Roland Schwarz

YANA – a software tool for analyzing flux modes, gene-expression and enzyme activities

BackgroundA number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendliness, METATOOL as a reliable high-performance implementation of the algorithm has been the instrument of choice up to now. As reported here, the analysis of metabolic networks has been improved by an editor and analyzer of metabolic flux modes. Analysis routines for expression levels and the most central, well connected metabolites and their metabolic connections are of particular interest.ResultsYANA features a platform-independent, dedicated toolbox for metabolic networks with a graphical user interface to calculate (integrating METATOOL), edit (including support for the SBML format), visualize, centralize, and compare elementary flux modes. Further, YANA calculates expected flux distributions for a given Elementary Mode (EM) activity pattern and vice versa. Moreover, a dissection algorithm, a centralization algorithm, and an average diameter routine can be used to simplify and analyze complex networks. Proteomics or gene expression data give a rough indication of some individual enzyme activities, whereas the complete flux distribution in the network is often not known. As such data are noisy, YANA features a fast evolutionary algorithm (EA) for the prediction of EM activities with minimum error, including alerts for inconsistent experimental data. We offer the possibility to include further known constraints (e.g. growth constraints) in the EA calculation process. The redox metabolism around glutathione reductase serves as an illustration example. All software and documentation are available for download at http://yana.bioapps.biozentrum.uni-wuerzburg.de.ConclusionA graphical toolbox and an editor for METATOOL as well as a series of additional routines for metabolic network analyses constitute a new user-friendly software for such efforts.

Integrated network reconstruction, visualization and analysis using YANAsquare

BackgroundModeling of metabolic networks includes tasks such as network assembly, network overview, calculation of metabolic fluxes and testing the robustness of the network.ResultsYANAsquare provides a software framework for rapid network assembly (flexible pathway browser with local or remote operation mode), network overview (visualization routine and YANAsquare editor) and network performance analysis (calculation of flux modes as well as target and robustness tests). YANAsquare comes as an easy-to-setup program package in Java. It is fully compatible and integrates the programs YANA (translation of gene expression values into flux distributions, metabolite network dissection) and Metatool (elementary mode calculation). As application examples we set-up and model the phospholipid network in the phagosome and genome-scale metabolic maps of S.aureus, S.epidermidis and S.saprophyticus as well as test their robustness against enzyme impairment.ConclusionYANAsquare is an application software for rapid setup, visualization and analysis of small, larger and genome-scale metabolic networks.

Comparative Genome Biology of a Serogroup B Carriage and Disease Strain Supports a Polygenic Nature of Meningococcal Virulence

Neisseria meningitidis serogroup B strains are responsible for most meningococcal cases in the industrialized countries, and strains belonging to the clonal complex ST-41/44 are among the most prevalent serogroup B strains in carriage and disease. Here, we report the first genome and transcriptome comparison of a serogroup B carriage strain from the clonal complex ST-41/44 to the serogroup B disease strain MC58 from the clonal complex ST-32. Both genomes are highly colinear, with only three major genome rearrangements that are associated with the integration of mobile genetic elements. They further differ in about 10% of their gene content, with the highest variability in gene presence as well as gene sequence found for proteins involved in host cell interactions, including Opc, NadA, TonB-dependent receptors, RTX toxin, and two-partner secretion system proteins. Whereas housekeeping genes coding for metabolic functions were highly conserved, there were considerable differences in their expression pattern upon adhesion to human nasopharyngeal cells between both strains, including differences in energy metabolism and stress response. In line with these genomic and transcriptomic differences, both strains also showed marked differences in their in vitro infectivity and in serum resistance. Taken together, these data support the concept of a polygenic nature of meningococcal virulence comprising differences in the repertoire of adhesins as well as in the regulation of metabolic genes and suggest a prominent role for immune selection and genetic drift in shaping the meningococcal genome.

Staphylococcus aureus physiological growth limitations: insights from flux calculations built on proteomics and external metabolite data.

