Engineering the spatial organization of metabolic enzymes: mimicking nature's synergy

A growing body of evidence indicates that many cellular reactions within metabolic pathways are catalyzed not by free-floating 'soluble' enzymes, but via one or more membrane-associated multienzyme complexes. This type of macromolecular organization has important implications for the overall efficiency, specificity, and regulation of metabolic pathways. An ever-increasing number of biochemical and genetic studies on primary and secondary metabolism have laid a solid foundation for this model, providing compelling evidence in favor of the so-called channeling of intermediates between enzyme active sites and colocalization of enzymes inside a cell. In this review, we discuss several of nature's most notable multifunctional enzyme systems including the AROM complex and tryptophan synthase, each of which provides new fundamental insights into the structural organization of metabolic machinery within living cells. We then focus on the growing body of literature related to engineering strategies using protein chimeras and post-translational assembly mechanisms. Common among these techniques is the desire to mimic natural enzyme organization for optimizing the production of valuable metabolites with industrial and medical importance.     

Exploring mitochondrial evolution and metabolism organization principles by comparative analysis of metabolic networks

The endosymbiotic theory proposed that mitochondrial genomes are derived from an alpha-proteobacterium-like endosymbiont, which was concluded from sequence analysis. We rebuilt the metabolic networks of mitochondria and 22 relative species, and studied the evolution of mitochondrial metabolism at the level of enzyme content and network topology. Our phylogenetic results based on network alignment and motif identification supported the endosymbiotic theory from the point of view of systems biology for the first time. It was found that the mitochondrial metabolic network were much more compact than the relative species, probably related to the higher efficiency of oxidative phosphorylation of the specialized organelle, and the network is highly clustered around the TCA cycle. Moreover, the mitochondrial metabolic network exhibited high functional specificity to the modules. This work provided insight to the understanding of mitochondria evolution, and the organization principle of mitochondrial metabolic network at the network level.     

Subcellular metabolic organization in the context of dynamic energy budget and biochemical systems theories

The dynamic modelling of metabolic networks aims to describe the temporal evolution of metabolite concentrations in cells. This area has attracted increasing attention in recent years owing to the availability of high-throughput data and the general development of systems biology as a promising approach to study living organisms. Biochemical Systems Theory (BST) provides an accurate formalism to describe biological dynamic phenomena. However, knowledge about the molecular organization level, used in these models, is not enough to explain phenomena such as the driving forces of these metabolic networks. Dynamic Energy Budget (DEB) theory captures the quantitative aspects of the organization of metabolism at the organism level in a way that is non-species-specific. This imposes constraints on the sub-organismal organization that are not present in the bottom-up approach of systems biology. We use in vivo data of lactic acid bacteria under various conditions to compare some aspects of BST and DEB approaches. Due to the large number of parameters to be estimated in the BST model, we applied powerful parameter identification techniques. Both models fitted equally well, but the BST model employs more parameters. The DEB model uses similarities of processes under growth and no-growth conditions and under aerobic and anaerobic conditions, which reduce the number of parameters. This paper discusses some future directions for the integration of knowledge from these two rich and promising areas, working top-down and bottom-up simultaneously. This middle-out approach is expected to bring new ideas and insights to both areas in terms of describing how living organisms operate.     

Phylogenetic comparison of metabolic capacities of organisms at genome level

Horizontal gene transfer (HGT) has been shown to widely spread in organisms by comparative genomic studies. However, its effect on the phylogenetic relationship of organisms, especially at a system level of different cellular functions, is still not well understood. In this work, we have constructed phylogenetic trees based on the enzyme, reaction, and gene contents of metabolic networks reconstructed from annotated genome information of 82 sequenced organisms. Results from different phylogenetic distance definitions and based on three different functional subsystems (i.e., metabolism, cellular processes, information storage and processing) were compared. Results based on the three different functional subsystems give different pictures on the phylogenetic relationship of organisms, reflecting the different extents of HGT in the different functional systems. In general, horizontal transfer is prevailing in genes for metabolism, but less in genes for information processing. Nevertheless, the major results of metabolic network-based phylogenetic trees are in good agreement with the tree based on 16S rRNA and genome trees, confirming the three domain classification and the close relationship between eukaryotes and archaea at the level of metabolic networks. These results strongly support the hypothesis that although HGT is widely distributed, it is nevertheless constrained by certain pre-existing metabolic organization principle(s) during the evolution. Further research is needed to identify the organization principle and constraints of metabolic network on HGT which have large impacts on understanding the evolution of life and in purposefully manipulating cellular metabolism.     

