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.

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.

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.

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/ .

Derivative processes for modelling metabolic fluxes.

MOTIVATION: One of the challenging questions in modelling biological systems is to characterize the functional forms of the processes that control and orchestrate molecular and cellular phenotypes. Recently proposed methods for the analysis of metabolic pathways, for example, dynamic flux estimation, can only provide estimates of the underlying fluxes at discrete time points but fail to capture the complete temporal behaviour. To describe the dynamic variation of the fluxes, we additionally require the assumption of specific functional forms that can capture the temporal behaviour. However, it also remains unclear how to address the noise which might be present in experimentally measured metabolite concentrations. RESULTS: Here we propose a novel approach to modelling metabolic fluxes: derivative processes that are based on multiple-output Gaussian processes (MGPs), which are a flexible non-parametric Bayesian modelling technique. The main advantages that follow from MGPs approach include the natural non-parametric representation of the fluxes and ability to impute the missing data in between the measurements. Our derivative process approach allows us to model changes in metabolite derivative concentrations and to characterize the temporal behaviour of metabolic fluxes from time course data. Because the derivative of a Gaussian process is itself a Gaussian process, we can readily link metabolite concentrations to metabolic fluxes and vice versa. Here we discuss how this can be implemented in an MGP framework and illustrate its application to simple models, including nitrogen metabolism in Escherichia coli. AVAILABILITY AND IMPLEMENTATION: R code is available from the authors upon request.

Analysis of metabolic evolution in bacteria using whole-genome metabolic models.

Recent advances in the automation of metabolic model reconstruction have led to the availability of draft-quality metabolic models (predicted reaction complements) for multiple bacterial species. These reaction complements can be considered as trait representations and can be used for ancestral state reconstruction to infer the most likely metabolic complements of common ancestors of all bacteria with generated metabolic models. We present here an ancestral state reconstruction for 141 extant bacteria and analyze the reaction gains and losses for these bacteria with respect to their lifestyles and pathogenic nature. A simulated annealing approach is used to look at coordinated metabolic gains and losses in two bacteria. The main losses of Onion yellows phytoplasma OY-M, an obligate intracellular pathogen, are shown (as expected) to be in cell wall biosynthesis. The metabolic gains made by Clostridium difficile CD196 in adapting to its current habitat in the human colon is also analyzed. Our analysis shows that the capability to utilize N-Acetyl-neuraminic acid as a carbon source has been gained, rather than having been present in the Clostridium ancestor, as has the capability to synthesize phthiocerol dimycocerosate, which could potentially aid the evasion of the host immune response. We have shown that the availability of large numbers of metabolic models, along with conventional approaches, has enabled a systematic method to analyze metabolic evolution in the bacterial domain.

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.

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.

Modular biological function is most effectively captured by combining molecular interaction data types.

Large-scale molecular interaction data sets have the potential to provide a comprehensive, system-wide understanding of biological function. Although individual molecules can be promiscuous in terms of their contribution to function, molecular functions emerge from the specific interactions of molecules giving rise to modular organisation. As functions often derive from a range of mechanisms, we demonstrate that they are best studied using networks derived from different sources. Implementing a graph partitioning algorithm we identify subnetworks in yeast protein-protein interaction (PPI), genetic interaction and gene co-regulation networks. Among these subnetworks we identify cohesive subgraphs that we expect to represent functional modules in the different data types. We demonstrate significant overlap between the subgraphs generated from the different data types and show these overlaps can represent related functions as represented by the Gene Ontology (GO). Next, we investigate the correspondence between our subgraphs and the Gene Ontology. This revealed varying degrees of coverage of the biological process, molecular function and cellular component ontologies, dependent on the data type. For example, subgraphs from the PPI show enrichment for 84%, 58% and 93% of annotated GO terms, respectively. Integrating the interaction data into a combined network increases the coverage of GO. Furthermore, the different annotation types of GO are not predominantly associated with one of the interaction data types. Collectively our results demonstrate that successful capture of functional relationships by network data depends on both the specific biological function being characterised and the type of network data being used. We identify functions that require integrated information to be accurately represented, demonstrating the limitations of individual data types. Combining interaction subnetworks across data types is therefore essential for fully understanding the complex and emergent nature of biological function.

AMBIENT: Active Modules for Bipartite Networks--using high-throughput transcriptomic data to dissect metabolic response.

BACKGROUND: With the continued proliferation of high-throughput biological experiments, there is a pressing need for tools to integrate the data produced in ways that produce biologically meaningful conclusions. Many microarray studies have analysed transcriptomic data from a pathway perspective, for instance by testing for KEGG pathway enrichment in sets of upregulated genes. However, the increasing availability of species-specific metabolic models provides the opportunity to analyse these data in a more objective, system-wide manner. RESULTS: Here we introduce ambient (Active Modules for Bipartite Networks), a simulated annealing approach to the discovery of metabolic subnetworks (modules) that are significantly affected by a given genetic or environmental change. The metabolic modules returned by ambient are connected parts of the bipartite network that change coherently between conditions, providing a more detailed view of metabolic changes than standard approaches based on pathway enrichment. CONCLUSIONS: ambient is an effective and flexible tool for the analysis of high-throughput data in a metabolic context. The same approach can be applied to any system in which reactions (or metabolites) can be assigned a score based on some biological observation, without the limitation of predefined pathways. A Python implementation of ambient is available at http://www.theosysbio.bio.ic.ac.uk/ambient.

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.

The biological context of HIV-1 host interactions reveals subtle insights into a system hijack.

