Design principles of a conditional futile cycle exploited for regulation

Futile cycles: Emerging utility from apparent futility

Futile cycles are biological phenomena where two opposing biochemical reactions run simultaneously, resulting in a net energy loss without appreciable productivity. Such a state was presumed to be a biological aberration and thus deemed an energy-wasting "futile" cycle. However, multiple pieces of evidence suggest that biological utilities emerge from futile cycles. A few established functions of futile cycles are to control metabolic sensitivity, modulate energy homeostasis, and drive adaptive thermogenesis. Yet, the physiological regulation, implication, and pathological relevance of most futile cycles remain poorly studied. In this review, we highlight the abundance and versatility of futile cycles and propose a classification scheme. We further discuss the energetic implications of various futile cycles and their impact on basal metabolic rate, their bona fide and tentative pathophysiological implications, and putative drug interactions.

Mechanistic Modeling of Biochemical Systems without A Priori Parameter Values Using the Design Space Toolbox v.3.0

Mechanistic models of biochemical systems provide a rigorous description of biological phenomena. They are indispensable for making predictions and elucidating biological design principles. To date, mathematical analysis and characterization of these models encounter a bottleneck consisting of large numbers of unknown parameter values. Here, we introduce the Design Space Toolbox v.3.0 (DST3), a software implementation of the Design Space formalism enabling mechanistic modeling without requiring previous knowledge of parameter values. This is achieved by using a phenotype-centric modeling approach, in which the system is first decomposed into a series of biochemical phenotypes. Parameter values realizing phenotypes of interest are subsequently predicted. DST3 represents the most generally applicable implementation of the Design Space formalism and offers unique advantages over earlier versions. By expanding the Design Space formalism and streamlining its distribution, DST3 represents a valuable tool for elucidating biological design principles and designing novel synthetic circuits.

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DST3 extends the Design Space formalism by identifying additional phenotypes

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Additional phenotypes arise from cycles, conservations, and metabolic imbalances

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DST3 enables mechanistic modeling without previous knowledge of parameter values

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It fully unlocks the potential of the novel phenotype-centric modeling strategy

Design Principle for Decoding Calcium Signals to Generate Specific Gene Expression Via Transcription

The second messenger calcium plays a key role in conveying specificity of signaling pathways in plant cells. Specific calcium signatures are decoded to generate correct gene expression responses and amplification of calcium signatures is vital to this process. (1) It is not known if this amplification is an intrinsic property of all calcium-regulated gene expression responses and whether all calcium signatures have the potential to be amplified, or (2) how a given calcium signature maintains specificity in cells containing a great number of transcription factors (TFs) and other proteins with the potential to be calcium-regulated. The work presented here uncovers the design principle by which it is possible to decode calcium signals into specific changes in gene transcription in plant cells. Regarding the first question, we found that the binding mechanism between protein components possesses an intrinsic property that will nonlinearly amplify any calcium signal. This nonlinear amplification allows plant cells to effectively distinguish the kinetics of different calcium signatures to produce specific and appropriate changes in gene expression. Regarding the second question, we found that the large number of calmodulin (CaM)-binding TFs or proteins in plant cells form a buffering system such that the concentration of an active CaM-binding TF is insensitive to the concentration of any other CaM-binding protein, thus maintaining specificity. The design principle revealed by this work can be used to explain how any CaM-binding TF decodes calcium signals to generate specific gene expression responses in plant cells via transcription.

Design Space Toolbox V2: Automated Software Enabling a Novel Phenotype-Centric Modeling Strategy for Natural and Synthetic Biological Systems

Mathematical models of biochemical systems provide a means to elucidate the link between the genotype, environment, and phenotype. A subclass of mathematical models, known as mechanistic models, quantitatively describe the complex non-linear mechanisms that capture the intricate interactions between biochemical components. However, the study of mechanistic models is challenging because most are analytically intractable and involve large numbers of system parameters. Conventional methods to analyze them rely on local analyses about a nominal parameter set and they do not reveal the vast majority of potential phenotypes possible for a given system design. We have recently developed a new modeling approach that does not require estimated values for the parameters initially and inverts the typical steps of the conventional modeling strategy. Instead, this approach relies on architectural features of the model to identify the phenotypic repertoire and then predict values for the parameters that yield specific instances of the system that realize desired phenotypic characteristics. Here, we present a collection of software tools, the Design Space Toolbox V2 based on the System Design Space method, that automates (1) enumeration of the repertoire of model phenotypes, (2) prediction of values for the parameters for any model phenotype, and (3) analysis of model phenotypes through analytical and numerical methods. The result is an enabling technology that facilitates this radically new, phenotype-centric, modeling approach. We illustrate the power of these new tools by applying them to a synthetic gene circuit that can exhibit multi-stability. We then predict values for the system parameters such that the design exhibits 2, 3, and 4 stable steady states. In one example, inspection of the basins of attraction reveals that the circuit can count between three stable states by transient stimulation through one of two input channels: a positive channel that increases the count, and a negative channel that decreases the count. This example shows the power of these new automated methods to rapidly identify behaviors of interest and efficiently predict parameter values for their realization. These tools may be applied to understand complex natural circuitry and to aid in the rational design of synthetic circuits.

