PathwayBooster: a tool to support the curation of metabolic pathways.

BACKGROUND: Despite several recent advances in the automated generation of draft metabolic reconstructions, the manual curation of these networks to produce high quality genome-scale metabolic models remains a labour-intensive and challenging task. RESULTS: We present PathwayBooster, an open-source software tool to support the manual comparison and curation of metabolic models. It combines gene annotations from GenBank files and other sources with information retrieved from the metabolic databases BRENDA and KEGG to produce a set of pathway diagrams and reports summarising the evidence for the presence of a reaction in a given organism's metabolic network. By comparing multiple sources of evidence within a common framework, PathwayBooster assists the curator in the identification of likely false positive (misannotated enzyme) and false negative (pathway hole) reactions. Reaction evidence may be taken from alternative annotations of the same genome and/or a set of closely related organisms. CONCLUSIONS: By integrating and visualising evidence from multiple sources, PathwayBooster reduces the manual effort required in the curation of a metabolic model. The software is available online at http://www.theosysbio.bio.ic.ac.uk/resources/pathwaybooster/ .

Simple topological properties predict functional misannotations in a metabolic network.

MOTIVATION: Misannotation in sequence databases is an important obstacle for automated tools for gene function annotation, which rely extensively on comparison with sequences with known function. To improve current annotations and prevent future propagation of errors, sequence-independent tools are, therefore, needed to assist in the identification of misannotated gene products. In the case of enzymatic functions, each functional assignment implies the existence of a reaction within the organism's metabolic network; a first approximation to a genome-scale metabolic model can be obtained directly from an automated genome annotation. Any obvious problems in the network, such as dead end or disconnected reactions, can, therefore, be strong indications of misannotation. RESULTS: We demonstrate that a machine-learning approach using only network topological features can successfully predict the validity of enzyme annotations. The predictions are tested at three different levels. A random forest using topological features of the metabolic network and trained on curated sets of correct and incorrect enzyme assignments was found to have an accuracy of up to 86% in 5-fold cross-validation experiments. Further cross-validation against unseen enzyme superfamilies indicates that this classifier can successfully extrapolate beyond the classes of enzyme present in the training data. The random forest model was applied to several automated genome annotations, achieving an accuracy of ~60% in most cases when validated against recent genome-scale metabolic models. We also observe that when applied to draft metabolic networks for multiple species, a clear negative correlation is observed between predicted annotation quality and phylogenetic distance to the major model organism for biochemistry (Escherichia coli for prokaryotes and Homo sapiens for eukaryotes). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deciphering the response of Mycobacterium smegmatis to nitrogen stress using bipartite active modules.

BACKGROUND: The ability to adapt to environments with fluctuating nutrient availability is vital for bacterial survival. Although essential for growth, few nitrogen metabolism genes have been identified or fully characterised in mycobacteria and nitrogen stress survival mechanisms are unknown. RESULTS: A global transcriptional analysis of the mycobacterial response to nitrogen stress, showed a significant change in the differential expression of 16% of the Mycobacterium smegmatis genome. Gene expression changes were mapped onto the metabolic network using Active Modules for Bipartite Networks (AMBIENT) to identify metabolic pathways showing coordinated transcriptional responses to the stress. AMBIENT revealed several key features of the metabolic response not identified by KEGG enrichment alone. Down regulated reactions were associated with the general reduction in cellular metabolism as a consequence of reduced growth rate. Up-regulated modules highlighted metabolic changes in nitrogen assimilation and scavenging, as well as reactions involved in hydrogen peroxide metabolism, carbon scavenging and energy generation. CONCLUSIONS: Application of an Active Modules algorithm to transcriptomic data identified key metabolic reactions and pathways altered in response to nitrogen stress, which are central to survival under nitrogen limiting environments.

Evolutionary characteristics of bacterial two-component systems.

