The Brown Algal Virus EsV-1 Particle Contains a Putative Hybrid Histidine Kinase

AbstractThe Ectocarpus siliculosus virus, EsV-1, occurs worldwide in all populations of the filamentous marine brown alga E. siliculosus. We have screened an expression library of EsV-1 restriction fragments and identified a DNA clone with the potential to code for a 52-kDa histidine protein kinase. The derived amino acid sequence includes all homology boxes diagnostic for histidine protein kinases and, in addition, amino acid motifs that are commonly found in response regulators of bacterial two-component signal transduction proteins. Thus, the novel viral protein can be classified as a hybrid histidine protein kinase of a type that has previously been detected in fungi, slime molds, and plants. By using purified antibodies, we found that the protein with its potential kinase activity is located on the outer shell of viral particles. This is the first report on a two-component regulator-like protein in viruses and could provide the basis for speculations with regard to the evolution of EsV-1 and related viruses

Transmembrane domain length of viral K+ channels is a signal for mitochondria targeting

K+ channels operate in the plasma membrane and in membranes of organelles including mitochondria. The mechanisms and topogenic information for their differential synthesis and targeting is unknown. This article describes 2 similar viral K+ channels that are differentially sorted; one protein (Kesv) is imported by the Tom complex into the mitochondria, the other (Kcv) to the plasma membrane. By creating chimeras we discovered that mitochondrial sorting of Kesv depends on a hierarchical combination of N- and C-terminal signals. Crucial is the length of the second transmembrane domain; extending its C terminus by \u3e2 hydrophobic amino acids redirects Kesv from the mitochondrial to the plasma membrane. Activity of Kesv in the plasma membrane is detected electrically or by yeast rescue assays only after this shift in sorting. Hence only minor structural alterations in a transmembrane domain are sufficient to switch sorting of a K+ channel between the plasma membrane and mitochondria

A novel evolutionary strategy revealed in the Phaeoviruses

Phaeoviruses infect the brown algae, which are major contributors to primary production of coastal waters and estuaries. They exploit a Persistent evolutionary strategy akin to a K- selected life strategy via genome integration and are the only known representatives to do so within the giant algal viruses that are typified by r- selected Acute lytic viruses. In screening the genomes of five species within the filamentous brown algal lineage, here we show an unprecedented diversity of viral gene sequence variants especially amongst the smaller phaeoviral genomes. Moreover, one variant shares features from both the two major sub-groups within the phaeoviruses. These phaeoviruses have exploited the reduction of their giant dsDNA genomes and accompanying loss of DNA proofreading capability, typical of an Acute life strategist, but uniquely retain a Persistent life strategy

Phycodnavirus Potassium Ion Channel Proteins Question the Virus Molecular Piracy Hypothesis

Phycodnaviruses are large dsDNA, algal-infecting viruses that encode many genes with homologs in prokaryotes and eukaryotes. Among the viral gene products are the smallest proteins known to form functional K+ channels. To determine if these viral K+ channels are the product of molecular piracy from their hosts, we compared the sequences of the K+ channel pore modules from seven phycodnaviruses to the K+ channels from Chlorella variabilis and Ectocarpus siliculosus, whose genomes have recently been sequenced. C. variabilis is the host for two of the viruses PBCV-1 and NY-2A and E. siliculosus is the host for the virus EsV-1. Systematic phylogenetic analyses consistently indicate that the viral K+ channels are not related to any lineage of the host channel homologs and that they are more closely related to each other than to their host homologs. A consensus sequence of the viral channels resembles a protein of unknown function from a proteobacterium. However, the bacterial protein lacks the consensus motif of all K+ channels and it does not form a functional channel in yeast, suggesting that the viral channels did not come from a proteobacterium. Collectively, our results indicate that the viruses did not acquire their K+ channel-encoding genes from their current algal hosts by gene transfer; thus alternative explanations are required. One possibility is that the viral genes arose from ancient organisms, which served as their hosts before the viruses developed their current host specificity. Alternatively the viral proteins could be the origin of K+ channels in algae and perhaps even all cellular organisms

