Showing papers by "Vladimir Brusic published in 2003"

Molecular Evolution and Phylogeny of Elapid Snake Venom Three-Finger Toxins

Abstract: Animal venom components are of considerable interest to researchers across a wide variety of disciplines, including molecular biology, biochemistry, medicine, and evolutionary genetics. The three-finger family of snake venom peptides is a particularly interesting and biochemically complex group of venom peptides, because they are encoded by a large multigene family and display a diverse array of functional activities. In addition, understanding how this complex and highly varied multigene family evolved is an interesting question to researchers investigating the biochemical diversity of these peptides and their impact on human health. Therefore, the purpose of our study was to investigate the long-term evolutionary patterns exhibited by these snake venom toxins to understand the mechanisms by which they diversified into a large, biochemically diverse, multigene family. Our results show a much greater diversity of family members than was previously known, including a number of subfamilies that did not fall within any previously identified groups with characterized activities. In addition, we found that the long-term evolutionary processes that gave rise to the diversity of three-finger toxins are consistent with the birth-and-death model of multigene family evolution. It is anticipated that this “three-finger toxin toolkit” will prove to be useful in providing a clearer picture of the diversity of investigational ligands or potential therapeutics available within this important family.

MAGE-6 Encodes HLA-DRβ1*0401-presented Epitopes Recognized by CD4+ T Cells from Patients with Melanoma or Renal Cell Carcinoma

Abstract: CD4+ T cells modulate the magnitude and durability of CTL responses in vivo and may serve as potent effector cells within the tumor microenvironment. The current study was undertaken to define novel epitopes from the broadly expressed tumor antigen MAGE-6 that are recognized by CD4+ T cells. We have combined the use of a HLA-DR4/peptide binding algorithm with the IFN-gamma enzyme-linked immunospot assay to identify four nonoverlapping sequences derived from the MAGE-6 protein that served as CD4+ T-cell epitopes in HLA-DR4+ donors. Strikingly, patients with active melanoma or renal cell carcinoma failed to secrete IFN-gamma in response to MAGE-6-derived epitopes, whereas both normal donors and cancer patients with no current evidence of disease were responsive, particularly after short-term in vitro stimulations with peptide-pulsed dendritic cells. Importantly, peptide-specific CD4+ T cells also recognized HLA-DRbeta1*0401+ tumor cells that constitutively expressed the MAGE-6 protein and autologous HLA-DRbeta1*0401+ dendritic cells transfected with MAGE-6 cDNA-elicited CD4+ T cells that reacted against individual peptide epitopes in vitro. These data suggest that MAGE-6-derived epitopes could serve as useful vaccine candidate components and may provide an immune-monitoring index of clinically important Th1-type immunity in patients with renal cell carcinoma or melanoma.

Computer model for recognition of functional transcription start sites in RNA polymerase II promoters of vertebrates.

Abstract: This paper introduces a new computer system for recognition of functional transcription start sites (TSSs) in RNA polymerase II promoter regions of vertebrates. This system allows scanning complete vertebrate genomes for promoters with significantly reduced number of false positive predictions. It can be used in the context of gene finding through its recognition of the 5′ end of genes. The implemented recognition model uses a composite-hierarchical approach, artificial intelligence, statistics, and signal processing techniques. It also exploits the separation of promoter sequences into those that are C+G-rich or C+G-poor. The system was evaluated on a large and diverse human sequence-set and exhibited several times higher accuracy than several publicly available TSS-finding programs. Results obtained using human chromosome 22 data showed even greater specificity than the evaluation set results. The system has been implemented in the Dragon Promoter Finder package, which can be accessed at http://sdmc.krdl.org.sg:8080/promoter/ .

Computational tools for the study of allergens

Abstract: Allergy is a major cause of morbidity worldwide. The number of characterized allergens and related information is increasing rapidly creating demands for advanced information storage, retrieval and analysis. Bioinformatics provides useful tools for analysing allergens and these are complementary to traditional laboratory techniques for the study of allergens. Specific applications include structural analysis of allergens, identification of B- and T-cell epitopes, assessment of allergenicity and cross-reactivity, and genome analysis. In this paper, the most important bioinformatic tools and methods with relevance to the study of allergy have been reviewed.

