Cytotoxic T-lymphocyte epitope vaccination protects against human metapneumovirus infection and disease in mice

Human metapneumovirus (hMPV) has emerged as an important human respiratory pathogen causing upper and lower respiratory tract infections in young children and older adults. In addition, hMPV infection is associated with asthma exacerbation in young children. Recent epidemiological evidence indicates that hMPV may cocircullate with human respiratory syncytial virus (hRSV) and mediate clinical disease similar to that seen with hRSV. Therefore, a vaccine for hMPV is highly desirable. In the present study, we used predictive bioinformatics, peptide immunization, and functional T-cell assays to define hMPV cytotoxic T-lymphocyte (CTL) epitopes recognized by mouse T cells restricted through several major histocompatibility complex class I alleles, including HILA-A*0201. We demonstrate that peptide immunization with hMPV CTL epitopes reduces viral load and immunopathollogy in the lungs of hMPV-challenged mice and enhances the expression of Th1-type cytokines (gamma interferon and interleukin-12 [IL-12]) in lungs and regional lymph nodes. In addition, we show that levels of Th2-type cytolkines (IL-10 and IL-4) are significantly lower in hMPV CTL epitope-vaccinated mice challenged with hMPV. These results demonstrate for the first time the efficacy of an hMPV CTL epitope vaccine in the control of hMPV infection in a murine model.

Discovery of cyclotide-like protein sequences in graminaceous crop plants: Ancestral precursors of circular proteins?

Cyclotides are peptides from plants of the Rubiaceae and Violaceae families that have the unusual characteristic of a macrocylic backbone. They are further characterized by their incorporation of a cystine knot in which two disulfides, along with the intervening backbone residues, form a ring through which a third disulfide is threaded. The cyclotides have been found in every Violaceae species screened to date but are apparently present in only a few Rubiaceae species. The selective distribution reported so far raises questions about the evolution of the cyclotides within the plant kingdom. In this study, we use a combined bioinformatics and expression analysis approach to elucidate the evolution and distribution of the cyclotides in the plant kingdom and report the discovery of related sequences widespread in the Poaceae family, including crop plants such as rice ( Oryza sativa), maize ( Zea mays), and wheat ( Triticum aestivum), which carry considerable economic and social importance. The presence of cyclotide-like sequences within these plants suggests that the cyclotides may be derived from an ancestral gene of great antiquity. Quantitative RT-PCR was used to show that two of the discovered cyclotide-like genes from rice and barley ( Hordeum vulgare) have tissue-specific expression patterns.

Reconstruction and in silico analysis of the MAPK signaling pathways in the human blood fluke, Schistosoma japonicum

At present, little is known about signal transduction mechanisms in schistosomes, which cause the disease of schistosomiasis. The mitogen-activated protein kinase (MAPK) signaling pathways, which are evolutionarily conserved from yeast to Homo sapiens, play key roles in multiple cellular processes. Here, we reconstructed the hypothetical MAPK signaling pathways in Schistosoma japonicum and compared the schistosome pathways with those of model eukaryote species. We identified 60 homologous components in the S. japoncium MAPK signaling pathways. Among these, 27 were predicted to be full-length sequences. Phylogenetic analysis of these proteins confirmed the evolutionary conservation of the MAPK signaling pathways. Remarkably, we identified S. japonicum homologues of GTP-binding protein beta and alpha-I subunits in the yeast mating pathway, which might be involved in the regulation of different life stages and female sexual maturation processes as well in schistosomes. In addition, several pathway member genes, including ERK, JNK, Sja-DSP, MRAS and RAS, were determined through quantitative PCR analysis to be expressed in a stage-specific manner, with ERK, JNK and their inhibitor Sja-DSP markedly upregulated in adult female schistosomes. (c) 2006 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Pattern-based phylogenetic distance estimation and tree reconstruction

We have developed an alignment-free method that calculates phylogenetic distances using a maximum-likelihood approach for a model of sequence change on patterns that are discovered in unaligned sequences. To evaluate the phylogenetic accuracy of our method, and to conduct a comprehensive comparison of existing alignment-free methods (freely available as Python package decaf+py at http://www.bioinformatics.org.au), we have created a data set of reference trees covering a wide range of phylogenetic distances. Amino acid sequences were evolved along the trees and input to the tested methods; from their calculated distances we infered trees whose topologies we compared to the reference trees. We find our pattern-based method statistically superior to all other tested alignment-free methods. We also demonstrate the general advantage of alignment-free methods over an approach based on automated alignments when sequences violate the assumption of collinearity. Similarly, we compare methods on empirical data from an existing alignment benchmark set that we used to derive reference distances and trees. Our pattern-based approach yields distances that show a linear relationship to reference distances over a substantially longer range than other alignment-free methods. The pattern-based approach outperforms alignment-free methods and its phylogenetic accuracy is statistically indistinguishable from alignment-based distances.