Links between Transcription, Environmental Adaptation and Gene Variability in Escherichia coli: Correlations between Gene Expression and Gene Variability Reflect Growth Efficiencies

Gene expression is known to be the principle factor explaining how fast genes evolve. Highly transcribed genes evolve slowly because any negative impact caused by a particular mutation is magnified by protein abundance. However, gene expression is a phenotype that depends both on the environment and on the strains or species. We studied this phenotypic plasticity by analyzing the transcriptome profiles of four Escherichia coli strains grown in three different culture media, and explored how expression variability was linked to gene allelic diversity. Genes whose expression changed according to the media and not to the strains were less polymorphic than other genes. Genes for which transcription depended predominantly on the strain were more polymorphic than other genes and were involved in sensing and responding to environmental changes, with an overrepresentation of two-component system genes. Surprisingly, we found that the correlation between transcription and gene diversity was highly variable among growth conditions and could be used to quantify growth efficiency of a strain in a medium. Genetic variability was found to increase with gene expression in poor growth conditions. As such conditions are also characterized by down-regulation of all DNA repair systems, including transcription-coupled repair, we suggest that gene expression under stressful conditions may be mutagenic and thus leads to a variability in mutation rate among genes in the genome which contributes to the pattern of protein evolution.

miR-378 Activates the Pyruvate-PEP Futile Cycle and Enhances Lipolysis to Ameliorate Obesity in Mice

Obesity has been linked to many health problems, such as diabetes. However, there is no drug that effectively treats obesity. Here, we reveal that miR-378 transgenic mice display reduced fat mass, enhanced lipolysis, and increased energy expenditure. Notably, administering AgomiR-378 prevents and ameliorates obesity in mice. We also found that the energy deficiency seen in miR-378 transgenic mice was due to impaired glucose metabolism. This impairment was caused by an activated pyruvate-PEP futile cycle via the miR-378-Akt1-FoxO1-PEPCK pathway in skeletal muscle and enhanced lipolysis in adipose tissues mediated by miR-378-SCD1. Our findings demonstrate that activating the pyruvate-PEP futile cycle in skeletal muscle is the primary cause of elevated lipolysis in adipose tissues of miR-378 transgenic mice, and it helps orchestrate the crosstalk between muscle and fat to control energy homeostasis in mice. Thus, miR-378 may serve as a promising agent for preventing and treating obesity in humans.

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Systemically administering AgomiR-378 prevents and ameliorates obesity in mice

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miR-378 transgenic mice show energy deficiency due to impaired glucose metabolism

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The impairment is caused by miR-378-activated pyruvate-PEP futile cycle in muscle

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miR-378 governs energy homeostasis by increased lipolysis via targeting Scd1 in fat

Futile cycles are generally regarded as energetically wasteful processes that are avoided in metabolic pathways. Zhang et al. demonstrate miR-378-activated pyruvate-phosphoenolpyruvate futile cycle plays a regulatory benefit for the energy homeostasis by orchestrating inter-organ crosstalk between skeletal muscle and fat in mice, resulting its implication in preventing and treating obesity.

Quantitative description of ion transport via plasma membrane of yeast and small cells

Modeling of ion transport via plasma membrane needs identification and quantitative understanding of the involved processes. Brief characterization of main ion transport systems of a yeast cell (Pma1, Ena1, TOK1, Nha1, Trk1, Trk2, non-selective cation conductance) and determining the exact number of molecules of each transporter per a typical cell allow us to predict the corresponding ion flows. In this review a comparison of ion transport in small yeast cell and several animal cell types is provided. The importance of cell volume to surface ratio is emphasized. The role of cell wall and lipid rafts is discussed in respect to required increase in spatial and temporary resolution of measurements. Conclusions are formulated to describe specific features of ion transport in a yeast cell. Potential directions of future research are outlined based on the assumptions.