The evolution of biological systems is influenced by a number of factors and forces that have acted in different combinations at different times to give rise to extant organisms. Here we illustrate some of the issues surrounding the data-driven evolutionary analysis of biological systems in the context of bacterial two-component systems (TCSs). TCSs are critical for bacteria to interact with their extracellular environment. A typical TCS consists of a histidine kinase on the membrane and a response regulator in the cytoplasm. Here we comprehensively characterise the extent to which these appear together across some 950 bacterial species and test for statistically significant patterns of correlated gain and loss. Our analysis provides evidence for correlated evolution but also a high level of evolutionary flexibility: at the sequence level, histidine kinases but especially response regulators belonging to different TCSs in a species show high levels of similarity, which may facilitate crosstalk as well as the recruitment of components into new compound signalling systems. We furthermore find that bacterial lifestyle has an overriding influence on the presence and absence of TCS; while in most TCSs either both or none of the two components are present, several TCSs tend to lose preferentially either the histidine kinase or response regulator component, which further supports the notion of reuse and reshuffling of these components in different TCS arrangements. We conclude by placing these findings in a wider context and discuss the implications for evolutionary systems biology more generally.

Patterns of HIV-1 protein interaction identify perturbed host-cellular subsystems.

Human immunodeficiency virus type 1 (HIV-1) exploits a diverse array of host cell functions in order to replicate. This is mediated through a network of virus-host interactions. A variety of recent studies have catalogued this information. In particular the HIV-1, Human Protein Interaction Database (HHPID) has provided a unique depth of protein interaction detail. However, as a map of HIV-1 infection, the HHPID is problematic, as it contains curation error and redundancy; in addition, it is based on a heterogeneous set of experimental methods. Based on identifying shared patterns of HIV-host interaction, we have developed a novel methodology to delimit the core set of host-cellular functions and their associated perturbation from the HHPID. Initially, using biclustering, we identify 279 significant sets of host proteins that undergo the same types of interaction. The functional cohesiveness of these protein sets was validated using a human protein-protein interaction network, gene ontology annotation and sequence similarity. Next, using a distance measure, we group host protein sets and identify 37 distinct higher-level subsystems. We further demonstrate the biological significance of these subsystems by cross-referencing with global siRNA screens that have been used to detect host factors necessary for HIV-1 replication, and investigate the seemingly small intersect between these data sets. Our results highlight significant host-cell subsystems that are perturbed during the course of HIV-1 infection. Moreover, we characterise the patterns of interaction that contribute to these perturbations. Thus, our work disentangles the complex set of HIV-1-host protein interactions in the HHPID, reconciles these with siRNA screens and provides an accessible and interpretable map of infection.

Making the right connections: biological networks in the light of evolution.

Our understanding of how evolution acts on biological networks remains patchy, as is our knowledge of how that action is best identified, modelled and understood. Starting with network structure and the evolution of protein-protein interaction networks, we briefly survey the ways in which network evolution is being addressed in the fields of systems biology, development and ecology. The approaches highlighted demonstrate a movement away from a focus on network topology towards a more integrated view, placing biological properties centre-stage. We argue that there remains great potential in a closer synergy between evolutionary biology and biological network analysis, although that may require the development of novel approaches and even different analogies for biological networks themselves.

Intrahost speciations and host switches played an important role in the evolution of herpesviruses.

In times when herpesvirus genomic data were scarce, the cospeciation between these viruses and their hosts was considered to be common knowledge. However, as more herpesviral sequences were made available, tree reconciliation analyses started to reveal topological incongruences between host and viral phylogenies, indicating that other cophylogenetic events, such as intrahost speciation and host switching, likely played important roles along more than 200 million years of evolutionary history of these viruses. Tree reconciliations performed with undated phylogenies can identify topological differences, but offer insufficient information to reveal temporal incongruences between the divergence timing of host and viral species. In this study, we performed cophylogenetic analyses using time-resolved trees of herpesviruses and their hosts, based on careful molecular clock modelling. This approach enabled us to infer cophylogenetic events over time and also integrate information on host biogeography to better understand host-virus evolutionary history. Given the increasing amount of sequence data now available, mismatches between host and viral phylogenies have become more evident, and to account for such phylogenetic differences, host switches, intrahost speciations and losses were frequently found in all tree reconciliations. For all subfamilies in Herpesviridae, under all scenarios we explored, intrahost speciation and host switching were more frequent than cospeciation, which was shown to be a rare event, restricted to contexts where topological and temporal patterns of viral and host evolution were in strict agreement.

