (AIDS). The major factor accountable for pathogenicity of C. albicans is host immune status. Various virulence molecules, or factors, of are also responsible for the disease progression. Virulence proteins are published in public databases but they normally lack detailed functional annotations. We have developed CandiVF, a specialized database of C. albicans virulence factors (http://antigen.i2r.a-star.edu.sg/Templar/DB/CandiVF/) to facilitate efficient extraction and analysis of data aimed to assist research on immune responses, pathogenesis, prevention, and control of candidiasis. CandiVF contains a large number of annotated virulence proteins, including secretory, cell wall-associated, membrane, cytoplasmic, and nuclear proteins. This database has in-built bioinformatics tools including keyword and BLAST search, visualization of 3D-structures, HLA-DR epitope prediction, virulence descriptors, and virulence factors ontology.

The v-MFG test: Investigating maternal, offspring and maternal-fetal genetic incompatibility effects on disease and viability

The MFG test is a family-based association test that detects genetic effects contributing to disease in offspring, including offspring allelic effects, maternal allelic effects and MFG incompatibility effects. Like many other family-based association tests, it assumes that the offspring survival and the offspring-parent genotypes are conditionally independent provided the offspring is affected. However, when the putative disease-increasing locus can affect another competing phenotype, for example, offspring viability, the conditional independence assumption fails and these tests could lead to incorrect conclusions regarding the role of the gene in disease. We propose the v-MFG test to adjust for the genetic effects on one phenotype, e.g., viability, when testing the effects of that locus on another phenotype, e.g., disease. Using genotype data from nuclear families containing parents and at least one affected offspring, the v-MFG test models the distribution of family genotypes conditional on offspring phenotypes. It simultaneously estimates genetic effects on two phenotypes, viability and disease. Simulations show that the v-MFG test produces accurate genetic effect estimates on disease as well as on viability under several different scenarios. It generates accurate type-I error rates and provides adequate power with moderate sample sizes to detect genetic effects on disease risk when viability is reduced. We demonstrate the v-MFG test with HLA-DRB1 data from study participants with rheumatoid arthritis (RA) and their parents, we show that the v-MFG test successfully detects an MFG incompatibility effect on RA while simultaneously adjusting for a possible viability loss.

MULTIPRED: A computational system for prediction of promiscuous HLA binding peptides

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules ( proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability ( area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets termed T-cell epitope hotspots. MULTIPRED is available at http:// antigen.i2r.a-star.edu.sg/ multipred/.

Extreme learning machine for predicting HLA-peptide binding

Machine learning techniques have been recognized as powerful tools for learning from data. One of the most popular learning techniques, the Back-Propagation (BP) Artificial Neural Networks, can be used as a computer model to predict peptides binding to the Human Leukocyte Antigens (HLA). The major advantage of computational screening is that it reduces the number of wet-lab experiments that need to be performed, significantly reducing the cost and time. A recently developed method, Extreme Learning Machine (ELM), which has superior properties over BP has been investigated to accomplish such tasks. In our work, we found that the ELM is as good as, if not better than, the BP in term of time complexity, accuracy deviations across experiments, and most importantly - prevention from over-fitting for prediction of peptide binding to HLA.

Prediction of HLA-DQ3.2 beta ligands: evidence of multiple registers in class II binding peptides

Motivation: While processing of MHC class II antigens for presentation to helper T-cells is essential for normal immune response, it is also implicated in the pathogenesis of autoimmune disorders and hypersensitivity reactions. Sequence-based computational techniques for predicting HLA-DQ binding peptides have encountered limited success, with few prediction techniques developed using three-dimensional models. Methods: We describe a structure-based prediction model for modeling peptide-DQ3.2 beta complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2 beta receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2 beta binding and non-binding peptides obtained from biochemical and functional studies. Results: Our model predicts DQ3.2 beta binding peptides with high accuracy [area under the receiver operating characteristic (ROC) curve A(ROC) > 0.90], compared with experimental data. We investigated the binding patterns of DQ3.2 beta peptides and illustrate that several registers exist within a candidate binding peptide. Further analysis reveals that peptides with multiple registers occur predominantly for high-affinity binders.