Differences in the mechanism of inoculation between a semi-persistent and a non-persistent aphid-transmitted plant virus

Inoculation of the semi-persistent cauliflower mosaic virus (CaMV, genus Caulimovirus) is associated with successive brief (5-10 s) intracellular stylet punctures (pd) when aphids probe in epidermal and mesophyll cells. In contrast to non-persistent viruses, there is no evidence for which of the pd subphases (II-1, II-2 and II-3) is involved in the inoculation of CaMV. Experiments were conducted using the electrical penetration graph (EPG) technique to investigate which particular subphases of the pd are associated with the inoculation of CaMV to turnip by its aphid vector Brevicoryne brassicae. In addition, the same aphid species/test plant combination was used to compare the role of the pd subphases in the inoculation of the non-persistent turnip mosaic virus (TuMV, genus Potyvirus). Inoculation of TuMV was found to be related to subphase II-1, confirming earlier results, but CaMV inoculation appeared to be related exclusively to subphase II-2 instead. The mechanism of CaMV inoculation and the possible nature of subphase II-2 are discussed in the scope of our findings

Impact of the solvent capacity constraint on E. coli metabolism

<p>Abstract</p> <p>Background</p> <p>Obtaining quantitative predictions for cellular metabolic activities requires the identification and modeling of the physicochemical constraints that are relevant at physiological growth conditions. Molecular crowding in a cell's cytoplasm is one such potential constraint, as it limits the solvent capacity available to metabolic enzymes.</p> <p>Results</p> <p>Using a recently introduced flux balance modeling framework (FBAwMC) here we demonstrate that this constraint determines a metabolic switch in <it>E. coli </it>cells when they are shifted from low to high growth rates. The switch is characterized by a change in effective optimization strategy, the excretion of acetate at high growth rates, and a global reorganization of <it>E. coli </it>metabolic fluxes, the latter being partially confirmed by flux measurements of central metabolic reactions.</p> <p>Conclusion</p> <p>These results implicate the solvent capacity as an important physiological constraint acting on <it>E. coli </it>cells operating at high metabolic rates and for the activation of a metabolic switch when they are shifted from low to high growth rates. The relevance of this constraint in the context of both the aerobic ethanol excretion seen in fast growing yeast cells (Crabtree effect) and the aerobic glycolysis observed in rapidly dividing cancer cells (Warburg effect) should be addressed in the future.</p

Optimisation of rubberised concrete with high rubber content: an experimental investigation

This article investigates experimentally the behaviour of rubberised concrete (RuC) with high rubber content so as to fully utilise the mechanical properties of vulcanised rubber. The fresh properties and short-term uniaxial compressive strength of 40 rubberised concrete mixes were assessed. The parameters examined included the volume (0–100%) and type of mineral aggregate replacement (fine or coarse), water or admixture contents, type of binder, rubber particle properties, and rubber surface pre-treatments. Microstructural analysis using a Scanning Electron Microscope (SEM) was used to investigate bond between rubber and concrete at the Interface Transition Zone (ITZ). This initial study led to the development of an “optimum” RuC mix, comprising mix parameters leading to the highest workability and strength at all rubber contents. Compared to a non-optimised concrete with 100% replacement of fine aggregates with rubber, the compressive strength of concrete with optimised binder material and moderate water/binder ratio was enhanced by up to 160% and the workability was improved significantly. The optimisation proposed in this study will lead to workable high rubber content RuC suitable for sustainable high-value applications

A Developmental Systems Perspective on Epistasis: Computational Exploration of Mutational Interactions in Model Developmental Regulatory Networks

The way in which the information contained in genotypes is translated into complex phenotypic traits (i.e. embryonic expression patterns) depends on its decoding by a multilayered hierarchy of biomolecular systems (regulatory networks). Each layer of this hierarchy displays its own regulatory schemes (i.e. operational rules such as +/− feedback) and associated control parameters, resulting in characteristic variational constraints. This process can be conceptualized as a mapping issue, and in the context of highly-dimensional genotype-phenotype mappings (GPMs) epistatic events have been shown to be ubiquitous, manifested in non-linear correspondences between changes in the genotype and their phenotypic effects. In this study I concentrate on epistatic phenomena pervading levels of biological organization above the genetic material, more specifically the realm of molecular networks. At this level, systems approaches to studying GPMs are specially suitable to shed light on the mechanistic basis of epistatic phenomena. To this aim, I constructed and analyzed ensembles of highly-modular (fully interconnected) networks with distinctive topologies, each displaying dynamic behaviors that were categorized as either arbitrary or functional according to early patterning processes in the Drosophila embryo. Spatio-temporal expression trajectories in virtual syncytial embryos were simulated via reaction-diffusion models. My in silico mutational experiments show that: 1) the average fitness decay tendency to successively accumulated mutations in ensembles of functional networks indicates the prevalence of positive epistasis, whereas in ensembles of arbitrary networks negative epistasis is the dominant tendency; and 2) the evaluation of epistatic coefficients of diverse interaction orders indicates that, both positive and negative epistasis are more prevalent in functional networks than in arbitrary ones. Overall, I conclude that the phenotypic and fitness effects of multiple perturbations are strongly conditioned by both the regulatory architecture (i.e. pattern of coupled feedback structures) and the dynamic nature of the spatio-temporal expression trajectories displayed by the simulated networks

