Observation volumes and {gamma}-factors in two-photon fluorescence fluctuation spectroscopy

Fluorescence fluctuation spectroscopy has become an important measurement tool for investigating molecular dynamics, molecular interactions, and chemical kinetics in biological systems. Although the basic theory of fluctuation spectroscopy is well established, it is not widely recognized that saturation of the fluorescence excitation can dramatically alter the size and profile of the fluorescence observation volume from which fluorescence fluctuations are measured, even at relatively modest excitation levels. A precise model for these changes is needed for accurate analysis and interpretation of fluctuation spectroscopy data. We here introduce a combined analytical and computational approach to characterize the observation volume under saturating conditions and demonstrate how the variation in the volume is important in two-photon fluorescence correlation spectroscopy. We introduce a simple approach for analysis of fluorescence correlation spectroscopy data that can fully account for the effects of saturation, and demonstrate its success for characterizing the observed changes in both the amplitude and relaxation timescale of measured correlation curves. We also discuss how a quantitative model for the observed phenomena may be of broader importance in fluorescence fluctuation spectroscopy.     

Hydrophobicity and charge shape cellular metabolite concentrations

What governs the concentrations of metabolites within living cells? Beyond specific metabolic and enzymatic considerations, are there global trends that affect their values? We hypothesize that the physico-chemical properties of metabolites considerably affect their in-vivo concentrations. The recently achieved experimental capability to measure the concentrations of many metabolites simultaneously has made the testing of this hypothesis possible. Here, we analyze such recently available data sets of metabolite concentrations within E. coli, S. cerevisiae, B. subtilis and human. Overall, these data sets encompass more than twenty conditions, each containing dozens (28-108) of simultaneously measured metabolites. We test for correlations with various physico-chemical properties and find that the number of charged atoms, non-polar surface area, lipophilicity and solubility consistently correlate with concentration. In most data sets, a change in one of these properties elicits a ∼100 fold increase in metabolite concentrations. We find that the non-polar surface area and number of charged atoms account for almost half of the variation in concentrations in the most reliable and comprehensive data set. Analyzing specific groups of metabolites, such as amino-acids or phosphorylated nucleotides, reveals even a higher dependence of concentration on hydrophobicity. We suggest that these findings can be explained by evolutionary constraints imposed on metabolite concentrations and discuss possible selective pressures that can account for them. These include the reduction of solute leakage through the lipid membrane, avoidance of deleterious aggregates and reduction of non-specific hydrophobic binding. By highlighting the global constraints imposed on metabolic pathways, future research could shed light onto aspects of biochemical evolution and the chemical constraints that bound metabolic engineering efforts.     

Evolvability of cell specification mechanisms

The architecture of gene action during development is relevant to phenotypic evolution as it links genotype to morphological phenotype. Analysis of development at the level of cell fate specification mechanisms illuminates some of the properties of developmental evolution. In this article, we first review examples of evolutionary change in mechanisms of cell fate specification, with an emphasis on evolution in the dependence on inductive signaling and on evolution of the mechanisms that result in spatial asymmetries. We then focus on properties of development that bias possible phenotypic change and present how the distribution of phenotypes that are available by mutational change of the starting genotype can be experimentally tested by systematic mutagenesis. We finally discuss ways in which selection pressures on phenotypes can be inferred from a comparison of the phenotypic spectrum found on mutation with that found in the wild.     