BACKGROUND: In order to replicate, HIV, like all viruses, needs to invade a host cell and hijack it for its own use, a process that involves multiple protein interactions between virus and host. The HIV-1, Human Protein Interaction Database available at NCBI's website captures this information from the primary literature, containing over 2,500 unique interactions. We investigate the general properties and biological context of these interactions and, thus, explore the molecular specificity of the HIV-host perturbation. In particular, we investigate (i) whether HIV preferentially interacts with highly connected and 'central' proteins, (ii) known phenotypic properties of host proteins inferred from essentiality and disease-association data, and (iii) biological context (molecular function, processes and location) of the host proteins to identify attributes most strongly associated with specific HIV interactions. RESULTS: After correcting for ascertainment bias in the literature, we demonstrate a significantly greater propensity for HIV to interact with highly connected and central host proteins. Unexpectedly, we find there are no associations between HIV interaction and inferred essentiality. Similarly, we find a tendency for HIV not to interact with proteins encoded by genes associated with disease. Crucially, we find that functional categories over-represented in HIV-host interactions are innately enriched for highly connected and central proteins in the host system. CONCLUSIONS: Our results imply that HIV's propensity to interact with highly connected and central proteins is a consequence of interactions with particular cellular functions, rather than being a direct effect of network topological properties. The lack of a propensity for interactions with phenotypically essential proteins suggests a selective pressure to minimise virulence in retroviral evolution. Thus, the specificity of HIV-host interactions is complex, and only superficially explained by network properties.

Low-complexity regions within protein sequences have position-dependent roles.

BACKGROUND: Regions of protein sequences with biased amino acid composition (so-called Low-Complexity Regions (LCRs)) are abundant in the protein universe. A number of studies have revealed that i) these regions show significant divergence across protein families; ii) the genetic mechanisms from which they arise lends them remarkable degrees of compositional plasticity. They have therefore proved difficult to compare using conventional sequence analysis techniques, and functions remain to be elucidated for most of them. Here we undertake a systematic investigation of LCRs in order to explore their possible functional significance, placed in the particular context of Protein-Protein Interaction (PPI) networks and Gene Ontology (GO)-term analysis. RESULTS: In keeping with previous results, we found that LCR-containing proteins tend to have more binding partners across different PPI networks than proteins that have no LCRs. More specifically, our study suggests i) that LCRs are preferentially positioned towards the protein sequence extremities and, in contrast with centrally-located LCRs, such terminal LCRs show a correlation between their lengths and degrees of connectivity, and ii) that centrally-located LCRs are enriched with transcription-related GO terms, while terminal LCRs are enriched with translation and stress response-related terms. CONCLUSIONS: Our results suggest not only that LCRs may be involved in flexible binding associated with specific functions, but also that their positions within a sequence may be important in determining both their binding properties and their biological roles.

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.

Proteomic analysis of integrin-associated complexes identifies RCC2 as a dual regulator of Rac1 and Arf6.

The binding of integrin adhesion receptors to their extracellular matrix ligands controls cell morphology, movement, survival, and differentiation in various developmental, homeostatic, and disease processes. Here, we report a methodology to isolate complexes associated with integrin adhesion receptors, which, like other receptor-associated signaling complexes, have been refractory to proteomic analysis. Quantitative, comparative analyses of the proteomes of two receptor-ligand pairs, alpha(4)beta(1)-vascular cell adhesion molecule-1 and alpha(5)beta(1)-fibronectin, defined both core and receptor-specific components. Regulator of chromosome condensation-2 (RCC2) was detected in the alpha(5)beta(1)-fibronectin signaling network at an intersection between the Rac1 and adenosine 5'-diphosphate ribosylation factor 6 (Arf6) subnetworks. RCC2 knockdown enhanced fibronectin-induced activation of both Rac1 and Arf6 and accelerated cell spreading, suggesting that RCC2 limits the signaling required for membrane protrusion and delivery. Dysregulation of Rac1 and Arf6 function by RCC2 knockdown also abolished persistent migration along fibronectin fibers, indicating a functional role for RCC2 in directional cell movement. This proteomics workflow now opens the way to further dissection and systems-level analyses of adhesion signaling.

Evolving proteins at Darwin's bicentenary.

A report of the Biochemical Society/Wellcome Trust meeting 'Protein Evolution - Sequences, Structures and Systems', Hinxton, UK, 26-27 January 2009.

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.

A unique dual activity amino acid hydroxylase in Toxoplasma gondii.

The genome of the protozoan parasite Toxoplasma gondii was found to contain two genes encoding tyrosine hydroxylase; that produces L-DOPA. The encoded enzymes metabolize phenylalanine as well as tyrosine with substrate preference for tyrosine. Thus the enzymes catabolize phenylalanine to tyrosine and tyrosine to L-DOPA. The catalytic domain descriptive of this class of enzymes is conserved with the parasite enzyme and exhibits similar kinetic properties to metazoan tyrosine hydroxylases, but contains a unique N-terminal extension with a signal sequence motif. One of the genes, TgAaaH1, is constitutively expressed while the other gene, TgAaaH2, is induced during formation of the bradyzoites of the cyst stages of the life cycle. This is the first description of an aromatic amino acid hydroxylase in an apicomplexan parasite. Extensive searching of apicomplexan genome sequences revealed an ortholog in Neospora caninum but not in Eimeria, Cryptosporidium, Theileria, or Plasmodium. Possible role(s) of these bi-functional enzymes during host infection are discussed.