Photorespiration: metabolic pathways and their role in stress protection

Photorespiration results from the oxygenase reaction catalysed by ribulose-1,5-bisphosphate carboxylase/ oxygenase. In this reaction glycollate-2-phosphate is produced and subsequently metabolized in the photorespiratory pathway to form the Calvin cycle intermediate glycerate-3-phosphate. During this metabolic process, CO2 and NH3 are produced and ATP and reducing equivalents are consumed, thus making photorespiration a wasteful process. However, precisely because of this ine¤ciency, photorespiration could serve as an energy sink preventing the overreduction of the photosynthetic electron transport chain and photoinhibition, especially under stress conditions that lead to reduced rates of photosynthetic CO2 assimilation. Furthermore, photorespiration provides metabolites for other metabolic processes, e.g. glycine for the synthesis of glutathione, which is also involved in stress protection. In this review, we describe the use of photorespiratory mutants to study the control and regulation of photorespiratory pathways. In addition, we discuss the possible role of photorespiration under stress conditions, such as drought, high salt concentrations and high light intensities encountered by alpine plants

Solving integral equations in η→3π\eta\to 3\pi

A dispersive analysis of $\eta\to 3\pi$ decays has been performed in the past by many authors. The numerical analysis of the pertinent integral equations is hampered by two technical difficulties: i) The angular averages of the amplitudes need to be performed along a complicated path in the complex plane. ii) The averaged amplitudes develop singularities along the path of integration in the dispersive representation of the full amplitudes. It is a delicate affair to handle these singularities properly, and independent checks of the obtained solutions are demanding and time consuming. In the present article, we propose a solution method that avoids these difficulties. It is based on a simple deformation of the path of integration in the dispersive representation (not in the angular average). Numerical solutions are then obtained rather straightforwardly. We expect that the method also works for $\omega\to 3\pi$.Comment: 11 pages, 10 Figures. Version accepted for publication in EPJC. The ancillary files contain an updated set of fundamental solutions. The numerical differences to the former set are tiny, see the READMEv2 file for detail

The causes of epistasis

[EN] Since Bateson's discovery that genes can suppress the phenotypic effects of other genes, gene interactions-called epistasis-have been the topic of a vast research effort. Systems and developmental biologists study epistasis to understand the genotype-phenotype map, whereas evolutionary biologists recognize the fundamental importance of epistasis for evolution. Depending on its form, epistasis may lead to divergence and speciation, provide evolutionary benefits to sex and affect the robustness and evolvability of organisms. That epistasis can itself be shaped by evolution has only recently been realized. Here, we review the empirical pattern of epistasis, and some of the factors that may affect the form and extent of epistasis. Based on their divergent consequences, we distinguish between interactions with or without mean effect, and those affecting the magnitude of fitness effects or their sign. Empirical work has begun to quantify epistasis in multiple dimensions in the context of metabolic and fitness landscape models. We discuss possible proximate causes (such as protein function and metabolic networks) and ultimate factors (including mutation, recombination, and the importance of natural selection and genetic drift). We conclude that, in general, pleiotropy is an important prerequisite for epistasis, and that epistasis may evolve as an adaptive or intrinsic consequence of changes in genetic robustness and evolvability.We thank Fons Debets, Ryszard Korona, Alexey Kondrashov, Joachim Krug, Sijmen Schoustra and an anonymous reviewer for constructive comments, and funds from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement 225167 (eFLUX), a visitor grant from Research School Production Ecology and Resource Conservation for S.F.E., and NSF grant DEB-0844355 for T.F.C.De Visser, JAGM.; Cooper, TF.; Elena Fito, SF. (2011). The causes of epistasis. Proceedings of the Royal Society B: Biological Sciences. 278(1725):3617-3624. https://doi.org/10.1098/rspb.2011.1537S361736242781725Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S., … Mostafavi, S. (2010). The Genetic Landscape of a Cell. Science, 327(5964), 425-431. doi:10.1126/science.1180823Moore, J. H., & Williams, S. M. (2005). Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. BioEssays, 27(6), 637-646. doi:10.1002/bies.20236Phillips, P. C. (2008). Epistasis — the essential role of gene interactions in the structure and evolution of genetic systems. Nature Reviews Genetics, 9(11), 855-867. doi:10.1038/nrg2452Azevedo, R. B. R., Lohaus, R., Srinivasan, S., Dang, K. K., & Burch, C. L. (2006). Sexual reproduction selects for robustness and negative epistasis in artificial gene networks. Nature, 440(7080), 87-90. doi:10.1038/nature04488Desai, M. 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Metabolic Control Analysis in a Cellular Model of Elevated MAO-B: Relevance to Parkinson’s Disease

