Getting into hot water:sick guppies frequent warmer thermal conditions

Ectotherms depend on the environmental temperature for thermoregulation and exploit thermal regimes that optimise physiological functioning. They may also frequent warmer conditions to up-regulate their immune response against parasite infection and/or impede parasite development. This adaptive response, known as ‘behavioural fever’, has been documented in various taxa including insects, reptiles and fish, but only in response to endoparasite infections. Here, a choice chamber experiment was used to investigate the thermal preferences of a tropical freshwater fish, the Trinidadian guppy (Poecilia reticulata), when infected with a common helminth ectoparasite Gyrodactylus turnbulli, in female-only and mixed-sex shoals. The temperature tolerance of G. turnbulli was also investigated by monitoring parasite population trajectories on guppies maintained at a continuous 18, 24 or 32 °C. Regardless of shoal composition, infected fish frequented the 32 °C choice chamber more often than when uninfected, significantly increasing their mean temperature preference. Parasites maintained continuously at 32 °C decreased to extinction within 3 days, whereas mean parasite abundance increased on hosts incubated at 18 and 24 °C. We show for the first time that gyrodactylid-infected fish have a preference for warmer waters and speculate that sick fish exploit the upper thermal tolerances of their parasites to self medicate

Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models

Background: Bacteria have evolved the ability to efficiently and resourcefully adapt to changing environments. A key means by which they optimize their use of available nutrients is through adjustments in gene expression with consequent changes in enzyme activity. We report a new method for drawing environmental inferences from gene expression data. Our method prioritizes a list of candidate carbon sources for their compatibility with a gene expression profile using the framework of flux balance analysis to model the organism’s metabolic network. Principal Findings: For each of six gene expression profiles for Escherichia coli grown under differing nutrient conditions, we applied our method to prioritize a set of eighteen different candidate carbon sources. Our method ranked the correct carbon source as one of the top three candidates for five of the six expression sets when used with a genome-scale model. The correct candidate ranked fifth in the remaining case. Additional analyses show that these rankings are robust with respect to biological and measurement variation, and depend on specific gene expression, rather than general expression level. The gene expression profiles are highly adaptive: simulated production of biomass averaged 94.84% of maximum when the in silico carbon source matched the in vitro source of the expression profile, and 65.97% when it did not. Conclusions: Inferences about a microorganism’s nutrient environment can be made by integrating gene expression data into a metabolic framework. This work demonstrates that reaction flux limits for a model can be computed which are realistic in the sense that they affect in silico growth in a manner analogous to that in which a microorganism’s alteration of gene expression is adaptive to its nutrient environment.National Institute of Allergy and Infectious Diseases (U.S.) (grant HHSN 2722008000059C)National Institute of Allergy and Infectious Diseases (U.S.) (grant HHSN 26620040000IC)Bill & Melinda Gates Foundation (grant 18651010-37352-A

Effect of Hydrogen Peroxide on Immersion Challenge of Rainbow Trout Fry with Flavobacterium psychrophilum

An experimental model for immersion challenge of rainbow trout fry (Oncorhynchus mykiss) with Flavobacterium psychrophilum, the causative agent of rainbow trout fry syndrome and bacterial cold water disease was established in the present study. Although injection-based infection models are reliable and produce high levels of mortality attempts to establish a reproducible immersion model have been less successful. Various concentrations of hydrogen peroxide (H₂O₂) were evaluated before being used as a pre-treatment stressor prior to immersion exposure to F. psychrophilum. H₂O₂ accelerated the onset of mortality and increased mortality approximately two-fold; from 9.1% to 19.2% and from 14.7% to 30.3% in two separate experiments. Clinical signs observed in the infected fish corresponded to symptoms characteristically seen during natural outbreaks. These findings indicate that pre-treatment with H₂O₂ can increase the level of mortality in rainbow trout fry after exposure to F. psychrophilum