Evolving noisy oscillatory dynamics in genetic regulatory networks

We introduce a genetic programming (GP) approach for evolving genetic networks that demonstrate desired dynamics when simulated as a discrete stochastic process. Our representation of genetic networks is based on a biochemical reaction model including key elements such as transcription, translation and post-translational modifications. The stochastic, reaction-based GP system is similar but not identical with algorithmic chemistries. We evolved genetic networks with noisy oscillatory dynamics. The results show the practicality of evolving particular dynamics in gene regulatory networks when modelled with intrinsic noise.

Patterns in complex systems modeling

The design, development, and use of complex systems models raises a unique class of challenges and potential pitfalls, many of which are commonly recurring problems. Over time, researchers gain experience in this form of modeling, choosing algorithms, techniques, and frameworks that improve the quality, confidence level, and speed of development of their models. This increasing collective experience of complex systems modellers is a resource that should be captured. Fields such as software engineering and architecture have benefited from the development of generic solutions to recurring problems, called patterns. Using pattern development techniques from these fields, insights from communities such as learning and information processing, data mining, bioinformatics, and agent-based modeling can be identified and captured. Collections of such 'pattern languages' would allow knowledge gained through experience to be readily accessible to less-experienced practitioners and to other domains. This paper proposes a methodology for capturing the wisdom of computational modelers by introducing example visualization patterns, and a pattern classification system for analyzing the relationship between micro and macro behaviour in complex systems models. We anticipate that a new field of complex systems patterns will provide an invaluable resource for both practicing and future generations of modelers.

Teaching principles of genetics through SNP analysis

An understanding of inheritance requires comprehension of genetic processes at all levels, from molecules to populations. Frequently genetics courses are separated into molecular and organismal genetics and students may fail to see the relationships between them. This is particularly true with human genetics, because of the difficulties in designing experimental approaches which are consistent with ethical restrictions, student abilities and background knowledge, and available time and materials. During 2005 we used analysis of single nucleotide polymorphisms (SNPs) in two genetic regions to enhance student learning and provide a practical experience in human genetics. Students scanned databases to discover SNPs in a gene of interest, used software to design PCR primers and a restriction enzyme based assay for the alleles, and carried out an analysis of the SNP on anonymous individual and family DNAs. The project occupied eight to ten hours per week for one semester, with some time spent in the laboratory and some spent in database searching, reading and writing the report. In completing their projects, students acquired a knowledge of Mendel’s first law (through looking at inheritance patterns), Mendel’s second law and the exceptions (the concepts of linkage and linkage disequilibrium), DNA structure (primer design and restriction enzyme analysis) and function (SNPs in coding and non-coding regions), population genetics and the statistical analysis of allele frequencies, genomics, bioinformatics and the ethical issues associated with the use of human samples. They also developed skills in presentation of results by publication and conference participation. Deficiencies in their understanding (for example of inheritance patterns, gene structure, statistical approaches and report writing) were detected and guidance given during the project. SNP analysis was found to be a powerful approach to enhance and integrate student understanding of genetic concepts.

A principled approach to interactive hierarchical non-linear visualization of high-dimensional data

Hierarchical visualization systems are desirable because a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex high-dimensional data sets. We extend an existing locally linear hierarchical visualization system PhiVis [1] in several directions: bf(1) we allow for em non-linear projection manifolds (the basic building block is the Generative Topographic Mapping -- GTM), bf(2) we introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree, bf(3) we describe folding patterns of low-dimensional projection manifold in high-dimensional data space by computing and visualizing the manifold's local directional curvatures. Quantities such as magnification factors [3] and directional curvatures are helpful for understanding the layout of the nonlinear projection manifold in the data space and for further refinement of the hierarchical visualization plot. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. We demonstrate the visualization system principle of the approach on a complex 12-dimensional data set and mention possible applications in the pharmaceutical industry.

Evaluating the effectiveness of Bayesian feature selection

A practical Bayesian approach for inference in neural network models has been available for ten years, and yet it is not used frequently in medical applications. In this chapter we show how both regularisation and feature selection can bring significant benefits in diagnostic tasks through two case studies: heart arrhythmia classification based on ECG data and the prognosis of lupus. In the first of these, the number of variables was reduced by two thirds without significantly affecting performance, while in the second, only the Bayesian models had an acceptable accuracy. In both tasks, neural networks outperformed other pattern recognition approaches.

Data visualization with simultaneous feature selection

Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections. © 2006 IEEE.

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.

MILVA:An interactive tool for the exploration of multidimensional microarray data

Clustering techniques such as k-means and hierarchical clustering are commonly used to analyze DNA microarray derived gene expression data. However, the interactions between processes underlying the cell activity suggest that the complexity of the microarray data structure may not be fully represented with discrete clustering methods.