We previously demonstrated that spare respiratory capacity of the TCA cycle enzyme alpha-ketoglutarate dehydrogenase (KGDH) was completely abolished upon increasing levels of MAO-B activity in a dopaminergic cell model system (Kumar et al., J Biol Chem 278:46432–46439, 2003). MAO-B mediated increases in H2O2 also appeared to result in direct oxidative inhibition of both mitochondrial complex I and aconitase. In order to elucidate the contribution that each of these components exerts over metabolic respiratory control as well as the impact of MAO-B elevation on their spare respiratory capacities, we performed metabolic respiratory control analysis. In addition to KGDH, we assessed the activities and substrate-mediated respiration of complex I, pyruvate dehydrogenase (PDH), succinate dehydrogenase (SDH), and mitochondrial aconitase in the absence and presence of complex-specific inhibitors in specific and mixed substrate conditions in mitochondria from our MAO-B elevated cells versus controls. Data from this study indicates that Complex I and KGDH are the most sensitive to inhibition by MAO-B mediated H2O2 generation, and could be instrumental in determining the fate of mitochondrial metabolism in this cellular PD model system

Modelling dominance in a flexible intercross analysis

<p>Abstract</p> <p>Background</p> <p>The aim of this paper is to develop a flexible model for analysis of quantitative trait loci (QTL) in outbred line crosses, which includes both additive and dominance effects. Our flexible intercross analysis (FIA) model accounts for QTL that are not fixed within founder lines and is based on the variance component framework. Genome scans with FIA are performed using a score statistic, which does not require variance component estimation.</p> <p>Results</p> <p>Simulations of a pedigree with 800 <it>F</it>₂individuals showed that the power of FIA including both additive and dominance effects was almost 50% for a QTL with equal allele frequencies in both lines with complete dominance and a moderate effect, whereas the power of a traditional regression model was equal to the chosen significance value of 5%. The power of FIA without dominance effects included in the model was close to those obtained for FIA with dominance for all simulated cases except for QTL with overdominant effects. A genome-wide linkage analysis of experimental data from an <it>F</it>₂intercross between Red Jungle Fowl and White Leghorn was performed with both additive and dominance effects included in FIA. The score values for chicken body weight at 200 days of age were similar to those obtained in FIA analysis without dominance.</p> <p>Conclusion</p> <p>We have extended FIA to include QTL dominance effects. The power of FIA was superior, or similar, to standard regression methods for QTL effects with dominance. The difference in power for FIA with or without dominance is expected to be small as long as the QTL effects are not overdominant. We suggest that FIA with only additive effects should be the standard model to be used, especially since it is more computationally efficient.</p

Final State Interactions and Khuri-Treiman Equations in η→3π\eta\to 3\pi decays

Using extended Khuri-Treiman equations, we evaluate the final state interactions due to two-pion rescatterings to the decays $\eta\to \pi^0 \pi^+ \pi^-$ and $\eta\to \pi^0 \pi^0 \pi^0$. As subtraction to the dispersion relation we take the one-loop chiral perturbation theory result of Gasser and Leutwyler. The calculated corrections are moderate and amount to about $14\%$ in the amplitude at the center of the decay region. A careful analysis of the errors inherent to our approach is given. As a consequence, the experimental rate of the decay can only be reproduced if the double quark mass ratio $Q^{-2}\equiv\frac{m_{d}-m_{u}} {m_{s}-{\hat m}} \cdot\frac{m_{d}+m_{u}}{m_{s}+{\hat m}}$ is increased from the usual value of $1/(24.1)^2$ to $1/(22.4 \pm 0.9)^2$. We have also calculated the ratio of the rates of the two decays and various Dalitz Plot parameters. In particular, the linear slope $a$ in the charged decay is different from the one-loop value and agrees better with experiment.Comment: 55 pages, 4 Postscript figure