Comparing proteomics and metabolomics allows insights into Staphylococcus aureus physiological growth. We update genome and proteome information and deliver strain‐specific metabolic models for three S. aureus strains (COL, N315, and Newman). We find a number of differences in metabolism and enzymes. Growth experiments (glucose or combined with oxygen limitation) were conducted to measure external metabolites. Fluxes of the central metabolism were calculated from these data with low error. In exponential phase, glycolysis is active and amino acids are used for growth. In later phases, dehydroquinate synthetase is suppressed and acetate metabolism starts. There are strain‐specific differences for these phases. A time series of 2‐D gel protein expression data on COL strain delivered a second data set (glucose limitation) on which fluxes were calculated. The comparison with the metabolite‐predicted fluxes shows, in general, good correlation. Outliers point to different regulated enzymes for S. aureus COL under these limitations. In exponential growth, there is lower activity for some enzymes in upper glycolysis and pentose phosphate pathway and stronger activity for some in lower glycolysis. In transition phase, aspartate kinase is expressed to meet amino acid requirements and in later phases there is high expression of glyceraldehyde‐3‐phosphate dehydrogenase and lysine synthetase. Central metabolite fluxes and protein expression of their enzymes correlate in S. aureus.

New trends in pharmacogenomic strategies against resistance development in microbial infections

This review summarizes some of the new trends in the fight against drug resistant bacteria. We review Gram-positive (e.g., S.aureus) and Gram-negative (e.g., Pseudomonas aeruginosa, Helicobacter pylori) bacteria, the current antibiotic resistance situation, as well as resistance spread and some recently discovered resistance mechanisms, such as those based on integrons and complex transposons. We then summarize several current routes to identify new drugs such as cationic antimicrobial peptides, novel acyldepsipeptides, RNA aptamers and lipopeptides. New drug strategies to treat resistant pathogens include eliciting growth in dormant bacteria, or a new way to attack efflux systems. Typical approaches from pharmacogenomics combined with systems biology and bioinformatics support these routes (simulations, metagenomics and metabolic network modeling), as well as the patient treatment (e.g., haplotyping and immune response).

Modelling phagosomal lipid networks that regulate actin assembly

BackgroundWhen purified phagosomes are incubated in the presence of actin under appropriate conditions, microfilaments start growing from the membrane in a process that is affected by ATP and the lipid composition of the membrane. Isolated phagosomes are metabolically active organelles that contain enzymes and metabolites necessary for lipid interconversion. Hence, addition of ATP, lipids, and actin to the system alter the steady-state composition of the phagosomal membrane at the same time that the actin nucleation is initiated. Our aim was to model all these processes in parallel.ResultsWe compiled detailed experimental data on the effects of different lipids and ATP on actin nucleation and we investigated experimentally lipid interconversion and ATP metabolism in phagosomes by using suitable radioactive compounds.In a first step, a complex lipid network interconnected by chemical reactions catalyzed by known enzymes was modelled in COPASI (Complex Pathway Simulator). However, several lines of experimental evidence indicated that only the phosphatidylinositol branch of the network was active, an observation that dramatically reduced the number of parameters in the model. The results also indicated that a lipid network-independent ATP-consuming activity should be included in the model. When this activity was introduced, the set of differential equations satisfactorily reproduced the experimental data. On the other hand, a molecular mechanism connecting membrane lipids, ATP, and the actin nucleation process is still missing. We therefore adopted a phenomenological (black-box) approach to represent the empirical observations. We proposed that lipids and ATP influence the dynamic interconversion between active and inactive actin nucleation sites. With this simple model, all the experimental data were satisfactorily fitted with a single positive parameter per lipid and ATP.ConclusionBy establishing an active dialogue between an initial complex model and experimental observations, we could narrow the set of differential equations and parameters required to characterize the time-dependent changes of metabolites influencing actin nucleation on phagosomes. For this, the global model was dissected into three sub-models: ATP consumption, lipid interconversion, and nucleation of actin on phagosomal membranes. This scheme allowed us to describe this complex system with a relatively small set of differential equations and kinetic parameters that satisfactorily reproduced the experimental data.

