Improving the iterative Linear Interaction Energy approach using automated recognition of configurational transitions

Recently an iterative method was proposed to enhance the accuracy and efficiency of ligand-protein binding affinity prediction through linear interaction energy (LIE) theory. For ligand binding to flexible Cytochrome P450s (CYPs), this method was shown to decrease the root-mean-square error and standard deviation of error prediction by combining interaction energies of simulations starting from different conformations. Thereby, different parts of protein-ligand conformational space are sampled in parallel simulations. The iterative LIE framework relies on the assumption that separate simulations explore different local parts of phase space, and do not show transitions to other parts of configurational space that are already covered in parallel simulations. In this work, a method is proposed to (automatically) detect such transitions during the simulations that are performed to construct LIE models and to predict binding affinities. Using noise-canceling techniques and splines to fit time series of the raw data for the interaction energies, transitions during simulation between different parts of phase space are identified. Boolean selection criteria are then applied to determine which parts of the interaction energy trajectories are to be used as input for the LIE calculations. Here we show that this filtering approach benefits the predictive quality of our previous CYP 2D6-aryloxypropanolamine LIE model. In addition, an analysis is performed of the gain in computational efficiency that can be obtained from monitoring simulations using the proposed filtering method and by prematurely terminating simulations accordingly.     

A Meta-Analysis of Retinoblastoma Copy Numbers Refines the List of Possible Driver Genes Involved in Tumor Progression

BackgroundWhile RB1 loss initiates retinoblastoma development, additional somatic copy number alterations (SCNAs) can drive tumor progression. Although SCNAs have been identified with good concordance between studies at a cytoband resolution, accurate identification of single genes for all recurrent SCNAs is still challenging. This study presents a comprehensive meta-analysis of genome-wide SCNAs integrated with gene expression profiling data, narrowing down the list of plausible retinoblastoma driver genes.MethodsWe performed SCNA profiling of 45 primary retinoblastoma samples and eight retinoblastoma cell lines by high-resolution microarrays. We combined our data with genomic, clinical and histopathological data of ten published genome-wide SCNA studies, which strongly enhanced the power of our analyses (N = 310).ResultsComprehensive recurrence analysis of SCNAs in all studies integrated with gene expression data allowed us to reduce candidate gene lists for 1q, 2p, 6p, 7q and 13q to a limited gene set. Besides the well-established driver genes RB1 (13q-loss) and MYCN (2p-gain) we identified CRB1 and NEK7 (1q-gain), SOX4 (6p-gain) and NUP205 (7q-gain) as novel retinoblastoma driver candidates. Depending on the sample subset and algorithms used, alternative candidates were identified including MIR181 (1q-gain) and DEK (6p gain). Remarkably, our study showed that copy number gains rarely exceeded change of one copy, even in pure tumor samples with 100% homozygosity at the RB1 locus (N = 34), which is indicative for intra-tumor heterogeneity. In addition, profound between-tumor variability was observed that was associated with age at diagnosis and differentiation grades.InterpretationSince focal alterations at commonly altered chromosome regions were rare except for 2p24.3 (MYCN), further functional validation of the oncogenic potential of the described candidate genes is now required. For further investigations, our study provides a refined and revised set of candidate retinoblastoma driver genes.     

Niche differentiation among clones in asexual grass thrips

Many asexual animal populations comprise a mixture of genetically different lineages, but to what degree this genetic diversity leads to ecological differences remains often unknown. Here, we test whether genetically different clonal lineages of Aptinothrips grass thrips differ in performance on a range of plants used as hosts in natural populations. We find a clear clone‐by‐plant species interactive effect on reproductive output, meaning that clonal lineages perform differently on different plant species and thus are characterized by disparate ecological niches. This implies that local clonal diversities can be driven and maintained by frequency‐dependent selection and that resource heterogeneity can generate diverse clone assemblies.     

Polybrominated Diphenyl Ethers (PBDEs) in Marine Fish and Dietary Exposure in Newfoundland

EcoHealth - Presence of PBDEs tested in 127 liver samples from Atlantic Cod (Gadus morhua) and Turbot (Scophthalmus Maximus) and 80 adult participants from two rural Newfoundland communities....     

Alternative mRNAs arising from trans-splicing code for mitochondrial and cytosolic variants of Echinococcus granulosus thioredoxin Glutathione reductase

Thioredoxin and glutathione systems are the major thiol-dependent redox systems in animal cells. They transfer via the reversible oxidoreduction of thiols the reducing equivalents of NADPH to numerous substrates and substrate reductases and constitute major defenses against oxidative stress. In this study, we cloned from the helminth parasite Echinococcus granulosus two trans-spliced mRNA variants that encode thioredoxin glutathione reductases (TGR). These variants code for mitochondrial and cytosolic selenocysteine-containing isoforms that possess identical glutaredoxin (Grx) and thioredoxin reductase (TR) domains and differ exclusively in their N termini. Western blot analysis of subcellular fractions with specific anti-TGR antibodies showed that TGR is present in both compartments. The biochemical characterization of the native purified TGR suggests that the Grx and TR domains of the enzyme can function either coupled or independently of each other, because the Grx domain can accept electrons from either TR domains or the glutathione system and the TR domains can transfer electrons to either the fused Grx domain or to E. granulosus thioredoxin.     

Index of Authors

Authors Index

Index of Authors     

Numerical bifurcation analysis of ecosystems in a spatially homogeneous environment

The dynamics of single populations up to ecosystems, are often described by one or a set of non-linear ordinary differential equations. In this paper we review the use of bifurcation theory to analyse these non-linear dynamical systems. Bifurcation analysis gives regimes in the parameter space with quantitatively different asymptotic dynamic behaviour of the system. In small-scale systems the underlying models for the populations and their interaction are simple Lotka-Volterra models or more elaborated models with more biological detail. The latter ones are more difficult to analyse, especially when the number of populations is large. Therefore for large-scale systems the Lotka-Volterra equations are still popular despite the limited realism. Various approaches are discussed in which the different time-scale of ecological and evolutionary biological processes are considered together.     

Quantitative steps in symbiogenesis and the evolution of homeostasis

The merging of two independent populations of heterotrophs and autotrophs into a single population of mixotrophs has occurred frequently in evolutionary history. It is an example of a wide class of related phenomena, known as symbiogenesis. The physiological basis is almost always (reciprocal) syntrophy, where each species uses the products of the other species. Symbiogenesis can repeat itself after specialization on particular assimilatory substrates. We discuss quantitative aspects and delineate eight steps from two free-living interacting populations to a single fully integrated endosymbiotic one. The whole process of gradual interlocking of the two populations could be mimicked by incremental changes of particular parameter values. The role of products gradually changes from an ecological to a physiological one. We found conditions where the free-living, epibiotic and endobiotic populations of symbionts can co-exist, as well as conditions where the endobiotic symbionts outcompete other symbionts. Our population dynamical analyses give new insights into the evolution of cellular homeostasis. We show how structural biomass with a constant chemical composition can evolve in a chemically varying environment if the parameters for the formation of products satisfy simple constraints. No additional regulation mechanisms are required for homeostasis within the context of the dynamic energy budget (DEB) theory for the uptake and use of substrates by organisms. The DEB model appears to be dosed under endosymbiosis. This means that when each free-living partner follows DEB rules for substrate uptake and use, and they become engaged in an endosymbiotic relationship, a gradual transition to a single fully integrated system is possible that again follows DEB rules for substrate uptake and use.