Preference for high concentrations of plant pyrrolizidine alkaloids in the specialist arctiid moth Utetheisa ornatrix depends on previous experience

Secondary metabolites are one the most pervasive defensive mechanisms in plants. Many specialist herbivores have evolved adaptations to overcome these defensive compounds. Some herbivores can even take advantage of these compounds by sequestering them for protection and/or mate attraction. One of the most studied specialist insects that sequesters secondary metabolites is the arctiid moth Utetheisa ornatrix. This species sequesters pyrrolizidine alkaloids (PAs) from its host plant, the legume Crotalaria spp. The sequestered PAs are used as a predator repellent and as a mating pheromone. We used this species to test larval preference for different concentrations of PAs. We purified PAs from plant material and added them at different concentrations to an artificial diet. Larvae of U. ornatrix previously feeding on low and high PA concentration artificial diets were allowed to choose between two new artificial diets with different PA concentrations. The amount of PAs sequestered and larval preference were dependent on their previous exposure to low or high PA content in the diet. Larvae that were pretreated with a low PA diet significantly consumed more diet with the high PA concentration, while larvae that were pretreated with a high PA diet showed no discrimination between future feeding of different PA concentration diets. We discuss our results using mechanistic and evolutionary approaches. Finally, we discuss how these results have important implications on the evolution of plant herbivore interactions and how specialist herbivores may decrease the levels of chemical defenses on plant populations.     

Structure and evolution of Streptomyces interaction networks in soil and in silico

Soil grains harbor an astonishing diversity of Streptomyces strains producing diverse secondary metabolites. However, it is not understood how this genotypic and chemical diversity is ecologically maintained. While secondary metabolites are known to mediate signaling and warfare among strains, no systematic measurement of the resulting interaction networks has been available. We developed a high-throughput platform to measure all pairwise interactions among 64 Streptomyces strains isolated from several individual grains of soil. We acquired more than 10,000 time-lapse movies of colony development of each isolate on media containing compounds produced by each of the other isolates. We observed a rich set of such sender-receiver interactions, including inhibition and promotion of growth and aerial mycelium formation. The probability that two random isolates interact is balanced; it is neither close to zero nor one. The interactions are not random: the distribution of the number of interactions per sender is bimodal and there is enrichment for reciprocity--if strain A inhibits or promotes B, it is likely that B also inhibits or promotes A. Such reciprocity is further enriched in strains derived from the same soil grain, suggesting that it may be a property of coexisting communities. Interactions appear to evolve rapidly: isolates with identical 16S rRNA sequences can have very different interaction patterns. A simple eco-evolutionary model of bacteria interacting through antibiotic production shows how fast evolution of production and resistance can lead to the observed statistical properties of the network. In the model, communities are evolutionarily unstable--they are constantly being invaded by strains with new sets of interactions. This combination of experimental and theoretical observations suggests that diverse Streptomyces communities do not represent a stable ecological state but an intrinsically dynamic eco-evolutionary phenomenon.     

Testing procedures for non-stationarity and non-linearity in physiological signals

Most of the physiological signals (EEG, ECG, blood flow, human gait, etc.) characterize by complex dynamics including both non-stationarities and non-linearities. These time series resemble red noise with long-range correlation and 1/(f beta) power spectrum. A question arises as to how to distinguish the characteristics of the process underlying the signal dynamics from the properties of the observed time series. The classical methods to determine possible non-linear (chaotic) dynamics (e.g. correlation dimension) often fail in such signals because of relatively short data records containing stochastic components and non-stationarities. We report an application of several approaches, aimed at (1) determining of the non-stationarities in the signals and (2) testing whether non-linear dynamics exists. Assessment of the intrinsic correlation properties of the dynamic process and distinguishing the same from external trends was performed using singular spectra and detrended fluctuation analysis. The existence of non-linear dynamics was tested by correlation dimension (modified algorithm of re-embedding) and by correlation integrals of real and surrogate data. The correlation integrals of real signal and surrogate data sets were statistically compared using Kolmogorov-Smirnov (K-S) test. The procedures were tested on EEG and laser-Doppler (LD) blood flow. Our suggestion is that no one approach taken alone is the best for our aims. Instead, a battery of methods should be used.     