Detecting species-site dependencies in large multiple sequence alignments

Multiple sequence alignments (MSAs) are one of the most important sources of information in sequence analysis. Many methods have been proposed to detect, extract and visualize their most significant properties. To the same extent that site-specific methods like sequence logos successfully visualize site conservations and sequence-based methods like clustering approaches detect relationships between sequences, both types of methods fail at revealing informational elements of MSAs at the level of sequence–site interactions, i.e. finding clusters of sequences and sites responsible for their clustering, which together account for a high fraction of the overall information of the MSA. To fill this gap, we present here a method that combines the Fisher score-based embedding of sequences from a profile hidden Markov model (pHMM) with correspondence analysis. This method is capable of detecting and visualizing group-specific or conflicting signals in an MSA and allows for a detailed explorative investigation of alignments of any size tractable by pHMMs. Applications of our methods are exemplified on an alignment of the Neisseria surface antigen LP2086, where it is used to detect sites of recombinatory horizontal gene transfer and on the vitamin K epoxide reductase family to distinguish between evolutionary and functional signals.

Pharmacogenomic strategies against resistance development in microbial infections.

There are several promising new strategies against resistance development in microbial infections. This paper discusses typical experimental and bioinformatical strategies to study the impact of infectious challenges on host-pathogen interaction, followed by several novel approaches and sources for new pharmaceutical strategies against resistance development. Genomics reveals promising new targets by providing a better understanding of cellular pathways, through the identification of new pathways, and by identifying new intervention areas, such as phospholipids, glycolipids, innate immunity, and antibiotic peptides. Additional antibiotic resources come from new genomes, including marine organisms, lytic phages and probiotic strategies. A system perspective regards all interactions between the host, pathogen and environment to develop new pharmacogenomic strategies against resistance development.

Unsupervised Meta-Analysis on Diverse Gene Expression Datasets Allows Insight into Gene Function and Regulation

Over the past years, microarray databases have increased rapidly in size. While they offer a wealth of data, it remains challenging to integrate data arising from different studies. Here we propose an unsupervised approach of a large-scale meta-analysis on Arabidopsis thaliana whole genome expression datasets to gain additional insights into the function and regulation of genes. Applying kernel principal component analysis and hierarchical clustering, we found three major groups of experimental contrasts sharing a common biological trait. Genes associated to two of these clusters are known to play an important role in indole-3-acetic acid (IAA) mediated plant growth and development or pathogen defense. Novel functions could be assigned to genes including a cluster of serine/threonine kinases that carry two uncharacterized domains (DUF26) in their receptor part implicated in host defense. With the approach shown here, hidden interrelations between genes regulated under different conditions can be unraveled.

A probabilistic model of cell size reduction in Pseudo-nitzschia delicatissima (Bacillariophyta)

The pennate planktonic diatom Pseudo-nitzschia delicatissima is very common in temperate marine waters and often responsible for blooms. Due to its surrounding rigid silicate frustrule the diatom undergoes successive size reduction as its vegetative reproduction cycle proceeds. Since a long time the life cycle of diatoms has raised scientific interest and some years ago extensive samples of Pseudo-nitzschia have been taken from coastal waters. Mating and cell size reduction experiments were carried out and served us as a data basis for a probabilistic model of cell size reduction. We applied a homogenous non-stationary continuous-time Markov chain to model the development of individual diatoms from an initial size of about 80 microm until cell death which occurred when the size reached its low at about 18 microm. In contrast to conventional curve fitting models we are capable of calculating confidence intervals for estimates of the population ages as well as integrate the process of auxospore formation into the model. We thus propose a unique way to describe the stationary size distribution in a diatom population in terms of cell division and auxospore formation probabilities of its individuals.