Bioinformatics for venom and toxin sciences

Abstract: Venomous animals produce a myriad of important pharmacological components. The individual components, or venoms (toxins), are used in ion channel and receptor studies, drug discovery, and formulation of insecticides. The toxin data are scattered across public databases which provide sequence and structural descriptions, but very limited functional annotation. The exponential growth of newly identified toxin data has created a need for better data management. Venominformatics is a systematic bioinformatics approach in which classified, consolidated and cleaned venom data are stored into repositories and integrated with advanced bioinformatics tools for the analysis of structure and function of toxins. Venominformatics complements experimental studies and helps reduce the number of essential experiments.

Immunoinformatics--the new kid in town.

Abstract: The astounding diversity of immune system components (e.g. immunoglobulins, lymphocyte receptors, or cytokines) together with the complexity of the regulatory pathways and network-type interactions makes immunology a combinatorial science. Currently available data represent only a tiny fraction of possible situations and data continues to accrue at an exponential rate. Computational analysis has therefore become an essential element of immunology research with a main role of immunoinformatics being the management and analysis of immunological data. More advanced analyses of the immune system using computational models typically involve conversion of an immunological question to a computational problem, followed by solving of the computational problem and translation of these results into biologically meaningful answers. Major immunoinformatics developments include immunological databases, sequence analysis, structure modelling, mathematical modelling of the immune system, simulation of laboratory experiments, statistical support for immunological experimentation, and immunogenomics. In this paper we describe the status and challenges within these sub-fields. We forsee the emergence of immunomics not only as a collective endeavour by researchers to decipher the sequences of T cell receptors, immunoglobulins, and other immune receptors, but also to functionally annotate the capacity of the immune system to interact with the whole array of self and non-self entities, including genome-to-genome interactions.

Bioinformatic strategies for better understanding of immune function.

Abstract: Novartis Foundation sponsored a Symposium which brought together a group of experimental immunologists, theoretical immunologists, and bioinformaticians to discuss the new field of immunoinformatics. The discussion focused on immunological databases, antigen processing and presentation, immunogenomics, host-pathogen interactions, and mathematical modelling of the immune system. A main conclusion of the meeting is the critical role played by immunoinformatics in current immunology research. In particular, immunoinformatics provides a foundation for the emerging fields of systems immunology and immunogenomics.

The future for computational modelling and prediction systems in clinical immunology.

Abstract: Advances in computational science, despite their enormous potential, have been surprisingly slow to impact on clinical practice. This paper examines the potential of bioinformatics to advance clinical immunology across a number of key examples including the use of computational immunology to improve renal transplantation outcomes, identify novel genes involved in immunological disorders, decipher the relationship between antigen presentation pathways and human disease, and predict allergenicity. These examples demonstrate the enormous potential for immunoinformatics to advance clinical and experimental immunology. The acceptance of immunoinformatic techniques by clinical and research immunologists will need robust standards of data quality, system integrity and properly validated immunoinformatic systems. Such validation, at a minimum, will require appropriately designed clinical studies conducted according to Good Clinical Practice standards. This strategy will enable immunoinformatics to achieve its full potential to advance and shape clinical immunology in the future.

From immunoinformatics to immunomics.

Abstract: The immune system comprises a complex network of organs, specialized tissues, cells, and molecules. Its main function is to protect the organism from external and internal challenges and provides the interface between the organism and its environment. Foreign agents such as viruses, bacteria, fungi, or parasites may cause infection and disease. Foreign chemicals can cause toxic effects and pathogenic mutations. A malfunction of the immune system may lead to cancers, autoimmunity, or susceptibility to infections. The function of immune system is a complicated balancing act based on the ability to respond to previously seen as well as to unknown foreign agents. The study of the immune system is of critical importance for basic and applied life sciences, particularly for health care. Immunological data are growing at an exponential rate. The main sources of immunological data are public databases, various “omics” data, and published articles. The majority of entries in public database have relevance to the immune system processes. Foreign antigens represent potential targets of the immune response and can be classified into pathogenic antigens and tolerable environmental antigens. Some environmental antigens, such as allergens, carry potential for causing undesirable immune responses. Specialist immunological databases contain wellannotated data of immunological interest with detailed annotation. Genomics and proteomics have provided enormous stimuli to biological sciences. They have provided huge amounts of new biological data and induced a major paradigm shift in modern life sciences. The classic hypothesis-driven research has been complemented with various omics approaches which focus on large-scale study of biological molecules in aggregate. The great scientific conquest of the 20th

