Carbohydrate Metabolism Differences between Subgroup A1 and B2 Strains of Bacillus anthracis as Assessed by Comparative Genomics and Functional Genetics

In France, Bacillus anthracis subgroup B2 strains do not metabolize starch or glycogen but can use gluconate, whereas subgroup A1 strains show the inverse pattern. Functional genetic analysis revealed that mutations in the amyS and gntK genes encoding an alpha-amylase and a gluconate kinase, respectively, were responsible for these phenotypes.Bacillus anthracis, the etiological agent of anthrax, is a gram-positive, aerobic soil bacterium. Multilocus variable-number tandem repeat analysis of a collection of French isolates shows that the main groups of B. anthracis groups A (subgroup A1) and B (subgroup B2) described worldwide are represented (1, 2). Subgroup B2 isolates are the most common isolates in France and are found particularly in southern mountain regions, but they are extremely rare elsewhere in the world. Biochemical characterization of French isolates indicates that subgroup A1 and B2 strains have different carbohydrate utilization patterns (P. Vaissaire, A. Fouet, K. L. Smith, C. Keys, C. Le Doujet, P. Sylvestre, M. Levy, P. Keim, and M. Mock, presented at the 5th International Conference on Anthrax and 3rd International Workshop on the Molecular Biology of Bacillus cereus, B. anthracis and B. thuringiensis, 30 March to 3 April 2003, Nice, France). French subgroup A1 strains metabolize starch and glycogen but not gluconate, and the inverse is true for subgroup B2 strains. The genomes of several B. anthracis strains are available on the NCBI website (http://www.ncbi.nlm.nih.gov/), and two of these strains, Ames and CNEVA, are representative of groups A and B, respectively. We compared the genomic sequences of Ames and CNEVA to identify mutations that may affect metabolic activities involved in the phenotypic differences.The Kegg pathway database (http://www.genome.jp/kegg/pathway.html) was used to select enzyme activities involved in the metabolic pathways for starch, glycogen, and gluconate. BLAST analysis of the corresponding open reading frame in the Ames (subgroup A3) and CNEVA (subgroup B2) genomes was then used to identify the selected genes that were interrupted or mutated. The functions and localizations of these open reading frames were then investigated with the Pfam (http://pfam.sanger.ac.uk/), CDD (http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml), SMART (http://smart.embl-heidelberg.de/), SignalP (http://www.cbs.dtu.dk/services/SignalP/), and TMHMM (http://www.cbs.dtu.dk/services/TMHMM-2.0/) search programs. A single-base deletion in the amyS gene (BA3551) encoding an alpha-amylase linked to starch and glycogen metabolism was found in the CNEVA genome. The wild-type AmyS protein contains 513 amino acids, and its predicted molecular mass is 58.4 kDa. In subgroup B2, there is a frameshift due to deletion of an adenosine in the 7th position of the nucleotide sequence that leads to a premature stop codon in the 13th position. In the Ames genome, a single-base substitution was found in the gntK gene (BA0162) encoding a gluconate kinase linked to gluconate metabolism. The predicted wild-type GntK protein contains 511 amino acids, and its predicted molecular mass is 56.7 kDa. The mutation identified is a cytosine-to-adenosine substitution at position 530 of the nucleotide sequence that leads to a premature stop codon at amino acid position 176. We confirmed the presence of these two mutations in the other B. anthracis subgroup genomes accessible in the NCBI unfinished microbial genome database and sequenced 12 isolates with various genotypes belonging to subgroups A1 and B2 (6 isolates in each subgroup) originating from outbreaks that occurred in different regions of France over the last 15 years. These analyses revealed that the deletion in amyS is restricted to strains belonging to group B subgroups, whereas the substitution in gntK is restricted to strains belonging to group A subgroups. The mutations identified in amyS and gntK both result in premature stop codons that lead to a loss of the enzymatic activities and may thus account for the observed phenotypic differences between subgroup A1 and B2 strains. We therefore focused on these two genes and used French strains 9602R and RA3R belonging to subgroups A1 and B2, respectively, for further analysis.     