Diversity and Evolution of Sensor Histidine Kinases in Eukaryotes

Histidine kinases (HKs) are primary sensor proteins that act in cell signaling pathways generically referred to as "two component systems" (TCSs). TCSs are among the most widely distributed transduction systems used by both prokaryotic and eukaryotic organisms to detect and respond to a broad range of environmental cues. The structure and distribution of HK proteins are now well documented in prokaryotes but information is still fragmentary for eukaryotes. Here, we have taken advantage of recent genomic resources to explore the structural diversity and the phylogenetic distribution of HKs in the prominent eukaryotic supergroups. Searches of the genomes of 67 eukaryotic species spread evenly throughout the phylogenetic tree of life identified 748 predicted HK proteins. Independent phylogenetic analyses of predicted HK proteins were carried out for each of the major eukaryotic supergroups. This allowed most of the compiled sequences to be categorised into previously described HK groups. Beyond the phylogenetic analysis of eukaryotic HKs, this study revealed some interesting findings: (i) characterisation of some previously undescribed eukaryotic HK groups with predicted functions putatively related to physiological traits; (ii) discovery of HK groups that were previously believed to be restricted to a single kingdom in additional supergroups and (iii) indications that some evolutionary paths have led to the appearance, transfer, duplication, and loss of HK genes in some phylogenetic lineages. This study provides an unprecedented overview of the structure and distribution of HKs in the Eukaryota and represents a first step towards deciphering the evolution of TCS signaling in living organisms

Life-Cycle and Genome of OtV5, a Large DNA Virus of the Pelagic Marine Unicellular Green Alga Ostreococcus tauri

Large DNA viruses are ubiquitous, infecting diverse organisms ranging from algae to man, and have probably evolved from an ancient common ancestor. In aquatic environments, such algal viruses control blooms and shape the evolution of biodiversity in phytoplankton, but little is known about their biological functions. We show that Ostreococcus tauri, the smallest known marine photosynthetic eukaryote, whose genome is completely characterized, is a host for large DNA viruses, and present an analysis of the life-cycle and 186,234 bp long linear genome of OtV5. OtV5 is a lytic phycodnavirus which unexpectedly does not degrade its host chromosomes before the host cell bursts. Analysis of its complete genome sequence confirmed that it lacks expected site-specific endonucleases, and revealed the presence of 16 genes whose predicted functions are novel to this group of viruses. OtV5 carries at least one predicted gene whose protein closely resembles its host counterpart and several other host-like sequences, suggesting that horizontal gene transfers between host and viral genomes may occur frequently on an evolutionary scale. Fifty seven percent of the 268 predicted proteins present no similarities with any known protein in Genbank, underlining the wealth of undiscovered biological diversity present in oceanic viruses, which are estimated to harbour 200Mt of carbon

The <i>Ectocarpus</i> genome and the independent evolution of multicellularity in brown algae

Brown algae (Phaeophyceae) are complex photosynthetic organisms with a very different evolutionary history to green plants, to which they are only distantly related1. These seaweeds are the dominant species in rocky coastal ecosystems and they exhibit many interesting adaptations to these, often harsh, environments. Brown algae are also one of only a small number of eukaryotic lineages that have evolved complex multicellularity (Fig. 1).We report the 214 million base pair (Mbp) genome sequence of the filamentous seaweed Ectocarpus siliculosus (Dillwyn) Lyngbye, a model organism for brown algae, closely related to the kelps (Fig. 1). Genome features such as the presence of an extended set of light-harvesting and pigment biosynthesis genes and new metabolic processes such as halide metabolism help explain the ability of this organism to cope with the highly variable tidal environment. The evolution of multicellularity in this lineage is correlated with the presence of a rich array of signal transduction genes. Of particular interest is the presence of a family of receptor kinases, as the independent evolution of related molecules has been linked with the emergence of multicellularity in both the animal and green plant lineages. The Ectocarpus genome sequence represents an important step towards developing this organism as a model species, providing the possibility to combine genomic and genetic2 approaches to explore these and other aspects of brown algal biology further

Élaboration d’un outil numérique pour la réduction et l’optimisation des mécanismes cinétiques pour les systèmes de combustion

