Organising metabolic networks: cycles in flux distributions

Metabolic networks are among the most widely studied biological systems. The topology and interconnections of metabolic reactions have been well described for many species, but are not sufficient to understand how their activity is regulated in living organisms. The principles directing the dynamic organisation of reaction fluxes remain poorly understood. Cyclic structures are thought to play a central role in the homeostasis of biological systems and in their resilience to a changing environment. In this work, we investigate the role of fluxes of matter cycling in metabolic networks. First, we introduce a methodology for the computation of cyclic and acyclic fluxes in metabolic networks, adapted from an algorithm initially developed to study cyclic fluxes in trophic networks. Subsequently, we apply this methodology to the analysis of three metabolic systems, including the central metabolism of wild type and a deletion mutant of Escherichia coli, erythrocyte metabolism and the central metabolism of the bacterium Methylobacterium extorquens. The role of cycles in driving and maintaining the performance of metabolic functions upon perturbations is unveiled through these examples. This methodology may be used to further investigate the role of cycles in living organisms, their pro-activity and organisational invariance, leading to a better understanding of biological entailment and information processing

Genomic and proteomic evidences unravel the UV-resistome of the poly-extremophile Acinetobacter sp. Ver3

Ultraviolet radiation can damage biomolecules, with detrimental or even lethal effects for life. Even though lower wavelengths are filtered by the ozone layer, a significant amount of harmful UV-B and UV-A radiation reach Earth?s surface, particularly in high altitude environments. High-Altitude Andean Lakes (HAAL) are a group of disperse shallow lakes and salterns, located at the Dry Central Andes region in South America at altitudes above 3,000 m. As it is considered one of the highest UV-exposed environments, HAAL microbes constitute model systems to study UV-resistance mechanisms in environmental bacteria at various complexity levels. Herein, we present the genome sequence of Acinetobacter sp. Ver3, a gammaproteobacterium isolated from Lake Verde (4,400 m), together with further experimental evidence supporting the phenomenological observations regarding this bacterium ability to cope with increased UV-induced DNA damage. Comparison with the genomes of other Acinetobacter strains highlighted a number of unique genes, such as a novel cryptochrome. An ?UV-resistome? was defined, encompassing mainly, genes related to UV-damage repair on DNA and genes conferring an enhanced capacity for scavenging the reactive molecular species responsible for oxidative damage. In accordance, proteomic profiling of UV-exposed cells identified up-regulated proteins such as a specific cytoplasmic catalase, a putative regulator, and proteins associated to amino acid and protein synthesis. Down-regulated proteins were related to several energy-generating pathways such as glycolysis, beta-oxidation of fatty acids and electronic respiratory chain. To the best of our knowledge, this is the first report on a genome from a polyextremophilic Acinetobacter strain.Fil: Kurth, Daniel German. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Planta Piloto de Procesos Industriales Microbiologicos; ArgentinaFil: Belfiore, Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Planta Piloto de Procesos Industriales Microbiologicos; ArgentinaFil: Gorriti, Marta Fabiana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Planta Piloto de Procesos Industriales Microbiologicos; ArgentinaFil: Cortez, Nestor Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; ArgentinaFil: Farias, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Planta Piloto de Procesos Industriales Microbiologicos; ArgentinaFil: Albarracín, Virginia Helena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Planta Piloto de Procesos Industriales Microbiologicos; Argentin

Modeling the contribution of allosteric regulation for flux control in the central carbon metabolism of E. coli

Modeling cellular metabolism is fundamental for many biotechnological applications, including drug discovery and rational cell factory design. Central carbon metabolism (CCM) is particularly important as it provides the energy and precursors for other biological processes. However, the complex regulation of CCM pathways has still not been fully unraveled and recent studies have shown that CCM is mostly regulated at post-transcriptional levels. In order to better understand the role of allosteric regulation in controlling the metabolic phenotype, we expand the reconstruction of CCM in Escherichia coli with allosteric interactions obtained from relevant databases. This model is used to integrate multi-omics datasets and analyze the coordinated changes in enzyme, metabolite, and flux levels between multiple experimental conditions. We observe cases where allosteric interactions have a major contribution to the metabolic flux changes. Inspired by these results, we develop a constraint-based method (arFBA) for simulation of metabolic flux distributions that accounts for allosteric interactions. This method can be used for systematic prediction of potential allosteric regulation under the given experimental conditions based on experimental data. We show that arFBA allows predicting coordinated flux changes that would not be predicted without considering allosteric regulation. The results reveal the importance of key regulatory metabolites, such as fructose-1,6-bisphosphate, in controlling the metabolic flux. Accounting for allosteric interactions in metabolic reconstructions reveals a hidden topology in metabolic networks, improving our understanding of cellular metabolism and fostering the development of novel simulation methods that account for this type of regulation.(undefined

Unraveling antimicrobial resistance using metabolomics

The emergence of antimicrobial resistance (AMR) in bacterial pathogens represents a global health threat. The metabolic state of bacteria is associated with a range of genetic and phenotypic resistance mechanisms. This review provides an overview of the roles of metabolic processes that are associated with AMR mechanisms, including energy production, cell wall synthesis, cell-cell communication, and bacterial growth. These metabolic processes can be targeted with the aim of re-sensitizing resistant pathogens to antibiotic treatments. We discuss how state-of-the-art metabolomics approaches can be used for comprehensive analysis of microbial AMR-related metabolism, which may facilitate the discovery of novel drug targets and treatment strategies.Analytical BioScience