PathogenFinder - Distinguishing Friend from Foe Using Bacterial Whole Genome Sequence Data

Although the majority of bacteria are harmless or even beneficial to their host, others are highly virulent and can cause serious diseases, and even death. Due to the constantly decreasing cost of high-throughput sequencing there are now many completely sequenced genomes available from both human pathogenic and innocuous strains. The data can be used to identify gene families that correlate with pathogenicity and to develop tools to predict the pathogenicity of newly sequenced strains, investigations that previously were mainly done by means of more expensive and time consuming experimental approaches. We describe PathogenFinder (http://cge.cbs.dtu.dk/services/PathogenFinder/), a web-server for the prediction of bacterial pathogenicity by analysing the input proteome, genome, or raw reads provided by the user. The method relies on groups of proteins, created without regard to their annotated function or known involvement in pathogenicity. The method has been built to work with all taxonomic groups of bacteria and using the entire training-set, achieved an accuracy of 88.6% on an independent test-set, by correctly classifying 398 out of 449 completely sequenced bacteria. The approach here proposed is not biased on sets of genes known to be associated with pathogenicity, thus the approach could aid the discovery of novel pathogenicity factors. Furthermore the pathogenicity prediction web-server could be used to isolate the potential pathogenic features of both known and unknown strains.     

Reliable B Cell Epitope Predictions: Impacts of Method Development and Improved Benchmarking

The interaction between antibodies and antigens is one of the most important immune system mechanisms for clearing infectious organisms from the host. Antibodies bind to antigens at sites referred to as B-cell epitopes. Identification of the exact location of B-cell epitopes is essential in several biomedical applications such as; rational vaccine design, development of disease diagnostics and immunotherapeutics. However, experimental mapping of epitopes is resource intensive making in silico methods an appealing complementary approach. To date, the reported performance of methods for in silico mapping of B-cell epitopes has been moderate. Several issues regarding the evaluation data sets may however have led to the performance values being underestimated: Rarely, all potential epitopes have been mapped on an antigen, and antibodies are generally raised against the antigen in a given biological context not against the antigen monomer. Improper dealing with these aspects leads to many artificial false positive predictions and hence to incorrect low performance values. To demonstrate the impact of proper benchmark definitions, we here present an updated version of the DiscoTope method incorporating a novel spatial neighborhood definition and half-sphere exposure as surface measure. Compared to other state-of-the-art prediction methods, Discotope-2.0 displayed improved performance both in cross-validation and in independent evaluations. Using DiscoTope-2.0, we assessed the impact on performance when using proper benchmark definitions. For 13 proteins in the training data set where sufficient biological information was available to make a proper benchmark redefinition, the average AUC performance was improved from 0.791 to 0.824. Similarly, the average AUC performance on an independent evaluation data set improved from 0.712 to 0.727. Our results thus demonstrate that given proper benchmark definitions, B-cell epitope prediction methods achieve highly significant predictive performances suggesting these tools to be a powerful asset in rational epitope discovery. The updated version of DiscoTope is available at www.cbs.dtu.dk/services/DiscoTope-2.0.     

Improved method for predicting linear B-cell epitopes

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.     

Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers

Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate binding affinity prediction of peptides of length 8, 10 and 11. The method gives the opportunity to predict peptides with a different length than nine for MHC alleles where no such peptides have been measured. As validation, the performance of this approach is compared to predictors trained on peptides of the peptide length in question. In this validation, the approximation method has an accuracy that is comparable to or better than methods trained on a peptide length identical to the predicted peptides. AVAILABILITY: The algorithm has been implemented in the web-accessible servers NetMHC-3.0: http://www.cbs.dtu.dk/services/NetMHC-3.0, and NetMHCpan-1.1: http://www.cbs.dtu.dk/services/NetMHCpan-1.1     

MatrixPlot: visualizing sequence constraints.