Reconstruction of ancestral protein interaction networks for the bZIP transcription factors.

As whole-genome protein-protein interaction datasets become available for a wide range of species, evolutionary biologists have the opportunity to address some of the unanswered questions surrounding the evolution of these complex systems. Protein interaction networks from divergent organisms may be compared to investigate how gene duplication, deletion, and rewiring processes have shaped the evolution of their contemporary structures. However, current approaches for comparing observed networks from multiple species lack the phylogenetic context necessary to reconstruct the evolutionary history of a network. Here we show how probabilistic modeling can provide a platform for the quantitative analysis of multiple protein interaction networks. We apply this technique to the reconstruction of ancestral networks for the bZIP family of transcription factors and find that excellent agreement is obtained with an alternative sequence-based method for the prediction of leucine zipper interactions. Further analysis shows our probabilistic method to be significantly more robust to the presence of noise in the observed network data than a simple parsimony-based approach. In addition, the integration of evidence over multiple species means that the same method may be used to improve the quality of noisy interaction data for extant species. The ancestral states of a protein interaction network have been reconstructed here by using an explicit probabilistic model of network evolution. We anticipate that this model will form the basis of more general methods for probing the evolutionary history of biochemical networks.

The evolution of protein domain repertoires: Shedding light on the origins of the Herpesviridae family.

Herpesviruses (HVs, Family: Herpesviridae) have large genomes that encode hundreds of proteins. Apart from amino acid mutations, protein domain acquisitions, duplications and losses are also common modes of evolution. HV domain repertoires differ across species, and only a core set is shared among all species, aspect that raises a question: How have HV domain repertoires diverged while keeping some similarities? To answer such question, we used profile Hidden Markov Models (HMMs) to search for domains in all possible translated open reading frames (ORFs) of fully sequenced HV genomes. With at least 274 domains being identified, we built a matrix of domain counts per species, and applied a parsimony method to reconstruct the ancestral states of these domains along the HV phylogeny. It revealed events of domain gain, duplication, and loss over more than 400 millions of years, where Alpha-, Beta-, and GammaHVs expanded and condensed their domain repertoires at distinct rates. Most of the acquired domains perform 'Modulation and Control', 'Envelope', or 'Auxiliary' functions, categories that showed high flexibility (number of domains) and redundancy (number of copies). Conversely, few gains and duplications were observed for domains involved in 'Capsid assembly and structure', and 'DNA Replication, recombination and metabolism'. Among the forty-one primordial domains encoded by Herpesviridae ancestors, twenty-eight are still found in all present-day HVs. Because of their distinct evolutionary strategies, HV domain repertoires are very specific at the subfamily, genus and species levels. Differences in domain composition may not only explain HV host range and tissue tropism, but also provide hints to the origins of HVs.

JNets: exploring networks by integrating annotation.

BACKGROUND: A common method for presenting and studying biological interaction networks is visualization. Software tools can enhance our ability to explore network visualizations and improve our understanding of biological systems, particularly when these tools offer analysis capabilities. However, most published network visualizations are static representations that do not support user interaction. RESULTS: JNets was designed as a network visualization tool that incorporates annotation to explore the underlying features of interaction networks. The software is available as an application and a configurable applet that can provide a flexible and dynamic online interface to many types of network data. As a case study, we use JNets to investigate approved drug targets present within the HIV-1 Human protein interaction network. Our software highlights the intricate influence that HIV-1 has on the host immune response. CONCLUSION: JNets is a software tool that allows interaction networks to be visualized and studied remotely, from within a standard web page. Therefore, using this free software, network data can be presented in an enhanced, interactive format. More information about JNets is available at http://www.manchester.ac.uk/bioinformatics/jnets.

Back to basics--how the evolution of the extracellular matrix underpinned vertebrate evolution.

The extracellular matrix (ECM) is a complex substrate that is involved in and influences a spectrum of behaviours such as growth and differentiation and is the basis for the structure of tissues. Although a characteristic of all metazoans, the ECM has elaborated into a variety of tissues unique to vertebrates, such as bone, tendon and cartilage. Here we review recent advances in our understanding of the molecular evolution of the ECM. Furthermore, we demonstrate that ECM genes represent a pivotal family of proteins the evolution of which appears to have played an important role in the evolution of vertebrates.