Determinants of intra-specific variation in basal metabolic rate

Basal metabolic rate (BMR) provides a widely accepted benchmark of metabolic expenditure for endotherms under laboratory and natural conditions. While most studies examining BMR have concentrated on inter-specific variation, relatively less attention has been paid to the determinants of within-species variation. Even fewer studies have analysed the determinants of within-species BMR variation corrected for the strong influence of body mass by appropriate means (e.g. ANCOVA). Here, we review recent advancements in studies on the quantitative genetics of BMR and organ mass variation, along with their molecular genetics. Next, we decompose BMR variation at the organ, tissue and molecular level. We conclude that within-species variation in BMR and its components have a clear genetic signature, and are functionally linked to key metabolic process at all levels of biological organization. We highlight the need to integrate molecular genetics with conventional metabolic field studies to reveal the adaptive significance of metabolic variation. Since comparing gene expressions inter-specifically is problematic, within-species studies are more likely to inform us about the genetic underpinnings of BMR. We also urge for better integration of animal and medical research on BMR; the latter is quickly advancing thanks to the application of imaging technologies and ‘omics’ studies. We also suggest that much insight on the biochemical and molecular underpinnings of BMR variation can be gained from integrating studies on the mammalian target of rapamycin (mTOR), which appears to be the major regulatory pathway influencing the key molecular components of BMR.     

Polyhedral organelles compartmenting bacterial metabolic processes

Bacterial polyhedral organelles are extremely large macromolecular complexes consisting of metabolic enzymes encased within a multiprotein shell that is somewhat reminiscent of a viral capsid. Recent investigations suggest that polyhedral organelles are widely used by bacteria for optimizing metabolic processes. The distribution and diversity of these unique structures has been underestimated because many are not formed during growth on standard laboratory media and because electron microscopy is required for their observation. However, recent physiological studies and genomic analyses tentatively indicate seven functionally distinct organelles distributed among over 40 genera of bacteria. Functional studies conducted thus far are consistent with the idea that polyhedral organelles act as microcompartments that enhance metabolic processes by selectively concentrating specific metabolites. Relatively little is known about how this is achieved at the molecular level. Possible mechanisms include regulation of enzyme activity or efficiency, substrate channeling, a selectively permeable protein shell, and/or differential solubility of metabolites within the organelle. Given their complexity and distinctive structure, it would not be surprising if aspects of their biochemical mechanism are unique. Therefore, the unusual structure of polyhedral organelles raises intriguing questions about their assembly, turnover, and molecular evolution, very little of which is understood.     

On the stability of metabolic cycles

We investigate the stability properties of two different classes of metabolic cycles using a combination of analytical and computational methods. Using principles from structural kinetic modeling (SKM), we show that the stability of metabolic networks with certain structural regularities can be studied using a combination of analytical and computational techniques. We then apply these techniques to a class of single input, single output metabolic cycles, and find that the cycles are stable under all conditions tested. Next, we extend our analysis to a small autocatalytic cycle, and determine parameter regimes within which the cycle is very likely to be stable. We demonstrate that analytical methods can be used to understand the relationship between kinetic parameters and stability, and that results from these analytical methods can be confirmed with computational experiments. In addition, our results suggest that elevated metabolite concentrations and certain crucial saturation parameters can strongly affect the stability of the entire metabolic cycle. We discuss our results in light of the possibility that evolutionary forces may select for metabolic network topologies with a high intrinsic probability of being stable. Furthermore, our conclusions support the hypothesis that certain types of metabolic cycles may have played a role in the development of primitive metabolism despite the absence of regulatory mechanisms.     

Diverse metabolic model parameters generate similar methionine cycle dynamics

Parameter estimation constitutes a major challenge in dynamic modeling of metabolic networks. Here we examine, via computational simulations, the influence of system nonlinearity and the nature of available data on the distribution and predictive capability of identified model parameters. Simulated methionine cycle metabolite concentration data (both with and without corresponding flux data) was inverted to identify model parameters consistent with it. Thousands of diverse parameter families were found to be consistent with the data to within moderate error, with most of the parameter values spanning over 1000-fold ranges irrespective of whether flux data was included. Due to strong correlations within the extracted parameter families, model predictions were generally reliable despite the broad ranges found for individual parameters. Inclusion of flux data, by strengthening these correlations, resulted in substantially more reliable flux predictions. These findings suggest that, despite the difficulty of extracting biochemically accurate model parameters from system level data, such data may nevertheless prove adequate for driving the development of predictive dynamic metabolic models.     

Recovering metabolic pathways via optimization

A metabolic pathway is a coherent set of enzyme catalysed biochemical reactions by which a living organism transforms an initial (source) compound into a final (target) compound. Some of the different metabolic pathways adopted within organisms have been experimentally determined. In this paper, we show that a number of experimentally determined metabolic pathways can be recovered by a mathematical optimization model.     