From informatics to bioinformatics

Abstract: Informatics has helped in launching molecular biology into the genomic era. It appears certain that informatics will continue to be a major factor in the success of molecular biology in the post-genome era. In this paper, we describe advances made in data integration and data mining technologies that are relevant to molecular biology and biomedical sciences. In particular, we discuss some past and present research results on topics such as (a) the taming of autonomous heterogeneous distributed data sources, (b) the prediction of immunogenic peptides, (c) the discovery of gene structure features, (d) the classification of gene expression profiles, and (e) the extraction of protein interaction information from literature.

Computational detection of vertebrate RNA polymerase II promoters.

Abstract: Publisher Summary This chapter describes the system and method termed as dragon promoter program. It aims at producing both high accuracy predictions of vertebrate promoters and high positional accuracy of TSS predictions. The DRAGON allows users to select among five specificity levels of predictions, ranging from a very high specificity to a very high sensitivity. Very high sensitivity predictions are suitable for screening relatively short fragments of a genomic sequence, whereas at the other extreme, very high specificity predictions are suitable for genome-scale predictions because of the significantly reduced number of false-positive predictions. It is expected that improvements in computational promoter analysis will follow two paths. First, the increased number of experimentally verified promoters and their TSSs will help further refine promoter prediction methods and increase the overall accuracy. Second, the improved analysis of promoter structure will help understand the diversity of promoters and identification of specific patterns that regulate gene expression. The analysis of promoters will remain a critical issue in the analysis of genes and genomes, particularly for the study of regulation of gene expression and gene networks. Because of the large size of vertebrate genomes, computational analysis will remain the key technology in the analysis of functional sites in DNA.

Cytokine-Related Genes Identified From the RIKEN Full-Length Mouse cDNA Data Set

Abstract: To identify novel cytokine-related genes, we searched the set of 60,770 annotated RIKEN mouse cDNA clones (FANTOM2 clones), using keywords such as cytokine itself or cytokine names (such as interferon, interleukin, epidermal growth factor, fibroblast growth factor, and transforming growth factor). This search produced 108 known cytokines and cytokine-related products such as cytokine receptors, cytokine-associated genes, or their products (enhancers, accessory proteins, cytokine-induced genes). We found 15 clusters of FANTOM2 clones that are candidates for novel cytokine-related genes. These encoded products with strong sequence similarity to guanylate-binding protein (GBP-5), interleukin-1 receptor-associated kinase 2 (IRAK-2), interleukin 20 receptor alpha isoform 3, a member of the interferon-inducible proteins of the Ifi 200 cluster, four members of the membrane-associated family 1-8 of interferon-inducible proteins, one p27-like protein, and a hypothetical protein containing a Toll/Interleukin receptor domain. All four clones representing novel candidates of gene products from the family contain a novel highly conserved cross-species domain. Clones similar to growth factor-related products included transforming growth factor beta-inducible early growth response protein 2 (TIEG-2), TGFbeta-induced factor 2, integrin beta-like 1, latent TGF-binding protein 4S, and FGF receptor 4B. We performed a detailed sequence analysis of the candidate novel genes to elucidate their likely functional properties.

Identification of novel "pathologs" (human disease-related gene candidates) from the RIKEN full-length mouse cDNA data set

Abstract: The majority of common diseases such as cancer, allergy, diabetes, or heart disease are characterized by complex genetic traits, in which genetic and environmental components contribute to disease susceptibility. Our knowledge of the genetic factors underlying most of such diseases is limited. A major goal in the post-genomic era is to identify and characterize disease susceptibility genes and to use this knowledge for disease treatment and prevention. More than 500 genes are conserved across the invertebrate and vertebrate genomes. Because of gene conservation, various organisms including yeast, fruitfly, zebrafish, rat, and mouse have been used as genetic models.