Epistasis in a Model of Molecular Signal Transduction

Biological functions typically involve complex interacting molecular networks, with numerous feedback and regulation loops. How the properties of the system are affected when one, or several of its parts are modified is a question of fundamental interest, with numerous implications for the way we study and understand biological processes and treat diseases. This question can be rephrased in terms of relating genotypes to phenotypes: to what extent does the effect of a genetic variation at one locus depend on genetic variation at all other loci? Systematic quantitative measurements of epistasis – the deviation from additivity in the effect of alleles at different loci – on a given quantitative trait remain a major challenge. Here, we take a complementary approach of studying theoretically the effect of varying multiple parameters in a validated model of molecular signal transduction. To connect with the genotype/phenotype mapping we interpret parameters of the model as different loci with discrete choices of these parameters as alleles, which allows us to systematically examine the dependence of the signaling output – a quantitative trait – on the set of possible allelic combinations. We show quite generally that quantitative traits behave approximately additively (weak epistasis) when alleles correspond to small changes of parameters; epistasis appears as a result of large differences between alleles. When epistasis is relatively strong, it is concentrated in a sparse subset of loci and in low order (e.g. pair-wise) interactions. We find that focusing on interaction between loci that exhibit strong additive effects is an efficient way of identifying most of the epistasis. Our model study defines a theoretical framework for interpretation of experimental data and provides statistical predictions for the structure of genetic interaction expected for moderately complex biological circuits

Industrial Systems Biology of Saccharomyces cerevisiae Enables Novel Succinic Acid Cell Factory.

Saccharomyces cerevisiae is the most well characterized eukaryote, the preferred microbial cell factory for the largest industrial biotechnology product (bioethanol), and a robust commerically compatible scaffold to be exploitted for diverse chemical production. Succinic acid is a highly sought after added-value chemical for which there is no native pre-disposition for production and accmulation in S. cerevisiae. The genome-scale metabolic network reconstruction of S. cerevisiae enabled in silico gene deletion predictions using an evolutionary programming method to couple biomass and succinate production. Glycine and serine, both essential amino acids required for biomass formation, are formed from both glycolytic and TCA cycle intermediates. Succinate formation results from the isocitrate lyase catalyzed conversion of isocitrate, and from the alpha-keto-glutarate dehydrogenase catalyzed conversion of alpha-keto-glutarate. Succinate is subsequently depleted by the succinate dehydrogenase complex. The metabolic engineering strategy identified included deletion of the primary succinate consuming reaction, Sdh3p, and interruption of glycolysis derived serine by deletion of 3-phosphoglycerate dehydrogenase, Ser3p/Ser33p. Pursuing these targets, a multi-gene deletion strain was constructed, and directed evolution with selection used to identify a succinate producing mutant. Physiological characterization coupled with integrated data analysis of transcriptome data in the metabolically engineered strain were used to identify 2nd-round metabolic engineering targets. The resulting strain represents a 30-fold improvement in succinate titer, and a 43-fold improvement in succinate yield on biomass, with only a 2.8-fold decrease in the specific growth rate compared to the reference strain. Intuitive genetic targets for either over-expression or interruption of succinate producing or consuming pathways, respectively, do not lead to increased succinate. Rather, we demonstrate how systems biology tools coupled with directed evolution and selection allows non-intuitive, rapid and substantial re-direction of carbon fluxes in S. cerevisiae, and hence show proof of concept that this is a potentially attractive cell factory for over-producing different platform chemicals

On the Accessibility of Adaptive Phenotypes of a Bacterial Metabolic Network

The mechanisms by which adaptive phenotypes spread within an evolving population after their emergence are understood fairly well. Much less is known about the factors that influence the evolutionary accessibility of such phenotypes, a pre-requisite for their emergence in a population. Here, we investigate the influence of environmental quality on the accessibility of adaptive phenotypes of Escherichia coli's central metabolic network. We used an established flux-balance model of metabolism as the basis for a genotype-phenotype map (GPM). We quantified the effects of seven qualitatively different environments (corresponding to both carbohydrate and gluconeogenic metabolic substrates) on the structure of this GPM. We found that the GPM has a more rugged structure in qualitatively poorer environments, suggesting that adaptive phenotypes could be intrinsically less accessible in such environments. Nevertheless, on average ∼74% of the genotype can be altered by neutral drift, in the environment where the GPM is most rugged; this could allow evolving populations to circumvent such ruggedness. Furthermore, we found that the normalized mutual information (NMI) of genotype differences relative to phenotype differences, which measures the GPM's capacity to transmit information about phenotype differences, is positively correlated with (simulation-based) estimates of the accessibility of adaptive phenotypes in different environments. These results are consistent with the predictions of a simple analytic theory that makes explicit the relationship between the NMI and the speed of adaptation. The results suggest an intuitive information-theoretic principle for evolutionary adaptation; adaptation could be faster in environments where the GPM has a greater capacity to transmit information about phenotype differences. More generally, our results provide insight into fundamental environment-specific differences in the accessibility of adaptive phenotypes, and they suggest opportunities for research at the interface between information theory and evolutionary biology

