Uptake of home-based voluntary HIV testing in sub-Saharan Africa: a systematic review and meta-analysis

Improving access to HIV testing is a key priority in scaling up HIV treatment and prevention services. Home-based voluntary counselling and testing (HBT) as an approach to delivering wide-scale HIV testing is explored here

A molecular insight into algal-oomycete warfare : cDNA analysis of Ectocarpus siliculosus infected with the basal oomycete Eurychasma dicksonii

Peer reviewedPublisher PD

Phaeoviruses discovered in kelp (Laminariales)

Phaeoviruses are latent double-stranded DNA viruses that insert their genomes into those of their brown algal (Phaeophyceae) hosts. So far these viruses are known only from members of the Ectocarpales, which are small and short-lived macroalgae. Here we report molecular and morphological evidence for a new Phaeovirus cluster, referred to as sub-group C, infecting kelps (Laminariales) of the genera Laminaria and Saccharina, which are ecologically and commercially important seaweeds. Epifluorescence and TEM observations indicate that the Laminaria digitata Virus (LdigV), the type species of sub-group C, targets the host nucleus for its genome replication, followed by gradual degradation of the chloroplast and assembly of virions in the cytoplasm of both vegetative and reproductive cells. This study is the first to describe phaeoviruses in kelp. In the field, these viruses infected two thirds of their host populations; however, their biological impact remains unknown

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

Emission of volatile halogenated compounds, speciation and localization of bromine and iodine in the brown algal genome model Ectocarpus siliculosus

This study explores key features of bromine and iodine metabolism in the filamentous brown alga and genomics model Ectocarpus siliculosus. Both elements are accumulated in Ectocarpus, albeit at much lower concentration factors (2-3 orders of magnitude for iodine, and < 1 order of magnitude for bromine) than e.g. in the kelp Laminaria digitata. Iodide competitively reduces the accumulation of bromide. Both iodide and bromide are accumulated in the cell wall (apoplast) of Ectocarpus, with minor amounts of bromine also detectable in the cytosol. Ectocarpus emits a range of volatile halogenated compounds, the most prominent of which by far is methyl iodide. Interestingly, biosynthesis of this compound cannot be accounted for by vanadium haloperoxidase since the latter have not been found to catalyze direct halogenation of an unactivated methyl group or hydrocarbon so a methyl halide transferase-type production mechanism is proposed

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

International audienceIncreasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells

The role of population PK-PD modelling in paediatric clinical research

Children differ from adults in their response to drugs. While this may be the result of changes in dose exposure (pharmacokinetics [PK]) and/or exposure response (pharmacodynamics [PD]) relationships, the magnitude of these changes may not be solely reflected by differences in body weight. As a consequence, dosing recommendations empirically derived from adults dosing regimens using linear extrapolations based on body weight, can result in therapeutic failure, occurrence of adverse effect or even fatalities. In order to define rational, patient-tailored dosing schemes, population PK-PD studies in children are needed. For the analysis of the data, population modelling using non-linear mixed effect modelling is the preferred tool since this approach allows for the analysis of sparse and unbalanced datasets. Additionally, it permits the exploration of the influence of different covariates such as body weight and age to explain the variability in drug response. Finally, using this approach, these PK-PD studies can be designed in the most efficient manner in order to obtain the maximum information on the PK-PD parameters with the highest precision. Once a population PK-PD model is developed, internal and external validations should be performed. If the model performs well in these validation procedures, model simulations can be used to define a dosing regimen, which in turn needs to be tested and challenged in a prospective clinical trial. This methodology will improve the efficacy/safety balance of dosing guidelines, which will be of benefit to the individual child

Evolutionary distinctiveness of fatty acid and polyketide synthesis in eukaryotes

© 2016 International Society for Microbial Ecology All rights reserved. Fatty acids, which are essential cell membrane constituents and fuel storage molecules, are thought to share a common evolutionary origin with polyketide toxins in eukaryotes. While fatty acids are primary metabolic products, polyketide toxins are secondary metabolites that are involved in ecologically relevant processes, such as chemical defence, and produce the adverse effects of harmful algal blooms. Selection pressures on such compounds may be different, resulting in differing evolutionary histories. Surprisingly, some studies of dinoflagellates have suggested that the same enzymes may catalyse these processes. Here we show the presence and evolutionary distinctiveness of genes encoding six key enzymes essential for fatty acid production in 13 eukaryotic lineages for which no previous sequence data were available (alveolates: dinoflagellates, Vitrella, Chromera; stramenopiles: bolidophytes, chrysophytes, pelagophytes, raphidophytes, dictyochophytes, pinguiophytes, xanthophytes; Rhizaria: chlorarachniophytes, haplosporida; euglenids) and 8 other lineages (apicomplexans, bacillariophytes, synurophytes, cryptophytes, haptophytes, chlorophyceans, prasinophytes, trebouxiophytes). The phylogeny of fatty acid synthase genes reflects the evolutionary history of the organism, indicating selection to maintain conserved functionality. In contrast, polyketide synthase gene families are highly expanded in dinoflagellates and haptophytes, suggesting relaxed constraints in their evolutionary history, while completely absent from some protist lineages. This demonstrates a vast potential for the production of bioactive polyketide compounds in some lineages of microbial eukaryotes, indicating that the evolution of these compounds may have played an important role in their ecological success

Pharmacokinetic-Pharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance.

Pharmacokinetic/pharmacodynamic (PKPD) modeling is important in the design and conduct of clinical pharmacology research in children. During drug development, PKPD modeling and simulation should underpin rational trial design and facilitate extrapolation to investigate efficacy and safety. The application of PKPD modeling to optimize dosing recommendations and therapeutic drug monitoring is also increasing, and PKPD model-based dose individualization will become a core feature of personalized medicine. Following extensive progress on pediatric PK modeling, a greater emphasis now needs to be placed on PD modeling to understand age-related changes in drug effects. This paper discusses the principles of PKPD modeling in the context of pediatric drug development, summarizing how important PK parameters, such as clearance (CL), are scaled with size and age, and highlights a standardized method for CL scaling in children. One standard scaling method would facilitate comparison of PK parameters across multiple studies, thus increasing the utility of existing PK models and facilitating optimal design of new studies