SUMMARY: MatrixPlot is a program for making high-quality matrix plots, such as mutual information plots of sequence alignments and distance matrices of sequences with known three-dimensional coordinates. The user can add information about the sequences (e.g. a sequence logo profile) along the edges of the plot, as well as zoom in on any region in the plot. AVAILABILITY: MatrixPlot can be obtained on request, and can also be accessed online at http://www. cbs.dtu.dk/services/MatrixPlot. CONTACT: gorodkin@cbs.dtu.dk     

GeneWiz browser: An Interactive Tool for Visualizing Sequenced Chromosomes

We present an interactive web application for visualizing genomic data of prokaryotic chromosomes. The tool (GeneWiz browser) allows users to carry out various analyses such as mapping alignments of homologous genes to other genomes, mapping of short sequencing reads to a reference chromosome, and calculating DNA properties such as curvature or stacking energy along the chromosome. The GeneWiz browser produces an interactive graphic that enables zooming from a global scale down to single nucleotides, without changing the size of the plot. Its ability to disproportionally zoom provides optimal readability and increased functionality compared to other browsers. The tool allows the user to select the display of various genomic features, color setting and data ranges. Custom numerical data can be added to the plot allowing, for example, visualization of gene expression and regulation data. Further, standard atlases are pre-generated for all prokaryotic genomes available in GenBank, providing a fast overview of all available genomes, including recently deposited genome sequences. The tool is available online from http://www.cbs.dtu.dk/services/gwBrowser. Supplemental material including interactive atlases is available online at http://www.cbs.dtu.dk/services/gwBrowser/suppl/.     

NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ

Major histocompatibility complex class II (MHCII) molecules play an important role in cell-mediated immunity. They present specific peptides derived from endosomal proteins for recognition by T helper cells. The identification of peptides that bind to MHCII molecules is therefore of great importance for understanding the nature of immune responses and identifying T cell epitopes for the design of new vaccines and immunotherapies. Given the large number of MHC variants, and the costly experimental procedures needed to evaluate individual peptide–MHC interactions, computational predictions have become particularly attractive as first-line methods in epitope discovery. However, only a few so-called pan-specific prediction methods capable of predicting binding to any MHC molecule with known protein sequence are currently available, and all of them are limited to HLA-DR. Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence. The method employs a strategy common for HLA-DR, HLA-DP and HLA-DQ molecules to define the peptide-binding MHC environment in terms of a pseudo sequence. This strategy allows the inclusion of new molecules even from other species. The method was evaluated in several benchmarks and demonstrates a significant improvement over molecule-specific methods as well as the ability to predict peptide binding of previously uncharacterised MHCII molecules. To the best of our knowledge, the NetMHCIIpan-3.0 method is the first pan-specific predictor covering all HLA class II molecules with known sequences including HLA-DR, HLA-DP, and HLA-DQ. The NetMHCpan-3.0 method is available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.0.     

Prediction of novel archaeal enzymes from sequence-derived features

The completely sequenced archaeal genomes potentially encode, among their many functionally uncharacterized genes, novel enzymes of biotechnological interest. We have developed a prediction method for detection and classification of enzymes from sequence alone (available at http://www.cbs.dtu.dk/services/ArchaeaFun/). The method does not make use of sequence similarity; rather, it relies on predicted protein features like cotranslational and posttranslational modifications, secondary structure, and simple physical/chemical properties.     