Ensemble Modeling for Aromatic Production in Escherichia coli

Ensemble Modeling (EM) is a recently developed method for metabolic modeling, particularly for utilizing the effect of enzyme tuning data on the production of a specific compound to refine the model. This approach is used here to investigate the production of aromatic products in Escherichia coli. Instead of using dynamic metabolite data to fit a model, the EM approach uses phenotypic data (effects of enzyme overexpression or knockouts on the steady state production rate) to screen possible models. These data are routinely generated during strain design. An ensemble of models is constructed that all reach the same steady state and are based on the same mechanistic framework at the elementary reaction level. The behavior of the models spans the kinetics allowable by thermodynamics. Then by using existing data from the literature for the overexpression of genes coding for transketolase (Tkt), transaldolase (Tal), and phosphoenolpyruvate synthase (Pps) to screen the ensemble, we arrive at a set of models that properly describes the known enzyme overexpression phenotypes. This subset of models becomes more predictive as additional data are used to refine the models. The final ensemble of models demonstrates the characteristic of the cell that Tkt is the first rate controlling step, and correctly predicts that only after Tkt is overexpressed does an increase in Pps increase the production rate of aromatics. This work demonstrates that EM is able to capture the result of enzyme overexpression on aromatic producing bacteria by successfully utilizing routinely generated enzyme tuning data to guide model learning

Decoupling Environment-Dependent and Independent Genetic Robustness across Bacterial Species

The evolutionary origins of genetic robustness are still under debate: it may arise as a consequence of requirements imposed by varying environmental conditions, due to intrinsic factors such as metabolic requirements, or directly due to an adaptive selection in favor of genes that allow a species to endure genetic perturbations. Stratifying the individual effects of each origin requires one to study the pertaining evolutionary forces across many species under diverse conditions. Here we conduct the first large-scale computational study charting the level of robustness of metabolic networks of hundreds of bacterial species across many simulated growth environments. We provide evidence that variations among species in their level of robustness reflect ecological adaptations. We decouple metabolic robustness into two components and quantify the extents of each: the first, environmental-dependent, is responsible for at least 20% of the non-essential reactions and its extent is associated with the species' lifestyle (specialized/generalist); the second, environmental-independent, is associated (correlation = ∼0.6) with the intrinsic metabolic capacities of a species—higher robustness is observed in fast growers or in organisms with an extensive production of secondary metabolites. Finally, we identify reactions that are uniquely susceptible to perturbations in human pathogens, potentially serving as novel drug-targets

Gene–Environment Interactions at Nucleotide Resolution

Interactions among genes and the environment are a common source of phenotypic variation. To characterize the interplay between genetics and the environment at single nucleotide resolution, we quantified the genetic and environmental interactions of four quantitative trait nucleotides (QTN) that govern yeast sporulation efficiency. We first constructed a panel of strains that together carry all 32 possible combinations of the 4 QTN genotypes in 2 distinct genetic backgrounds. We then measured the sporulation efficiencies of these 32 strains across 8 controlled environments. This dataset shows that variation in sporulation efficiency is shaped largely by genetic and environmental interactions. We find clear examples of QTN:environment, QTN: background, and environment:background interactions. However, we find no QTN:QTN interactions that occur consistently across the entire dataset. Instead, interactions between QTN only occur under specific combinations of environment and genetic background. Thus, what might appear to be a QTN:QTN interaction in one background and environment becomes a more complex QTN:QTN:environment:background interaction when we consider the entire dataset as a whole. As a result, the phenotypic impact of a set of QTN alleles cannot be predicted from genotype alone. Our results instead demonstrate that the effects of QTN and their interactions are inextricably linked both to genetic background and to environmental variation