Identifying the important HIV-1 recombination breakpoints.

Recombinant HIV-1 genomes contribute significantly to the diversity of variants within the HIV/AIDS pandemic. It is assumed that some of these mosaic genomes may have novel properties that have led to their prevalence, particularly in the case of the circulating recombinant forms (CRFs). In regions of the HIV-1 genome where recombination has a tendency to convey a selective advantage to the virus, we predict that the distribution of breakpoints--the identifiable boundaries that delimit the mosaic structure--will deviate from the underlying null distribution. To test this hypothesis, we generate a probabilistic model of HIV-1 copy-choice recombination and compare the predicted breakpoint distribution to the distribution from the HIV/AIDS pandemic. Across much of the HIV-1 genome, we find that the observed frequencies of inter-subtype recombination are predicted accurately by our model. This observation strongly indicates that in these regions a probabilistic model, dependent on local sequence identity, is sufficient to explain breakpoint locations. In regions where there is a significant over- (either side of the env gene) or under- (short regions within gag, pol, and most of env) representation of breakpoints, we infer natural selection to be influencing the recombination pattern. The paucity of recombination breakpoints within most of the envelope gene indicates that recombinants generated in this region are less likely to be successful. The breakpoints at a higher frequency than predicted by our model are approximately at either side of env, indicating increased selection for these recombinants as a consequence of this region, or at least part of it, having a tendency to be recombined as an entire unit. Our findings thus provide the first clear indication of the existence of a specific portion of the genome that deviates from a probabilistic null model for recombination. This suggests that, despite the wide diversity of recombinant forms seen in the viral population, only a minority of recombination events appear to be of significance to the evolution of HIV-1.

Methodology capture: discriminating between the "best" and the rest of community practice.

BACKGROUND: The methodologies we use both enable and help define our research. However, as experimental complexity has increased the choice of appropriate methodologies has become an increasingly difficult task. This makes it difficult to keep track of available bioinformatics software, let alone the most suitable protocols in a specific research area. To remedy this we present an approach for capturing methodology from literature in order to identify and, thus, define best practice within a field. RESULTS: Our approach is to implement data extraction techniques on the full-text of scientific articles to obtain the set of experimental protocols used by an entire scientific discipline, molecular phylogenetics. Our methodology for identifying methodologies could in principle be applied to any scientific discipline, whether or not computer-based. We find a number of issues related to the nature of best practice, as opposed to community practice. We find that there is much heterogeneity in the use of molecular phylogenetic methods and software, some of which is related to poor specification of protocols. We also find that phylogenetic practice exhibits field-specific tendencies that have increased through time, despite the generic nature of the available software. We used the practice of highly published and widely collaborative researchers ("expert" researchers) to analyse the influence of authority on community practice. We find expert authors exhibit patterns of practice common to their field and therefore act as useful field-specific practice indicators. CONCLUSION: We have identified a structured community of phylogenetic researchers performing analyses that are customary in their own local community and significantly different from those in other areas. Best practice information can help to bridge such subtle differences by increasing communication of protocols to a wider audience. We propose that the practice of expert authors from the field of evolutionary biology is the closest to contemporary best practice in phylogenetic experimental design. Capturing best practice is, however, a complex task and should also acknowledge the differences between fields such as the specific context of the analysis.

Bioinformatic analysis of the neprilysin (M13) family of peptidases reveals complex evolutionary and functional relationships.