Too Much of a Good Thing: The Unique and Repeated Paths Toward Copper Adaptation

Copper is a micronutrient essential for growth due to its role as a cofactor in enzymes involved in respiration, defense against oxidative damage, and iron uptake. Yet too much of a good thing can be lethal, and yeast cells typically do not have tolerance to copper levels much beyond the concentration in their ancestral environment. Here, we report a short-term evolutionary study of Saccharomyces cerevisiae exposed to levels of copper sulfate that are inhibitory to the initial strain. We isolated and identified adaptive mutations soon after they arose, reducing the number of neutral mutations, to determine the first genetic steps that yeast take when adapting to copper. We analyzed 34 such strains through whole-genome sequencing and by assaying fitness within different environments; we also isolated a subset of mutations through tetrad analysis of four lines. We identified a multilayered evolutionary response. In total, 57 single base-pair mutations were identified across the 34 lines. In addition, gene amplification of the copper metallothionein protein, CUP1-1, was rampant, as was chromosomal aneuploidy. Four other genes received multiple, independent mutations in different lines (the vacuolar transporter genes VTC1 and VTC4; the plasma membrane H+-ATPase PMA1; and MAM3, a protein required for normal mitochondrial morphology). Analyses indicated that mutations in all four genes, as well as CUP1-1 copy number, contributed significantly to explaining variation in copper tolerance. Our study thus finds that evolution takes both common and less trodden pathways toward evolving tolerance to an essential, but highly toxic, micronutrient.     

NetTepi: an integrated method for the prediction of T cell epitopes

Multiple factors determine the ability of a peptide to elicit a cytotoxic T cell lymphocyte response. Binding to a major histocompatibility complex class I (MHC-I) molecule is one of the most essential factors, as no peptide can become a T cell epitope unless presented on the cell surface in complex with an MHC-I molecule. As such, peptide-MHC (pMHC) binding affinity predictors are currently the premier methods for T cell epitope prediction, and these prediction methods have been shown to have high predictive performances in multiple studies. However, not all MHC-I binders are T cell epitopes, and multiple studies have investigated what additional factors are important for determining the immunogenicity of a peptide. A recent study suggested that pMHC stability plays an important role in determining if a peptide can become a T cell epitope. Likewise, a T cell propensity model has been proposed for identifying MHC binding peptides with amino acid compositions favoring T cell receptor interactions. In this study, we investigate if improved accuracy for T cell epitope discovery can be achieved by integrating predictions for pMHC binding affinity, pMHC stability, and T cell propensity. We show that a weighted sum approach allows pMHC stability and T cell propensity predictions to enrich pMHC binding affinity predictions. The integrated model leads to a consistent and significant increase in predictive performance and we demonstrate how this can be utilized to decrease the experimental workload of epitope screens. The final method, NetTepi, is publically available at www.cbs.dtu.dk/services/NetTepi.     

Estimating Individual Admixture Proportions from Next Generation Sequencing Data

Inference of population structure and individual ancestry is important both for population genetics and for association studies. With next generation sequencing technologies it is possible to obtain genetic data for all accessible genetic variations in the genome. Existing methods for admixture analysis rely on known genotypes. However, individual genotypes cannot be inferred from low-depth sequencing data without introducing errors. This article presents a new method for inferring an individual’s ancestry that takes the uncertainty introduced in next generation sequencing data into account. This is achieved by working directly with genotype likelihoods that contain all relevant information of the unobserved genotypes. Using simulations as well as publicly available sequencing data, we demonstrate that the presented method has great accuracy even for very low-depth data. At the same time, we demonstrate that applying existing methods to genotypes called from the same data can introduce severe biases. The presented method is implemented in the NGSadmix software available at http://www.popgen.dk/software.     

The Abundant Histone Chaperones Spt6 and FACT Collaborate to Assemble,Inspect, and Maintain Chromatin Structure in Saccharomyces cerevisiae

