Multiple Metabolic States Research Articles

Activity of Moxifloxacin against Mycobacterium tuberculosis in Acid Phase and Nonreplicative-Persister Phenotype Phase in a Hollow-Fiber Infection Model.

A major goal for improving tuberculosis therapy is to identify drug regimens with improved efficacy and shorter treatment durations. Shorter therapies improve patient adherence to the antibiotic regimens, which, in turn, decreases resistance emergence. Mycobacterium tuberculosis exists in multiple metabolic states. At the initiation of therapy, the bulk of the population is in log-phase growth. Consequently, it is logical to focus initial therapy on those organisms. Moxifloxacin has good early bactericidal activity against log-phase bacteria and is a logical component of initial therapy. It would be optimal if this agent also possessed activity against acid-phase and nonreplicative-persister (NRP) phenotype organisms. In our hollow-fiber infection model, we studied multiple exposures to moxifloxacin (equivalent to 200 mg to 800 mg daily) against strain H37Rv in the acid phase and against strain 18b in streptomycin starvation, which is a model for NRP-phase organisms. Moxifloxacin possesses good activity against acid-phase organisms, generating cell killing of 3.75 log10(CFU/ml) (200 mg daily) to 5.16 log10(CFU/ml) (800 mg daily) over the 28 days of the experiment. Moxifloxacin also has activity against streptomycin-starved strain 18b. The 400- to 800-mg daily regimens achieved extinction at day 28, while the no-treatment control still had 1.96 log10(CFU/ml) culturable. The lowest dose (200 mg daily) still had 0.7 log10(CFU/ml) measurable at day 28, a net kill of 1.26 log10(CFU/ml). Moxifloxacin is an attractive agent for early therapy, because it possesses activity against three metabolic states of M. tuberculosis.

Poly-pathway model, a novel approach to simulate multiple metabolic states by reaction network-based model – Application to amino acid depletion in CHO cell culture

Mammalian cell lines are characterized by a complex and flexible metabolism. A single model that could describe the variations in metabolic behavior triggered by variations in the culture conditions would be a precious tool in bioprocess development. In this paper, we introduce an approach to generate a poly-pathway model and use it to simulate diverse metabolic states triggered in response to removal, reduction or doubling of amino acids in the culture medium of an antibody-producing CHO cell line. Macro-reactions were obtained from a metabolic network via elementary flux mode enumeration and the fluxes were modeled by kinetic equations with saturation and inhibition effects from external medium components. Importantly, one set of kinetic parameters was estimated using experimental data of the multiple metabolic states. A good fit between the model and the data was obtained for the majority of the metabolites and the experimentally observed flux variations. We find that the poly-pathway modeling approach is promising for the simulation of multiple metabolic states.

RETRACTED: Poly-pathway model, a novel approach to simulate multiple metabolic states by reaction network-based model – Application to amino acid depletion in CHO cell culture

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). The authors of the paper wish to retract the paper due to the discovery of a calculation error in the processing of the raw data. The discovered error concerns the calculation of the specific uptake/secretion rates for several metabolites in one of the experimental conditions, i.e. glutamine omission (called Q0). In other words, in Figure 2, the variations of the metabolic fluxes for the condition Q0 are not correct. When this error is corrected, the resulting mathematical model changes (in particular for the results associated with Q0 conditions), several figures and tables are modified, and the interpretation of the fluxes in Q0 has to be slightly modified. Therefore the authors wish to retract the article. However, the error does not affect the modelling approach or the methodology presented in the article. Therefore, a revised version with the correct data has since been published: http://www.sciencedirect.com/science/article/pii/S0168165617302663. We apologize to the scientific community for the need to retract the article and the inconvenience caused.

Genes and associated peptides involved with aestivation in a land snail

Some animals can undergo a remarkable transition from active normal life to a dormant state called aestivation; entry into this hypometabolic state ensures that life continues even during long periods of environmental hardship. In this study, we aimed to identify those central nervous system (CNS) peptides that may regulate metabolic suppression leading to aestivation in land snails. Mass spectral-based neuropeptidome analysis of the CNS comparing active and aestivating states, revealed 19 differentially produced peptides; 2 were upregulated in active animals and 17 were upregulated in aestivated animals. Of those, the buccalin neuropeptide was further investigated since there is existing evidence in molluscs that buccalin modulates physiology by muscle contraction. The Theba pisana CNS contains two buccalin transcripts that encode precursor proteins that are capable of releasing numerous buccalin peptides. Of these, Tpi-buccalin-2 is most highly expressed within our CNS transcriptome derived from multiple metabolic states. No significant difference was observed at the level of gene expression levels for Tpi-buccalin-2 between active and aestivated animals, suggesting that regulation may reside at the level of post-translational control of peptide abundance. Spatial gene and peptide expression analysis of aestivated snail CNS demonstrated that buccalin-2 has widespread distribution within regions that control several physiological roles. In conclusion, we provide the first detailed molecular analysis of the peptides and associated genes that are related to hypometabolism in a gastropod snail known to undergo extended periods of aestivation.

Enhanced glucose tolerance in pancreatic-derived factor (PANDER) knockout C57BL/6 mice.