BACKGROUND: The neprilysin (M13) family of endopeptidases are zinc-metalloenzymes, the majority of which are type II integral membrane proteins. The best characterised of this family is neprilysin, which has important roles in inactivating signalling peptides involved in modulating neuronal activity, blood pressure and the immune system. Other family members include the endothelin converting enzymes (ECE-1 and ECE-2), which are responsible for the final step in the synthesis of potent vasoconstrictor endothelins. The ECEs, as well as neprilysin, are considered valuable therapeutic targets for treating cardiovascular disease. Other members of the M13 family have not been functionally characterised, but are also likely to have biological roles regulating peptide signalling. The recent sequencing of animal genomes has greatly increased the number of M13 family members in protein databases, information which can be used to reveal evolutionary relationships and to gain insight into conserved biological roles. RESULTS: The phylogenetic analysis successfully resolved vertebrate M13 peptidases into seven classes, one of which appears to be specific to mammals, and insect genes into five functional classes and a series of expansions, which may include inactive peptidases. Nematode genes primarily resolved into groups containing no other taxa, bar the two nematode genes associated with Drosophila DmeNEP1 and DmeNEP4. This analysis reconstructed only one relationship between chordate and invertebrate clusters, that of the ECE sub-group and the DmeNEP3 related genes. Analysis of amino acid utilisation in the active site of M13 peptidases reveals a basis for their biochemical properties. A relatively invariant S1' subsite gives the majority of M13 peptidases their strong preference for hydrophobic residues in P1' position. The greater variation in the S2' subsite may be instrumental in determining the specificity of M13 peptidases for their substrates and thus allows M13 peptidases to fulfil a broad range of physiological roles. CONCLUSION: The M13 family of peptidases have diversified extensively in all species examined, indicating wide ranging roles in numerous physiological processes. It is predicted that differences in the S2' subsite are fundamental to determining the substrate specificities that facilitate this functional diversity.

metaSHARK: a WWW platform for interactive exploration of metabolic networks.

The metaSHARK (metabolic search and reconstruction kit) web server offers users an intuitive, fully interactive way to explore the KEGG metabolic network via a WWW browser. Metabolic reconstruction information for specific organisms, produced by our automated SHARKhunt tool or from other programs or genome annotations, may be uploaded to the website and overlaid on the generic network. Additional data from gene expression experiments can also be incorporated, allowing the visualization of differential gene expression in the context of the predicted metabolic network. metaSHARK is available at http://bioinformatics.leeds.ac.uk/shark/.

Arabidopsis Co-expression Tool (ACT): web server tools for microarray-based gene expression analysis.

The Arabidopsis Co-expression Tool, ACT, ranks the genes across a large microarray dataset according to how closely their expression follows the expression of a query gene. A database stores pre-calculated co-expression results for approximately 21,800 genes based on data from over 300 arrays. These results can be corroborated by calculation of co-expression results for user-defined sub-sets of arrays or experiments from the NASC/GARNet array dataset. Clique Finder (CF) identifies groups of genes which are consistently co-expressed with each other across a user-defined co-expression list. The parameters can be altered easily to adjust cluster size and the output examined for optimal inclusion of genes with known biological roles. Alternatively, a Scatter Plot tool displays the correlation coefficients for all genes against two user-selected queries on a scatter plot which can be useful for visual identification of clusters of genes with similar r-values. User-input groups of genes can be highlighted on the scatter plots. Inclusion of genes with known biology in sets of genes identified using CF and Scatter Plot tools allows inferences to be made about the roles of the other genes in the set and both tools can therefore be used to generate short lists of genes for further characterization. ACT is freely available at www.Arabidopsis.leeds.ac.uk/ACT.

An exploration of alternative visualisations of the basic helix-loop-helix protein interaction network.

BACKGROUND: Alternative representations of biochemical networks emphasise different aspects of the data and contribute to the understanding of complex biological systems. In this study we present a variety of automated methods for visualisation of a protein-protein interaction network, using the basic helix-loop-helix (bHLH) family of transcription factors as an example. RESULTS: Network representations that arrange nodes (proteins) according to either continuous or discrete information are investigated, revealing the existence of protein sub-families and the retention of interactions following gene duplication events. Methods of network visualisation in conjunction with a phylogenetic tree are presented, highlighting the evolutionary relationships between proteins, and clarifying the context of network hubs and interaction clusters. Finally, an optimisation technique is used to create a three-dimensional layout of the phylogenetic tree upon which the protein-protein interactions may be projected. CONCLUSION: We show that by incorporating secondary genomic, functional or phylogenetic information into network visualisation, it is possible to move beyond simple layout algorithms based on network topology towards more biologically meaningful representations. These new visualisations can give structure to complex networks and will greatly help in interpreting their evolutionary origins and functional implications. Three open source software packages (InterView, TVi and OptiMage) implementing our methods are available.