OptCom: A Multi-Level Optimization Framework for the Metabolic Modeling and Analysis of Microbial Communities

Microorganisms rarely live isolated in their natural environments but rather function in consolidated and socializing communities. Despite the growing availability of high-throughput sequencing and metagenomic data, we still know very little about the metabolic contributions of individual microbial players within an ecological niche and the extent and directionality of interactions among them. This calls for development of efficient modeling frameworks to shed light on less understood aspects of metabolism in microbial communities. Here, we introduce OptCom, a comprehensive flux balance analysis framework for microbial communities, which relies on a multi-level and multi-objective optimization formulation to properly describe trade-offs between individual vs. community level fitness criteria. In contrast to earlier approaches that rely on a single objective function, here, we consider species-level fitness criteria for the inner problems while relying on community-level objective maximization for the outer problem. OptCom is general enough to capture any type of interactions (positive, negative or combinations thereof) and is capable of accommodating any number of microbial species (or guilds) involved. We applied OptCom to quantify the syntrophic association in a well-characterized two-species microbial system, assess the level of sub-optimal growth in phototrophic microbial mats, and elucidate the extent and direction of inter-species metabolite and electron transfer in a model microbial community. We also used OptCom to examine addition of a new member to an existing community. Our study demonstrates the importance of trade-offs between species- and community-level fitness driving forces and lays the foundation for metabolic-driven analysis of various types of interactions in multi-species microbial systems using genome-scale metabolic models

Genomic Analysis of QTLs and Genes Altering Natural Variation in Stochastic Noise

Quantitative genetic analysis has long been used to study how natural variation of genotype can influence an organism's phenotype. While most studies have focused on genetic determinants of phenotypic average, it is rapidly becoming understood that stochastic noise is genetically determined. However, it is not known how many traits display genetic control of stochastic noise nor how broadly these stochastic loci are distributed within the genome. Understanding these questions is critical to our understanding of quantitative traits and how they relate to the underlying causal loci, especially since stochastic noise may be directly influenced by underlying changes in the wiring of regulatory networks. We identified QTLs controlling natural variation in stochastic noise of glucosinolates, plant defense metabolites, as well as QTLs for stochastic noise of related transcripts. These loci included stochastic noise QTLs unique for either transcript or metabolite variation. Validation of these loci showed that genetic polymorphism within the regulatory network alters stochastic noise independent of effects on corresponding average levels. We examined this phenomenon more globally, using transcriptomic datasets, and found that the Arabidopsis transcriptome exhibits significant, heritable differences in stochastic noise. Further analysis allowed us to identify QTLs that control genomic stochastic noise. Some genomic QTL were in common with those altering average transcript abundance, while others were unique to stochastic noise. Using a single isogenic population, we confirmed that natural variation at ELF3 alters stochastic noise in the circadian clock and metabolism. Since polymorphisms controlling stochastic noise in genomic phenotypes exist within wild germplasm for naturally selected phenotypes, this suggests that analysis of Arabidopsis evolution should account for genetic control of stochastic variance and average phenotypes. It remains to be determined if natural genetic variation controlling stochasticity is equally distributed across the genomes of other multi-cellular eukaryotes

Duox, Flotillin-2, and Src42A Are Required to Activate or Delimit the Spread of the Transcriptional Response to Epidermal Wounds in Drosophila

The epidermis is the largest organ of the body for most animals, and the first line of defense against invading pathogens. A breach in the epidermal cell layer triggers a variety of localized responses that in favorable circumstances result in the repair of the wound. Many cellular and genetic responses must be limited to epidermal cells that are close to wounds, but how this is regulated is still poorly understood. The order and hierarchy of epidermal wound signaling factors are also still obscure. The Drosophila embryonic epidermis provides an excellent system to study genes that regulate wound healing processes. We have developed a variety of fluorescent reporters that provide a visible readout of wound-dependent transcriptional activation near epidermal wound sites. A large screen for mutants that alter the activity of these wound reporters has identified seven new genes required to activate or delimit wound-induced transcriptional responses to a narrow zone of cells surrounding wound sites. Among the genes required to delimit the spread of wound responses are Drosophila Flotillin-2 and Src42A, both of which are transcriptionally activated around wound sites. Flotillin-2 and constitutively active Src42A are also sufficient, when overexpressed at high levels, to inhibit wound-induced transcription in epidermal cells. One gene required to activate epidermal wound reporters encodes Dual oxidase, an enzyme that produces hydrogen peroxide. We also find that four biochemical treatments (a serine protease, a Src kinase inhibitor, methyl-ß-cyclodextrin, and hydrogen peroxide) are sufficient to globally activate epidermal wound response genes in Drosophila embryos. We explore the epistatic relationships among the factors that induce or delimit the spread of epidermal wound signals. Our results define new genetic functions that interact to instruct only a limited number of cells around puncture wounds to mount a transcriptional response, mediating local repair and regeneration

A framework for evolutionary systems biology

<p>Abstract</p> <p>Background</p> <p>Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.</p> <p>Results</p> <p>Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions <it>in silico</it>. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.</p> <p>Conclusion</p> <p>EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.</p