Fluorescence correlation spectroscopy and quantitative cell biology

Fluorescence correlation spectroscopy (FCS) analyzes fluctuations in fluorescence within a small observation volume. Autocorrelation analysis of FCS fluctuation data can be used to measure concentrations, diffusion properties, and kinetic constants for individual fluorescent molecules. Photon count histogram analysis of fluorescence fluctuation data can be used to study oligomerization of individual fluorescent molecules. If the FCS observation volume is positioned inside a living cell, these parameters can be measured in vivo. FCS can provide the requisite quantitative data for analysis of molecular interaction networks underlying complex cell biological processes.     

What have two decades of laboratory life-history evolution studies onDrosophila melanogaster taught us?

A series of laboratory selection experiments onDrosophila melanogaster over the past two decades has provided insights into the specifics of life-history tradeoffs in the species and greatly refined our understanding of how ecology and genetics interact in life-history evolution. Much of what has been learnt from these studies about the subtlety of the microevolutionary process also has significant implications for experimental design and inference in organismal biology beyond life-history evolution, as well as for studies of evolution in the wild. Here we review work on the ecology and evolution of life-histories in laboratory populations ofD. melanogaster, emphasizing how environmental effects on life-history-related traits can influence evolutionary change. We discuss life-history tradeoffs—many unexpected—revealed by selection experiments, and also highlight recent work that underscores the importance to life-history evolution of cross-generation and cross-life-stage effects and interactions, sexual antagonism and sexual dimorphism, population dynamics, and the possible role of biological clocks in timing life-history events. Finally, we discuss some of the limitations of typical selection experiments, and how these limitations might be transcended in the future by a combination of more elaborate and realistic selection experiments, developmental evolutionary biology, and the emerging discipline of phenomics.     

An emerging synthesis between community ecology and evolutionary biology

A synthesis between community ecology and evolutionary biology is emerging that identifies how genetic variation and evolution within one species can shape the ecological properties of entire communities and, in turn, how community context can govern evolutionary processes and patterns. This synthesis incorporates research on the ecology and evolution within communities over short timescales (community genetics and diffuse coevolution), as well as macroevolutionary timescales (community phylogenetics and co-diversification of communities). As we discuss here, preliminary evidence supports the hypothesis that there is a dynamic interplay between ecology and evolution within communities, yet researchers have not yet demonstrated convincingly whether, and under what circumstances, it is important for biologists to bridge community ecology and evolutionary biology. Answering this question will have important implications for both basic and applied problems in biology.     

Evolution of dominance in metabolic pathways

Dominance is a form of phenotypic robustness to mutations. Understanding how such robustness can evolve provides a window into how the relation between genotype and phenotype can evolve. As such, the issue of dominance evolution is a question about the evolution of inheritance systems. Attempts at explaining the evolution of dominance have run into two problems. One is that selection for dominance is sensitive to the frequency of heterozygotes. Accordingly, dominance cannot evolve unless special conditions lead to the presence of a high frequency of mutant alleles in the population. Second, on the basis of theoretical results in metabolic control analysis, it has been proposed that metabolic systems possess inherent constraints. These hypothetical constraints imply the default manifestation of dominance of the wild type with respect to the effects of mutations at most loci. Hence, some biologists have maintained that an evolutionary explanation is not relevant to dominance. In this article, we put into question the hypothetical assumption of default metabolic constraints. We show that this assumption is based on an exclusion of important nonlinear interactions that can occur between enzymes in a pathway. With an a priori exclusion of such interactions, the possibility of epistasis and hence dominance modification is eliminated. We present a theoretical model that integrates enzyme kinetics and population genetics to address dominance evolution in metabolic pathways. In the case of mutations that decrease enzyme concentrations, and given the mechanistic constraints of Michaelis-Menten-type catalysis, it is shown that dominance of the wild type can be extensively modified in a two-enzyme pathway. Moreover, we discuss analytical results indicating that the conclusions from the two-enzyme case can be generalized to any number of enzymes. Dominance modification is achieved chiefly through changes in enzyme concentrations or kinetic parameters such as k(cat), both of which can alter saturation levels. Low saturation translates into higher levels of dominance with respect to mutations that decrease enzyme concentrations. Furthermore, it is shown that in the two-enzyme example, dominance evolves as a by-product of selection in a manner that is insensitive to the frequency of heterozygotes. Using variation in k(cat) as an example of modifier mutations, it is shown that the latter can have direct fitness effects in addition to dominance modification effects. Dominance evolution can occur in a frequency-insensitive manner as a result of selection for such dual-effects alleles. This type of selection may prove to be a common pattern for the evolution of phenotypic robustness to mutations.     