In the modeling of a combustion process, obtention of global data such as flame speed can, under certain circumstances, be achieved through extremely reduced mechanisms. On the contrary, prediction of detailed data such as polluant species requires the use of detailed kinetic mechanisms involving many chemical species. Due to the size and to the presence of many differents time scales, the integration of those models to complex numerical simulations is a non trivial task. A reduction tool based on Directed Relation Graph and sensitivity analysis methods is proposed to tackle this issue. Reduced mechanisms fitting user defined tolerances for quantities of interest such as global (flame speed, ignition delay, etc) and detailed data (concentration profiles) are automatically generated. The reduction process is paired up with an optimisation of reaction rates through a genetic algorithm to make up for the error induced by the loss of information. This process can use both numerical and experimental reference entries. The complete numerical tool has been tested on several canonical configurations for several fuels (methane, ethane and n-heptane) and reduction rates up to 90% have been observed.Lors de la modélisation d’un processus de combustion, l’obtention de données globales telles que la vitesse de flamme peut sous certaines condition être réalisée à l’aide de mécanismes très réduits. En revanche, la prédiction de données détaillées comme la concentration d’espèces polluantes minoritaires nécessite l’utilisation de mécanismes cinétiques détaillés mettant en jeu de nombreuses espèces chimiques. Du fait de leur taille et des différences d’échelles de temps, l’intégration de tels modèles chimiques à des simulations numériques complexes est cependant extrêmement coûteuse en temps de calcul. Pour s’affranchir de cette limite, un outil de réduction basé sur les méthodes de Directed Relation Graph et d’analyse de sensibilité a été développé. Il permet la génération automatique de mécanismes réduits en fonction de quantités d’intérêt telles que les données globales (vitesse de flamme, délai d’auto-allumage, etc) et détaillées (profils de concentration) en fonction de tolérances d’erreur définies par l’utilisateur. Les opérations de réduction sont couplées à une optimisation par algorithme génétique des constantes de réaction afin de compenser au maximum les erreurs issues de la perte d’information. Cette optimisation peut être réalisée par rapport à des données issues de simulations numériques mais également par rapport à des mesures expérimentales. L’outil complet a été testé sur différentes configurations canoniques pour différents combustibles (méthane, éthane et n-heptane) et des taux de réduction supérieurs à 80% ont pu être obtenus

Development of a numerical tool for the reduction and optimisation of detailed kinetic mechanisms in combustion systems

Lors de la modélisation d’un processus de combustion, l’obtention de données globales telles que la vitesse de flamme peut sous certaines condition être réalisée à l’aide de mécanismes très réduits. En revanche, la prédiction de données détaillées comme la concentration d’espèces polluantes minoritaires nécessite l’utilisation de mécanismes cinétiques détaillés mettant en jeu de nombreuses espèces chimiques. Du fait de leur taille et des différences d’échelles de temps, l’intégration de tels modèles chimiques à des simulations numériques complexes est cependant extrêmement coûteuse en temps de calcul. Pour s’affranchir de cette limite, un outil de réduction basé sur les méthodes de Directed Relation Graph et d’analyse de sensibilité a été développé. Il permet la génération automatique de mécanismes réduits en fonction de quantités d’intérêt telles que les données globales (vitesse de flamme, délai d’auto-allumage, etc) et détaillées (profils de concentration) en fonction de tolérances d’erreur définies par l’utilisateur. Les opérations de réduction sont couplées à une optimisation par algorithme génétique des constantes de réaction afin de compenser au maximum les erreurs issues de la perte d’information. Cette optimisation peut être réalisée par rapport à des données issues de simulations numériques mais également par rapport à des mesures expérimentales. L’outil complet a été testé sur différentes configurations canoniques pour différents combustibles (méthane, éthane et n-heptane) et des taux de réduction supérieurs à 80% ont pu être obtenus.In the modeling of a combustion process, obtention of global data such as flame speed can, under certain circumstances, be achieved through extremely reduced mechanisms. On the contrary, prediction of detailed data such as polluant species requires the use of detailed kinetic mechanisms involving many chemical species. Due to the size and to the presence of many differents time scales, the integration of those models to complex numerical simulations is a non trivial task. A reduction tool based on Directed Relation Graph and sensitivity analysis methods is proposed to tackle this issue. Reduced mechanisms fitting user defined tolerances for quantities of interest such as global (flame speed, ignition delay, etc) and detailed data (concentration profiles) are automatically generated. The reduction process is paired up with an optimisation of reaction rates through a genetic algorithm to make up for the error induced by the loss of information. This process can use both numerical and experimental reference entries. The complete numerical tool has been tested on several canonical configurations for several fuels (methane, ethane and n-heptane) and reduction rates up to 90% have been observed