Spleen Vagal Denervation Inhibits the Production of Antibodies to Circulating Antigens

BACKGROUND: Recently the vagal output of the central nervous system has been shown to suppress the innate immune defense to pathogens. Here we investigated by anatomical and physiological techniques the communication of the brain with the spleen and provided evidence that the brain has the capacity to stimulate the production of antigen specific antibodies by its parasympathetic autonomic output. METHODOLOGY/PRINCIPAL FINDINGS: This conclusion was reached by successively demonstrating that: 1. The spleen receives not only sympathetic input but also parasympathetic input. 2. Intravenous trinitrophenyl-ovalbumin (TNP-OVA) does not activate the brain and does not induce an immune response. 3. Intravenous TNP-OVA with an inducer of inflammation; lipopolysaccharide (LPS), activates the brain and induces TNP-specific IgM. 4. LPS activated neurons are in the same areas of the brain as those that provide parasympathetic autonomic information to the spleen, suggesting a feed back circuit between brain and immune system. Consequently we investigated the interaction of the brain with the spleen and observed that specific parasympathetic denervation but not sympathetic denervation of the spleen eliminates the LPS-induced antibody response to TNP-OVA. CONCLUSIONS/SIGNIFICANCE: These findings not only show that the brain can stimulate antibody production by its autonomic output, it also suggests that the power of LPS as adjuvant to stimulate antibody production may also depend on its capacity to activate the brain. The role of the autonomic nervous system in the stimulation of the adaptive immune response may explain why mood and sleep have an influence on antibody production

Contrasted Effects of Diversity and Immigration on Ecological Insurance in Marine Bacterioplankton Communities

The ecological insurance hypothesis predicts a positive effect of species richness on ecosystem functioning in a variable environment. This effect stems from temporal and spatial complementarity among species within metacommunities coupled with optimal levels of dispersal. Despite its importance in the context of global change by human activities, empirical evidence for ecological insurance remains scarce and controversial. Here we use natural aquatic bacterial communities to explore some of the predictions of the spatial and temporal aspects of the ecological insurance hypothesis. Addressing ecological insurance with bacterioplankton is of strong relevance given their central role in fundamental ecosystem processes. Our experimental set up consisted of water and bacterioplankton communities from two contrasting coastal lagoons. In order to mimic environmental fluctuations, the bacterioplankton community from one lagoon was successively transferred between tanks containing water from each of the two lagoons. We manipulated initial bacterial diversity for experimental communities and immigration during the experiment. We found that the abundance and production of bacterioplankton communities was higher and more stable (lower temporal variance) for treatments with high initial bacterial diversity. Immigration was only marginally beneficial to bacterial communities, probably because microbial communities operate at different time scales compared to the frequency of perturbation selected in this study, and of their intrinsic high physiologic plasticity. Such local “physiological insurance” may have a strong significance for the maintenance of bacterial abundance and production in the face of environmental perturbations

Transcriptome Kinetics of Circulating Neutrophils during Human Experimental Endotoxemia

Polymorphonuclear cells (neutrophils) play an important role in the systemic inflammatory response syndrome and the development of sepsis. These cells are essential for the defense against microorganisms, but may also cause tissue damage. Therefore, neutrophil numbers and activity are considered to be tightly regulated. Previous studies have investigated gene transcription during experimental endotoxemia in whole blood and peripheral blood mononuclear cells. However, the gene transcription response of the circulating pool of neutrophils to systemic inflammatory stimulation in vivo is currently unclear. We examined neutrophil gene transcription kinetics in healthy human subjects (n = 4) administered a single dose of endotoxin (LPS, 2 ng/kg iv). In addition, freshly isolated neutrophils were stimulated ex vivo with LPS, TNFα, G-CSF and GM-CSF to identify stimulus-specific gene transcription responses. Whole transcriptome microarray analysis of circulating neutrophils at 2, 4 and 6 hours after LPS infusion revealed activation of inflammatory networks which are involved in signaling of TNFα and IL-1α and IL-1β. The transcriptome profile of inflammatory activated neutrophils in vivo reflects extended survival and regulation of inflammatory responses. These changes in neutrophil transcriptome suggest a combination of early activation of circulating neutrophils by TNFα and G-CSF and a mobilization of young neutrophils from the bone marrow

Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here