Pancreatic-derived factor (PANDER; also known as FAM3B) is a uniquely structured protein strongly expressed within and secreted from the endocrine pancreas. PANDER has been hypothesized to regulate fasting and fed glucose homeostasis, hepatic lipogenesis and insulin signaling, and to serve a potential role in the onset or progression of type 2 diabetes (T2D). Despite having potentially pivotal pleiotropic roles in glycemic regulation and T2D, there has been limited generation of stable animal models for the investigation of PANDER function, and there are no models on well-established genetic murine backgrounds for T2D. Our aim was to generate an enhanced murine model to further elucidate the biological function of PANDER. Therefore, a pure-bred PANDER knockout C57BL/6 (PANKO-C57) model was created and phenotypically characterized with respect to glycemic regulation and hepatic insulin signaling. The PANKO-C57 model exhibited an enhanced metabolic phenotype, particularly with regard to enhanced glucose tolerance. Male PANKO-C57 mice displayed decreased fasting plasma insulin and C-peptide levels, whereas leptin levels were increased as compared with matched C57BL/6J wild-type mice. Despite similar peripheral insulin sensitivity between both groups, hepatic insulin signaling was significantly increased during fasting conditions, as demonstrated by increased phosphorylation of hepatic PKB/Akt and AMPK, along with mature SREBP-1 expression. Insulin stimulation of PANKO-C57 mice resulted in increased hepatic triglyceride and glycogen content as compared with wild-type C57BL/6 mice. In summary, the PANKO-C57 mouse represents a suitable model for the investigation of PANDER in multiple metabolic states and provides an additional tool to elucidate the biological function and potential role in T2D.

Complex Nonlinear Behavior in Metabolic Processes: Global Bifurcation Analysis of Escherichia coli Growth on Multiple Substrates

The nonlinear behavior of metabolic systems can arise from at least two different sources. One comes from the nonlinear kinetics of chemical reactions in metabolism and the other from nonlinearity associated with regulatory processes. Consequently, organisms at a constant growth rate (as experienced in a chemostat) could display multiple metabolic states or display complex oscillatory behavior both with potentially serious implications to process operation. This paper explores the nonlinear behavior of a metabolic model of Escherichia coli growth on mixed substrates with sufficient detail to include regulatory features through the cybernetic postulate that metabolic regulation is the consequence of a dynamic objective function ensuring the organism’s survival. The chief source of nonlinearity arises from the optimal formulation with the metabolic state determined by a convex combination of reactions contributing to the objective function. The model for anaerobic growth of E. coli was previously examined for multiple steady states in a chemostat fed by a mixture of glucose and pyruvate substrates under very specific conditions and experimentally verified. In this article, we explore the foregoing model for nonlinear behavior over the full range of parameters, γ (the fractional concentration of glucose in the feed mixture) and D (the dilution rate). The observed multiplicity is in the cybernetic variables combining elementary modes. The results show steady-state multiplicity up to seven. No Hopf bifurcation was encountered, however. Bifurcation analysis of cybernetic models is complicated by the non-differentiability of the cybernetic variables for enzyme activities. A methodology is adopted here to overcome this problem, which is applicable to more complicated metabolic networks.

APL-1, the Alzheimer’s Amyloid Precursor Protein inCaenorhabditis elegans, Modulates Multiple Metabolic Pathways Throughout Development

Mutations in the amyloid precursor protein (APP) gene or in genes that process APP are correlated with familial Alzheimer's disease (AD). The biological function of APP remains unclear. APP is a transmembrane protein that can be sequentially cleaved by different secretases to yield multiple fragments, which can potentially act as signaling molecules. Caenorhabditis elegans encodes one APP-related protein, APL-1, which is essential for viability. Here, we show that APL-1 signaling is dependent on the activity of the FOXO transcription factor DAF-16 and the nuclear hormone receptor DAF-12 and influences metabolic pathways such as developmental progression, body size, and egg-laying rate. Furthermore, apl-1(yn5) mutants, which produce high levels of the extracellular APL-1 fragment, show an incompletely penetrant temperature-sensitive embryonic lethality. In a genetic screen to isolate mutants in which the apl-1(yn5) lethality rate is modified, we identified a suppressor mutation in MOA-1/R155.2, a receptor-protein tyrosine phosphatase, and an enhancer mutation in MOA-2/B0495.6, a protein involved in receptor-mediated endocytosis. Knockdown of apl-1 in an apl-1(yn5) background caused lethality and molting defects at all larval stages, suggesting that apl-1 is required for each transitional molt. We suggest that signaling of the released APL-1 fragment modulates multiple metabolic states and that APL-1 is required throughout development.

Analysis of metabolomic PCA data using tree diagrams

Large amounts of data from high-throughput metabolomic experiments are commonly visualized using a principal component analysis (PCA) two-dimensional scores plot. The question of the similarity or difference between multiple metabolic states then becomes a question of the degree of overlap between their respective data point clusters in principal component (PC) scores space. A qualitative visual inspection of the clustering pattern in PCA scores plots is a common protocol. This article describes the application of tree diagrams and bootstrapping techniques for an improved quantitative analysis of metabolic PCA data clustering. Our PCAtoTree program creates a distance matrix with 100 bootstrap steps that describes the separation of all clusters in a metabolic data set. Using accepted phylogenetic software, the distance matrix resulting from the various metabolic states is organized into a phylogenetic-like tree format, where bootstrap values ⩾50 indicate a statistically relevant branch separation. PCAtoTree analysis of two previously published data sets demonstrates the improved resolution of metabolic state differences using tree diagrams. In addition, for metabolomic studies of large numbers of different metabolic states, the tree format provides a better description of similarities and differences between each metabolic state. The approach is also tolerant of sample size variations between different metabolic states.