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.

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

Herpesviruses (HVs, Family: ) 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

From evidence to inference: probing the evolution of protein interaction networks.

The evolutionary mechanisms by which protein interaction networks grow and change are beginning to be appreciated as a major factor shaping their present-day structures and properties. Starting with a consideration of the biases and errors inherent in our current views of these networks, we discuss the dangers of constructing evolutionary arguments from naïve analyses of network topology. We argue that progress in understanding the processes of network evolution is only possible when hypotheses are formulated as plausible evolutionary models and compared against the observed data within the framework of probabilistic modeling.

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.

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.

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.

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.

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.

Cataloguing the HIV type 1 human protein interaction network.

Although many interactions between HIV-1 and human proteins have been reported in the scientific literature, no publicly accessible source for efficiently reviewing this information was available. Therefore, a project was initiated in an attempt to catalogue all published interactions between HIV-1 and human proteins. HIV-related articles in PubMed were used to develop a database containing names, Entrez GeneIDs, and RefSeq protein accession numbers of interacting proteins.

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.

Rapid absorption of nicotine from new nicotine gum formulations.

Rationale: A clinically limiting feature of currently-available nicotine gum is its slow rate of nicotine delivery and consequently slow onset of therapeutic effects. Previous research suggested that a nicotine hydrogen tartrate gum (NHTG1) that delivered nicotine more rapidly provided more effective craving relief. A subsequent gum formulation (NTHG2) was developed to further increase speed of delivery.