The evolution of catalytic function in the HIV-1 protease

The evolution of species is a complex phenomenon based on the optimization of a multidimensional function referred to as fitness. At the level of biomolecular evolution, the fitness function can be reduced to include physiochemical properties relevant to the biological function of a particular molecule. In this work, questions involving the physical-chemical mechanisms underlying the evolution of HIV-1 protease are addressed through molecular simulation and subsequent analysis of thermodynamic properties related to the activity of the enzyme. Specifically, the impact of 40 single amino acid mutations on the binding affinity toward the matrix/capsid (MA/CA) substrate and corresponding transition state intermediate has been characterized using a molecular mechanics Poisson-Boltzmann surface area approach. We demonstrate that this approach is capable of extracting statistically significant information relevant to experimentally determined catalytic activity. Further, no correlation was observed between the effect of mutations on substrate and transition state binding, suggesting independent evolutionary pathways toward optimizing substrate specificity and catalytic activity. In addition, a detailed analysis of calculated binding affinity data suggests that ground-state destabilization (reduced binding affinity for the substrate) could be a contributing factor in the evolutionary optimization of HIV-1 protease. A numerical model is developed to demonstrate that ground-state destabilization is a valid mechanism for activity optimization given the high concentrations of substrate experienced by the functional enzyme in vivo.     

Application of Magnetic Resonance Spectroscopy in metabolic research

The etiology of metabolic disease in humans is far from understood, and even though potential pathways are identified in animal models and cell studies, it is often difficult to determine their relevance in humans, as the possibilities of tissue sampling are limited. The application of non-invasive imaging techniques can provide essential metabolic information and this mini review focuses on the opportunities of Magnetic Resonance Spectroscopy (MRS) to add to our understanding of the metabolic processes during health and disease. MRS is a volatile technique that can give us information about the concentrations of endogenous metabolites in a completely non-invasive way. In this mini review we discuss the opportunities that MRS is giving us by describing how the investigation of ectopic fat depots has gained a lot of attention and has really taken off after ¹H-MRS for quantification of lipid content became widely available. We furthermore discuss how other MRS techniques, such as ³¹P-MRS and ¹³C-MRS can add valuable information and especially highlight the strength of MRS to be applied dynamically and therefore monitor metabolic changes during physiological challenges such as exercise or meal tests.     

The partition matrix: exploring variable phylogenetic signals along nucleotide sequence alignments

The partition matrix is a graphical tool for comparative analysis of nucleotide sequences following alignment. It is particularly useful for investigating the divergent phylogenies of sequence regions undergoing reticulate evolution. A partition matrix is generated by determining the consistency of the parsimoniously informative sites in a set of aligned sequences with the binary partitions inferred from the sequences. Since the linear order of sites is maintained, the matrix can be used to assess whether the distribution of sites either supporting or conflicting with particular partitions changes along the length of the alignment. The usefulness of the matrix in allowing visual identification of differences in evolutionary history among regions depends on the order in which partitions are shown; several suitable ordering schemes are proposed. We demonstrate the use of the partition matrix in interpreting the evolution of the pseudoautosomal boundary region on the sex chromosome of catarrhine primates. Its routine use should help to avoid attempts to derive single phylogenies from sequences whose evolution has been reticulate and to identify the gene conversion or recombination events underlying the reticulation. The method is relatively fast. It is exploratory, and it can form the basis for more formal analysis, which we discuss.      