Quantitative label-free imaging of lipid composition and packing of individual cellular lipid droplets using multiplex CARS microscopy

Lipid droplets (LDs) are highly dynamic organelles that perform multiple functions, including the regulated storage and release of cholesterol and fatty acids. Information on the molecular composition of individual LDs within their cellular context is crucial in understanding the diverse biological functions of LDs, as well as their involvement in the development of metabolic disorders such as obesity, type II diabetes, and atherosclerosis. Although ensembles of LDs isolated from cells and tissues were analyzed in great detail, quantitative information on the heterogeneity in lipid composition of individual droplets, and possible variations within single lipid droplets, is lacking. Therefore, we used a label-free quantitative method to image lipids within LDs in 3T3-L1 cells. The method combines submicron spatial resolution in three dimensions, using label-free coherent anti-Stokes Raman scattering microscopy, with quantitative analysis based on the maximum entropy method. Our method allows quantitative imaging of the chemistry (level of acyl unsaturation) and physical state (acyl chain order) of individual LDs. Our results reveal variations in lipid composition and physical state between LDs contained in the same cell, and even within a single LD.     

Origin and diversification of a metabolic cycle in oligomer world

Based on the oligomer-world hypothesis we propose an abstract model where the molecular recognition among oligomers is described in the shape space. The origin of life in the oligomer world is regarded as the establishment of a metabolic cycle in a primitive cell. The cycle is sustained by the molecular recognition. If an original cell acquires the ability of the replication of oligomers, the relationship among oligomers changes due to the poor fidelity of the replication. This change leads to the diversification of metabolic cycles. The selection among diverse cycles is the basis of the evolution. The evolvability is one of the essential characters of life. We demonstrate the origin and diversification of the metabolic cycle by the computer simulation of our model. Such a simulation is expected to be the simplified demonstration of what actually occurred in the primordial soup. Our model describes an analog era preceding the digital era based on the genetic code.     

Beyond the '3/4-power law': variation in the intra- and interspecific scaling of metabolic rate in animals

In this review I show that the '3/4-power scaling law' of metabolic rate is not universal, either within or among animal species. Significant variation in the scaling of metabolic rate with body mass is described mainly for animals, but also for unicells and plants. Much of this variation, which can be related to taxonomic, physiological, and/or environmental differences, is not adequately explained by existing theoretical models, which are also reviewed. As a result, synthetic explanatory schemes based on multiple boundary constraints and on the scaling of multiple energy-using processes are advocated. It is also stressed that a complete understanding of metabolic scaling will require the identification of both proximate (functional) and ultimate (evolutionary) causes. Four major types of intraspecific metabolic scaling with body mass are recognized [based on the power function R=aMb, where R is respiration (metabolic) rate, a is a constant, M is body mass, and b is the scaling exponent]: Type I: linear, negatively allometric (b<1); Type II: linear, isometric (b=1); Type III: nonlinear, ontogenetic shift from isometric (b=1), or nearly isometric, to negatively allometric (b<1); and Type IV: nonlinear, ontogenetic shift from positively allometric (b>1) to one or two later phases of negative allometry (b<1). Ontogenetic changes in the metabolic intensity of four component processes (i.e. growth, reproduction, locomotion, and heat production) appear to be important in these different patterns of metabolic scaling. These changes may, in turn, be shaped by age (size)-specific patterns of mortality. In addition, major differences in interspecific metabolic scaling are described, especially with respect to mode of temperature regulation, body-size range, and activity level. A 'metabolic-level boundaries hypothesis' focusing on two major constraints (surface-area limits on resource/waste exchange processes and mass/volume limits on power production) can explain much, but not all of this variation. My analysis indicates that further empirical and theoretical work is needed to understand fully the physiological and ecological bases for the considerable variation in metabolic scaling that is observed both within and among species. Recommended approaches for doing this are discussed. I conclude that the scaling of metabolism is not the simple result of a physical law, but rather appears to be the more complex result of diverse adaptations evolved in the context of both physico-chemical and ecological constraints.     

Crosstalk between adipose tissue and the microbiota-gut-brain axis in metabolic diseases

Recently, the microbiota-gut-brain axis (MGBA) has emerged as a target for therapeutic innovation. Impairment of dynamic relationships within the MGBA promotes the pathological features of metabolic diseases. However, experimental data on the MGBA has limited clinical application. This review summarizes recent studies and proposes that exploring the interaction among peripheral organs and the MGBA could verify the dominant role of the latter in the onset of metabolic diseases and promote the clinical application of research outcomes. We first emphasize the molecular basis of metabolic diseases caused by MGBA disorders, which manifests as bidirectional relationship. We also summarize related therapeutic strategies, along with limitations in their clinical application. Adipose tissue (AT) is dynamic during metabolic activities and might interact with components in the MGBA. Therefore, it is interesting to explore the interplay among the MGBA and different kinds of AT, including thermogenic adipose tissue and white adipose tissue (WAT). In addition, we also evaluate the functional specificity of adipose tissue derived mesenchymal stem cells (ADSCs) and the beige adipose tissue. Understanding the heterogeneity and molecular basis of the interaction between different kinds of AT and the MGBA could accelerate innovation in the diagnosis and therapy of metabolic diseases.     