Metabolic reconstruction and analysis for parasite genomes.

With the completion of sequencing projects for several parasite genomes, efforts are ongoing to make sense of this mass of information in terms of the gene products encoded and their interactions in the growth, development and survival of parasites. The emerging science of systems biology aims to explain the complex relationship between genotype and phenotype by using network models. One area in which this approach has been particularly successful is in the modeling of metabolism. With an accurate picture of the set of metabolic reactions encoded in a genome, it is now possible to identify enzymes or transporters that might be viable targets for new drugs. Because these predictions greatly depend on the quality and completeness of the genome annotation, there are substantial efforts in the scientific community to increase the numbers of metabolic enzymes identified. In this review, we discuss the opportunities for using metabolic reconstruction and analysis tools in parasitology research, and their applications to protozoan parasites.

metaSHARK: software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparum and Eimeria tenella.

The metabolic SearcH And Reconstruction Kit (metaSHARK) is a new fully automated software package for the detection of enzyme-encoding genes within unannotated genome data and their visualization in the context of the surrounding metabolic network. The gene detection package (SHARKhunt) runs on a Linux system and requires only a set of raw DNA sequences (genomic, expressed sequence tag and/or genome survey sequence) as input. Its output may be uploaded to our web-based visualization tool (SHARKview) for exploring and comparing data from different organisms. We first demonstrate the utility of the software by comparing its results for the raw Plasmodium falciparum genome with the manual annotations available at the PlasmoDB and PlasmoCyc websites. We then apply SHARKhunt to the unannotated genome sequences of the coccidian parasite Eimeria tenella and observe that, at an E-value cut-off of 10(-20), our software makes 142 additional assertions of enzymatic function compared with a recent annotation package working with translated open reading frame sequences. The ability of the software to cope with low levels of sequence coverage is investigated by analyzing assemblies of the E.tenella genome at estimated coverages from 0.5x to 7.5x. Lastly, as an example of how metaSHARK can be used to evaluate the genomic evidence for specific metabolic pathways, we present a study of coenzyme A biosynthesis in P.falciparum and E.tenella.

Protein-Protein Interactions in Virus-Host Systems.

To study virus-host protein interactions, knowledge about viral and host protein architectures and repertoires, their particular evolutionary mechanisms, and information on relevant sources of biological data is essential. The purpose of this review article is to provide a thorough overview about these aspects. Protein domains are basic units defining protein interactions, and the uniqueness of viral domain repertoires, their mode of evolution, and their roles during viral infection make viruses interesting models of study. Mutations at protein interfaces can reduce or increase their binding affinities by changing protein electrostatics and structural properties. During the course of a viral infection, both pathogen and cellular proteins are constantly competing for binding partners. Endogenous interfaces mediating intraspecific interactions-viral-viral or host-host interactions-are constantly targeted and inhibited by exogenous interfaces mediating viral-host interactions. From a biomedical perspective, blocking such interactions is the main mechanism underlying antiviral therapies. Some proteins are able to bind multiple partners, and their modes of interaction define how fast these "hub proteins" evolve. "Party hubs" have multiple interfaces; they establish simultaneous/stable (domain-domain) interactions, and tend to evolve slowly. On the other hand, "date hubs" have few interfaces; they establish transient/weak (domain-motif) interactions by means of short linear peptides (15 or fewer residues), and can evolve faster. Viral infections are mediated by several protein-protein interactions (PPIs), which can be represented as networks (protein interaction networks, PINs), with proteins being depicted as nodes, and their interactions as edges. It has been suggested that viral proteins tend to establish interactions with more central and highly connected host proteins. In an evolutionary arms race, viral and host proteins are constantly changing their interface residues, either to evade or to optimize their binding capabilities. Apart from gaining and losing interactions via rewiring mechanisms, virus-host PINs also evolve via gene duplication (paralogy); conservation (orthology); horizontal gene transfer (HGT) (xenology); and molecular mimicry (convergence). The last sections of this review focus on PPI experimental approaches and their limitations, and provide an overview of sources of biomolecular data for studying virus-host protein interactions.