Using Correlation Proximity Graphs to Study Phenotypic Integration

Characterizing and comparing the covariance or correlation structure of phenotypic traits lies at the heart of studies concerned with multivariate evolution. I describe an approach that represents the geometric structure of a correlation matrix as a type of proximity graph called a Correlation Proximity graph. Correlation Proximity graphs provide a compact representation of the geometric relationships inherent in correlation matrices, and these graphs have simple and intuitive properties. I demonstrate how this framework can be used to study patterns of phenotypic integration by employing this approach to compare phenotypic and additive genetic correlation matrices within and between species. I also outline a graph-based method for testing whether an inferred correlation proximity graph is one of a number of possible models that are consistent with a “soft” biological hypothesis.     

Engineered enzymes for chemical production

In order to enable competitive manufacturing routes, most biocatalysts must be tailor-made for their processes. Enzymes from nature rarely have the combined properties necessary for industrial chemical production such as high activity and selectivity on non-natural substrates and toleration of high concentrations of organic media over the wide range of conditions (decreasing substrate, increasing product concentrations, solvents, etc.,) that will be present over the course of a manufacturing process. With the advances in protein engineering technologies, a variety of enzyme properties can be altered simultaneously, if the appropriate screening parameters are employed. Here we discuss the process of directed evolution for the generation of commercially viable biocatalysts for the production of fine chemicals, and how novel approaches have helped to overcome some of the challenges.     

Cell behaviour as a dynamic attractor in the intracellular signalling system

We present a model of the cell signalling network based on the generic properties of interactions between protein kinases (PKs) and protein phosphatases (PPs) inside cells. The model is designed to examine the global properties and intrinsic dynamics of the phosphorylation system. A genetic algorithm (GA) is used to evolve populations of "cells". The GA selects cells and ranks them based on an analysis of the dynamics of the proteins within the networks from a series of different random starting conditions. The fittest cells are taken to be those which can generate a variety of different "behaviours" from a series of different initial conditions. During the GA, intracellular protein interactions evolve via mutation and an analogue of domain shuffling between protein types that is thought to occur during biological evolution. The dynamics of the simulated networks are presented and we discuss the hypothesis that changes in the behaviour of a cell may be interpretable as a switch between attractor basins in the intracellular signalling network.     

Graph-theoretic methods for the analysis of chemical and biochemical networks. I. Multistability and oscillations in ordinary differential equation models

A chemical mechanism is a model of a chemical reaction network consisting of a set of elementary reactions that express how molecules react with each other. In classical mass-action kinetics, a mechanism implies a set of ordinary differential equations (ODEs) which govern the time evolution of the concentrations. In this article, ODE models of chemical kinetics that have the potential for multiple positive equilibria or oscillations are studied. We begin by considering some methods of stability analysis based on the digraph of the Jacobian matrix. We then prove two theorems originally given by A. N. Ivanova which correlate the bifurcation structure of a mass-action model to the properties of a bipartite graph with nodes representing chemical species and reactions. We provide several examples of the application of these theorems.     

Fluctuation domains in adaptive evolution

We derive an expression for the variation between parallel trajectories in phenotypic evolution, extending the well known result that predicts the mean evolutionary path in adaptive dynamics or quantitative genetics. We show how this expression gives rise to the notion of fluctuation domains-parts of the fitness landscape where the rate of evolution is very predictable (due to fluctuation dissipation) and parts where it is highly variable (due to fluctuation enhancement). These fluctuation domains are determined by the curvature of the fitness landscape. Regions of the fitness landscape with positive curvature, such as adaptive valleys or branching points, experience enhancement. Regions with negative curvature, such as adaptive peaks, experience dissipation. We explore these dynamics in the ecological scenarios of implicit and explicit competition for a limiting resource.