Chronobiological aspects of obesity and metabolic syndrome

Circadian rhythms (approximately 24h) are widely characterized at molecular level and their generation is acknowledged to originate from oscillations in expression of several clock genes and from regulation of their protein products. While general entrainment of organisms to environmental light-dark cycles is mainly achieved through the master clock of the suprachiasmatic nucleus in mammals, this molecular clockwork is functional in several organs and tissues. Some studies have suggested that disruption of the circadian system (chronodisruption (CD)) may be causal for manifestations of the metabolic syndrome. This review summarizes (1) how molecular clocks coordinate metabolism and their specific role in the adipocyte; (2) the genetic aspects of and scientific evidence for obesity as a chronobiological illness; and (3) CD and its causes and pathological consequences. Finally, ideas about use of chronobiology for the treatment of obesity are discussed.     

PathFinder: reconstruction and dynamic visualization of metabolic pathways

MOTIVATION: Beyond methods for a gene-wise annotation and analysis of sequenced genomes new automated methods for functional analysis on a higher level are needed. The identification of realized metabolic pathways provides valuable information on gene expression and regulation. Detection of incomplete pathways helps to improve a constantly evolving genome annotation or discover alternative biochemical pathways. To utilize automated genome analysis on the level of metabolic pathways new methods for the dynamic representation and visualization of pathways are needed. RESULTS: PathFinder is a tool for the dynamic visualization of metabolic pathways based on annotation data. Pathways are represented as directed acyclic graphs, graph layout algorithms accomplish the dynamic drawing and visualization of the metabolic maps. A more detailed analysis of the input data on the level of biochemical pathways helps to identify genes and detect improper parts of annotations. As an Relational Database Management System (RDBMS) based internet application PathFinder reads a list of EC-numbers or a given annotation in EMBL- or Genbank-format and dynamically generates pathway graphs.     

Temperature effects on a whole metabolic reaction cannot be inferred from its components

Changes in temperature affect the kinetic energy of the constituents of a system at the molecular level and have pervasive effects on the physiology of the whole organism. A mechanistic link between these levels of organization has been assumed and made explicit through the use of values of organismal Q10 to infer control of metabolic rate. To be valid this postulate requires linearity and independence of the isolated reaction steps, assumptions not accepted by all. We address this controversy by applying dynamic systems theory and metabolic control analysis to a metabolic pathway model. It is shown that temperature effects on isolated steps cannot rigorously be extrapolated to higher levels of organization.     

基于Matlab对解脂耶氏酵母基因组规模代谢网络模型仿真结果的可视化分析

【目的】近十年来,基因组代谢网络模型迅速发展。通过构建基因组代谢网络模型进行计算机仿真模拟已成为研究生物体复杂的生理代谢不可或缺的工具。实现对仿真结果的可视化分析,可以直观地追踪模型中的代谢流向,从而更好地对仿真结果进行分析。【方法】在简要概述目前可视化方法的基础上,提出了一种基于Matlab实现基因组规模代谢网络模型仿真结果可视化的方法：通过CellDesigner预先绘制与模型相匹配的图,通过RAVEN toolbox中的函数于Matlab进行读图、并实现仿真结果的可视化。【结果】以解脂耶氏酵母基因组规模代谢网络模型iYL619_PCP v1.7为对象,实现并阐明其仿真结果的可视化。【结论】通过该方法可以清晰地监测模型中的流量和流向变化,提高仿真结果的分析效率。     

A manipulative test of competing theories for metabolic scaling

The reasons why metabolic rate (B) scales allometrically with body mass (M) remain hotly debated. The field is dominated by correlational analyses of the relationship between B and M; these struggle to disentangle competing explanations because both B and M are confounded with ontogeny, life history, and ecology. Here, we overcome these problems by using an experimental approach to test among competing metabolic theories. We examined the scaling of B in size-manipulated and intact colonies of a bryozoan and show that B scales with M(0.5). To explain this, we apply a general model based on the dynamic energy budget theory for metabolic organization that predicts B on the basis of energy allocation to assimilation, maintenance, growth, and maturation. Uniquely, this model predicts the absolute value of B, emphasizes that there is no single scaling exponent of B, and demonstrates that a single model can explain the variation in B seen in nature.