Saccharomyces cerevisiae Spt6 protein is a conserved chromatin factor with several distinct functional domains, including a natively unstructured 30-residue N-terminal region that binds competitively with Spn1 or nucleosomes. To uncover physiological roles of these interactions, we isolated histone mutations that suppress defects caused by weakening Spt6:Spn1 binding with the spt6-F249K mutation. The strongest suppressor was H2A-N39K, which perturbs the point of contact between the two H2A-H2B dimers in an assembled nucleosome. Substantial suppression also was observed when the H2A-H2B interface with H3-H4 was altered, and many members of this class of mutations also suppressed a defect in another essential histone chaperone, FACT. Spt6 is best known as an H3-H4 chaperone, but we found that it binds with similar affinity to H2A-H2B or H3-H4. Like FACT, Spt6 is therefore capable of binding each of the individual components of a nucleosome, but unlike FACT, Spt6 did not produce endonuclease-sensitive reorganized nucleosomes and did not displace H2A-H2B dimers from nucleosomes. Spt6 and FACT therefore have distinct activities, but defects can be suppressed by overlapping histone mutations. We also found that Spt6 and FACT together are nearly as abundant as nucleosomes, with ∼24,000 Spt6 molecules, ∼42,000 FACT molecules, and ∼75,000 nucleosomes per cell. Histone mutations that destabilize interfaces within nucleosomes therefore reveal multiple spatial regions that have both common and distinct roles in the functions of these two essential and abundant histone chaperones. We discuss these observations in terms of different potential roles for chaperones in both promoting the assembly of nucleosomes and monitoring their quality.     

A neural network method for identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites

We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. The method performs significantly better than previous prediction schemes, and can easily be applied to genome-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision. Predictions can be made on a publicly available WWW server: http://www.cbs.dtu.dk/services/SignalP/.     

The Mechanism of Nucleotide Excision Repair-Mediated UV-Induced Mutagenesis in Nonproliferating Cells

Following the irradiation of nondividing yeast cells with ultraviolet (UV) light, most induced mutations are inherited by both daughter cells, indicating that complementary changes are introduced into both strands of duplex DNA prior to replication. Early analyses demonstrated that such two-strand mutations depend on functional nucleotide excision repair (NER), but the molecular mechanism of this unique type of mutagenesis has not been further explored. In the experiments reported here, an ade2 adeX colony-color system was used to examine the genetic control of UV-induced mutagenesis in nondividing cultures of Saccharomyces cerevisiae. We confirmed a strong suppression of two-strand mutagenesis in NER-deficient backgrounds and demonstrated that neither mismatch repair nor interstrand crosslink repair affects the production of these mutations. By contrast, proteins involved in the error-prone bypass of DNA damage (Rev3, Rev1, PCNA, Rad18, Pol32, and Rad5) and in the early steps of the DNA-damage checkpoint response (Rad17, Mec3, Ddc1, Mec1, and Rad9) were required for the production of two-strand mutations. There was no involvement, however, for the Pol η translesion synthesis DNA polymerase, the Mms2-Ubc13 postreplication repair complex, downstream DNA-damage checkpoint factors (Rad53, Chk1, and Dun1), or the Exo1 exonuclease. Our data support models in which UV-induced mutagenesis in nondividing cells occurs during the Pol ζ-dependent filling of lesion-containing, NER-generated gaps. The requirement for specific DNA-damage checkpoint proteins suggests roles in recruiting and/or activating factors required to fill such gaps.     

GenePublisher: Automated analysis of DNA microarray data

GenePublisher, a system for automatic analysis of data from DNA microarray experiments, has been implemented with a web interface at http://www.cbs.dtu.dk/services/GenePublisher. Raw data are uploaded to the server together with a specification of the data. The server performs normalization, statistical analysis and visualization of the data. The results are run against databases of signal transduction pathways, metabolic pathways and promoter sequences in order to extract more information. The results of the entire analysis are summarized in report form and returned to the user.     

NetPhosYeast: prediction of protein phosphorylation sites in yeast

We here present a neural network-based method for the prediction of protein phosphorylation sites in yeast--an important model organism for basic research. Existing protein phosphorylation site predictors are primarily based on mammalian data and show reduced sensitivity on yeast phosphorylation sites compared to those in humans, suggesting the need for an yeast-specific phosphorylation site predictor. NetPhosYeast achieves a correlation coefficient close to 0.75 with a sensitivity of 0.84 and specificity of 0.90 and outperforms existing predictors in the identification of phosphorylation sites in yeast. AVAILABILITY: The NetPhosYeast prediction service is available as a public web server at http://www.cbs.dtu.dk/services/NetPhosYeast/.