A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism

The interplay between nutrient-induced signaling and metabolism plays an important role in maintaining homeostasis and its malfunction has been implicated in many different human diseases such as obesity, type 2 diabetes, cancer, and neurological disorders. Therefore, unraveling the role of nutrients as signaling molecules and metabolites together with their interconnectivity may provide a deeper understanding of how these conditions occur. Both signaling and metabolism have been extensively studied using various systems biology approaches. However, they are mainly studied individually and in addition, current models lack both the complexity of the dynamics and the effects of the crosstalk in the signaling system. To gain a better understanding of the interconnectivity between nutrient signaling and metabolism in yeast cells, we developed a hybrid model, combining a Boolean module, describing the main pathways of glucose and nitrogen signaling, and an enzyme-constrained model accounting for the central carbon metabolism of Saccharomyces cerevisiae, using a regulatory network as a link. The resulting hybrid model was able to capture a diverse utalization of isoenzymes and to our knowledge outperforms constraint-based models in the prediction of individual enzymes for both respiratory and mixed metabolism. The model showed that during fermentation, enzyme utilization has a major contribution in governing protein allocation, while in low glucose conditions robustness and control are prioritized. In addition, the model was capable of reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression, as well as regulatory effects associated with lifespan increase during caloric restriction. Overall, we show that our hybrid model provides a comprehensive framework for the study of the non-trivial effects of the interplay between signaling and metabolism, suggesting connections between the Snf1 signaling pathways and processes that have been related to chronological lifespan of yeast cells

Metabolic engineering of microorganisms for the overproduction of fatty acids

Fatty acids naturally synthesized in many organisms are promising starting points for the catalytic production of industrial chemicals and diesel-like biofuels. However, bio-production of fatty acids in microbial hosts relies heavily on manipulating tightly regulated fatty acid biosynthetic pathways, thus complicating the engineering for higher yields. With the advent of systems metabolic engineering, we demonstrated an iterative metabolic engineering effort that integrates computationally driven predictions and metabolic flux analysis (MFA) was demonstrated to meet this challenge. With wild type E. coli fluxomic data, the OptForce procedure was employed to suggest genetic manipulations for fatty acid overproduction. In accordance with the OptForce prioritization of interventions, fabZ and acyl-ACP thioesterase were upregulated and fadD was deleted to arrive at a strain that produces 1.70 g/L and 0.14 g fatty acid/g glucose of C14-16 fatty acid in minimal medium. However, OptForce does not infer gene regulation, enzyme inhibition and metabolic toxicity. Along with transcriptomics and metabolomics analysis, we re-deployed OptForce simulation using the redefined flux distribution as constraints to generate predictions for the second generation fatty acid-overproducing strain. MFA identified the up-regulation of the TCA cycle and down-regulation of pentose phosphate pathway under fatty acid overproduction to replenish the need of energy and reducing molecules. The elevation of intracellular metabolite levels in the TCA cycle complemented the flux findings. With re-defined flux boundary of the first generation strain, OptForce suggested the interruption of TCA cycle such as removal of succinate dehydrogenase as the most prioritized genetic intervention to further improve fatty acid production. Meanwhilem, the whole genome transcriptional analysis revealed acid stress response, membrane disruption, colanic acid and biofilm formation during fatty acid production, thus pinpointing the targets for future metabolic engineering effort. These results highlight the benefit of using computational strain design and system metabolic engineering tools in systematically guiding the strain design to produce free fatty acids. Nonetheless, Saccharomyces cerevisiae is another attractive host organism for the production of biochemicals and biofuels. However, S. cerevisiae is very susceptible to octanoic acid toxicity. Transcriptomics analysis revealed membrane stress and intracellular acidification during octanoic acid stress. MFA illustrated the increase of flux in the TCA cycle possibly to facilitate the ATP-binding-cassette transporter activities. Further efforts can focus on improving membrane integrity or explore oleaginious yeasts to enhance the tolerance against fatty acids

Systems biology in animal sciences

Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed ‘omics’ technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A ‘system’ approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with ‘system approaches’ in animal sciences, providing exciting opportunities to predict and modulate animal traits

Cancer Cell Metabolism: One Hallmark, Many Faces

Cancer cells must rewire cellular metabolism to satisfy the demands of growth and proliferation. Although many of the metabolic alterations are largely similar to those in normal proliferating cells, they are aberrantly driven in cancer by a combination of genetic lesions and nongenetic factors such as the tumor microenvironment. However, a single model of altered tumor metabolism does not describe the sum of metabolic changes that can support cell growth. Instead, the diversity of such changes within the metabolic program of a cancer cell can dictate by what means proliferative rewiring is driven, and can also impart heterogeneity in the metabolic dependencies of the cell. A better understanding of this heterogeneity may enable the development and optimization of therapeutic strategies that target tumor metabolism. Significance: Altered tumor metabolism is now a generally regarded hallmark of cancer. Nevertheless, the recognition of metabolic heterogeneity in cancer is becoming clearer as a result of advancements in several tools used to interrogate metabolic rewiring and dependencies. Deciphering this context-dependent heterogeneity will supplement our current understanding of tumor metabolism and may yield promising therapeutic and diagnostic utilities.National Institutes of Health (U.S.) (Grant CA129105